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Sample records for multi population neural

  1. Using multi-neuron population recordings for neural prosthetics.

    PubMed

    Chapin, John K

    2004-05-01

    Classical single-neuron recording methods led to 'neuron-centric' concepts of neural coding, whereas more recent multi-neuron population recordings have inspired 'population-centric' concepts of distributed processing in neural systems. Because most neocortical neurons code information coarsely, sensory or motor processing tends to be widely distributed across neuronal populations. Dynamic fluctuations in neural population functions thus involve subtle changes in the overall pattern of neural activity. Mathematical analysis of neural population codes allows extraction of 'motor signals' from neuronal population recordings in the motor cortices, which can then be used in real-time to directly control movement of a robot arm. This technique holds promise for the development of neurally controlled prosthetic devices and provides insights into how information is distributed across several brain regions.

  2. Multi-scale approaches for high-speed imaging and analysis of large neural populations

    PubMed Central

    Ahrens, Misha B.; Yuste, Rafael; Peterka, Darcy S.; Paninski, Liam

    2017-01-01

    Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to “zoom out” by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution. PMID:28771570

  3. Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes

    NASA Astrophysics Data System (ADS)

    Bauer, Johannes; Dávila-Chacón, Jorge; Wermter, Stefan

    2015-10-01

    Humans and other animals have been shown to perform near-optimally in multi-sensory integration tasks. Probabilistic population codes (PPCs) have been proposed as a mechanism by which optimal integration can be accomplished. Previous approaches have focussed on how neural networks might produce PPCs from sensory input or perform calculations using them, like combining multiple PPCs. Less attention has been given to the question of how the necessary organisation of neurons can arise and how the required knowledge about the input statistics can be learned. In this paper, we propose a model of learning multi-sensory integration based on an unsupervised learning algorithm in which an artificial neural network learns the noise characteristics of each of its sources of input. Our algorithm borrows from the self-organising map the ability to learn latent-variable models of the input and extends it to learning to produce a PPC approximating a probability density function over the latent variable behind its (noisy) input. The neurons in our network are only required to perform simple calculations and we make few assumptions about input noise properties and tuning functions. We report on a neurorobotic experiment in which we apply our algorithm to multi-sensory integration in a humanoid robot to demonstrate its effectiveness and compare it to human multi-sensory integration on the behavioural level. We also show in simulations that our algorithm performs near-optimally under certain plausible conditions, and that it reproduces important aspects of natural multi-sensory integration on the neural level.

  4. Neural population coding: combining insights from microscopic and mass signals

    PubMed Central

    Panzeri, Stefano; Macke, Jakob H.; Gross, Joachim; Kayser, Christoph

    2015-01-01

    Behavior relies on the distributed and coordinated activity of neural populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local neural population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed populations that collectively shape behavior. PMID:25670005

  5. Neural Simulations on Multi-Core Architectures

    PubMed Central

    Eichner, Hubert; Klug, Tobias; Borst, Alexander

    2009-01-01

    Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i.e. user-transparent load balancing. PMID:19636393

  6. Independent Optical Excitation of Distinct Neural Populations

    PubMed Central

    Klapoetke, Nathan C; Murata, Yasunobu; Kim, Sung Soo; Pulver, Stefan R.; Birdsey-Benson, Amanda; Cho, Yong Ku; Morimoto, Tania K; Chuong, Amy S; Carpenter, Eric J; Tian, Zhijian; Wang, Jun; Xie, Yinlong; Yan, Zhixiang; Zhang, Yong; Chow, Brian Y; Surek, Barbara; Melkonian, Michael; Jayaraman, Vivek; Constantine-Paton, Martha; Wong, Gane Ka-Shu; Boyden, Edward S

    2014-01-01

    Optogenetic tools enable the causal examination of how specific cell types contribute to brain circuit functions. A long-standing question is whether it is possible to independently activate two distinct neural populations in mammalian brain tissue. Such a capability would enable the examination of how different synapses or pathways interact to support computation. Here we report two new channelrhodopsins, Chronos and Chrimson, obtained through the de novo sequencing and physiological characterization of opsins from over 100 species of algae. Chrimson is 45 nm red-shifted relative to any previous channelrhodopsin, important for scenarios where red light would be preferred; we show minimal visual system mediated behavioral artifact in optogenetically stimulated Drosophila. Chronos has faster kinetics than any previous channelrhodopsin, yet is effectively more light-sensitive. Together, these two reagents enable crosstalk-free two-color activation of neural spiking and downstream synaptic transmission in independent neural populations in mouse brain slice. PMID:24509633

  7. Optimal attentional modulation of a neural population

    PubMed Central

    Borji, Ali; Itti, Laurent

    2014-01-01

    Top-down attention has often been separately studied in the contexts of either optimal population coding or biasing of visual search. Yet, both are intimately linked, as they entail optimally modulating sensory variables in neural populations according to top-down goals. Designing experiments to probe top-down attentional modulation is difficult because non-linear population dynamics are hard to predict in the absence of a concise theoretical framework. Here, we describe a unified framework that encompasses both contexts. Our work sheds light onto the ongoing debate on whether attention modulates neural response gain, tuning width, and/or preferred feature. We evaluate the framework by conducting simulations for two tasks: (1) classification (discrimination) of two stimuli sa and sb and (2) searching for a target T among distractors D. Results demonstrate that all of gain, tuning, and preferred feature modulation happen to different extents, depending on stimulus conditions and task demands. The theoretical analysis shows that task difficulty (linked to difference Δ between sa and sb, or T, and D) is a crucial factor in optimal modulation, with different effects in discrimination vs. search. Further, our framework allows us to quantify the relative utility of neural parameters. In easy tasks (when Δ is large compared to the density of the neural population), modulating gains and preferred features is sufficient to yield nearly optimal performance; however, in difficult tasks (smaller Δ), modulating tuning width becomes necessary to improve performance. This suggests that the conflicting reports from different experimental studies may be due to differences in tasks and in their difficulties. We further propose future electrophysiology experiments to observe different types of attentional modulation in a same neuron. PMID:24723881

  8. Dynamical criticality in the collective activity of a neural population

    NASA Astrophysics Data System (ADS)

    Mora, Thierry

    The past decade has seen a wealth of physiological data suggesting that neural networks may behave like critical branching processes. Concurrently, the collective activity of neurons has been studied using explicit mappings to classic statistical mechanics models such as disordered Ising models, allowing for the study of their thermodynamics, but these efforts have ignored the dynamical nature of neural activity. I will show how to reconcile these two approaches by learning effective statistical mechanics models of the full history of the collective activity of a neuron population directly from physiological data, treating time as an additional dimension. Applying this technique to multi-electrode recordings from retinal ganglion cells, and studying the thermodynamics of the inferred model, reveals a peak in specific heat reminiscent of a second-order phase transition.

  9. Population clocks: motor timing with neural dynamics

    PubMed Central

    Buonomano, Dean V.; Laje, Rodrigo

    2010-01-01

    An understanding of sensory and motor processing will require elucidation of the mechanisms by which the brain tells time. Open questions relate to whether timing relies on dedicated or intrinsic mechanisms and whether distinct mechanisms underlie timing across scales and modalities. Although experimental and theoretical studies support the notion that neural circuits are intrinsically capable of sensory timing on short scales, few general models of motor timing have been proposed. For one class of models, population clocks, it is proposed that time is encoded in the time-varying patterns of activity of a population of neurons. We argue that population clocks emerge from the internal dynamics of recurrently connected networks, are biologically realistic and account for many aspects of motor timing. PMID:20889368

  10. Neural population densities shape network correlations

    NASA Astrophysics Data System (ADS)

    Lefebvre, Jérémie; Perkins, Theodore J.

    2012-02-01

    The way sensory microcircuits manage cellular response correlations is a crucial question in understanding how such systems integrate external stimuli and encode information. Most sensory systems exhibit heterogeneities in terms of population sizes and features, which all impact their dynamics. This work addresses how correlations between the dynamics of neural ensembles depend on the relative size or density of excitatory and inhibitory populations. To do so, we study an apparently symmetric system of coupled stochastic differential equations that model the evolution of the populations’ activities. Excitatory and inhibitory populations are connected by reciprocal recurrent connections, and both receive different stimuli exhibiting a certain level of correlation with each other. A stability analysis is performed, which reveals an intrinsic asymmetry in the distribution of the fixed points with respect to the threshold of the nonlinearities. Based on this, we show how the cross correlation between the population responses depends on the density of the inhibitory population, and that a specific ratio between both population sizes leads to a state of zero correlation. We show that this so-called asynchronous state subsists, despite the presence of stimulus correlation, and most importantly, that it occurs only in asymmetrical systems where one population outnumbers the other. Using linear approximations, we derive analytical expressions for the root of the cross-correlation function and study how the asynchronous state is impacted by the model's parameters. This work suggests a possible explanation for why inhibitory cells outnumber excitatory cells in the visual system.

  11. Neural decoding of collective wisdom with multi-brain computing.

    PubMed

    Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry

    2012-01-02

    Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally

  12. From neural responses to population behavior: neural focus group predicts population-level media effects.

    PubMed

    Falk, Emily B; Berkman, Elliot T; Lieberman, Matthew D

    2012-05-01

    Can neural responses of a small group of individuals predict the behavior of large-scale populations? In this investigation, brain activations were recorded while smokers viewed three different television campaigns promoting the National Cancer Institute's telephone hotline to help smokers quit (1-800-QUIT-NOW). The smokers also provided self-report predictions of the campaigns' relative effectiveness. Population measures of the success of each campaign were computed by comparing call volume to 1-800-QUIT-NOW in the month before and the month after the launch of each campaign. This approach allowed us to directly compare the predictive value of self-reports with neural predictors of message effectiveness. Neural activity in a medial prefrontal region of interest, previously associated with individual behavior change, predicted the population response, whereas self-report judgments did not. This finding suggests a novel way of connecting neural signals to population responses that has not been previously demonstrated and provides information that may be difficult to obtain otherwise.

  13. Intermediate intrinsic diversity enhances neural population coding.

    PubMed

    Tripathy, Shreejoy J; Padmanabhan, Krishnan; Gerkin, Richard C; Urban, Nathaniel N

    2013-05-14

    Cell-to-cell variability in molecular, genetic, and physiological features is increasingly recognized as a critical feature of complex biological systems, including the brain. Although such variability has potential advantages in robustness and reliability, how and why biological circuits assemble heterogeneous cells into functional groups is poorly understood. Here, we develop analytic approaches toward answering how neuron-level variation in intrinsic biophysical properties of olfactory bulb mitral cells influences population coding of fluctuating stimuli. We capture the intrinsic diversity of recorded populations of neurons through a statistical approach based on generalized linear models. These models are flexible enough to predict the diverse responses of individual neurons yet provide a common reference frame for comparing one neuron to the next. We then use Bayesian stimulus decoding to ask how effectively different populations of mitral cells, varying in their diversity, encode a common stimulus. We show that a key advantage provided by physiological levels of intrinsic diversity is more efficient and more robust encoding of stimuli by the population as a whole. However, we find that the populations that best encode stimulus features are not simply the most heterogeneous, but those that balance diversity with the benefits of neural similarity.

  14. Intermediate intrinsic diversity enhances neural population coding

    PubMed Central

    Tripathy, Shreejoy J.; Padmanabhan, Krishnan; Gerkin, Richard C.; Urban, Nathaniel N.

    2013-01-01

    Cell-to-cell variability in molecular, genetic, and physiological features is increasingly recognized as a critical feature of complex biological systems, including the brain. Although such variability has potential advantages in robustness and reliability, how and why biological circuits assemble heterogeneous cells into functional groups is poorly understood. Here, we develop analytic approaches toward answering how neuron-level variation in intrinsic biophysical properties of olfactory bulb mitral cells influences population coding of fluctuating stimuli. We capture the intrinsic diversity of recorded populations of neurons through a statistical approach based on generalized linear models. These models are flexible enough to predict the diverse responses of individual neurons yet provide a common reference frame for comparing one neuron to the next. We then use Bayesian stimulus decoding to ask how effectively different populations of mitral cells, varying in their diversity, encode a common stimulus. We show that a key advantage provided by physiological levels of intrinsic diversity is more efficient and more robust encoding of stimuli by the population as a whole. However, we find that the populations that best encode stimulus features are not simply the most heterogeneous, but those that balance diversity with the benefits of neural similarity. PMID:23630284

  15. Multi/infinite dimensional neural networks, multi/infinite dimensional logic theory.

    PubMed

    Murthy, Garimella Rama

    2005-06-01

    A mathematical model of an arbitrary multi-dimensional neural network is developed and a convergence theorem for an arbitrary multi-dimensional neural network represented by a fully symmetric tensor is stated and proved. The input and output signal states of a multi-dimensional neural network/logic gate are related through an energy function, defined over the fully symmetric tensor (representing the connection structure of a multi-dimensional neural network). The inputs and outputs are related such that the minimum/maximum energy states correspond to the output states of the logic gate/neural network realizing a logic function. Similarly, a logic circuit consisting of the interconnection of logic gates, represented by a block symmetric tensor, is associated with a quadratic/higher degree energy function. Infinite dimensional logic theory is discussed through the utilization of infinite dimension/order tensors.

  16. Ventrally emigrating neural tube (VENT) cells: a second neural tube-derived cell population.

    PubMed

    Dickinson, Douglas P; Machnicki, Michal; Ali, Mohammed M; Zhang, Zhanying; Sohal, Gurkirpal S

    2004-08-01

    Two embryological fates for cells of the neural tube are well established. Cells from the dorsal part of the developing neural tube emigrate and become neural crest cells, which in turn contribute to the development of the peripheral nervous system and a variety of non-neural structures. Other neural tube cells form the neurons and glial cells of the central nervous system (CNS). This has led to the neural crest being treated as the sole neural tube-derived emigrating cell population, with the remaining neural tube cells assumed to be restricted to forming the CNS. However, this restriction has not been tested fully. Our investigations of chick, quail and duck embryos utilizing a variety of different labelling techniques (DiI, LacZ, GFP and quail chimera) demonstrate the existence of a second neural tube-derived emigrating cell population. These cells originate from the ventral part of the cranial neural tube, emigrate at the exit/entry site of the cranial nerves, migrate in association with the nerves and populate their target tissues. On the basis of its site of origin and route of migration we have named this cell population the ventrally emigrating neural tube (VENT) cells. VENT cells also differ from neural crest cells in that they emigrate considerably after the emigration of neural crest cells, and lack expression of the neural crest cell antigen HNK-1. VENT cells are multipotent, differentiating into cell types belonging to all four basic tissues in the body: the nerve, muscle, connective and epithelium. Thus, the neural tube provides at least two cell populations--neural crest and VENT cells--that contribute to the development of the peripheral nervous system and various non-neural structures. This review describes the origin of the idea of VENT cells, and discusses evidence for their existence and subsequent fates.

  17. Distinguishing causal interactions in neural populations.

    PubMed

    Seth, Anil K; Edelman, Gerald M

    2007-04-01

    We describe a theoretical network analysis that can distinguish statistically causal interactions in population neural activity leading to a specific output. We introduce the concept of a causal core to refer to the set of neuronal interactions that are causally significant for the output, as assessed by Granger causality. Because our approach requires extensive knowledge of neuronal connectivity and dynamics, an illustrative example is provided by analysis of Darwin X, a brain-based device that allows precise recording of the activity of neuronal units during behavior. In Darwin X, a simulated neuronal model of the hippocampus and surrounding cortical areas supports learning of a spatial navigation task in a real environment. Analysis of Darwin X reveals that large repertoires of neuronal interactions contain comparatively small causal cores and that these causal cores become smaller during learning, a finding that may reflect the selection of specific causal pathways from diverse neuronal repertoires.

  18. A thesaurus for a neural population code

    PubMed Central

    Ganmor, Elad; Segev, Ronen; Schneidman, Elad

    2015-01-01

    Information is carried in the brain by the joint spiking patterns of large groups of noisy, unreliable neurons. This noise limits the capacity of the neural code and determines how information can be transmitted and read-out. To accurately decode, the brain must overcome this noise and identify which patterns are semantically similar. We use models of network encoding noise to learn a thesaurus for populations of neurons in the vertebrate retina responding to artificial and natural videos, measuring the similarity between population responses to visual stimuli based on the information they carry. This thesaurus reveals that the code is organized in clusters of synonymous activity patterns that are similar in meaning but may differ considerably in their structure. This organization is highly reminiscent of the design of engineered codes. We suggest that the brain may use this structure and show how it allows accurate decoding of novel stimuli from novel spiking patterns. DOI: http://dx.doi.org/10.7554/eLife.06134.001 PMID:26347983

  19. Multi-Agent Reinforcement Learning and Adaptive Neural Networks.

    DTIC Science & Technology

    2007-11-02

    learning method. The objective was to study the utility of reinforcement learning as an approach to complex decentralized control problems. The major...accomplishment was a detailed study of multi-agent reinforcement learning applied to a large-scale decentralized stochastic control problem. This study...included a very successful demonstration that a multi-agent reinforcement learning system using neural networks could learn high-performance

  20. A wireless multi-channel neural amplifier for freely moving animals.

    PubMed

    Szuts, Tobi A; Fadeyev, Vitaliy; Kachiguine, Sergei; Sher, Alexander; Grivich, Matthew V; Agrochão, Margarida; Hottowy, Pawel; Dabrowski, Wladyslaw; Lubenov, Evgueniy V; Siapas, Athanassios G; Uchida, Naoshige; Litke, Alan M; Meister, Markus

    2011-02-01

    Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from rats. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them into one signal and transmits that output by radio frequency to a receiver up to 60 m away. The system introduces <4 μV of electrode-referred noise, comparable to wired recording systems, and outperforms existing rodent telemetry systems in channel count, weight and transmission range. This allows effective recording of brain signals in freely behaving animals. We report measurements of neural population activity taken outdoors and in tunnels. Neural firing in the visual cortex was relatively sparse, correlated even across large distances and was strongly influenced by locomotor activity.

  1. ART-Based Neural Networks for Multi-label Classification

    NASA Astrophysics Data System (ADS)

    Sapozhnikova, Elena P.

    Multi-label classification is an active and rapidly developing research area of data analysis. It becomes increasingly important in such fields as gene function prediction, text classification or web mining. This task corresponds to classification of instances labeled by multiple classes rather than just one. Traditionally, it was solved by learning independent binary classifiers for each class and combining their outputs to obtain multi-label predictions. Alternatively, a classifier can be directly trained to predict a label set of an unknown size for each unseen instance. Recently, several direct multi-label machine learning algorithms have been proposed. This paper presents a novel approach based on ART (Adaptive Resonance Theory) neural networks. The Fuzzy ARTMAP and ARAM algorithms were modified in order to improve their multi-label classification performance and were evaluated on benchmark datasets. Comparison of experimental results with the results of other multi-label classifiers shows the effectiveness of the proposed approach.

  2. Neural networks within multi-core optic fibers

    PubMed Central

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

    2016-01-01

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks. PMID:27383911

  3. Neural networks within multi-core optic fibers

    NASA Astrophysics Data System (ADS)

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

    2016-07-01

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

  4. Neural networks within multi-core optic fibers.

    PubMed

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

    2016-07-07

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

  5. Spontaneous Neural Dynamics and Multi-scale Network Organization

    PubMed Central

    Foster, Brett L.; He, Biyu J.; Honey, Christopher J.; Jerbi, Karim; Maier, Alexander; Saalmann, Yuri B.

    2016-01-01

    Spontaneous neural activity has historically been viewed as task-irrelevant noise that should be controlled for via experimental design, and removed through data analysis. However, electrophysiology and functional MRI studies of spontaneous activity patterns, which have greatly increased in number over the past decade, have revealed a close correspondence between these intrinsic patterns and the structural network architecture of functional brain circuits. In particular, by analyzing the large-scale covariation of spontaneous hemodynamics, researchers are able to reliably identify functional networks in the human brain. Subsequent work has sought to identify the corresponding neural signatures via electrophysiological measurements, as this would elucidate the neural origin of spontaneous hemodynamics and would reveal the temporal dynamics of these processes across slower and faster timescales. Here we survey common approaches to quantifying spontaneous neural activity, reviewing their empirical success, and their correspondence with the findings of neuroimaging. We emphasize invasive electrophysiological measurements, which are amenable to amplitude- and phase-based analyses, and which can report variations in connectivity with high spatiotemporal precision. After summarizing key findings from the human brain, we survey work in animal models that display similar multi-scale properties. We highlight that, across many spatiotemporal scales, the covariance structure of spontaneous neural activity reflects structural properties of neural networks and dynamically tracks their functional repertoire. PMID:26903823

  6. Stimulus-dependent Maximum Entropy Models of Neural Population Codes

    PubMed Central

    Segev, Ronen; Schneidman, Elad

    2013-01-01

    Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population. PMID:23516339

  7. Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

    PubMed

    Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

    2017-08-13

    The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Beta band oscillations in motor cortex reflect neural population signals that delay movement onset

    PubMed Central

    Khanna, Preeya; Carmena, Jose M

    2017-01-01

    Motor cortical beta oscillations have been reported for decades, yet their behavioral correlates remain unresolved. Some studies link beta oscillations to changes in underlying neural activity, but the specific behavioral manifestations of these reported changes remain elusive. To investigate how changes in population neural activity, beta oscillations, and behavior are linked, we recorded multi-scale neural activity from motor cortex while three macaques performed a novel neurofeedback task. Subjects volitionally brought their beta oscillatory power to an instructed state and subsequently executed an arm reach. Reaches preceded by a reduction in beta power exhibited significantly faster movement onset times than reaches preceded by an increase in beta power. Further, population neural activity was found to shift farther from a movement onset state during beta oscillations that were neurofeedback-induced or naturally occurring during reaching tasks. This finding establishes a population neural basis for slowed movement onset following periods of beta oscillatory activity. DOI: http://dx.doi.org/10.7554/eLife.24573.001 PMID:28467303

  9. Internal models for interpreting neural population activity during sensorimotor control.

    PubMed

    Golub, Matthew D; Yu, Byron M; Chase, Steven M

    2015-12-08

    To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects' internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.

  10. Surrogate population models for large-scale neural simulations.

    PubMed

    Tripp, Bryan P

    2015-06-01

    Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to multiscale modeling of neural systems that involves constructing efficient surrogate models of populations. Given a population of neuron models with correlated activity and with specific, nonrandom connections, a surrogate model is constructed in order to approximate the aggregate outputs of the population. The surrogate model requires less computation than the neural model, but it has a clear and specific relationship with the neural model. For example, approximate spike rasters for specific neurons can be derived from a simulation of the surrogate model. This article deals specifically with neural engineering framework (NEF) circuits of leaky-integrate-and-fire point neurons. Weighted sums of spikes are modeled by interpolating over latent variables in the population activity, and linear filters operate on gaussian random variables to approximate spike-related fluctuations. It is found that the surrogate models can often closely approximate network behavior with orders-of-magnitude reduction in computational demands, although there are certain systematic differences between the spiking and surrogate models. Since individual spikes are not modeled, some simulations can be performed with much longer steps sizes (e.g., 20 ms). Possible extensions to non-NEF networks and to more complex neuron models are discussed.

  11. Simultaneous multi-plane imaging of neural circuits

    PubMed Central

    Yang, Weijian; Miller, Jae-eun Kang; Carrillo-Reid, Luis; Pnevmatikakis, Eftychios; Paninski, Liam; Yuste, Rafael; Peterka, Darcy S.

    2015-01-01

    Summary Recording the activity of large populations of neurons is an important step towards understanding the emergent function of neural circuits. Here we present a simple holographic method to simultaneously perform two-photon calcium imaging of neuronal populations across multiple areas and layers of mouse cortex in vivo. We use prior knowledge of neuronal locations, activity sparsity and a constrained nonnegative matrix factorization algorithm to extract signals from neurons imaged simultaneously and located in different focal planes or fields of view. Our laser multiplexing approach is simple and fast and could be used as a general method to image the activity of neural circuits in three dimensions across multiple areas in the brain. PMID:26774159

  12. Probabilistic models for neural populations that naturally capture global coupling and criticality.

    PubMed

    Humplik, Jan; Tkačik, Gašper

    2017-09-01

    Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system's state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.

  13. What Can Population Calcium Imaging Tell Us About Neural Circuits?

    PubMed Central

    Kwan, Alex C.

    2008-01-01

    Calcium imaging of bulk-loaded fluorescent indicators can be used to record the spiking activity of hundreds of neurons. Recent advances promise imaging technologies that are faster, more efficient, and applicable to awake animals, thereby moving imaging capabilities closer to the traditional strengths of multi-electrode arrays. This article reviews these technical achievements and discusses how they can help us achieve the goal of understanding neural circuits. PMID:18829844

  14. Understanding the Implications of Neural Population Activity on Behavior

    NASA Astrophysics Data System (ADS)

    Briguglio, John

    Learning how neural activity in the brain leads to the behavior we exhibit is one of the fundamental questions in Neuroscience. In this dissertation, several lines of work are presented to that use principles of neural coding to understand behavior. In one line of work, we formulate the efficient coding hypothesis in a non-traditional manner in order to test human perceptual sensitivity to complex visual textures. We find a striking agreement between how variable a particular texture signal is and how sensitive humans are to its presence. This reveals that the efficient coding hypothesis is still a guiding principle for neural organization beyond the sensory periphery, and that the nature of cortical constraints differs from the peripheral counterpart. In another line of work, we relate frequency discrimination acuity to neural responses from auditory cortex in mice. It has been previously observed that optogenetic manipulation of auditory cortex, in addition to changing neural responses, evokes changes in behavioral frequency discrimination. We are able to account for changes in frequency discrimination acuity on an individual basis by examining the Fisher information from the neural population with and without optogenetic manipulation. In the third line of work, we address the question of what a neural population should encode given that its inputs are responses from another group of neurons. Drawing inspiration from techniques in machine learning, we train Deep Belief Networks on fake retinal data and show the emergence of Garbor-like filters, reminiscent of responses in primary visual cortex. In the last line of work, we model the state of a cortical excitatory-inhibitory network during complex adaptive stimuli. Using a rate model with Wilson-Cowan dynamics, we demonstrate that simple non-linearities in the signal transferred from inhibitory to excitatory neurons can account for real neural recordings taken from auditory cortex. This work establishes and tests

  15. Braided Multi-Electrode Probes (BMEPs) for Neural Interfaces

    NASA Astrophysics Data System (ADS)

    Kim, Tae Gyo

    Although clinical use of invasive neural interfaces is very limited, due to safety and reliability concerns, the potential benefits of their use in brain machine interfaces (BMIs) seem promising and so they have been widely used in the research field. Microelectrodes as invasive neural interfaces are the core tool to record neural activities and their failure is a critical issue for BMI systems. Possible sources of this failure are neural tissue motions and their interactions with stiff electrode arrays or probes fixed to the skull. To overcome these tissue motion problems, we have developed novel braided multi-electrode probes (BMEPs). By interweaving ultra-fine wires into a tubular braid structure, we obtained a highly flexible multi-electrode probe. In this thesis we described BMEP designs and how to fabricate BMEPs, and explore experiments to show the advantages of BMEPs through a mechanical compliance comparison and a chronic immunohistological comparison with single 50microm nichrome wires used as a reference electrode type. Results from the mechanical compliance test showed that the bodies of BMEPs have 4 to 21 times higher compliance than the single 50microm wire and the tethers of BMEPs were 6 to 96 times higher compliance, depending on combinations of the wire size (9.6microm or 12.7microm), the wire numbers (12 or 24), and the length of tether (3, 5 or 10 mm). Results from the immunohistological comparison showed that both BMEPs and 50microm wires anchored to the skull caused stronger tissue reactions than unanchored BMEPs and 50microm wires, and 50microm wires caused stronger tissue reactions than BMEPs. In in-vivo tests with BMEPs, we succeeded in chronic recordings from the spinal cord of freely jumping frogs and in acute recordings from the spinal cord of decerebrate rats during air stepping which was evoked by mesencephalic locomotor region (MLR) stimulation. This technology may provide a stable and reliable neural interface to spinal cord

  16. Multi-Layer and Recursive Neural Networks for Metagenomic Classification.

    PubMed

    Ditzler, Gregory; Polikar, Robi; Rosen, Gail

    2015-09-01

    Recent advances in machine learning, specifically in deep learning with neural networks, has made a profound impact on fields such as natural language processing, image classification, and language modeling; however, feasibility and potential benefits of the approaches to metagenomic data analysis has been largely under-explored. Deep learning exploits many layers of learning nonlinear feature representations, typically in an unsupervised fashion, and recent results have shown outstanding generalization performance on previously unseen data. Furthermore, some deep learning methods can also represent the structure in a data set. Consequently, deep learning and neural networks may prove to be an appropriate approach for metagenomic data. To determine whether such approaches are indeed appropriate for metagenomics, we experiment with two deep learning methods: i) a deep belief network, and ii) a recursive neural network, the latter of which provides a tree representing the structure of the data. We compare these approaches to the standard multi-layer perceptron, which has been well-established in the machine learning community as a powerful prediction algorithm, though its presence is largely missing in metagenomics literature. We find that traditional neural networks can be quite powerful classifiers on metagenomic data compared to baseline methods, such as random forests. On the other hand, while the deep learning approaches did not result in improvements to the classification accuracy, they do provide the ability to learn hierarchical representations of a data set that standard classification methods do not allow. Our goal in this effort is not to determine the best algorithm in terms accuracy-as that depends on the specific application-but rather to highlight the benefits and drawbacks of each of the approach we discuss and provide insight on how they can be improved for predictive metagenomic analysis.

  17. Multi-channel fiber photometry for population neuronal activity recording.

    PubMed

    Guo, Qingchun; Zhou, Jingfeng; Feng, Qiru; Lin, Rui; Gong, Hui; Luo, Qingming; Zeng, Shaoqun; Luo, Minmin; Fu, Ling

    2015-10-01

    Fiber photometry has become increasingly popular among neuroscientists as a convenient tool for the recording of genetically defined neuronal population in behaving animals. Here, we report the development of the multi-channel fiber photometry system to simultaneously monitor neural activities in several brain areas of an animal or in different animals. In this system, a galvano-mirror modulates and cyclically couples the excitation light to individual multimode optical fiber bundles. A single photodetector collects excited light and the configuration of fiber bundle assembly and the scanner determines the total channel number. We demonstrated that the system exhibited negligible crosstalk between channels and optical signals could be sampled simultaneously with a sample rate of at least 100 Hz for each channel, which is sufficient for recording calcium signals. Using this system, we successfully recorded GCaMP6 fluorescent signals from the bilateral barrel cortices of a head-restrained mouse in a dual-channel mode, and the orbitofrontal cortices of multiple freely moving mice in a triple-channel mode. The multi-channel fiber photometry system would be a valuable tool for simultaneous recordings of population activities in different brain areas of a given animal and different interacting individuals.

  18. Multi-channel fiber photometry for population neuronal activity recording

    PubMed Central

    Guo, Qingchun; Zhou, Jingfeng; Feng, Qiru; Lin, Rui; Gong, Hui; Luo, Qingming; Zeng, Shaoqun; Luo, Minmin; Fu, Ling

    2015-01-01

    Fiber photometry has become increasingly popular among neuroscientists as a convenient tool for the recording of genetically defined neuronal population in behaving animals. Here, we report the development of the multi-channel fiber photometry system to simultaneously monitor neural activities in several brain areas of an animal or in different animals. In this system, a galvano-mirror modulates and cyclically couples the excitation light to individual multimode optical fiber bundles. A single photodetector collects excited light and the configuration of fiber bundle assembly and the scanner determines the total channel number. We demonstrated that the system exhibited negligible crosstalk between channels and optical signals could be sampled simultaneously with a sample rate of at least 100 Hz for each channel, which is sufficient for recording calcium signals. Using this system, we successfully recorded GCaMP6 fluorescent signals from the bilateral barrel cortices of a head-restrained mouse in a dual-channel mode, and the orbitofrontal cortices of multiple freely moving mice in a triple-channel mode. The multi-channel fiber photometry system would be a valuable tool for simultaneous recordings of population activities in different brain areas of a given animal and different interacting individuals. PMID:26504642

  19. Gamma oscillations of spiking neural populations enhance signal discrimination.

    PubMed

    Masuda, Naoki; Doiron, Brent

    2007-11-01

    Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive.

  20. Internal models for interpreting neural population activity during sensorimotor control

    PubMed Central

    Golub, Matthew D; Yu, Byron M; Chase, Steven M

    2015-01-01

    To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects’ internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output. DOI: http://dx.doi.org/10.7554/eLife.10015.001 PMID:26646183

  1. Critical behavior of large maximally informative neural populations

    NASA Astrophysics Data System (ADS)

    Berkowitz, John; Sharpee, Tatyana

    We consider maximally informative encoding of scalar signals by neural populations. In a small time window, neural responses are binary, with spiking probability that follows a sigmoidal tuning curve. The width of the tuning curve represents effective noise in neural transmission. Previous analyses of this problem for relatively small numbers of neurons with identical noise parameters indicated the presence of multiple bifurcations that occurred with decreasing noise value. For very high noise values, maximal information is achieved when all neurons have the same threshold values. With decreasing noise, the threshold values split into two or more groups via a series of bifurcations, until finally each neuron has a different threshold. Analyzing this problem in the large N limit, we found instead that there is a single phase transition from redundant coding to coding based on distributed thresholds. The order parameter of this transition is the threshold standard deviation across the population; differences in noise parameter from the mean are analogous to local magnetic fields. Near the bifurcation point, information transmitted follows a Landau expansion. We use this expansion to quantify the scaling of the order parameter with noise and effective magnetic field. NSF CAREER Award IIS-1254123, NSF Ideas Lab Collaborative Research IOS 1556388.

  2. Measuring Fisher Information Accurately in Correlated Neural Populations

    PubMed Central

    Kohn, Adam; Pouget, Alexandre

    2015-01-01

    Neural responses are known to be variable. In order to understand how this neural variability constrains behavioral performance, we need to be able to measure the reliability with which a sensory stimulus is encoded in a given population. However, such measures are challenging for two reasons: First, they must take into account noise correlations which can have a large influence on reliability. Second, they need to be as efficient as possible, since the number of trials available in a set of neural recording is usually limited by experimental constraints. Traditionally, cross-validated decoding has been used as a reliability measure, but it only provides a lower bound on reliability and underestimates reliability substantially in small datasets. We show that, if the number of trials per condition is larger than the number of neurons, there is an alternative, direct estimate of reliability which consistently leads to smaller errors and is much faster to compute. The superior performance of the direct estimator is evident both for simulated data and for neuronal population recordings from macaque primary visual cortex. Furthermore we propose generalizations of the direct estimator which measure changes in stimulus encoding across conditions and the impact of correlations on encoding and decoding, typically denoted by Ishuffle and Idiag respectively. PMID:26030735

  3. Multi-Photon Time Lapse Imaging to Visualize Development in Real-time: Visualization of Migrating Neural Crest Cells in Zebrafish Embryos.

    PubMed

    Williams, Antionette L; Bohnsack, Brenda L

    2017-08-09

    Congenital eye and craniofacial anomalies reflect disruptions in the neural crest, a transient population of migratory stem cells that give rise to numerous cell types throughout the body. Understanding the biology of the neural crest has been limited, reflecting a lack of genetically tractable models that can be studied in vivo and in real-time. Zebrafish is a particularly important developmental model for studying migratory cell populations, such as the neural crest. To examine neural crest migration into the developing eye, a combination of the advanced optical techniques of laser scanning microscopy with long wavelength multi-photon fluorescence excitation was implemented to capture high-resolution, three-dimensional, real-time videos of the developing eye in transgenic zebrafish embryos, namely Tg(sox10:EGFP) and Tg(foxd3:GFP), as sox10 and foxd3 have been shown in numerous animal models to regulate early neural crest differentiation and likely represent markers for neural crest cells. Multi-photon time-lapse imaging was used to discern the behavior and migratory patterns of two neural crest cell populations contributing to early eye development. This protocol provides information for generating time-lapse videos during zebrafish neural crest migration, as an example, and can be further applied to visualize the early development of many structures in the zebrafish and other model organisms.

  4. Neural Population Dynamics Modeled by Mean-Field Graphs

    NASA Astrophysics Data System (ADS)

    Kozma, Robert; Puljic, Marko

    2011-09-01

    In this work we apply random graph theory approach to describe neural population dynamics. There are important advantages of using random graph theory approach in addition to ordinary and partial differential equations. The mathematical theory of large-scale random graphs provides an efficient tool to describe transitions between high- and low-dimensional spaces. Recent advances in studying neural correlates of higher cognition indicate the significance of sudden changes in space-time neurodynamics, which can be efficiently described as phase transitions in the neuropil medium. Phase transitions are rigorously defined mathematically on random graph sequences and they can be naturally generalized to a class of percolation processes called neuropercolation. In this work we employ mean-field graphs with given vertex degree distribution and edge strength distribution. We demonstrate the emergence of collective oscillations in the style of brains.

  5. Multi-Population Classical HLA Type Imputation

    PubMed Central

    Moutsianas, Loukas; Shen, Judong; Cox, Charles; Nelson, Matthew R.; McVean, Gil

    2013-01-01

    Statistical imputation of classical HLA alleles in case-control studies has become established as a valuable tool for identifying and fine-mapping signals of disease association in the MHC. Imputation into diverse populations has, however, remained challenging, mainly because of the additional haplotypic heterogeneity introduced by combining reference panels of different sources. We present an HLA type imputation model, HLA*IMP:02, designed to operate on a multi-population reference panel. HLA*IMP:02 is based on a graphical representation of haplotype structure. We present a probabilistic algorithm to build such models for the HLA region, accommodating genotyping error, haplotypic heterogeneity and the need for maximum accuracy at the HLA loci, generalizing the work of Browning and Browning (2007) and Ron et al. (1998). HLA*IMP:02 achieves an average 4-digit imputation accuracy on diverse European panels of 97% (call rate 97%). On non-European samples, 2-digit performance is over 90% for most loci and ethnicities where data available. HLA*IMP:02 supports imputation of HLA-DPB1 and HLA-DRB3-5, is highly tolerant of missing data in the imputation panel and works on standard genotype data from popular genotyping chips. It is publicly available in source code and as a user-friendly web service framework. PMID:23459081

  6. Seismic response modeling of multi-story buildings using neural networks

    NASA Astrophysics Data System (ADS)

    Conte, Joel P.; Durrani, Ahmad J.; Shelton, Robert O.

    1994-05-01

    A neural network based approach to model the seismic response of multi-story frame buildings is presented. The seismic response of frames is emulated using multi-layer feedforward neural networks with a backpropagation learning algorithm. Actual earthquake accelerograms and corresponding structural response obtained from analytical models of buildings are used in training the neural networks. The application of the neural network model is demonstrated by studying one to six story high building frames subjected to seismic base excitation. Furthermore, the learning ability of the network is examined for the case of multiple inputs where lateral forces at floor levels are included simultaneously with the base excitation. The effects of the network parameters on learning and accuracy of predictions are discussed. Based on this study, it is found that appropriately configured neural network models can successfully learn and simulate the linear elastic dynamic behavior of multi-story buildings.

  7. Sensory signals in neural populations underlying tactile perception and manipulation.

    PubMed

    Goodwin, Antony W; Wheat, Heather E

    2004-01-01

    For humans to manipulate an object successfully, the motor control system must have accurate information about parameters such as the shape of the stimulus, its position of contact on the skin, and the magnitude and direction of contact force. The same information is required for perception during haptic exploration of an object. Much of these data are relayed by the mechanoreceptive afferents innervating the glabrous skin of the digits. Single afferent responses are modulated by all the relevant stimulus parameters. Thus, only in complete population reconstructions is it clear how each of the parameters can be signaled to the brain independently when many are changing simultaneously, as occurs in most normal movements or haptic exploration. Modeling population responses reveals how resolution is affected by neural noise and intrinsic properties of the population such as the pattern and density of innervation and the covariance of response variability.

  8. A multi-views multi-learners approach towards dysarthric speech recognition using multi-nets artificial neural networks.

    PubMed

    Shahamiri, Seyed Reza; Salim, Siti Salwah Binti

    2014-09-01

    Automatic speech recognition (ASR) can be very helpful for speakers who suffer from dysarthria, a neurological disability that damages the control of motor speech articulators. Although a few attempts have been made to apply ASR technologies to sufferers of dysarthria, previous studies show that such ASR systems have not attained an adequate level of performance. In this study, a dysarthric multi-networks speech recognizer (DM-NSR) model is provided using a realization of multi-views multi-learners approach called multi-nets artificial neural networks, which tolerates variability of dysarthric speech. In particular, the DM-NSR model employs several ANNs (as learners) to approximate the likelihood of ASR vocabulary words and to deal with the complexity of dysarthric speech. The proposed DM-NSR approach was presented as both speaker-dependent and speaker-independent paradigms. In order to highlight the performance of the proposed model over legacy models, multi-views single-learner models of the DM-NSRs were also provided and their efficiencies were compared in detail. Moreover, a comparison among the prominent dysarthric ASR methods and the proposed one is provided. The results show that the DM-NSR recorded improved recognition rate by up to 24.67% and the error rate was reduced by up to 8.63% over the reference model.

  9. Reconstruction of Flaw Profiles Using Neural Networks and Multi-Frequency Eddy Current System

    SciTech Connect

    Chady, T.; Caryk, M.

    2005-04-09

    The objective of this paper is to identify profiles of flaws in conducting plates. To solve this problem, application of a multi-frequency eddy current system (MFES) and artificial neural networks is proposed. Dynamic feed-forward neural networks with various architectures are investigated. Extended experiments with all neural models are carried out in order to select the most promising configuration. Data utilized for the experiments were obtained from the measurements performed on the Inconel plates with EDM flaws.

  10. Mapping a Complete Neural Population in the Retina

    PubMed Central

    Amodei, Dario; Deshmukh, Nikhil; Sadeghi, Kolia; Soo, Frederick; Holy, Timothy E.; Berry, Michael J.

    2012-01-01

    Recording simultaneously from essentially all of the relevant neurons in a local circuit is crucial to understand how they collectively represent information. Here we show that the combination of a large, dense multielectrode array and a novel, mostly automated spike-sorting algorithm allowed us to record simultaneously from a highly overlapping population of >200 ganglion cells in the salamander retina. By combining these methods with labeling and imaging, we showed that up to 95% of the ganglion cells over the area of the array were recorded. By measuring the coverage of visual space by the receptive fields of the recorded cells, we concluded that our technique captured a neural population that forms an essentially complete representation of a region of visual space. This completeness allowed us to determine the spatial layout of different cell types as well as identify a novel group of ganglion cells that responded reliably to a set of naturalistic and artificial stimuli but had no measurable receptive field. Thus, our method allows unprecedented access to the complete neural representation of visual information, a crucial step for the understanding of population coding in sensory systems. PMID:23100409

  11. Locally optimal extracellular stimulation for chaotic desynchronization of neural populations.

    PubMed

    Wilson, Dan; Moehlis, Jeff

    2014-10-01

    We use optimal control theory to design a methodology to find locally optimal stimuli for desynchronization of a model of neurons with extracellular stimulation. This methodology yields stimuli which lead to positive Lyapunov exponents, and hence desynchronizes a neural population. We analyze this methodology in the presence of interneuron coupling to make predictions about the strength of stimulation required to overcome synchronizing effects of coupling. This methodology suggests a powerful alternative to pulsatile stimuli for deep brain stimulation as it uses less energy than pulsatile stimuli, and could eliminate the time consuming tuning process.

  12. Maximizing the potential of multi-parental crop populations.

    PubMed

    Ladejobi, Olufunmilayo; Elderfield, James; Gardner, Keith A; Gaynor, R Chris; Hickey, John; Hibberd, Julian M; Mackay, Ian J; Bentley, Alison R

    2016-12-01

    Most agriculturally significant crop traits are quantitatively inherited which limits the ease and efficiency of trait dissection. Multi-parent populations overcome the limitations of traditional trait mapping and offer new potential to accurately define the genetic basis of complex crop traits. The increasing popularity and use of nested association mapping (NAM) and multi-parent advanced generation intercross (MAGIC) populations raises questions about the optimal design and allocation of resources in their creation. In this paper we review strategies for the creation of multi-parent populations and describe two complementary in silico studies addressing the design and construction of NAM and MAGIC populations. The first simulates the selection of diverse founder parents and the second the influence of multi-parent crossing schemes (and number of founders) on haplotype creation and diversity. We present and apply two open software resources to simulate alternate strategies for the development of multi-parent populations.

  13. Global exponential periodicity and stability of recurrent neural networks with multi-proportional delays.

    PubMed

    Zhou, Liqun; Zhang, Yanyan

    2016-01-01

    In this paper, a class of recurrent neural networks with multi-proportional delays is studied. The nonlinear transformation transforms a class of recurrent neural networks with multi-proportional delays into a class of recurrent neural networks with constant delays and time-varying coefficients. By constructing Lyapunov functional and establishing the delay differential inequality, several delay-dependent and delay-independent sufficient conditions are derived to ensure global exponential periodicity and stability of the system. And several examples and their simulations are given to illustrate the effectiveness of obtained results.

  14. Simultaneous multi-electrode array recording and two-photon calcium imaging of neural activity

    PubMed Central

    Shew, Woodrow L.; Bellay, Timothy; Plenz, Dietmar

    2010-01-01

    A complete understanding of how brain circuits function will require measurement techniques which monitor large scale network activity simultaneously with the activity of local neural populations at a small scale. Here we present a useful step towards achieving this aim: simultaneous two-photon calcium imaging and multi-electrode array (MEA) recordings. The primary challenge of this method is removing an electrical artifact from the MEA signals that is caused by the imaging laser. Here we show that artifact removal can be achieved with a simple filtering scheme. As a demonstration of this technique we compare large-scale local field potential signals to single-neuron activity in a small-scale group of cells recorded from rat acute slices under two conditions: suppressed vs. intact inhibitory interactions between neurons. PMID:20659501

  15. Degraded attentional modulation of cortical neural populations in strabismic amblyopia

    PubMed Central

    Hou, Chuan; Kim, Yee-Joon; Lai, Xin Jie; Verghese, Preeti

    2016-01-01

    Behavioral studies have reported reduced spatial attention in amblyopia, a developmental disorder of spatial vision. However, the neural populations in the visual cortex linked with these behavioral spatial attention deficits have not been identified. Here, we use functional MRI–informed electroencephalography source imaging to measure the effect of attention on neural population activity in the visual cortex of human adult strabismic amblyopes who were stereoblind. We show that compared with controls, the modulatory effects of selective visual attention on the input from the amblyopic eye are substantially reduced in the primary visual cortex (V1) as well as in extrastriate visual areas hV4 and hMT+. Degraded attentional modulation is also found in the normal-acuity fellow eye in areas hV4 and hMT+ but not in V1. These results provide electrophysiological evidence that abnormal binocular input during a developmental critical period may impact cortical connections between the visual cortex and higher level cortices beyond the known amblyopic losses in V1 and V2, suggesting that a deficit of attentional modulation in the visual cortex is an important component of the functional impairment in amblyopia. Furthermore, we find that degraded attentional modulation in V1 is correlated with the magnitude of interocular suppression and the depth of amblyopia. These results support the view that the visual suppression often seen in strabismic amblyopia might be a form of attentional neglect of the visual input to the amblyopic eye. PMID:26885628

  16. Super-linear Precision in Simple Neural Population Codes

    NASA Astrophysics Data System (ADS)

    Schwab, David; Fiete, Ila

    2015-03-01

    A widely used tool for quantifying the precision with which a population of noisy sensory neurons encodes the value of an external stimulus is the Fisher Information (FI). Maximizing the FI is also a commonly used objective for constructing optimal neural codes. The primary utility and importance of the FI arises because it gives, through the Cramer-Rao bound, the smallest mean-squared error achievable by any unbiased stimulus estimator. However, it is well-known that when neural firing is sparse, optimizing the FI can result in codes that perform very poorly when considering the resulting mean-squared error, a measure with direct biological relevance. Here we construct optimal population codes by minimizing mean-squared error directly and study the scaling properties of the resulting network, focusing on the optimal tuning curve width. We then extend our results to continuous attractor networks that maintain short-term memory of external stimuli in their dynamics. Here we find similar scaling properties in the structure of the interactions that minimize diffusive information loss.

  17. Independent components of neural activity carry information on individual populations.

    PubMed

    Głąbska, Helena; Potworowski, Jan; Łęski, Szymon; Wójcik, Daniel K

    2014-01-01

    Local field potential (LFP), the low-frequency part of the potential recorded extracellularly in the brain, reflects neural activity at the population level. The interpretation of LFP is complicated because it can mix activity from remote cells, on the order of millimeters from the electrode. To understand better the relation between the recordings and the local activity of cells we used a large-scale network thalamocortical model to compute simultaneous LFP, transmembrane currents, and spiking activity. We used this model to study the information contained in independent components obtained from the reconstructed Current Source Density (CSD), which smooths transmembrane currents, decomposed further with Independent Component Analysis (ICA). We found that the three most robust components matched well the activity of two dominating cell populations: superior pyramidal cells in layer 2/3 (rhythmic spiking) and tufted pyramids from layer 5 (intrinsically bursting). The pyramidal population from layer 2/3 could not be well described as a product of spatial profile and temporal activation, but by a sum of two such products which we recovered in two of the ICA components in our analysis, which correspond to the two first principal components of PCA decomposition of layer 2/3 population activity. At low noise one more cell population could be discerned but it is unlikely that it could be recovered in experiment given typical noise ranges.

  18. A Neural Population Model Incorporating Dopaminergic Neurotransmission during Complex Voluntary Behaviors

    PubMed Central

    Simonyan, Kristina

    2014-01-01

    Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number of extensions of

  19. Multi-layer neural networks for robot control

    NASA Technical Reports Server (NTRS)

    Pourboghrat, Farzad

    1989-01-01

    Two neural learning controller designs for manipulators are considered. The first design is based on a neural inverse-dynamics system. The second is the combination of the first one with a neural adaptive state feedback system. Both types of controllers enable the manipulator to perform any given task very well after a period of training and to do other untrained tasks satisfactorily. The second design also enables the manipulator to compensate for unpredictable perturbations.

  20. Multi-structure segmentation of multi-modal brain images using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Kim, Eun Young; Johnson, Hans

    2010-03-01

    A method for simultaneous segmentation of multiple anatomical brain structures from multi-modal MR images has been developed. An artificial neural network (ANN) was trained from a set of feature vectors created by a combination of high-resolution registration methods, atlas based spatial probability distributions, and a training set of 16 expert traced data sets. A set of feature vectors were adapted to increase performance of ANN segmentation; 1) a modified spatial location for structural symmetry of human brain, 2) neighbors along the priors descent for directional consistency, and 3) candidate vectors based on the priors for the segmentation of multiple structures. The trained neural network was then applied to 8 data sets, and the results were compared with expertly traced structures for validation purposes. Comparing several reliability metrics, including a relative overlap, similarity index, and intraclass correlation of the ANN generated segmentations to a manual trace are similar or higher to those measures previously developed methods. The ANN provides a level of consistency between subjects and time efficiency comparing human labor that allows it to be used for very large studies.

  1. Multi-bump solutions in a neural field model with external inputs

    NASA Astrophysics Data System (ADS)

    Ferreira, Flora; Erlhagen, Wolfram; Bicho, Estela

    2016-07-01

    We study the conditions for the formation of multiple regions of high activity or "bumps" in a one-dimensional, homogeneous neural field with localized inputs. Stable multi-bump solutions of the integro-differential equation have been proposed as a model of a neural population representation of remembered external stimuli. We apply a class of oscillatory coupling functions and first derive criteria to the input width and distance, which relate to the synaptic couplings that guarantee the existence and stability of one and two regions of high activity. These input-induced patterns are attracted by the corresponding stable one-bump and two-bump solutions when the input is removed. We then extend our analytical and numerical investigation to N-bump solutions showing that the constraints on the input shape derived for the two-bump case can be exploited to generate a memory of N > 2 localized inputs. We discuss the pattern formation process when either the conditions on the input shape are violated or when the spatial ranges of the excitatory and inhibitory connections are changed. An important aspect for applications is that the theoretical findings allow us to determine for a given coupling function the maximum number of localized inputs that can be stored in a given finite interval.

  2. Noise in neural populations accounts for errors in working memory.

    PubMed

    Bays, Paul M

    2014-03-05

    Errors in short-term memory increase with the quantity of information stored, limiting the complexity of cognition and behavior. In visual memory, attempts to account for errors in terms of allocation of a limited pool of working memory resources have met with some success, but the biological basis for this cognitive architecture is unclear. An alternative perspective attributes recall errors to noise in tuned populations of neurons that encode stimulus features in spiking activity. I show that errors associated with decreasing signal strength in probabilistically spiking neurons reproduce the pattern of failures in human recall under increasing memory load. In particular, deviations from the normal distribution that are characteristic of working memory errors and have been attributed previously to guesses or variability in precision are shown to arise as a natural consequence of decoding populations of tuned neurons. Observers possess fine control over memory representations and prioritize accurate storage of behaviorally relevant information, at a cost to lower priority stimuli. I show that changing the input drive to neurons encoding a prioritized stimulus biases population activity in a manner that reproduces this empirical tradeoff in memory precision. In a task in which predictive cues indicate stimuli most probable for test, human observers use the cues in an optimal manner to maximize performance, within the constraints imposed by neural noise.

  3. Demixed principal component analysis of neural population data

    PubMed Central

    Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K

    2016-01-01

    Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure. DOI: http://dx.doi.org/10.7554/eLife.10989.001 PMID:27067378

  4. Noise in Neural Populations Accounts for Errors in Working Memory

    PubMed Central

    2014-01-01

    Errors in short-term memory increase with the quantity of information stored, limiting the complexity of cognition and behavior. In visual memory, attempts to account for errors in terms of allocation of a limited pool of working memory resources have met with some success, but the biological basis for this cognitive architecture is unclear. An alternative perspective attributes recall errors to noise in tuned populations of neurons that encode stimulus features in spiking activity. I show that errors associated with decreasing signal strength in probabilistically spiking neurons reproduce the pattern of failures in human recall under increasing memory load. In particular, deviations from the normal distribution that are characteristic of working memory errors and have been attributed previously to guesses or variability in precision are shown to arise as a natural consequence of decoding populations of tuned neurons. Observers possess fine control over memory representations and prioritize accurate storage of behaviorally relevant information, at a cost to lower priority stimuli. I show that changing the input drive to neurons encoding a prioritized stimulus biases population activity in a manner that reproduces this empirical tradeoff in memory precision. In a task in which predictive cues indicate stimuli most probable for test, human observers use the cues in an optimal manner to maximize performance, within the constraints imposed by neural noise. PMID:24599462

  5. Material depth reconstruction method of multi-energy X-ray images using neural network.

    PubMed

    Lee, Woo-Jin; Kim, Dae-Seung; Kang, Sung-Won; Yi, Won-Jin

    2012-01-01

    With the advent of technology, multi-energy X-ray imaging is promising technique that can reduce the patient's dose and provide functional imaging. Two-dimensional photon-counting detector to provide multi-energy imaging is under development. In this work, we present a material decomposition method using multi-energy images. To acquire multi-energy images, Monte Carlo simulation was performed. The X-ray spectrum was modeled and ripple effect was considered. Using the dissimilar characteristics in energy-dependent X-ray attenuation of each material, multiple energy X-ray images were decomposed into material depth images. Feedforward neural network was used to fit multi-energy images to material depth images. In order to use the neural network, step wedge phantom images were used for training neuron. Finally, neural network decomposed multi-energy X-ray images into material depth image. To demonstrate the concept of this method, we applied it to simulated images of a 3D head phantom. The results show that neural network method performed effectively material depth reconstruction.

  6. Optimal neural population coding of an auditory spatial cue.

    PubMed

    Harper, Nicol S; McAlpine, David

    2004-08-05

    A sound, depending on the position of its source, can take more time to reach one ear than the other. This interaural (between the ears) time difference (ITD) provides a major cue for determining the source location. Many auditory neurons are sensitive to ITDs, but the means by which such neurons represent ITD is a contentious issue. Recent studies question whether the classical general model (the Jeffress model) applies across species. Here we show that ITD coding strategies of different species can be explained by a unifying principle: that the ITDs an animal naturally encounters should be coded with maximal accuracy. Using statistical techniques and a stochastic neural model, we demonstrate that the optimal coding strategy for ITD depends critically on head size and sound frequency. For small head sizes and/or low-frequency sounds, the optimal coding strategy tends towards two distinct sub-populations tuned to ITDs outside the range created by the head. This is consistent with recent observations in small mammals. For large head sizes and/or high frequencies, the optimal strategy is a homogeneous distribution of ITD tunings within the range created by the head. This is consistent with observations in the barn owl. For humans, the optimal strategy to code ITDs from an acoustically measured distribution depends on frequency; above 400 Hz a homogeneous distribution is optimal, and below 400 Hz distinct sub-populations are optimal.

  7. Neural population models for perception of motion in depth.

    PubMed

    Peng, Qiuyan; Shi, Bertram E

    2014-08-01

    Changing disparity (CD) and interocular velocity difference (IOVD) are two possible mechanisms for stereomotion perception. We propose two neurally plausible models for the representation of motion-in-depth (MID) via the CD and IOVD mechanisms. These models create distributed representations of MID velocity as the responses from a population of neurons selective to different MID velocity. Estimates of perceived MID velocity can be computed from the population response. They can be applied directly to binocular image sequences commonly used to characterize MID perception in psychophysical experiments. Contrary to common assumptions, we find that the CD and IOVD mechanisms cannot be distinguished easily by random dot stereograms that disrupt correlations between the two eyes or through time. We also demonstrate that the assumed spatial connectivity between the units in these models can be learned through exposure to natural binocular stimuli. Our experiments with these developmental models of MID selectivity suggest that neurons selective to MID are more likely to develop via the CD mechanism than the IOVD mechanism. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Genomic selection in multi-breed dairy cattle populations

    USDA-ARS?s Scientific Manuscript database

    Genomic selection has been a valuable tool for increasing the rate of genetic improvement in purebred dairy cattle populations. However, there also are many large populations of crossbred dairy cattle in the world, and multi-breed genomic evaluations may be a valuable tool for improving rates of gen...

  9. Location coding by opponent neural populations in the auditory cortex.

    PubMed

    Stecker, G Christopher; Harrington, Ian A; Middlebrooks, John C

    2005-03-01

    Although the auditory cortex plays a necessary role in sound localization, physiological investigations in the cortex reveal inhomogeneous sampling of auditory space that is difficult to reconcile with localization behavior under the assumption of local spatial coding. Most neurons respond maximally to sounds located far to the left or right side, with few neurons tuned to the frontal midline. Paradoxically, psychophysical studies show optimal spatial acuity across the frontal midline. In this paper, we revisit the problem of inhomogeneous spatial sampling in three fields of cat auditory cortex. In each field, we confirm that neural responses tend to be greatest for lateral positions, but show the greatest modulation for near-midline source locations. Moreover, identification of source locations based on cortical responses shows sharp discrimination of left from right but relatively inaccurate discrimination of locations within each half of space. Motivated by these findings, we explore an opponent-process theory in which sound-source locations are represented by differences in the activity of two broadly tuned channels formed by contra- and ipsilaterally preferring neurons. Finally, we demonstrate a simple model, based on spike-count differences across cortical populations, that provides bias-free, level-invariant localization-and thus also a solution to the "binding problem" of associating spatial information with other nonspatial attributes of sounds.

  10. Teaching Multi-Cultural Populations: Five Heritages.

    ERIC Educational Resources Information Center

    Stone, James C., Ed.; DeNevi, Donald P., Ed.

    This book is an attempt to help fill the tremendous gap that presently exists between teachers' will to become more skillful with multicultural student populations and the as-yet short supply of the quality materials they urgently need in order to do so. Its organizing principle is that, inasmuch as America is an immense living laboratory for…

  11. Multi-channel holographic birfurcative neural network system for real-time adaptive EOS data analysis

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang; Diep, J.; Huang, K.

    1991-01-01

    Viewgraphs on multi-channel holographic bifurcative neural network system for real-time adaptive Earth Observing System (EOS) data analysis are presented. The objective is to research and develop an optical bifurcating neuromorphic pattern recognition system for making optical data array comparisons and to evaluate the use of the system for EOS data classification, reduction, analysis, and other applications.

  12. Multi-channel holographic birfurcative neural network system for real-time adaptive EOS data analysis

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang; Diep, J.; Huang, K.

    1991-01-01

    Viewgraphs on multi-channel holographic bifurcative neural network system for real-time adaptive Earth Observing System (EOS) data analysis are presented. The objective is to research and develop an optical bifurcating neuromorphic pattern recognition system for making optical data array comparisons and to evaluate the use of the system for EOS data classification, reduction, analysis, and other applications.

  13. Identification of feedback loops in neural networks based on multi-step Granger causality.

    PubMed

    Dong, Chao-Yi; Shin, Dongkwan; Joo, Sunghoon; Nam, Yoonkey; Cho, Kwang-Hyun

    2012-08-15

    Feedback circuits are crucial network motifs, ubiquitously found in many intra- and inter-cellular regulatory networks, and also act as basic building blocks for inducing synchronized bursting behaviors in neural network dynamics. Therefore, the system-level identification of feedback circuits using time-series measurements is critical to understand the underlying regulatory mechanism of synchronized bursting behaviors. Multi-Step Granger Causality Method (MSGCM) was developed to identify feedback loops embedded in biological networks using time-series experimental measurements. Based on multivariate time-series analysis, MSGCM used a modified Wald test to infer the existence of multi-step Granger causality between a pair of network nodes. A significant bi-directional multi-step Granger causality between two nodes indicated the existence of a feedback loop. This new identification method resolved the drawback of the previous non-causal impulse response component method which was only applicable to networks containing no co-regulatory forward path. MSGCM also significantly improved the ratio of correct identification of feedback loops. In this study, the MSGCM was testified using synthetic pulsed neural network models and also in vitro cultured rat neural networks using multi-electrode array. As a result, we found a large number of feedback loops in the in vitro cultured neural networks with apparent synchronized oscillation, indicating a close relationship between synchronized oscillatory bursting behavior and underlying feedback loops. The MSGCM is an efficient method to investigate feedback loops embedded in in vitro cultured neural networks. The identified feedback loop motifs are considered as an important design principle responsible for the synchronized bursting behavior in neural networks.

  14. [Research on Early Identification of Bipolar Disorder Based on Multi-layer Perceptron Neural Network].

    PubMed

    Zhang, Haowei; Gao, Yanni; Yuan, Chengmei; Liu, Ying; Ding, Yuqing

    2015-06-01

    Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identifica- tion of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neu- ral network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder.

  15. Population red blood cell folate concentrations for prevention of neural tube defects: bayesian model

    PubMed Central

    Devine, Owen; Hao, Ling; Dowling, Nicole F; Li, Song; Molloy, Anne M; Li, Zhu; Zhu, Jianghui; Berry, Robert J

    2014-01-01

    Objective To determine an optimal population red blood cell (RBC) folate concentration for the prevention of neural tube birth defects. Design Bayesian model. Setting Data from two population based studies in China. Participants 247 831 participants in a prospective community intervention project in China (1993-95) to prevent neural tube defects with 400 μg/day folic acid supplementation and 1194 participants in a population based randomized trial (2003-05) to evaluate the effect of folic acid supplementation on blood folate concentration among Chinese women of reproductive age. Intervention Folic acid supplementation (400 μg/day). Main outcome measures Estimated RBC folate concentration at time of neural tube closure (day 28 of gestation) and risk of neural tube defects. Results Risk of neural tube defects was high at the lowest estimated RBC folate concentrations (for example, 25.4 (95% uncertainty interval 20.8 to 31.2) neural tube defects per 10 000 births at 500 nmol/L) and decreased as estimated RBC folate concentration increased. Risk of neural tube defects was substantially attenuated at estimated RBC folate concentrations above about 1000 nmol/L (for example, 6 neural tube defects per 10 000 births at 1180 (1050 to 1340) nmol/L). The modeled dose-response relation was consistent with the existing literature. In addition, neural tube defect risk estimates developed using the proposed model and population level RBC information were consistent with the prevalence of neural tube defects in the US population before and after food fortification with folic acid. Conclusions A threshold for “optimal” population RBC folate concentration for the prevention of neural tube defects could be defined (for example, approximately 1000 nmol/L). Population based RBC folate concentrations, as a biomarker for risk of neural tube defects, can be used to facilitate evaluation of prevention programs as well as to identify subpopulations at elevated risk for a neural

  16. Unsupervised boundary delineation of spinal neural foramina using a multi-feature and adaptive spectral segmentation.

    PubMed

    He, Xiaoxu; Zhang, Heye; Landis, Mark; Sharma, Manas; Warrington, James; Li, Shuo

    2017-02-01

    As a common disease in the elderly, neural foramina stenosis (NFS) brings a significantly negative impact on the quality of life due to its symptoms including pain, disability, fall risk and depression. Accurate boundary delineation is essential to the clinical diagnosis and treatment of NFS. However, existing clinical routine is extremely tedious and inefficient due to the requirement of physicians' intensively manual delineation. Automated delineation is highly needed but faces big challenges from the complexity and variability in neural foramina images. In this paper, we propose a pure image-driven unsupervised boundary delineation framework for the automated neural foramina boundary delineation. This framework is based on a novel multi-feature and adaptive spectral segmentation (MFASS) algorithm. MFASS firstly utilizes the combination of region and edge features to generate reliable spectral features with a good separation between neural foramina and its surroundings, then estimates an optimal separation threshold for each individual image to separate neural foramina from its surroundings. This self-adjusted optimal separation threshold, estimated from spectral features, successfully overcome the diverse appearance and shape variations. With the robustness from the multi-feature fusion and the flexibility from the adaptively optimal separation threshold estimation, the proposed framework, based on MFASS, provides an automated and accurate boundary delineation. Validation was performed in 280 neural foramina MR images from 56 clinical subjects. Our method was benchmarked with manual boundary obtained by experienced physicians. Results demonstrate that the proposed method enjoys a high and stable consistency with experienced physicians (Dice: 90.58% ± 2.79%; SMAD: 0.5657 ± 0.1544 mm). Therefore, the proposed framework enables an efficient and accurate clinical tool in the diagnosis of neural foramina stenosis.

  17. Spatiotemporal multi-resolution approximation of the Amari type neural field model.

    PubMed

    Aram, P; Freestone, D R; Dewar, M; Scerri, K; Jirsa, V; Grayden, D B; Kadirkamanathan, V

    2013-02-01

    Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.

  18. Simultaneous silence organizes structured higher-order interactions in neural populations

    PubMed Central

    Shimazaki, Hideaki; Sadeghi, Kolia; Ishikawa, Tomoe; Ikegaya, Yuji; Toyoizumi, Taro

    2015-01-01

    Activity patterns of neural population are constrained by underlying biological mechanisms. These patterns are characterized not only by individual activity rates and pairwise correlations but also by statistical dependencies among groups of neurons larger than two, known as higher-order interactions (HOIs). While HOIs are ubiquitous in neural activity, primary characteristics of HOIs remain unknown. Here, we report that simultaneous silence (SS) of neurons concisely summarizes neural HOIs. Spontaneously active neurons in cultured hippocampal slices express SS that is more frequent than predicted by their individual activity rates and pairwise correlations. The SS explains structured HOIs seen in the data, namely, alternating signs at successive interaction orders. Inhibitory neurons are necessary to maintain significant SS. The structured HOIs predicted by SS were observed in a simple neural population model characterized by spiking nonlinearity and correlated input. These results suggest that SS is a ubiquitous feature of HOIs that constrain neural activity patterns and can influence information processing. PMID:25919985

  19. Knowledge extraction from evolving spiking neural networks with rank order population coding.

    PubMed

    Soltic, Snjezana; Kasabov, Nikola

    2010-12-01

    This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

  20. Novel multi-sided, microelectrode arrays for implantable neural applications

    PubMed Central

    Seymour, John P.; Langhals, Nick B.; Anderson, David J.; Kipke, Daryl R.

    2014-01-01

    A new parylene-based microfabrication process is presented for neural recording and drug delivery applications. We introduce a large design space for electrode placement and structural flexibility with a six mask process. By using chemical mechanical polishing, electrode sites may be created top-side, back-side, or on the edge of the device having three exposed sides. Added surface area was achieved on the exposed edge through electroplating. Poly(3,4-ethylenedioxythiophene) (PEDOT) modified edge electrodes having an 85-μm2 footprint resulted in an impedance of 200 kΩ at 1 kHz. Edge electrodes were able to successfully record single unit activity in acute animal studies. A finite element model of planar and edge electrodes relative to neuron position reveals that edge electrodes should be beneficial for increasing the volume of tissue being sampled in recording applications. PMID:21301965

  1. Novel multi-sided, microelectrode arrays for implantable neural applications.

    PubMed

    Seymour, John P; Langhals, Nick B; Anderson, David J; Kipke, Daryl R

    2011-06-01

    A new parylene-based microfabrication process is presented for neural recording and drug delivery applications. We introduce a large design space for electrode placement and structural flexibility with a six mask process. By using chemical mechanical polishing, electrode sites may be created top-side, back-side, or on the edge of the device having three exposed sides. Added surface area was achieved on the exposed edge through electroplating. Poly(3,4-ethylenedioxythiophene) (PEDOT) modified edge electrodes having an 85-μm(2) footprint resulted in an impedance of 200 kΩ at 1 kHz. Edge electrodes were able to successfully record single unit activity in acute animal studies. A finite element model of planar and edge electrodes relative to neuron position reveals that edge electrodes should be beneficial for increasing the volume of tissue being sampled in recording applications.

  2. Image exploitation using multi-sensor/neural network systems

    SciTech Connect

    Uberbacher, E.C.; Xu, Y.; Lee, R.W.

    1995-12-31

    We have developed and evaluated a tool for change detection and other analysis tasks relevant to image exploitation. The tool, visGRAIL, integrates three key elements: (1) the use of multiple algorithms to extract information from images - feature extractors or {open_quotes}sensors{close_quotes}, (2) an algorithm to fuse the information - presently a neural network, and (3) empirical estimation of the fusion parameters based on a representative set of images. The system was applied to test images in the RADIUS Common Development Environment (RCDE). In a task designed to distinguish natural scenes from those containing various amounts of human-made objects and structure, the system classified correctly 95% of 350 images in a test set. This paper describes details of the feature extractors, and presents analyses of the discriminatory characteristics of the features. visGRAIL has been integrated into the RCDE.

  3. Modeling analysis of the relationship between EEG rhythms and connectivity among different neural populations.

    PubMed

    Ursino, Mauro; Zavaglia, Melissa

    2007-12-01

    In our research, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. First, we showed that a single neural population, stimulated with white noise, can oscillate with its intrinsic rhythm, and that the position of this rhythm can be finely tuned (in the alpha, beta or gamma frequency ranges), acting on the population synaptic kinetics. Subsequently, we analyzed more complex circuits, composed of two or three interconnected populations, each with a different synaptic kinetics between neural groups within a population (hence, with a different intrinsic rhythm). The results demonstrates apex that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). The analysis of coherence, and of the position of the peaks in power spectral density, reveals important information on the possible connections among populations, and is especially useful to follow temporal changes in connectivity. In perspective, the results may be of value for a deeper comprehension of the mechanisms causing EEGs rhythms, for the study of connectivity among different neural populations and for the test of neurophysiological hypotheses.

  4. A multi-populations multi-strategies differential evolution algorithm for structural optimization of metal nanoclusters

    NASA Astrophysics Data System (ADS)

    Fan, Tian-E.; Shao, Gui-Fang; Ji, Qing-Shuang; Zheng, Ji-Wen; Liu, Tun-dong; Wen, Yu-Hua

    2016-11-01

    Theoretically, the determination of the structure of a cluster is to search the global minimum on its potential energy surface. The global minimization problem is often nondeterministic-polynomial-time (NP) hard and the number of local minima grows exponentially with the cluster size. In this article, a multi-populations multi-strategies differential evolution algorithm has been proposed to search the globally stable structure of Fe and Cr nanoclusters. The algorithm combines a multi-populations differential evolution with an elite pool scheme to keep the diversity of the solutions and avoid prematurely trapping into local optima. Moreover, multi-strategies such as growing method in initialization and three differential strategies in mutation are introduced to improve the convergence speed and lower the computational cost. The accuracy and effectiveness of our algorithm have been verified by comparing the results of Fe clusters with Cambridge Cluster Database. Meanwhile, the performance of our algorithm has been analyzed by comparing the convergence rate and energy evaluations with the classical DE algorithm. The multi-populations, multi-strategies mutation and growing method in initialization in our algorithm have been considered respectively. Furthermore, the structural growth pattern of Cr clusters has been predicted by this algorithm. The results show that the lowest-energy structure of Cr clusters contains many icosahedra, and the number of the icosahedral rings rises with increasing size.

  5. Retinal Metric: A Stimulus Distance Measure Derived from Population Neural Responses

    NASA Astrophysics Data System (ADS)

    Tkačik, Gašper; Granot-Atedgi, Einat; Segev, Ronen; Schneidman, Elad

    2013-02-01

    The ability of an organism to distinguish between various stimuli is limited by the structure and noise in the population code of its sensory neurons. Here we infer a distance measure on the stimulus space directly from the recorded activity of 100 neurons in the salamander retina. In contrast to previously used measures of stimulus similarity, this “neural metric” tells us how distinguishable a pair of stimulus clips is to the retina, based on the similarity between the induced distributions of population responses. We show that the retinal distance strongly deviates from Euclidean, or any static metric, yet has a simple structure: we identify the stimulus features that the neural population is jointly sensitive to, and show the support-vector-machine-like kernel function relating the stimulus and neural response spaces. We show that the non-Euclidean nature of the retinal distance has important consequences for neural decoding.

  6. Flexible and extendible neural stimulation/recording device based on cooperative multi-chip CMOS LSI architecture.

    PubMed

    Tokuda, Takashi; Pan, Yi-Li; Uehara, Akihiro; Kagawa, Keiichiro; Ohta, Jun; Nunoshita, Masahiro

    2004-01-01

    An LSI-based cooperative multi-chip neural stimulation/recording device is proposed and fabricated. The proposed multi-chip device consists of small (600 microm x 600 microm in the present design) intelligent neural stimulation/recording chips (unit chip). The unit chip has a neural stimulation/recording electrode array and individual control circuit. It can work with other unit chips cooperatively. One can configure any number of the unit chips as the multi-chip neural stimulation/recording device. Compared to conventional single-chip architecture, the proposed multi-chip architecture has advantages in thinness, mechanical strength and flexibility, and extendibility. That makes the multi-chip neural stimulation/ recording device more suitable for in vivo applications than conventional single-chip devices. Packaging technology for cooperative multi-chip device is also discussed. We developed a thin, flexible packaging technique for the multi-chip neural stimulation/recording device and LSI-compatible Pt/Au stacked biocompatible bump electrode.

  7. Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

    PubMed Central

    Sun, Baoliang; Jiang, Chunlan; Li, Ming

    2016-01-01

    An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271

  8. Simultaneous Measurement of Neural Spike Recordings and Multi-Photon Calcium Imaging in Neuroblastoma Cells

    PubMed Central

    Kim, Suhwan; Jung, Unsang; Baek, Juyeong; Kang, Shinwon; Kim, Jeehyun

    2012-01-01

    This paper proposes the design and implementation of a micro-electrode array (MEA) for neuroblastoma cell culturing. It also explains the implementation of a multi-photon microscope (MPM) customized for neuroblastoma cell excitation and imaging under ambient light. Electrical signal and fluorescence images were simultaneously acquired from the neuroblastoma cells on the MEA. MPM calcium images of the cultured neuroblastoma cell on the MEA are presented and also the neural activity was acquired through the MEA recording. A calcium green-1 (CG-1) dextran conjugate of 10,000 D molecular weight was used in this experiment for calcium imaging. This study also evaluated the calcium oscillations and neural spike recording of neuroblastoma cells in an epileptic condition. Based on our observation of neural spikes in neuroblastoma cells with our proposed imaging modality, we report that neuroblastoma cells can be an important model for epileptic activity studies. PMID:23202210

  9. Simultaneous measurement of neural spike recordings and multi-photon calcium imaging in neuroblastoma cells.

    PubMed

    Kim, Suhwan; Jung, Unsang; Baek, Juyeong; Kang, Shinwon; Kim, Jeehyun

    2012-11-08

    This paper proposes the design and implementation of a micro-electrode array (MEA) for neuroblastoma cell culturing. It also explains the implementation of a multi-photon microscope (MPM) customized for neuroblastoma cell excitation and imaging under ambient light. Electrical signal and fluorescence images were simultaneously acquired from the neuroblastoma cells on the MEA. MPM calcium images of the cultured neuroblastoma cell on the MEA are presented and also the neural activity was acquired through the MEA recording. A calcium green-1 (CG-1) dextran conjugate of 10,000 D molecular weight was used in this experiment for calcium imaging. This study also evaluated the calcium oscillations and neural spike recording of neuroblastoma cells in an epileptic condition. Based on our observation of neural spikes in neuroblastoma cells with our proposed imaging modality, we report that neuroblastoma cells can be an important model for epileptic activity studies.

  10. Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysis

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang; Huang, K. S.; Diep, J.

    1993-01-01

    Optical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. the proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photofractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feedforward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.

  11. Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysis

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang; Huang, K.; Diep, J.

    1992-01-01

    Optical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. The proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photorefractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feed forward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.

  12. A multi-channel fully differential programmable integrated circuit for neural recording application

    NASA Astrophysics Data System (ADS)

    Yun, Gui; Xu, Zhang; Yuan, Wang; Ming, Liu; Weihua, Pei; Kai, Liang; Suibiao, Huang; Bin, Li; Hongda, Chen

    2013-10-01

    A multi-channel, fully differential programmable chip for neural recording application is presented. The integrated circuit incorporates eight neural recording amplifiers with tunable bandwidth and gain, eight 4th-order Bessel switch capacitor filters, an 8-to-1 analog time-division multiplexer, a fully differential successive approximation register analog-to-digital converter (SAR ADC), and a serial peripheral interface for communication. The neural recording amplifier presents a programmable gain from 53 dB to 68 dB, a tunable low cut-off frequency from 0.1 Hz to 300 Hz, and 3.77 μVrms input-referred noise over a 5 kHz bandwidth. The SAR ADC digitizes signals at maximum sampling rate of 20 kS/s per channel and achieves an ENOB of 7.4. The integrated circuit is designed and fabricated in 0.18-μm CMOS mix-signal process. We successfully performed a multi-channel in-vivo recording experiment from a rat cortex using the neural recording chip.

  13. Improved multi-unit decoding at the brain-machine interface using population temporal linear filtering

    NASA Astrophysics Data System (ADS)

    Herzfeld, D. J.; Beardsley, S. A.

    2010-08-01

    Current efforts to decode control signals from multi-unit (MU) recordings rely on the use of spike sorting to differentiate neurons and the use of firing rates estimated over tens of milliseconds to reconstruct sensorimotor signals. The computational bottleneck associated with the need to identify and sort individual neuron responses poses challenges for the development of portable, real-time, neural decoding systems that can be incorporated into assistive and prosthetic devices for the disabled. Here, we investigate the ability of spike-based linear filtering to reduce computational overhead and improve the accuracy of decoding neuronal signals for populations of spiking neurons. Using a population temporal (PT) decoding framework, the speed and accuracy of spike-based MU decoding were compared with firing rate-based approaches using simulated populations of motor neurons tuned for the velocity of intended movement. For the two linear filtering approaches, the accuracy of decoded movements was examined as a function of the number of recorded neurons, amount of noise, with and without spike sorting, and for training and test motions whose statistics were either similar or dissimilar. Our results suggest that the use of a PT decoding framework can offset the loss in accuracy associated with decoding unsorted MU neural signals. Coupled with up to a 20-fold reduction in the number of decoding weights and the ability to implement the filtering in hardware, this approach could reduce the computational requirements and thus increase the portability of next generation brain-machine interfaces.

  14. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

    PubMed Central

    Gerstner, Wulfram

    2017-01-01

    Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. PMID:28422957

  15. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

    PubMed

    Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

    2017-04-01

    Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

  16. Nonlinear resonances and multi-stability in simple neural circuits

    NASA Astrophysics Data System (ADS)

    Alonso, Leandro M.

    2017-01-01

    This article describes a numerical procedure designed to tune the parameters of periodically driven dynamical systems to a state in which they exhibit rich dynamical behavior. This is achieved by maximizing the diversity of subharmonic solutions available to the system within a range of the parameters that define the driving. The procedure is applied to a problem of interest in computational neuroscience: a circuit composed of two interacting populations of neurons under external periodic forcing. Depending on the parameters that define the circuit, such as the weights of the connections between the populations, the response of the circuit to the driving can be strikingly rich and diverse. The procedure is employed to find circuits that, when driven by external input, exhibit multiple stable patterns of periodic activity organized in complex tuning diagrams and signatures of low dimensional chaos.

  17. Bilingualism provides a neural reserve for aging populations.

    PubMed

    Abutalebi, Jubin; Guidi, Lucia; Borsa, Virginia; Canini, Matteo; Della Rosa, Pasquale A; Parris, Ben A; Weekes, Brendan S

    2015-03-01

    It has been postulated that bilingualism may act as a cognitive reserve and recent behavioral evidence shows that bilinguals are diagnosed with dementia about 4-5 years later compared to monolinguals. In the present study, we investigated the neural basis of these putative protective effects in a group of aging bilinguals as compared to a matched monolingual control group. For this purpose, participants completed the Erikson Flanker task and their performance was correlated to gray matter (GM) volume in order to investigate if cognitive performance predicts GM volume specifically in areas affected by aging. We performed an ex-Gaussian analysis on the resulting RTs and report that aging bilinguals performed better than aging monolinguals on the Flanker task. Bilingualism was overall associated with increased GM in the ACC. Likewise, aging induced effects upon performance correlated only for monolinguals to decreased gray matter in the DLPFC. Taken together, these neural regions might underlie the benefits of bilingualism and act as a neural reserve that protects against the cognitive decline that occurs during aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models.

    PubMed

    Williamson, Ryan C; Cowley, Benjamin R; Litwin-Kumar, Ashok; Doiron, Brent; Kohn, Adam; Smith, Matthew A; Yu, Byron M

    2016-12-01

    Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction-shared dimensionality and percent shared variance-with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

  19. Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

    PubMed Central

    Cowley, Benjamin R.; Doiron, Brent; Kohn, Adam

    2016-01-01

    Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure. PMID:27926936

  20. A neural network multi-task learning approach to biomedical named entity recognition.

    PubMed

    Crichton, Gamal; Pyysalo, Sampo; Chiu, Billy; Korhonen, Anna

    2017-08-15

    Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance. To investigate this, we develop supervised, multi-task, convolutional neural network models and apply them to a large number of varied existing biomedical named entity datasets. Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings. We present a single-task model for NER, a Multi-output multi-task model and a Dependent multi-task model. We apply the three models to 15 biomedical datasets containing multiple named entities including Anatomy, Chemical, Disease, Gene/Protein and Species. Each dataset represent a task. The results from the single-task model and the multi-task models are then compared for evidence of benefits from Multi-task Learning. With the Multi-output multi-task model we observed an average F-score improvement of 0.8% when compared to the single-task model from an average baseline of 78.4%. Although there was a significant drop in performance on one dataset, performance improves significantly for five datasets by up to 6.3%. For the Dependent multi-task model we observed an average improvement of 0.4% when compared to the single-task model. There were no significant drops in performance on any dataset, and performance improves significantly for six datasets by up to 1.1%. The dataset size experiments found that as dataset size decreased, the multi-output model's performance increased compared to the single-task model's. Using 50, 25 and 10% of the training data resulted in an average drop of approximately 3.4, 8 and 16.7% respectively for the single-task model but approximately 0.2, 3.0 and 9.8% for the multi-task model. Our

  1. Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

    NASA Astrophysics Data System (ADS)

    Lee, Jongpil; Nam, Juhan

    2017-08-01

    Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

  2. Cortical neural excitations in rats in vivo with using a prototype of a wireless multi-channel microstimulation system.

    PubMed

    Hayashida, Yuki; Umehira, Yuichi; Takatani, Kouki; Futami, Shigetoshi; Kameda, Seiji; Kamata, Takatsugu; Khan, Arif Ullah; Takeuchi, Yoshinori; Imai, Masaharu; Yagi, Tetsuya

    2015-08-01

    Understanding neural responses to multi-site electrical stimuli would be of essential importance for developing cortical neural prostheses. In order to provide a tool for such studies in experimental animals, we recently constructed a prototype of a wireless multi-channel microstimulation system, consisting of a stimulator chip, wireless data/power transmitters and receivers, and microcomputers. The proper operations of the system in cortical neural excitations were examined in anesthetized rats in vivo, with utilizing the voltage-sensitive dye imaging technique.

  3. Demystifying Multi-Task Deep Neural Networks for Quantitative Structure-Activity Relationships.

    PubMed

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

    2017-09-05

    Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision and natural language processing. In the past four years, DNNs also generated promising results for quantitative structure-activity relationship (QSAR) tasks. Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR datasets. It was also found that multi-task DNN models - those trained on and predicting multiple QSAR properties simultaneously - outperform DNNs trained separately on the individual datasets in many but not all tasks. Up to now there is no satisfactory explanation as to why the QSAR of one task, embedded in a multi-task DNN, can borrow information from other unrelated QSAR tasks. Thus using multi-task DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explore why multi-task DNNs make a difference in predictive performance. Our results show that, during prediction, a multi-task 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. Based on this realization, we develop a strategy to use multi-task DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples.

  4. High-Lift Optimization Design Using Neural Networks on a Multi-Element Airfoil

    NASA Technical Reports Server (NTRS)

    Greenman, Roxana M.; Roth, Karlin R.; Smith, Charles A. (Technical Monitor)

    1998-01-01

    The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag, and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural networks were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 83% compared with traditional gradient-based optimization procedures for multiple optimization runs.

  5. System identification: a multi-signal approach for probing neural cardiovascular regulation.

    PubMed

    Xiao, Xinshu; Mullen, Thomas J; Mukkamala, Ramakrishna

    2005-06-01

    Short-term, beat-to-beat cardiovascular variability reflects the dynamic interplay between ongoing perturbations to the circulation and the compensatory response of neurally mediated regulatory mechanisms. This physiologic information may be deciphered from the subtle, beat-to-beat variations by using digital signal processing techniques. While single signal analysis techniques (e.g., power spectral analysis) may be employed to quantify the variability itself, the multi-signal approach of system identification permits the dynamic characterization of the neural regulatory mechanisms responsible for coupling the variability between signals. In this review, we provide an overview of applications of system identification to beat-to-beat variability for the quantitative characterization of cardiovascular regulatory mechanisms. After briefly summarizing the history of the field and basic principles, we take a didactic approach to describe the practice of system identification in the context of probing neural cardiovascular regulation. We then review studies in the literature over the past two decades that have applied system identification for characterizing the dynamical properties of the sinoatrial node, respiratory sinus arrhythmia, and the baroreflex control of sympathetic nerve activity, heart rate and total peripheral resistance. Based on this literature review, we conclude by advocating specific methods of practice and that future research should focus on nonlinear and time-varying behaviors, validation of identification methods, and less understood neural regulatory mechanisms. Ultimately, we hope that this review stimulates such future investigations by both new and experienced system identification researchers.

  6. DETECTING ACTIVE GALACTIC NUCLEI USING MULTI-FILTER IMAGING DATA. II. INCORPORATING ARTIFICIAL NEURAL NETWORKS

    SciTech Connect

    Dong, X. Y.; De Robertis, M. M.

    2013-10-01

    This is the second paper of the series Detecting Active Galactic Nuclei Using Multi-filter Imaging Data. In this paper we review shapelets, an image manipulation algorithm, which we employ to adjust the point-spread function (PSF) of galaxy images. This technique is used to ensure the image in each filter has the same and sharpest PSF, which is the preferred condition for detecting AGNs using multi-filter imaging data as we demonstrated in Paper I of this series. We apply shapelets on Canada-France-Hawaii Telescope Legacy Survey Wide Survey ugriz images. Photometric parameters such as effective radii, integrated fluxes within certain radii, and color gradients are measured on the shapelets-reconstructed images. These parameters are used by artificial neural networks (ANNs) which yield: photometric redshift with an rms of 0.026 and a regression R-value of 0.92; galaxy morphological types with an uncertainty less than 2 T types for z ≤ 0.1; and identification of galaxies as AGNs with 70% confidence, star-forming/starburst (SF/SB) galaxies with 90% confidence, and passive galaxies with 70% confidence for z ≤ 0.1. The incorporation of ANNs provides a more reliable technique for identifying AGN or SF/SB candidates, which could be very useful for large-scale multi-filter optical surveys that also include a modest set of spectroscopic data sufficient to train neural networks.

  7. Neural Predictors of Visuomotor Adaptation Rate and Multi-Day Savings

    NASA Technical Reports Server (NTRS)

    Cassady, Kaitlin; Ruitenberg, Marit; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos Castenada, Roy; Kofman, Igor; Bloomberg, Jacob; hide

    2017-01-01

    Recent studies of sensorimotor adaptation have found that individual differences in task-based functional brain activation are associated with the rate of adaptation and savings at subsequent sessions. However, few studies to date have investigated offline neural predictors of adaptation and multi-day savings. In the present study, we explore whether individual differences in the rate of visuomotor adaptation and multi-day savings are associated with differences in resting state functional connectivity and gray matter volume. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. We found that resting state functional connectivity strength between sensorimotor, anterior cingulate, and temporoparietal areas of the brain was a significant predictor of adaptation rate during the early, cognitive phase of practice. In contrast, default mode network functional connectivity strength was found to predict late adaptation rate and savings on day two, which suggests that these behaviors may rely on overlapping processes. We also found that gray matter volume in temporoparietal and occipital regions was a significant predictor of early learning, whereas gray matter volume in superior posterior regions of the cerebellum was a significant predictor of late adaptation. The results from this study suggest that offline neural predictors of early adaptation facilitate the cognitive mechanisms of sensorimotor adaptation, with support from by the involvement of temporoparietal and cingulate networks. In contrast, the neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations. These findings provide novel insights into the neural processes associated with individual differences in sensorimotor adaptation.

  8. Multi-point optical manipulation and simultaneous imaging of neural circuits through wavefront phase modulation (Presentation Recording)

    NASA Astrophysics Data System (ADS)

    Aghayee, Samira; Winkowski, Dan; Kanold, Patrick; Losert, Wolfgang

    2015-08-01

    The spatial connectivity of neural circuits and the various activity patterns they exert is what forms the brain function. How these patterns link to a certain perception or a behavior is a key question in neuroscience. Recording the activity of neural circuits while manipulating arbitrary neurons leads to answering this question. That is why acquiring a fast and reliable method of stimulation and imaging a population of neurons at a single cell resolution is of great importance. Owing to the recent advancements in calcium imaging and optogenetics, tens to hundreds of neurons in a living system can be imaged and manipulated optically. We describe the adaptation of a multi-point optical method that can be used to address the specific challenges faced in the in-vivo study of neuronal networks in the cerebral cortex. One specific challenge in the cerebral cortex is that the information flows perpendicular to the surface. Therefore, addressing multiple points in a three dimensional space simultaneously is of great interest. Using a liquid crystal spatial light modulator, the wavefront of the input laser beam is modified to produce multiple focal points at different depths of the sample for true multipoint two-photon excitation.

  9. Propagating Neural Source Revealed by Doppler Shift of Population Spiking Frequency

    PubMed Central

    Zhang, Mingming; Shivacharan, Rajat S.; Chiang, Chia-Chu; Gonzalez-Reyes, Luis E.

    2016-01-01

    Electrical activity in the brain during normal and abnormal function is associated with propagating waves of various speeds and directions. It is unclear how both fast and slow traveling waves with sometime opposite directions can coexist in the same neural tissue. By recording population spikes simultaneously throughout the unfolded rodent hippocampus with a penetrating microelectrode array, we have shown that fast and slow waves are causally related, so a slowly moving neural source generates fast-propagating waves at ∼0.12 m/s. The source of the fast population spikes is limited in space and moving at ∼0.016 m/s based on both direct and Doppler measurements among 36 different spiking trains among eight different hippocampi. The fact that the source is itself moving can account for the surprising direction reversal of the wave. Therefore, these results indicate that a small neural focus can move and that this phenomenon could explain the apparent wave reflection at tissue edges or multiple foci observed at different locations in neural tissue. SIGNIFICANCE STATEMENT The use of novel techniques with an unfolded hippocampus and penetrating microelectrode array to record and analyze neural activity has revealed the existence of a source of neural signals that propagates throughout the hippocampus. The source itself is electrically silent, but its location can be inferred by building isochrone maps of population spikes that the source generates. The movement of the source can also be tracked by observing the Doppler frequency shift of these spikes. These results have general implications for how neural signals are generated and propagated in the hippocampus; moreover, they have important implications for the understanding of seizure generation and foci localization. PMID:27013678

  10. Multi-Scale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification.

    PubMed

    Wang, Qiangchang; Zheng, Yuanjie; Yang, Gongping; Jin, Weidong; Chen, Xinjian; Yin, Yilong

    2017-03-21

    We propose a new Multi-scale Rotation-invariant Convolutional Neural Network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography (HRCT). MRCNN employs Gabor-local binary pattern (Gabor-LBP) which introduces a good property in image analysis - invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public Interstitial Lung Disease (ILD) database show a superior performance of the proposed method to state-of-the-art.

  11. Multi-population model of a microbial electrolysis cell.

    PubMed

    Pinto, R P; Srinivasan, B; Escapa, A; Tartakovsky, B

    2011-06-01

    This work presents a multi-population dynamic model of a microbial electrolysis cell (MEC). The model describes the growth and metabolic activity of fermentative, electricigenic, methanogenic acetoclastic, and methanogenic hydrogenophilic microorganisms and is capable of simulating hydrogen production in a MEC fed with complex organic matter, such as wastewater. The model parameters were estimated with the experimental results obtained in continuous flow MECs fed with acetate or synthetic wastewater. Following successful model validation with an independent data set, the model was used to analyze and discuss the influence of applied voltage and organic load on hydrogen production and COD removal.

  12. Artificial neural network cascade identifies multi-P450 inhibitors in natural compounds.

    PubMed

    Li, Zhangming; Li, Yan; Sun, Lu; Tang, Yun; Liu, Lanru; Zhu, Wenliang

    2015-01-01

    Substantial evidence has shown that most exogenous substances are metabolized by multiple cytochrome P450 (P450) enzymes instead of by merely one P450 isoform. Thus, multi-P450 inhibition leads to greater drug-drug interaction risk than specific P450 inhibition. Herein, we innovatively established an artificial neural network cascade (NNC) model composed of 23 cascaded networks in a ladder-like framework to identify potential multi-P450 inhibitors among natural compounds by integrating 12 molecular descriptors into a P450 inhibition score (PIS). Experimental data reporting in vitro inhibition of five P450 isoforms (CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4) were obtained for 8,148 compounds from the Cytochrome P450 Inhibitors Database (CPID). The results indicate significant positive correlation between the PIS values and the number of inhibited P450 isoforms (Spearman's ρ = 0.684, p < 0.0001). Thus, a higher PIS indicates a greater possibility for a chemical to inhibit the enzyme activity of at least three P450 isoforms. Ten-fold cross-validation of the NNC model suggested an accuracy of 78.7% for identifying whether a compound is a multi-P450 inhibitor or not. Using our NNC model, 22.2% of the approximately 160,000 natural compounds in TCM Database@Taiwan were identified as potential multi-P450 inhibitors. Furthermore, chemical similarity calculations suggested that the prevailing parent structures of natural multi-P450 inhibitors were alkaloids. Our findings show that dissection of chemical structure contributes to confident identification of natural multi-P450 inhibitors and provides a feasible method for virtually evaluating multi-P450 inhibition risk for a known structure.

  13. Artificial neural network cascade identifies multi-P450 inhibitors in natural compounds

    PubMed Central

    Li, Zhangming; Li, Yan; Sun, Lu; Tang, Yun; Liu, Lanru

    2015-01-01

    Substantial evidence has shown that most exogenous substances are metabolized by multiple cytochrome P450 (P450) enzymes instead of by merely one P450 isoform. Thus, multi-P450 inhibition leads to greater drug-drug interaction risk than specific P450 inhibition. Herein, we innovatively established an artificial neural network cascade (NNC) model composed of 23 cascaded networks in a ladder-like framework to identify potential multi-P450 inhibitors among natural compounds by integrating 12 molecular descriptors into a P450 inhibition score (PIS). Experimental data reporting in vitro inhibition of five P450 isoforms (CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4) were obtained for 8,148 compounds from the Cytochrome P450 Inhibitors Database (CPID). The results indicate significant positive correlation between the PIS values and the number of inhibited P450 isoforms (Spearman’s ρ = 0.684, p < 0.0001). Thus, a higher PIS indicates a greater possibility for a chemical to inhibit the enzyme activity of at least three P450 isoforms. Ten-fold cross-validation of the NNC model suggested an accuracy of 78.7% for identifying whether a compound is a multi-P450 inhibitor or not. Using our NNC model, 22.2% of the approximately 160,000 natural compounds in TCM Database@Taiwan were identified as potential multi-P450 inhibitors. Furthermore, chemical similarity calculations suggested that the prevailing parent structures of natural multi-P450 inhibitors were alkaloids. Our findings show that dissection of chemical structure contributes to confident identification of natural multi-P450 inhibitors and provides a feasible method for virtually evaluating multi-P450 inhibition risk for a known structure. PMID:26719820

  14. Characterization of a population of neural progenitor cells in the infant hippocampus

    PubMed Central

    Paine, S M L; Willsher, A R; Nicholson, S L; Sebire, N J; Jacques, T S

    2014-01-01

    Aims Abnormalities of the hippocampus are associated with a range of diseases in children, including epilepsy and sudden death. A population of rod cells in part of the hippocampus, the polymorphic layer of the dentate gyrus, has long been recognized in infants. Previous work suggested that these cells were microglia and that their presence was associated with chronic illness and sudden infant death syndrome. Prompted by the observations that a sensitive immunohistochemical marker of microglia used in diagnostic practice does not typically stain these cells and that the hippocampus is a site of postnatal neurogenesis, we hypothesized that this transient population of cells were not microglia but neural progenitors. Methods Using archived post mortem tissue, we applied a broad panel of antibodies to establish the immunophenotype of these cells in 40 infants dying suddenly of causes that were either explained or remained unexplained, following post mortem investigation. Results The rod cells were consistently negative for the microglial markers CD45, CD68 and HLA-DR. The cells were positive, in varying proportions, for the neural progenitor marker, doublecortin, the neural stem cell marker, nestin and the neural marker, TUJ1. Conclusions These data support our hypothesis that the rod cells of the polymorphic layer of the dentate gyrus in the infant hippocampus are not microglia but a population of neural progenitors. These findings advance our understanding of postnatal neurogenesis in the human hippocampus in health and disease and are of diagnostic importance, allowing reactive microglia to be distinguished from the normal population of neural progenitors. PMID:23742713

  15. Applying the multivariate time-rescaling theorem to neural population models

    PubMed Central

    Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

    2011-01-01

    Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436

  16. Enrichment of skin-derived neural precursor cells from dermal cell populations by altering culture conditions.

    PubMed

    Bayati, Vahid; Gazor, Rohoullah; Nejatbakhsh, Reza; Negad Dehbashi, Fereshteh

    2016-01-01

    As stem cells play a critical role in tissue repair, their manipulation for being applied in regenerative medicine is of great importance. Skin-derived precursors (SKPs) may be good candidates for use in cell-based therapy as the only neural stem cells which can be isolated from an accessible tissue, skin. Herein, we presented a simple protocol to enrich neural SKPs by monolayer adherent cultivation to prove the efficacy of this method. To enrich neural SKPs from dermal cell populations, we have found that a monolayer adherent cultivation helps to increase the numbers of neural precursor cells. Indeed, we have cultured dermal cells as monolayer under serum-supplemented (control) and serum-supplemented culture, followed by serum free cultivation (test) and compared. Finally, protein markers of SKPs were assessed and compared in both experimental groups and differentiation potential was evaluated in enriched culture. The cells of enriched culture concurrently expressed fibronectin, vimentin and nestin, an intermediate filament protein expressed in neural and skeletal muscle precursors as compared to control culture. In addition, they possessed a multipotential capacity to differentiate into neurogenic, glial, adipogenic, osteogenic and skeletal myogenic cell lineages. It was concluded that serum-free adherent culture reinforced by growth factors have been shown to be effective on proliferation of skin-derived neural precursor cells (skin-NPCs) and drive their selective and rapid expansion.

  17. A novel lead design enables selective deep brain stimulation of neural populations in the subthalamic region

    NASA Astrophysics Data System (ADS)

    van Dijk, Kees J.; Verhagen, Rens; Chaturvedi, Ashutosh; McIntyre, Cameron C.; Bour, Lo J.; Heida, Ciska; Veltink, Peter H.

    2015-08-01

    Objective. The clinical effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN-DBS) as a treatment for Parkinson’s disease are sensitive to the location of the DBS lead within the STN. New high density (HD) lead designs have been created which are hypothesized to provide additional degrees of freedom in shaping the stimulating electric field. The objective of this study is to compare the performances of a new HD lead with a conventional cylindrical contact (CC) lead. Approach. A computational model, consisting of a finite element electric field model combined with multi-compartment neuron and axon models representing different neural populations in the subthalamic region, was used to evaluate the two leads. We compared ring-mode and steering-mode stimulation with the HD lead to single contact stimulation with the CC lead. These stimulation modes were tested for the lead: (1) positioned in the centroid of the STN, (2) shifted 1 mm towards the internal capsule (IC), and (3) shifted 2 mm towards the IC. Under these conditions, we quantified the number of STN neurons that were activated without activating IC fibers, which are known to cause side-effects. Main results. The modeling results show that the HD lead is able to mimic the stimulation effect of the CC lead. Additionally, in steering-mode stimulation there was a significant increase of activated STN neurons compared to the CC mode. Significance. From the model simulations we conclude that the HD lead in steering-mode with optimized stimulation parameter selection can stimulate more STN cells. Next, the clinical impact of the increased number of activated STN cells should be tested and balanced across the increased complexity of identifying the optimized stimulation parameter settings for the HD lead.

  18. A novel lead design enables selective deep brain stimulation of neural populations in the subthalamic region.

    PubMed

    van Dijk, Kees J; Verhagen, Rens; Chaturvedi, Ashutosh; McIntyre, Cameron C; Bour, Lo J; Heida, Ciska; Veltink, Peter H

    2015-08-01

    The clinical effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN-DBS) as a treatment for Parkinson's disease are sensitive to the location of the DBS lead within the STN. New high density (HD) lead designs have been created which are hypothesized to provide additional degrees of freedom in shaping the stimulating electric field. The objective of this study is to compare the performances of a new HD lead with a conventional cylindrical contact (CC) lead. A computational model, consisting of a finite element electric field model combined with multi-compartment neuron and axon models representing different neural populations in the subthalamic region, was used to evaluate the two leads. We compared ring-mode and steering-mode stimulation with the HD lead to single contact stimulation with the CC lead. These stimulation modes were tested for the lead: (1) positioned in the centroid of the STN, (2) shifted 1 mm towards the internal capsule (IC), and (3) shifted 2 mm towards the IC. Under these conditions, we quantified the number of STN neurons that were activated without activating IC fibers, which are known to cause side-effects. The modeling results show that the HD lead is able to mimic the stimulation effect of the CC lead. Additionally, in steering-mode stimulation there was a significant increase of activated STN neurons compared to the CC mode. From the model simulations we conclude that the HD lead in steering-mode with optimized stimulation parameter selection can stimulate more STN cells. Next, the clinical impact of the increased number of activated STN cells should be tested and balanced across the increased complexity of identifying the optimized stimulation parameter settings for the HD lead.

  19. Random versus maximum entropy models of neural population activity

    NASA Astrophysics Data System (ADS)

    Ferrari, Ulisse; Obuchi, Tomoyuki; Mora, Thierry

    2017-04-01

    The principle of maximum entropy provides a useful method for inferring statistical mechanics models from observations in correlated systems, and is widely used in a variety of fields where accurate data are available. While the assumptions underlying maximum entropy are intuitive and appealing, its adequacy for describing complex empirical data has been little studied in comparison to alternative approaches. Here, data from the collective spiking activity of retinal neurons is reanalyzed. The accuracy of the maximum entropy distribution constrained by mean firing rates and pairwise correlations is compared to a random ensemble of distributions constrained by the same observables. For most of the tested networks, maximum entropy approximates the true distribution better than the typical or mean distribution from that ensemble. This advantage improves with population size, with groups as small as eight being almost always better described by maximum entropy. Failure of maximum entropy to outperform random models is found to be associated with strong correlations in the population.

  20. Random versus maximum entropy models of neural population activity.

    PubMed

    Ferrari, Ulisse; Obuchi, Tomoyuki; Mora, Thierry

    2017-04-01

    The principle of maximum entropy provides a useful method for inferring statistical mechanics models from observations in correlated systems, and is widely used in a variety of fields where accurate data are available. While the assumptions underlying maximum entropy are intuitive and appealing, its adequacy for describing complex empirical data has been little studied in comparison to alternative approaches. Here, data from the collective spiking activity of retinal neurons is reanalyzed. The accuracy of the maximum entropy distribution constrained by mean firing rates and pairwise correlations is compared to a random ensemble of distributions constrained by the same observables. For most of the tested networks, maximum entropy approximates the true distribution better than the typical or mean distribution from that ensemble. This advantage improves with population size, with groups as small as eight being almost always better described by maximum entropy. Failure of maximum entropy to outperform random models is found to be associated with strong correlations in the population.

  1. A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks.

    PubMed

    Wang, Juan; Fang, Zhiyuan; Lang, Ning; Yuan, Huishu; Su, Min-Ying; Baldi, Pierre

    2017-05-01

    Spinal metastasis, a metastatic cancer of the spine, is the most common malignant disease in the spine. In this study, we investigate the feasibility of automated spinal metastasis detection in magnetic resonance imaging (MRI) by using deep learning methods. To accommodate the large variability in metastatic lesion sizes, we develop a Siamese deep neural network approach comprising three identical subnetworks for multi-resolution analysis and detection of spinal metastasis. At each location of interest, three image patches at three different resolutions are extracted and used as the input to the networks. To further reduce the false positives (FPs), we leverage the similarity between neighboring MRI slices, and adopt a weighted averaging strategy to aggregate the results obtained by the Siamese neural networks. The detection performance is evaluated on a set of 26 cases using a free-response receiver operating characteristic (FROC) analysis. The results show that the proposed approach correctly detects all the spinal metastatic lesions while producing only 0.40 FPs per case. At a true positive (TP) rate of 90%, the use of the aggregation reduces the FPs from 0.375 FPs per case to 0.207 FPs per case, a nearly 44.8% reduction. The results indicate that the proposed Siamese neural network method, combined with the aggregation strategy, provide a viable strategy for the automated detection of spinal metastasis in MRI images. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Behavioral dynamics and neural grounding of a dynamic field theory of multi-object tracking.

    PubMed

    Spencer, J P; Barich, K; Goldberg, J; Perone, S

    2012-09-01

    The ability to dynamically track moving objects in the environment is crucial for efficient interaction with the local surrounds. Here, we examined this ability in the context of the multi-object tracking (MOT) task. Several theories have been proposed to explain how people track moving objects; however, only one of these previous theories is implemented in a real-time process model, and there has been no direct contact between theories of object tracking and the growing neural literature using ERPs and fMRI. Here, we present a neural process model of object tracking that builds from a Dynamic Field Theory of spatial cognition. Simulations reveal that our dynamic field model captures recent behavioral data examining the impact of speed and tracking duration on MOT performance. Moreover, we show that the same model with the same trajectories and parameters can shed light on recent ERP results probing how people distribute attentional resources to targets vs. distractors. We conclude by comparing this new theory of object tracking to other recent accounts, and discuss how the neural grounding of the theory might be effectively explored in future work.

  3. Estimate the effective connectivity in multi-coupled neural mass model using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Shan, Bonan; Wang, Jiang; Deng, Bin; Zhang, Zhen; Wei, Xile

    2017-03-01

    Assessment of the effective connectivity among different brain regions during seizure is a crucial problem in neuroscience today. As a consequence, a new model inversion framework of brain function imaging is introduced in this manuscript. This framework is based on approximating brain networks using a multi-coupled neural mass model (NMM). NMM describes the excitatory and inhibitory neural interactions, capturing the mechanisms involved in seizure initiation, evolution and termination. Particle swarm optimization method is used to estimate the effective connectivity variation (the parameters of NMM) and the epileptiform dynamics (the states of NMM) that cannot be directly measured using electrophysiological measurement alone. The estimated effective connectivity includes both the local connectivity parameters within a single region NMM and the remote connectivity parameters between multi-coupled NMMs. When the epileptiform activities are estimated, a proportional-integral controller outputs control signal so that the epileptiform spikes can be inhibited immediately. Numerical simulations are carried out to illustrate the effectiveness of the proposed framework. The framework and the results have a profound impact on the way we detect and treat epilepsy.

  4. Modelling innovation performance of European regions using multi-output neural networks.

    PubMed

    Hajek, Petr; Henriques, Roberto

    2017-01-01

    Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

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

    PubMed

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

    2017-08-03

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-09-01

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

  7. Modelling Multi-Pulse Population Dynamics from Ultrafast Spectroscopy

    PubMed Central

    van Wilderen, Luuk J. G. W.; Lincoln, Craig N.; van Thor, Jasper J.

    2011-01-01

    Current advanced laser, optics and electronics technology allows sensitive recording of molecular dynamics, from single resonance to multi-colour and multi-pulse experiments. Extracting the occurring (bio-) physical relevant pathways via global analysis of experimental data requires a systematic investigation of connectivity schemes. Here we present a Matlab-based toolbox for this purpose. The toolbox has a graphical user interface which facilitates the application of different reaction models to the data to generate the coupled differential equations. Any time-dependent dataset can be analysed to extract time-independent correlations of the observables by using gradient or direct search methods. Specific capabilities (i.e. chirp and instrument response function) for the analysis of ultrafast pump-probe spectroscopic data are included. The inclusion of an extra pulse that interacts with a transient phase can help to disentangle complex interdependent pathways. The modelling of pathways is therefore extended by new theory (which is included in the toolbox) that describes the finite bleach (orientation) effect of single and multiple intense polarised femtosecond pulses on an ensemble of randomly oriented particles in the presence of population decay. For instance, the generally assumed flat-top multimode beam profile is adapted to a more realistic Gaussian shape, exposing the need for several corrections for accurate anisotropy measurements. In addition, the (selective) excitation (photoselection) and anisotropy of populations that interact with single or multiple intense polarised laser pulses is demonstrated as function of power density and beam profile. Using example values of real world experiments it is calculated to what extent this effectively orients the ensemble of particles. Finally, the implementation includes the interaction with multiple pulses in addition to depth averaging in optically dense samples. In summary, we show that mathematical modelling is

  8. Modelling multi-pulse population dynamics from ultrafast spectroscopy.

    PubMed

    van Wilderen, Luuk J G W; Lincoln, Craig N; van Thor, Jasper J

    2011-03-21

    Current advanced laser, optics and electronics technology allows sensitive recording of molecular dynamics, from single resonance to multi-colour and multi-pulse experiments. Extracting the occurring (bio-) physical relevant pathways via global analysis of experimental data requires a systematic investigation of connectivity schemes. Here we present a Matlab-based toolbox for this purpose. The toolbox has a graphical user interface which facilitates the application of different reaction models to the data to generate the coupled differential equations. Any time-dependent dataset can be analysed to extract time-independent correlations of the observables by using gradient or direct search methods. Specific capabilities (i.e. chirp and instrument response function) for the analysis of ultrafast pump-probe spectroscopic data are included. The inclusion of an extra pulse that interacts with a transient phase can help to disentangle complex interdependent pathways. The modelling of pathways is therefore extended by new theory (which is included in the toolbox) that describes the finite bleach (orientation) effect of single and multiple intense polarised femtosecond pulses on an ensemble of randomly oriented particles in the presence of population decay. For instance, the generally assumed flat-top multimode beam profile is adapted to a more realistic Gaussian shape, exposing the need for several corrections for accurate anisotropy measurements. In addition, the (selective) excitation (photoselection) and anisotropy of populations that interact with single or multiple intense polarised laser pulses is demonstrated as function of power density and beam profile. Using example values of real world experiments it is calculated to what extent this effectively orients the ensemble of particles. Finally, the implementation includes the interaction with multiple pulses in addition to depth averaging in optically dense samples. In summary, we show that mathematical modelling is

  9. Artificial bee colony algorithm with dynamic multi-population

    NASA Astrophysics Data System (ADS)

    Zhang, Ming; Ji, Zhicheng; Wang, Yan

    2017-07-01

    To improve the convergence rate and make a balance between the global search and local turning abilities, this paper proposes a decentralized form of artificial bee colony (ABC) algorithm with dynamic multi-populations by means of fuzzy C-means (FCM) clustering. Each subpopulation periodically enlarges with the same size during the search process, and the overlapping individuals among different subareas work for delivering information acting as exploring the search space with diffusion of solutions. Moreover, a Gaussian-based search equation with redefined local attractor is proposed to further accelerate the diffusion of the best solution and guide the search towards potential areas. Experimental results on a set of benchmarks demonstrate the competitive performance of our proposed approach.

  10. A MULTI-PATCH MALARIA MODEL WITH LOGISTIC GROWTH POPULATIONS*

    PubMed Central

    GAO, DAOZHOU; RUAN, SHIGUI

    2013-01-01

    In this paper, we propose a multi-patch model to study the effects of population dispersal on the spatial spread of malaria between patches. The basic reproduction number R0 is derived and it is shown that the disease-free equilibrium is locally asymptotically stable if R0<1 and unstable if R0>1. Bounds on the disease-free equilibrium and R0 are given. A sufficient condition for the existence of an endemic equilibrium when R0>1 is obtained. For the two-patch submodel, the dependence of R0 on the movement of exposed, infectious, and recovered humans between the two patches is investigated. Numerical simulations indicate that travel can help the disease to become endemic in both patches, even though the disease dies out in each isolated patch. However, if travel rates are continuously increased, the disease may die out again in both patches. PMID:23723531

  11. Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning

    PubMed Central

    Dann, Benjamin

    2016-01-01

    Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity. PMID:27814352

  12. Tfap2a and Foxd3 regulate early steps in the development of the neural crest progenitor population.

    PubMed

    Wang, Wen-Der; Melville, David B; Montero-Balaguer, Mercedes; Hatzopoulos, Antonis K; Knapik, Ela W

    2011-12-01

    The neural crest is a stem cell-like population exclusive to vertebrates that gives rise to many different cell types including chondrocytes, neurons and melanocytes. Arising from the neural plate border at the intersection of Wnt and Bmp signaling pathways, the complexity of neural crest gene regulatory networks has made the earliest steps of induction difficult to elucidate. Here, we report that tfap2a and foxd3 participate in neural crest induction and are necessary and sufficient for this process to proceed. Double mutant tfap2a (mont blanc, mob) and foxd3 (mother superior, mos) mob;mos zebrafish embryos completely lack all neural crest-derived tissues. Moreover, tfap2a and foxd3 are expressed during gastrulation prior to neural crest induction in distinct, complementary, domains; tfap2a is expressed in the ventral non-neural ectoderm and foxd3 in the dorsal mesendoderm and ectoderm. We further show that Bmp signaling is expanded in mob;mos embryos while expression of dkk1, a Wnt signaling inhibitor, is increased and canonical Wnt targets are suppressed. These changes in Bmp and Wnt signaling result in specific perturbations of neural crest induction rather than general defects in neural plate border or dorso-ventral patterning. foxd3 overexpression, on the other hand, enhances the ability of tfap2a to ectopically induce neural crest around the neural plate, overriding the normal neural plate border limit of the early neural crest territory. Although loss of either Tfap2a or Foxd3 alters Bmp and Wnt signaling patterns, only their combined inactivation sufficiently alters these signaling gradients to abort neural crest induction. Collectively, our results indicate that tfap2a and foxd3, in addition to their respective roles in the differentiation of neural crest derivatives, also jointly maintain the balance of Bmp and Wnt signaling in order to delineate the neural crest induction domain.

  13. Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations

    PubMed Central

    Obermayer, Klaus

    2017-01-01

    The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons. Here by introducing multiple analytic approximation methods to a state-space model of neural population activity, we make it possible to estimate dynamic pairwise interactions of up to 60 neurons. More specifically, we applied the pseudolikelihood approximation to the state-space model, and combined it with the Bethe or TAP mean-field approximation to make the sequential Bayesian estimation of the model parameters possible. The large-scale analysis allows us to investigate dynamics of macroscopic properties of neural circuitries underlying stimulus processing and behavior. We show that the model accurately estimates dynamics of network properties such as sparseness, entropy, and heat capacity by simulated data, and demonstrate utilities of these measures by analyzing activity of monkey V4 neurons as well as a simulated balanced network of spiking neurons. PMID:28095421

  14. Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex.

    PubMed

    Murray, John D; Bernacchia, Alberto; Roy, Nicholas A; Constantinidis, Christos; Romo, Ranulfo; Wang, Xiao-Jing

    2017-01-10

    Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

  15. Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

    PubMed Central

    Murray, John D.; Bernacchia, Alberto; Roy, Nicholas A.; Constantinidis, Christos; Romo, Ranulfo; Wang, Xiao-Jing

    2017-01-01

    Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain’s WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM. PMID:28028221

  16. C3G regulates the size of the cerebral cortex neural precursor population

    PubMed Central

    Voss, Anne K; Krebs, Danielle L; Thomas, Tim

    2006-01-01

    The mechanisms regulating the size of the cerebral cortex are poorly understood. Here, we demonstrate that the Rap1 guanine nucleotide exchange factor, C3G (Grf2, Rapgef1), controls the size of the cerebral precursor population. Mice lacking C3G show overproliferation of the cortical neuroepithelium. C3G-deficient neuroepithelial cells accumulate nuclear β-catenin and fail to exit the cell cycle in vivo. C3G mutant neural precursor cells fail to activate Rap1, exhibit activation of Akt/PKB, inhibition of the β-catenin-degrading enzyme, Gsk3β and accumulation of cytosolic and nuclear β-catenin when exposed to growth factors, in vitro. Our results show that the size of the cortical neural precursor population is controlled by C3G-mediated inhibition of the Ras signalling pathway. PMID:16858399

  17. Neural-tube defects in a prehistoric south-western Indian population.

    PubMed

    Devor, E J; Cordell, L S

    1981-01-01

    Concern with the frequency and patterning of the occurrence of midline neural-tube defects among contemporary human populations is widespread. These defects are, however, quite old and occur in unusually high numbers of prehistoric skeletons. A common explanation offered for such high incidence has been inbreeding among small, reproductively isolated populations. In a sample of 54 skeletons from the prehistoric south-western Indian site of Tijeras Pueblo in New Mexico, failure of neural-tube closure occurs in 10% of sacra recovered. While a more homogeneous genetic background and inbreeding may account for a portion of this elevated prevalence, the cause appears to lie with cultural-environmental factors. It is suggested that the aetiology of these conditions has become more complex in recent human history.

  18. Artificial vision by multi-layered neural networks: neocognitron and its advances.

    PubMed

    Fukushima, Kunihiko

    2013-01-01

    The neocognitron is a neural network model proposed by Fukushima (1980). Its architecture was suggested by neurophysiological findings on the visual systems of mammals. It is a hierarchical multi-layered network. It acquires the ability to robustly recognize visual patterns through learning. Although the neocognitron has a long history, modifications of the network to improve its performance are still going on. For example, a recent neocognitron uses a new learning rule, named add-if-silent, which makes the learning process much simpler and more stable. Nevertheless, a high recognition rate can be kept with a smaller scale of the network. Referring to the history of the neocognitron, this paper discusses recent advances in the neocognitron. We also show that various new functions can be realized by, for example, introducing top-down connections to the neocognitron: mechanism of selective attention, recognition and completion of partly occluded patterns, restoring occluded contours, and so on.

  19. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

    NASA Astrophysics Data System (ADS)

    Serb, Alexander; Bill, Johannes; Khiat, Ali; Berdan, Radu; Legenstein, Robert; Prodromakis, Themis

    2016-09-01

    In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.

  20. Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults

    NASA Astrophysics Data System (ADS)

    Zhao, Lin; Jia, Yingmin

    2016-06-01

    In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

  1. Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks

    NASA Astrophysics Data System (ADS)

    Ozcan, H. Kurtulus; Bilgili, Erdem; Sahin, Ulku; Ucan, O. Nuri; Bayat, Cuma

    2007-09-01

    Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.

  2. Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network

    PubMed Central

    Pham, Tuyen Danh; Lee, Dong Eun; Park, Kang Ryoung

    2017-01-01

    Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods. PMID:28698466

  3. Intelligent detection of impulse noise using multilayer neural network with multi-valued neurons

    NASA Astrophysics Data System (ADS)

    Aizenberg, Igor; Wallace, Glen

    2012-03-01

    In this paper, we solve the impulse noise detection problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent impulse noise detector. MLMVN was already used for point spread function identification and intelligent edge enhancement. So it is very attractive to apply it for solving another image processing problem. The main result, which is presented in the paper, is the proven ability of MLMVN to detect impulse noise on different images after a learning session with the data taken just from a single noisy image. Hence MLMVN can be used as a robust impulse detector. It is especially efficient for salt and pepper noise detection and outperforms all competitive techniques. It also shows comparable results in detection of random impulse noise. Moreover, for random impulse noise detection, MLMVN with the output neuron with a periodic activation function is used for the first time.

  4. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

    PubMed Central

    Serb, Alexander; Bill, Johannes; Khiat, Ali; Berdan, Radu; Legenstein, Robert; Prodromakis, Themis

    2016-01-01

    In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors. PMID:27681181

  5. Convolutional Neural Network for Multi-Source Deep Learning Crop Classification in Ukraine

    NASA Astrophysics Data System (ADS)

    Lavreniuk, M. S.

    2016-12-01

    Land cover and crop type maps are one of the most essential inputs when dealing with environmental and agriculture monitoring tasks [1]. During long time neural network (NN) approach was one of the most efficient and popular approach for most applications, including crop classification using remote sensing data, with high an overall accuracy (OA) [2]. In the last years the most popular and efficient method for multi-sensor and multi-temporal land cover classification is convolution neural networks (CNNs). Taking into account presence clouds in optical data, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of optical imagery from Landsat-8 satellite. After missing data restoration, optical data from Landsat-8 was merged with Sentinel-1A radar data for better crop types discrimination [3]. An ensemble of CNNs is proposed for multi-temporal satellite images supervised classification. Each CNN in the corresponding ensemble is a 1-d CNN with 4 layers implemented using the Google's library TensorFlow. The efficiency of the proposed approach was tested on a time-series of Landsat-8 and Sentinel-1A images over the JECAM test site (Kyiv region) in Ukraine in 2015. Overall classification accuracy for ensemble of CNNs was 93.5% that outperformed an ensemble of multi-layer perceptrons (MLPs) by +0.8% and allowed us to better discriminate summer crops, in particular maize and soybeans. For 2016 we would like to validate this method using Sentinel-1 and Sentinel-2 data for Ukraine territory within ESA project on country level demonstration Sen2Agri. 1. A. Kolotii et al., "Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine," The Int. Arch. of Photogram., Rem. Sens. and Spatial Inform. Scie., vol. 40, no. 7, pp. 39-44, 2015. 2. F. Waldner et al., "Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity," Int. Journal of Rem. Sens. vol. 37, no. 14, pp

  6. A design philosophy for multi-layer neural networks with applications to robot control

    NASA Technical Reports Server (NTRS)

    Vadiee, Nader; Jamshidi, MO

    1989-01-01

    A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.

  7. Distributed Adaptive Coordinated Control of Multi-Manipulator Systems Using Neural Networks

    NASA Astrophysics Data System (ADS)

    Hou, Zeng-Guang; Cheng, Long; Tan, Min; Wang, Xu

    On many occasions, all the manipulators in the multi-manipulator system need to achieve the same joint configuration to fulfill certain coordination tasks. In this chapter, a distributed adaptive approach is proposed for solving this coordination problem based on the leader-follower strategy. The proposed algorithm is distributed because the controller for each follower manipulator is solely based on the information of connected neighbor manipulators, and the joint value of leader manipulator is only accessible to partial follower manipulators. The uncertain term in the manipulator's dynamics is considered in the controller design, and it is approximated by the adaptive neural network scheme. The neural network weight matrix is adjusted on-line by the projection method, and the pre-training phase is no longer required. Effects of approximation error and external disturbances are counteracted by employing the robustness signal. According to the theoretical analysis, all the joints of follower manipulators can be regulated into an arbitrary small neighborhood of the value of leader's joint. Finally, simulation results are given to demonstrate the satisfactory performance of the proposed method.

  8. Energy-efficient multi-mode compressed sensing system for implantable neural recordings.

    PubMed

    Suo, Yuanming; Zhang, Jie; Xiong, Tao; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D

    2014-10-01

    Widely utilized in the field of Neuroscience, implantable neural recording devices could capture neuron activities with an acquisition rate on the order of megabytes per second. In order to efficiently transmit neural signals through wireless channels, these devices require compression methods that reduce power consumption. Although recent Compressed Sensing (CS) approaches have successfully demonstrated their power, their full potential is yet to be explored. Built upon our previous on-chip CS implementation, we propose an energy efficient multi-mode CS framework that focuses on improving the off-chip components, including (i) a two-stage sensing strategy, (ii) a sparsifying dictionary directly using data, (iii) enhanced compression performance from Full Signal CS mode and Spike Restoration mode to Spike CS + Restoration mode and; (iv) extension of our framework to the Tetrode CS recovery using joint sparsity. This new framework achieves energy efficiency, implementation simplicity and system flexibility simultaneously. Extensive experiments are performed on simulation and real datasets. For our Spike CS + Restoration mode, we achieve a compression ratio of 6% with a reconstruction SNDR > 10 dB and a classification accuracy > 95% for synthetic datasets. For real datasets, we get a 10% compression ratio with  ∼  10 dB for Spike CS + Restoration mode.

  9. Hydrophilic modification of neural microelectrode arrays based on multi-walled carbon nanotubes

    NASA Astrophysics Data System (ADS)

    Chen, Chang-Hsiao; Su, Huan-Chieh; Chuang, Shih-Chang; Yen, Shiang-Jie; Chen, Yung-Chan; Lee, Yu-Tao; Chen, Hsin; Yew, Tri-Rung; Chang, Yen-Chung; Yeh, Shih-Rung; Yao, Da-Jeng

    2010-12-01

    To decrease the impedance of microelectrode arrays, for neuroscience applications we have fabricated and tested MEA based on multi-walled carbon nanotubes. With decreasing physical size of a microelectrode, its impedance increases and charge-transfer capability decreases. To decrease the impedance, the effective surface area of the electrode must generally be increased. We explored the effect of plasma treatment on the surface wettability of MWCNT. With a steam-plasma treatment the surface of MWCNT becomes converted from superhydrophobic to superhydrophilic; this hydrophilic property is attributed to -OH bonding on the surface of MWCNT. We reported the synthesis at 400 °C of MWCNT on nickel-titanium multilayered metal catalysts by thermal chemical vapor deposition. Applying plasma with a power less than 25 W for 10 s improved the electrochemical and biological properties, and circumvented the limitation of the surface reverting to a hydrophobic condition; a hydrophilic state is maintained for at least one month. The MEA was used to record neural signals of a lateral giant cell from an American crayfish. The response amplitude of the action potential was about 275 µV with 1 ms period; the recorded data had a ratio of signal to noise up to 40.12 dB. The improved performance of the electrode makes feasible the separation of neural signals and the recognition of their distinct shapes. With further development the rapid treatment will be useful for long-term recording applications.

  10. Aspiration dynamics of multi-player games in finite populations

    PubMed Central

    Du, Jinming; Wu, Bin; Altrock, Philipp M.; Wang, Long

    2014-01-01

    On studying strategy update rules in the framework of evolutionary game theory, one can differentiate between imitation processes and aspiration-driven dynamics. In the former case, individuals imitate the strategy of a more successful peer. In the latter case, individuals adjust their strategies based on a comparison of their pay-offs from the evolutionary game to a value they aspire, called the level of aspiration. Unlike imitation processes of pairwise comparison, aspiration-driven updates do not require additional information about the strategic environment and can thus be interpreted as being more spontaneous. Recent work has mainly focused on understanding how aspiration dynamics alter the evolutionary outcome in structured populations. However, the baseline case for understanding strategy selection is the well-mixed population case, which is still lacking sufficient understanding. We explore how aspiration-driven strategy-update dynamics under imperfect rationality influence the average abundance of a strategy in multi-player evolutionary games with two strategies. We analytically derive a condition under which a strategy is more abundant than the other in the weak selection limiting case. This approach has a long-standing history in evolutionary games and is mostly applied for its mathematical approachability. Hence, we also explore strong selection numerically, which shows that our weak selection condition is a robust predictor of the average abundance of a strategy. The condition turns out to differ from that of a wide class of imitation dynamics, as long as the game is not dyadic. Therefore, a strategy favoured under imitation dynamics can be disfavoured under aspiration dynamics. This does not require any population structure, and thus highlights the intrinsic difference between imitation and aspiration dynamics. PMID:24598208

  11. Antibiotic resistance shaping multi-level population biology of bacteria

    PubMed Central

    Baquero, Fernando; Tedim, Ana P.; Coque, Teresa M.

    2013-01-01

    Antibiotics have natural functions, mostly involving cell-to-cell signaling networks. The anthropogenic production of antibiotics, and its release in the microbiosphere results in a disturbance of these networks, antibiotic resistance tending to preserve its integrity. The cost of such adaptation is the emergence and dissemination of antibiotic resistance genes, and of all genetic and cellular vehicles in which these genes are located. Selection of the combinations of the different evolutionary units (genes, integrons, transposons, plasmids, cells, communities and microbiomes, hosts) is highly asymmetrical. Each unit of selection is a self-interested entity, exploiting the higher hierarchical unit for its own benefit, but in doing so the higher hierarchical unit might acquire critical traits for its spread because of the exploitation of the lower hierarchical unit. This interactive trade-off shapes the population biology of antibiotic resistance, a composed-complex array of the independent “population biologies.” Antibiotics modify the abundance and the interactive field of each of these units. Antibiotics increase the number and evolvability of “clinical” antibiotic resistance genes, but probably also many other genes with different primary functions but with a resistance phenotype present in the environmental resistome. Antibiotics influence the abundance, modularity, and spread of integrons, transposons, and plasmids, mostly acting on structures present before the antibiotic era. Antibiotics enrich particular bacterial lineages and clones and contribute to local clonalization processes. Antibiotics amplify particular genetic exchange communities sharing antibiotic resistance genes and platforms within microbiomes. In particular human or animal hosts, the microbiomic composition might facilitate the interactions between evolutionary units involved in antibiotic resistance. The understanding of antibiotic resistance implies expanding our knowledge on multi

  12. Antibiotic resistance shaping multi-level population biology of bacteria.

    PubMed

    Baquero, Fernando; Tedim, Ana P; Coque, Teresa M

    2013-01-01

    Antibiotics have natural functions, mostly involving cell-to-cell signaling networks. The anthropogenic production of antibiotics, and its release in the microbiosphere results in a disturbance of these networks, antibiotic resistance tending to preserve its integrity. The cost of such adaptation is the emergence and dissemination of antibiotic resistance genes, and of all genetic and cellular vehicles in which these genes are located. Selection of the combinations of the different evolutionary units (genes, integrons, transposons, plasmids, cells, communities and microbiomes, hosts) is highly asymmetrical. Each unit of selection is a self-interested entity, exploiting the higher hierarchical unit for its own benefit, but in doing so the higher hierarchical unit might acquire critical traits for its spread because of the exploitation of the lower hierarchical unit. This interactive trade-off shapes the population biology of antibiotic resistance, a composed-complex array of the independent "population biologies." Antibiotics modify the abundance and the interactive field of each of these units. Antibiotics increase the number and evolvability of "clinical" antibiotic resistance genes, but probably also many other genes with different primary functions but with a resistance phenotype present in the environmental resistome. Antibiotics influence the abundance, modularity, and spread of integrons, transposons, and plasmids, mostly acting on structures present before the antibiotic era. Antibiotics enrich particular bacterial lineages and clones and contribute to local clonalization processes. Antibiotics amplify particular genetic exchange communities sharing antibiotic resistance genes and platforms within microbiomes. In particular human or animal hosts, the microbiomic composition might facilitate the interactions between evolutionary units involved in antibiotic resistance. The understanding of antibiotic resistance implies expanding our knowledge on multi

  13. Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations

    NASA Astrophysics Data System (ADS)

    Zylberberg, Joel; Shea-Brown, Eric

    2015-12-01

    While recent recordings from neural populations show beyond-pairwise, or higher-order, correlations (HOC), we have little understanding of how HOC arise from network interactions and of how they impact encoded information. Here, we show that input nonlinearities imply HOC in spin-glass-type statistical models. We then discuss one such model with parametrized pairwise- and higher-order interactions, revealing conditions under which beyond-pairwise interactions increase the mutual information between a given stimulus type and the population responses. For jointly Gaussian stimuli, coding performance is improved by shaping output HOC only when neural firing rates are constrained to be low. For stimuli with skewed probability distributions (like natural image luminances), performance improves for all firing rates. Our work suggests surprising connections between nonlinear integration of neural inputs, stimulus statistics, and normative theories of population coding. Moreover, it suggests that the inclusion of beyond-pairwise interactions could improve the performance of Boltzmann machines for machine learning and signal processing applications.

  14. Expectation and surprise determine neural population responses in the ventral visual stream.

    PubMed

    Egner, Tobias; Monti, Jim M; Summerfield, Christopher

    2010-12-08

    Visual cortex is traditionally viewed as a hierarchy of neural feature detectors, with neural population responses being driven by bottom-up stimulus features. Conversely, "predictive coding" models propose that each stage of the visual hierarchy harbors two computationally distinct classes of processing unit: representational units that encode the conditional probability of a stimulus and provide predictions to the next lower level; and error units that encode the mismatch between predictions and bottom-up evidence, and forward prediction error to the next higher level. Predictive coding therefore suggests that neural population responses in category-selective visual regions, like the fusiform face area (FFA), reflect a summation of activity related to prediction ("face expectation") and prediction error ("face surprise"), rather than a homogenous feature detection response. We tested the rival hypotheses of the feature detection and predictive coding models by collecting functional magnetic resonance imaging data from the FFA while independently varying both stimulus features (faces vs houses) and subjects' perceptual expectations regarding those features (low vs medium vs high face expectation). The effects of stimulus and expectation factors interacted, whereby FFA activity elicited by face and house stimuli was indistinguishable under high face expectation and maximally differentiated under low face expectation. Using computational modeling, we show that these data can be explained by predictive coding but not by feature detection models, even when the latter are augmented with attentional mechanisms. Thus, population responses in the ventral visual stream appear to be determined by feature expectation and surprise rather than by stimulus features per se.

  15. Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations.

    PubMed

    Zylberberg, Joel; Shea-Brown, Eric

    2015-12-01

    While recent recordings from neural populations show beyond-pairwise, or higher-order, correlations (HOC), we have little understanding of how HOC arise from network interactions and of how they impact encoded information. Here, we show that input nonlinearities imply HOC in spin-glass-type statistical models. We then discuss one such model with parametrized pairwise- and higher-order interactions, revealing conditions under which beyond-pairwise interactions increase the mutual information between a given stimulus type and the population responses. For jointly Gaussian stimuli, coding performance is improved by shaping output HOC only when neural firing rates are constrained to be low. For stimuli with skewed probability distributions (like natural image luminances), performance improves for all firing rates. Our work suggests surprising connections between nonlinear integration of neural inputs, stimulus statistics, and normative theories of population coding. Moreover, it suggests that the inclusion of beyond-pairwise interactions could improve the performance of Boltzmann machines for machine learning and signal processing applications.

  16. Population coding and decoding in a neural field: a computational study.

    PubMed

    Wu, Si; Amari, Shun-Ichi; Nakahara, Hiroyuki

    2002-05-01

    This study uses a neural field model to investigate computational aspects of population coding and decoding when the stimulus is a single variable. A general prototype model for the encoding process is proposed, in which neural responses are correlated, with strength specified by a gaussian function of their difference in preferred stimuli. Based on the model, we study the effect of correlation on the Fisher information, compare the performances of three decoding methods that differ in the amount of encoding information being used, and investigate the implementation of the three methods by using a recurrent network. This study not only rediscovers main results in existing literatures in a unified way, but also reveals important new features, especially when the neural correlation is strong. As the neural correlation of firing becomes larger, the Fisher information decreases drastically. We confirm that as the width of correlation increases, the Fisher information saturates and no longer increases in proportion to the number of neurons. However, we prove that as the width increases further--wider than (sqrt)2 times the effective width of the turning function--the Fisher information increases again, and it increases without limit in proportion to the number of neurons. Furthermore, we clarify the asymptotic efficiency of the maximum likelihood inference (MLI) type of decoding methods for correlated neural signals. It shows that when the correlation covers a nonlocal range of population (excepting the uniform correlation and when the noise is extremely small), the MLI type of method, whose decoding error satisfies the Cauchy-type distribution, is not asymptotically efficient. This implies that the variance is no longer adequate to measure decoding accuracy.

  17. A reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems

    NASA Astrophysics Data System (ADS)

    Zhong, Xu; Zhou, Yu

    2014-05-01

    This paper presents a decentralized multi-robot motion control strategy to facilitate a multi-robot system, comprised of collaborative mobile robots coordinated through wireless communications, to form and maintain desired wireless communication coverage in a realistic environment with unstable wireless signaling condition. A fuzzy neural network controller is proposed for each robot to maintain the wireless link quality with its neighbors. The controller is trained through reinforcement learning to establish the relationship between the wireless link quality and robot motion decision, via consecutive interactions between the controller and environment. The tuned fuzzy neural network controller is applied to a multi-robot deployment process to form and maintain desired wireless communication coverage. The effectiveness of the proposed control scheme is verified through simulations under different wireless signal propagation conditions.

  18. Measures of Coupling between Neural Populations Based on Granger Causality Principle

    PubMed Central

    Kaminski, Maciej; Brzezicka, Aneta; Kaminski, Jan; Blinowska, Katarzyna J.

    2016-01-01

    This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a “weak node.” Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures. PMID:27833546

  19. Measures of Coupling between Neural Populations Based on Granger Causality Principle.

    PubMed

    Kaminski, Maciej; Brzezicka, Aneta; Kaminski, Jan; Blinowska, Katarzyna J

    2016-01-01

    This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a "weak node." Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

  20. Neural Crest Cells Contribute an Astrocyte-like Glial Population to the Spleen

    PubMed Central

    Barlow-Anacker, Amanda J.; Fu, Ming; Erickson, Christopher S.; Bertocchini, Federica; Gosain, Ankush

    2017-01-01

    Neural crest cells (NCC) are multi-potent cells of ectodermal origin that colonize diverse organs, including the gastrointestinal tract to form the enteric nervous system (ENS) and hematopoietic organs (bone marrow, thymus) where they participate in lymphocyte trafficking. Recent studies have implicated the spleen as an anatomic site for integration of inflammatory signals from the intestine with efferent neural inputs. We have previously observed alterations in splenic lymphocyte subsets in animals with defective migration of NCC that model Hirschsprung’s disease, leading us to hypothesize that there may be a direct cellular contribution of NCC to the spleen. Here, we demonstrate that NCC colonize the spleen during embryogenesis and persist into adulthood. Splenic NCC display markers indicating a glial lineage and are arranged anatomically adjacent to blood vessels, pericytes and nerves, suggesting an astrocyte-like phenotype. Finally, we identify similar neural-crest derived cells in both the avian and non-human primate spleen, showing evolutionary conservation of these cells. PMID:28349968

  1. Multi-Level Interval Estimation for Locating damage in Structures by Using Artificial Neural Networks

    SciTech Connect

    Pan Danguang; Gao Yanhua; Song Junlei

    2010-05-21

    A new analysis technique, called multi-level interval estimation method, is developed for locating damage in structures. In this method, the artificial neural networks (ANN) analysis method is combined with the statistics theory to estimate the range of damage location. The ANN is multilayer perceptron trained by back-propagation. Natural frequencies and modal shape at a few selected points are used as input to identify the location and severity of damage. Considering the large-scale structures which have lots of elements, multi-level interval estimation method is developed to reduce the estimation range of damage location step-by-step. Every step, estimation range of damage location is obtained from the output of ANN by using the method of interval estimation. The next ANN training cases are selected from the estimation range after linear transform, and the output of new ANN estimation range of damage location will gained a reduced estimation range. Two numerical example analyses on 10-bar truss and 100-bar truss are presented to demonstrate the effectiveness of the proposed method.

  2. Lung nodule malignancy prediction using multi-task convolutional neural network

    NASA Astrophysics Data System (ADS)

    Li, Xiuli; Kao, Yueying; Shen, Wei; Li, Xiang; Xie, Guotong

    2017-03-01

    In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.

  3. Multi-Objective Calibration of Conceptual and Artificial Neural Network Models for Improved Runoff Forecasting

    NASA Astrophysics Data System (ADS)

    de Vos, N. J.; Rientjes, T. H.; Gupta, H. V.

    2006-12-01

    The forecasting of river discharges and water levels requires models that simulate the transformation of rainfall on a watershed into the runoff. The most popular approach to this complex modeling issue is to use conceptual hydrological models. In recent years, however, data-driven model alternatives have gained significant attention. Such models extract and re-use information that is implicit in hydrological data and do not directly take into account the physical laws that underlie rainfall-runoff processes. In this study, we have made a comparison between a conceptual hydrological model and the popular data-driven approach of Artificial Neural Network (ANN) modeling. ANNs use flexible model structures that simulate rainfall-runoff processes by mapping the transformation from system input and/or system states (e.g., rainfall, evaporation, soil moisture content) to system output (e.g. river discharge). Special attention was paid to the procedure of calibration of both approaches. Singular objective functions based on squared-error-based performance measures, such as the Mean Squared Error (MSE) are commonly used in rainfall-runoff modeling. However, not all differences between modeled and observed hydrograph characteristics can be adequately expressed by a single performance measure. Nowadays it is acknowledged that the calibration of rainfall-runoff models is inherently multi-objective. Therefore, Multi-Objective Evolutionary Algorithms (MOEAs) were tested as alternatives to traditional single-objective algorithms for calibration of both a conceptual and an ANN model for forecasting runoff. The MOEAs compare favorably to traditional single-objective methods in terms of performance, and they shed more light on the trade-offs between various objective functions. Additionally, the distribution of model parameter values gives insights into model parameter uncertainty and model structural deficiencies. Summarizing, the current study presents interesting and promising

  4. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.

    PubMed

    Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

    2015-03-01

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement.

  5. Classifying Multi-year Land Use and Land Cover using Deep Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Seo, B.

    2015-12-01

    Cultivated ecosystems constitute a particularly frequent form of human land use. Long-term management of a cultivated ecosystem requires us to know temporal change of land use and land cover (LULC) of the target system. Land use and land cover changes (LUCC) in agricultural ecosystem is often rapid and unexpectedly occurs. Thus, longitudinal LULC is particularly needed to examine trends of ecosystem functions and ecosystem services of the target system. Multi-temporal classification of land use and land cover (LULC) in complex heterogeneous landscape remains a challenge. Agricultural landscapes often made up of a mosaic of numerous LULC classes, thus spatial heterogeneity is large. Moreover, temporal and spatial variation within a LULC class is also large. Under such a circumstance, standard classifiers would fail to identify the LULC classes correctly due to the heterogeneity of the target LULC classes. Because most standard classifiers search for a specific pattern of features for a class, they fail to detect classes with noisy and/or transformed feature data sets. Recently, deep learning algorithms have emerged in the machine learning communities and shown superior performance on a variety of tasks, including image classification and object recognition. In this paper, we propose to use convolutional neural networks (CNN) to learn from multi-spectral data to classify agricultural LULC types. Based on multi-spectral satellite data, we attempted to classify agricultural LULC classes in Soyang watershed, South Korea for the three years' study period (2009-2011). The classification performance of support vector machine (SVM) and CNN classifiers were compared for different years. Preliminary results demonstrate that the proposed method can improve classification performance compared to the SVM classifier. The SVM classifier failed to identify classes when trained on a year to predict another year, whilst CNN could reconstruct LULC maps of the catchment over the study

  6. Micro mirrors based coupling of light to multi-core fiber realizing in-fiber photonic neural network processor

    NASA Astrophysics Data System (ADS)

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; London, Michael; Zalevsky, Zeev

    2017-02-01

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a DMD based approaches to realize energetically efficient light coupling into a multi-core fiber realizing a unique design for in-fiber optical neural networks. Neurons and synapses are realized as individual cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in Erbium-doped cores mimics synaptic interactions. In order to dynamically and efficiently couple light into the multi-core fiber a DMD based micro mirror device is used to perform proper beam shaping operation. The beam shaping reshapes the light into a large set of points in space matching the positions of the required cores in the entrance plane to the multi-core fiber.

  7. Neural decoding of attentional selection in multi-speaker environments without access to clean sources

    NASA Astrophysics Data System (ADS)

    O'Sullivan, James; Chen, Zhuo; Herrero, Jose; McKhann, Guy M.; Sheth, Sameer A.; Mehta, Ashesh D.; Mesgarani, Nima

    2017-10-01

    Objective. People who suffer from hearing impairments can find it difficult to follow a conversation in a multi-speaker environment. Current hearing aids can suppress background noise; however, there is little that can be done to help a user attend to a single conversation amongst many without knowing which speaker the user is attending to. Cognitively controlled hearing aids that use auditory attention decoding (AAD) methods are the next step in offering help. Translating the successes in AAD research to real-world applications poses a number of challenges, including the lack of access to the clean sound sources in the environment with which to compare with the neural signals. We propose a novel framework that combines single-channel speech separation algorithms with AAD. Approach. We present an end-to-end system that (1) receives a single audio channel containing a mixture of speakers that is heard by a listener along with the listener’s neural signals, (2) automatically separates the individual speakers in the mixture, (3) determines the attended speaker, and (4) amplifies the attended speaker’s voice to assist the listener. Main results. Using invasive electrophysiology recordings, we identified the regions of the auditory cortex that contribute to AAD. Given appropriate electrode locations, our system is able to decode the attention of subjects and amplify the attended speaker using only the mixed audio. Our quality assessment of the modified audio demonstrates a significant improvement in both subjective and objective speech quality measures. Significance. Our novel framework for AAD bridges the gap between the most recent advancements in speech processing technologies and speech prosthesis research and moves us closer to the development of cognitively controlled hearable devices for the hearing impaired.

  8. Gap junctions facilitate propagation of synchronous firing in the cortical neural population: a numerical simulation study.

    PubMed

    Shinozaki, Takashi; Naruse, Yasushi; Câteau, Hideyuki

    2013-10-01

    This study investigates the effect of gap junctions on firing propagation in a feedforward neural network by a numerical simulation with biologically plausible parameters. Gap junctions are electrical couplings between two cells connected by a binding protein, connexin. Recent electrophysiological studies have reported that a large number of inhibitory neurons in the mammalian cortex are mutually connected by gap junctions, and synchronization of gap junctions, spread over several hundred microns, suggests that these have a strong effect on the dynamics of the cortical network. However, the effect of gap junctions on firing propagation in cortical circuits has not been examined systematically. In this study, we perform numerical simulations using biologically plausible parameters to clarify this effect on population firing in a feedforward neural network. The results suggest that gap junctions switch the temporally uniform firing in a layer to temporally clustered firing in subsequent layers, resulting in an enhancement in the propagation of population firing in the feedforward network. Because gap junctions are often modulated in physiological conditions, we speculate that gap junctions could be related to a gating function of population firing in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

    PubMed Central

    Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

    2015-01-01

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

  10. Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

    NASA Astrophysics Data System (ADS)

    Itai, Akitoshi; Yasukawa, Hiroshi; Takumi, Ichi; Hata, Masayasu

    It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These observed signals contain the seismic radiation from the earth's crust, but also include several undesired signals. Our research focuses on the signal detection technique to identify an anomalous signal corresponding to the seismic radiation in the observed signal. Conventional anomalous signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be observed at the same time or the same sensor. In order to overcome the limitation related to the observed signal, we proposed the anomalous signals detection based on a multi-layer neural network which is trained by digital data observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous signal from the electromagnetic wave. The training data for the proposed network is the decomposed signal of the observed signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous signal that indicates seismic activity.

  11. Organ detection in thorax abdomen CT using multi-label convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Humpire Mamani, Gabriel Efrain; Setio, Arnaud Arindra Adiyoso; van Ginneken, Bram; Jacobs, Colin

    2017-03-01

    A convolutional network architecture is presented to determine bounding boxes around six organs in thoraxabdomen CT scans. A single network for each orthogonal view determines the presence of lungs, kidneys, spleen and liver. We show that an architecture that takes additional slices before and after the slice of interest as an additional input outperforms an architecture that processes single slices. From the slice-based analysis, a bounding box around the structures of interest can be computed. The system uses 6 convolutional, 4 pooling and one fully connected layer and uses 333 scans for training and 110 for validation. The test set contains 110 scans. The average Dice score of the proposed method was 0.95 and 0.95 for the lungs, 0.59 and 0.58 for the kidneys, 0.83 for the liver and 0.63 for the spleen. This paper shows that automatic localization of organs using multi-label convolution neural networks is possible. This architecture can likely be used to identify other organs of interest as well.

  12. 3D High Resolution Mesh Deformation Based on Multi Library Wavelet Neural Network Architecture

    NASA Astrophysics Data System (ADS)

    Dhibi, Naziha; Elkefi, Akram; Bellil, Wajdi; Amar, Chokri Ben

    2016-12-01

    This paper deals with the features of a novel technique for large Laplacian boundary deformations using estimated rotations. The proposed method is based on a Multi Library Wavelet Neural Network structure founded on several mother wavelet families (MLWNN). The objective is to align features of mesh and minimize distortion with a fixed feature that minimizes the sum of the distances between all corresponding vertices. New mesh deformation method worked in the domain of Region of Interest (ROI). Our approach computes deformed ROI, updates and optimizes it to align features of mesh based on MLWNN and spherical parameterization configuration. This structure has the advantage of constructing the network by several mother wavelets to solve high dimensions problem using the best wavelet mother that models the signal better. The simulation test achieved the robustness and speed considerations when developing deformation methodologies. The Mean-Square Error and the ratio of deformation are low compared to other works from the state of the art. Our approach minimizes distortions with fixed features to have a well reconstructed object.

  13. Cross-Species Analyses of the Cortical GABAergic and Subplate Neural Populations

    PubMed Central

    Clancy, Barbara; Teague-Ross, Terri J.; Nagarajan, Radhakrishnan

    2009-01-01

    Cortical GABAergic (γ-aminobutyric acidergic) neurons include a recently identified subset whose projections extend over relatively long distances in adult rodents and primates. A number of these inhibitory projection neurons are located in and above the conventionally identified white matter, suggesting their persistence from, or a correspondence with, the developmental subplate. GABAergic and subplate neurons share some unique properties unlike those of the more prevalent pyramidal neurons. To better understand the GABAergic and subplate populations, we constructed a database of neural developmental events common to the three species most frequently used in experimental studies: rat, mouse, and macaque, using data from the online database www.translatingtime.net as well as GABAergic and subplate developmental data from the empirical literature. We used a general linear model to test for similarities and differences, a valid approach because the sequence of most neurodevelopmental events is remarkably conserved across mammalian species. Similarities between the two rodent populations are striking, permitting us to identify developmental dates for GABAergic and subplate neural events in rats that were previously identified only in mice, as well as the timing in mouse development for events previously identified in rats. Primate comparative data are also compelling, although slight variability in statistical error measurement indicates differences in primate GABAergic and subplate events when compared to rodents. Although human extrapolations are challenging because fewer empirical data points are available, and because human data display more variability, we also produce estimates of dates for GABAergic and subplate neural events that have not yet been, or cannot be, determined empirically in humans. PMID:19936319

  14. Suppression of seizures based on the multi-coupled neural mass model

    NASA Astrophysics Data System (ADS)

    Cao, Yuzhen; Ren, Kaili; Su, Fei; Deng, Bin; Wei, Xile; Wang, Jiang

    2015-10-01

    Epilepsy is one of the most common serious neurological disorders, which affects approximately 1% of population in the world. In order to effectively control the seizures, we propose a novel control methodology, which combines the feedback linearization control (FLC) with the underlying mechanism of epilepsy, to achieve the suppression of seizures. The three coupled neural mass model is constructed to study the property of the electroencephalographs (EEGs). Meanwhile, with the model we research on the propagation of epileptiform waves and the synchronization of populations, which are taken as the foundation of our control method. Results show that the proposed approach not only yields excellent performances in clamping the pathological spiking patterns to the reference signals derived under the normal state but also achieves the normalization of the pathological parameter, where the parameters are estimated from EEGs with Unscented Kalman Filter. The specific contribution of this paper is to treat the epilepsy from its pathogenesis with the FLC, which provides critical theoretical basis for the clinical treatment of neurological disorders.

  15. Nanoplankton population dynamics and dissolved oxygen change across the Bay of Izmir by neural networks.

    PubMed

    Sunlu, F S; Demir, I; Onkal Engin, G; Buyukisik, B; Sunlu, U; Koray, T; Kukrer, S

    2009-06-01

    The bay of Izmir, which is the biggest harbor on the Aegean Sea, is of upmost economical importance for Izmir, the third largest city in Turkey. Most of the studies carried out focused on the effects of intensive industrial activity and agricultural production on the bay pollution within the region. These studies, most of the time, are limited to monitoring the level of pollution. However, it is believed that these studies should be supported with models and statistical analysis techniques, as the models, especially the prediction ones, provide an important approach to assessing risk and assessment. In this study, neural network analysis was used to construct prediction models for nanoplankton population change with nutrients and other environmentally important parameters. The results indicated that, using data over a 52 week period, it is possible to predict nanoplankton population dynamics and dissolved oxygen change for the future.

  16. Thirst driving and suppressing signals encoded by distinct neural populations in the brain.

    PubMed

    Oka, Yuki; Ye, Mingyu; Zuker, Charles S

    2015-04-16

    Thirst is the basic instinct to drink water. Previously, it was shown that neurons in several circumventricular organs of the hypothalamus are activated by thirst-inducing conditions. Here we identify two distinct, genetically separable neural populations in the subfornical organ that trigger or suppress thirst. We show that optogenetic activation of subfornical organ excitatory neurons, marked by the expression of the transcription factor ETV-1, evokes intense drinking behaviour, and does so even in fully water-satiated animals. The light-induced response is highly specific for water, immediate and strictly locked to the laser stimulus. In contrast, activation of a second population of subfornical organ neurons, marked by expression of the vesicular GABA transporter VGAT, drastically suppresses drinking, even in water-craving thirsty animals. These results reveal an innate brain circuit that can turn an animal's water-drinking behaviour on and off, and probably functions as a centre for thirst control in the mammalian brain.

  17. Increasing magnetite contents of polymeric magnetic particles dramatically improves labeling of neural stem cell transplant populations.

    PubMed

    Adams, Christopher F; Rai, Ahmad; Sneddon, Gregor; Yiu, Humphrey H P; Polyak, Boris; Chari, Divya M

    2015-01-01

    Safe and efficient delivery of therapeutic cells to sites of injury/disease in the central nervous system is a key goal for the translation of clinical cell transplantation therapies. Recently, 'magnetic cell localization strategies' have emerged as a promising and safe approach for targeted delivery of magnetic particle (MP) labeled stem cells to pathology sites. For neuroregenerative applications, this approach is limited by the lack of available neurocompatible MPs, and low cell labeling achieved in neural stem/precursor populations. We demonstrate that high magnetite content, self-sedimenting polymeric MPs [unfunctionalized poly(lactic acid) coated, without a transfecting component] achieve efficient labeling (≥90%) of primary neural stem cells (NSCs)-a 'hard-to-label' transplant population of major clinical relevance. Our protocols showed high safety with respect to key stem cell regenerative parameters. Critically, labeled cells were effectively localized in an in vitro flow system by magnetic force highlighting the translational potential of the methods used. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Anesthetic action on extra-synaptic receptors: effects in neural population models of EEG activity

    PubMed Central

    Hashemi, Meysam; Hutt, Axel; Sleigh, Jamie

    2014-01-01

    The role of extra-synaptic receptors in the regulation of excitation and inhibition in the brain has attracted increasing attention. Because activity in the extra-synaptic receptors plays a role in regulating the level of excitation and inhibition in the brain, they may be important in determining the level of consciousness. This paper reviews briefly the literature on extra-synaptic GABA and NMDA receptors and their affinity to anesthetic drugs. We propose a neural population model that illustrates how the effect of the anesthetic drug propofol on GABAergic extra-synaptic receptors results in changes in neural population activity and the electroencephalogram (EEG). Our results show that increased tonic inhibition in inhibitory cortical neurons cause a dramatic increase in the power of both δ− and α− bands. Conversely, the effects of increased tonic inhibition in cortical excitatory neurons and thalamic relay neurons have the opposite effect and decrease the power in these bands. The increased δ-activity is in accord with observed data for deepening propofol anesthesia; but is absolutely dependent on the inclusion of extrasynaptic (tonic) GABA action in the model. PMID:25540612

  19. Novel image fusion method based on adaptive pulse coupled neural network and discrete multi-parameter fractional random transform

    NASA Astrophysics Data System (ADS)

    Lang, Jun; Hao, Zhengchao

    2014-01-01

    In this paper, we first propose the discrete multi-parameter fractional random transform (DMPFRNT), which can make the spectrum distributed randomly and uniformly. Then we introduce this new spectrum transform into the image fusion field and present a new approach for the remote sensing image fusion, which utilizes both adaptive pulse coupled neural network (PCNN) and the discrete multi-parameter fractional random transform in order to meet the requirements of both high spatial resolution and low spectral distortion. In the proposed scheme, the multi-spectral (MS) and panchromatic (Pan) images are converted into the discrete multi-parameter fractional random transform domains, respectively. In DMPFRNT spectrum domain, high amplitude spectrum (HAS) and low amplitude spectrum (LAS) components carry different informations of original images. We take full advantage of the synchronization pulse issuance characteristics of PCNN to extract the HAS and LAS components properly, and give us the PCNN ignition mapping images which can be used to determine the fusion parameters. In the fusion process, local standard deviation of the amplitude spectrum is chosen as the link strength of pulse coupled neural network. Numerical simulations are performed to demonstrate that the proposed method is more reliable and superior than several existing methods based on Hue Saturation Intensity representation, Principal Component Analysis, the discrete fractional random transform etc.

  20. Detecting a hierarchical genetic population structure via Multi-InDel markers on the X chromosome

    PubMed Central

    Fan, Guang Yao; Ye, Yi; Hou, Yi Ping

    2016-01-01

    Detecting population structure and estimating individual biogeographical ancestry are very important in population genetics studies, biomedical research and forensics. Single-nucleotide polymorphism (SNP) has long been considered to be a primary ancestry-informative marker (AIM), but it is constrained by complex and time-consuming genotyping protocols. Following up on our previous study, we propose that a multi-insertion-deletion polymorphism (Multi-InDel) with multiple haplotypes can be useful in ancestry inference and hierarchical genetic population structures. A validation study for the X chromosome Multi-InDel marker (X-Multi-InDel) as a novel AIM was conducted. Genetic polymorphisms and genetic distances among three Chinese populations and 14 worldwide populations obtained from the 1000 Genomes database were analyzed. A Bayesian clustering method (STRUCTURE) was used to discern the continental origins of Europe, East Asia, and Africa. A minimal panel of ten X-Multi-InDels was verified to be sufficient to distinguish human ancestries from three major continental regions with nearly the same efficiency of the earlier panel with 21 insertion-deletion AIMs. Along with the development of more X-Multi-InDels, an approach using this novel marker has the potential for broad applicability as a cost-effective tool toward more accurate determinations of individual biogeographical ancestry and population stratification. PMID:27535707

  1. Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

    NASA Technical Reports Server (NTRS)

    Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

    2017-01-01

    Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

  2. Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

    NASA Technical Reports Server (NTRS)

    Ferreria, Paulo; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale

    2017-01-01

    Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

  3. Real time coarse orientation detection in MR scans using multi-planar deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Bhatia, Parmeet S.; Reda, Fitsum; Harder, Martin; Zhan, Yiqiang; Zhou, Xiang Sean

    2017-02-01

    Automatically detecting anatomy orientation is an important task in medical image analysis. Specifically, the ability to automatically detect coarse orientation of structures is useful to minimize the effort of fine/accurate orientation detection algorithms, to initialize non-rigid deformable registration algorithms or to align models to target structures in model-based segmentation algorithms. In this work, we present a deep convolution neural network (DCNN)-based method for fast and robust detection of the coarse structure orientation, i.e., the hemi-sphere where the principal axis of a structure lies. That is, our algorithm predicts whether the principal orientation of a structure is in the northern hemisphere or southern hemisphere, which we will refer to as UP and DOWN, respectively, in the remainder of this manuscript. The only assumption of our method is that the entire structure is located within the scan's field-of-view (FOV). To efficiently solve the problem in 3D space, we formulated it as a multi-planar 2D deep learning problem. In the training stage, a large number coronal-sagittal slice pairs are constructed as 2-channel images to train a DCNN to classify whether a scan is UP or DOWN. During testing, we randomly sample a small number of coronal-sagittal 2-channel images and pass them through our trained network. Finally, coarse structure orientation is determined using majority voting. We tested our method on 114 Elbow MR Scans. Experimental results suggest that only five 2-channel images are sufficient to achieve a high success rate of 97.39%. Our method is also extremely fast and takes approximately 50 milliseconds per 3D MR scan. Our method is insensitive to the location of the structure in the FOV.

  4. Multi-Valued Neural Network Modified Model and Its Optical Realization,

    DTIC Science & Technology

    1995-03-07

    This paper presents a modified optical neural networK model, and the optical system, constructed with spatial light modulator PROM, can materialize...modified model improves cognitive ability of an optical neural network and also improves the storage capacity to a certain extent.

  5. Higher-order correlations in common input shapes the output spiking activity of a neural population

    NASA Astrophysics Data System (ADS)

    Montangie, Lisandro; Montani, Fernando

    2017-04-01

    Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from q-Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter q. We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.

  6. Modelling of multi-nutrient interactions in growth of the dinoflagellate microalga Protoceratium reticulatum using artificial neural networks.

    PubMed

    López-Rosales, L; Gallardo-Rodríguez, J J; Sánchez-Mirón, A; Contreras-Gómez, A; García-Camacho, F; Molina-Grima, E

    2013-10-01

    This study examines the use of artificial neural networks as predictive tools for the growth of the dinoflagellate microalga Protoceratium reticulatum. Feed-forward back-propagation neural networks (FBN), using Levenberg-Marquardt back-propagation or Bayesian regularization as training functions, offered the best results in terms of representing the nonlinear interactions among all nutrients in a culture medium containing 26 different components. A FBN configuration of 26-14-1 layers was selected. The FBN model was trained using more than 500 culture experiments on a shake flask scale. Garson's algorithm provided a valuable means of evaluating the relative importance of nutrients in terms of microalgal growth. Microelements and vitamins had a significant importance (approximately 70%) in relation to macronutrients (nearly 25%), despite their concentrations in the culture medium being various orders of magnitude smaller. The approach presented here may be useful for modelling multi-nutrient interactions in photobioreactors.

  7. Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

    PubMed

    Valdés, Julio J; Barton, Alan J

    2007-05-01

    A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

  8. Invited review: genomic selection in multi-breed dairy cattle populations

    USDA-ARS?s Scientific Manuscript database

    Genomic selection has been a valuable tool for increasing the rate of genetic improvement in purebred dairy cattle populations. However, there also are many large populations of crossbred dairy cattle in the world, and multi-breed genomic evaluations may be a valuable tool for improving rates of gen...

  9. The effect of connectivity on EEG rhythms, power spectral density and coherence among coupled neural populations: analysis with a neural mass model.

    PubMed

    Zavaglia, Melissa; Astolfi, Laura; Babiloni, Fabio; Ursino, Mauro

    2008-01-01

    In the present work, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. In particular, we analyze a circuit, composed of three interconnected populations, each with a different synaptic kinetics (hence, with a different intrinsic rhythm). Results demonstrate that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). Analysis of coherence, and of the position of peaks in power spectral density, reveals important information on the possible connections among populations, especially useful to follow temporal changes in connectivity. Subsequently, the model is validated by comparing the power spectral density simulated in one population with that computed in the controlateral cingulated cortex (a region involved in motion preparation) during a right foot movement task in four normal subjects. The model is able to simulate real spectra quite well with only moderate parameter changes within the subject. In perspective, the results may be of value for a deeper comprehension of mechanism causing EEGs rhythms, for the study of brain connectivity and for the test of neurophysiological hypotheses.

  10. Evolutionary artificial neural networks by multi-dimensional particle swarm optimization.

    PubMed

    Kiranyaz, Serkan; Ince, Turker; Yildirim, Alper; Gabbouj, Moncef

    2009-12-01

    In this paper, we propose a novel technique for the automatic design of Artificial Neural Networks (ANNs) by evolving to the optimal network configuration(s) within an architecture space. It is entirely based on a multi-dimensional Particle Swarm Optimization (MD PSO) technique, which re-forms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. With the proper encoding of the network configurations and parameters into particles, MD PSO can then seek the positional optimum in the error space and the dimensional optimum in the architecture space. The optimum dimension converged at the end of a MD PSO process corresponds to a unique ANN configuration where the network parameters (connections, weights and biases) can then be resolved from the positional optimum reached on that dimension. In addition to this, the proposed technique generates a ranked list of network configurations, from the best to the worst. This is indeed a crucial piece of information, indicating what potential configurations can be alternatives to the best one, and which configurations should not be used at all for a particular problem. In this study, the architecture space is defined over feed-forward, fully-connected ANNs so as to use the conventional techniques such as back-propagation and some other evolutionary methods in this field. The proposed technique is applied over the most challenging synthetic problems to test its optimality on evolving networks and over the benchmark problems to test its generalization capability as well as to make comparative evaluations with the several competing techniques. The experimental

  11. Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis

    PubMed Central

    Gigante, Guido; Del Giudice, Paolo

    2017-01-01

    Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define

  12. Population-based structural variation discovery with Hydra-Multi.

    PubMed

    Lindberg, Michael R; Hall, Ira M; Quinlan, Aaron R

    2015-04-15

    Current strategies for SNP and INDEL discovery incorporate sequence alignments from multiple individuals to maximize sensitivity and specificity. It is widely accepted that this approach also improves structural variant (SV) detection. However, multisample SV analysis has been stymied by the fundamental difficulties of SV calling, e.g. library insert size variability, SV alignment signal integration and detecting long-range genomic rearrangements involving disjoint loci. Extant tools suffer from poor scalability, which limits the number of genomes that can be co-analyzed and complicates analysis workflows. We have developed an approach that enables multisample SV analysis in hundreds to thousands of human genomes using commodity hardware. Here, we describe Hydra-Multi and measure its accuracy, speed and scalability using publicly available datasets provided by The 1000 Genomes Project and by The Cancer Genome Atlas (TCGA). Hydra-Multi is written in C++ and is freely available at https://github.com/arq5x/Hydra. aaronquinlan@gmail.com or ihall@genome.wustl.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

  13. Genetically encoded calcium indicators for multi-color neural activity imaging and combination with optogenetics

    PubMed Central

    Akerboom, Jasper; Carreras Calderón, Nicole; Tian, Lin; Wabnig, Sebastian; Prigge, Matthias; Tolö, Johan; Gordus, Andrew; Orger, Michael B.; Severi, Kristen E.; Macklin, John J.; Patel, Ronak; Pulver, Stefan R.; Wardill, Trevor J.; Fischer, Elisabeth; Schüler, Christina; Chen, Tsai-Wen; Sarkisyan, Karen S.; Marvin, Jonathan S.; Bargmann, Cornelia I.; Kim, Douglas S.; Kügler, Sebastian; Lagnado, Leon; Hegemann, Peter; Gottschalk, Alexander; Schreiter, Eric R.; Looger, Loren L.

    2013-01-01

    Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Here we describe red, single-wavelength GECIs, “RCaMPs,” engineered from circular permutation of the thermostable red fluorescent protein mRuby. High-resolution crystal structures of mRuby, the red sensor RCaMP, and the recently published red GECI R-GECO1 give insight into the chromophore environments of the Ca2+-bound state of the sensors and the engineered protein domain interfaces of the different indicators. We characterized the biophysical properties and performance of RCaMP sensors in vitro and in vivo in Caenorhabditis elegans, Drosophila larvae, and larval zebrafish. Further, we demonstrate 2-color calcium imaging both within the same cell (registering mitochondrial and somatic [Ca2+]) and between two populations of cells: neurons and astrocytes. Finally, we perform integrated optogenetics experiments, wherein neural activation via channelrhodopsin-2 (ChR2) or a red-shifted variant, and activity imaging via RCaMP or GCaMP, are conducted simultaneously, with the ChR2/RCaMP pair providing independently addressable spectral channels. Using this paradigm, we measure calcium responses of naturalistic and ChR2-evoked muscle contractions in vivo in crawling C. elegans. We systematically compare the RCaMP sensors to R-GECO1, in terms of action potential-evoked fluorescence increases in neurons, photobleaching, and photoswitching. R-GECO1 displays higher Ca2+ affinity and larger dynamic range than RCaMP, but exhibits significant photoactivation with blue and green light, suggesting that integrated channelrhodopsin-based optogenetics using R-GECO1 may be subject to artifact. Finally, we create and test blue, cyan, and yellow variants engineered from GCaMP by rational design. This engineered set of chromatic variants facilitates new experiments in functional imaging and optogenetics. PMID:23459413

  14. Multi-InDel Analysis for Ancestry Inference of Sub-Populations in China

    PubMed Central

    Sun, Kuan; Ye, Yi; Luo, Tao; Hou, Yiping

    2016-01-01

    Ancestry inference is of great interest in diverse areas of scientific researches, including the forensic biology, medical genetics and anthropology. Various methods have been published for distinguishing populations. However, few reports refer to sub-populations (like ethnic groups) within Asian populations for the limitation of markers. Several InDel loci located very tightly in physical positions were treated as one marker by us, which is multi-InDel. The multi-InDel shows potential as Ancestry Inference Marker (AIM). In this study, we performed a genome-wide scan for multi-InDels as AIM. After examining the FST distributions in the 1000 Genomes Database, 12 candidates were selected and validated for eastern Asian populations. A multiplexed assay was developed as a panel to genotype 12 multi-InDel markers simultaneously. Ancestry component analysis with STRUCTURE and principal component analysis (PCA) were employed to estimate its capability for ancestry inference. Furthermore, ancestry assignments of trial individuals were conducted. It proved to be very effective when 210 samples from Han and Tibetan individuals in China were tested. The panel consisting of multi-InDel markers exhibited considerable potency in ancestry inference, and was suggested to be applied in forensic practices and genetic population studies. PMID:28004788

  15. Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

    PubMed Central

    Muthmann, Jens-Oliver; Amin, Hayder; Sernagor, Evelyne; Maccione, Alessandro; Panas, Dagmara; Berdondini, Luca; Bhalla, Upinder S.; Hennig, Matthias H.

    2015-01-01

    An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits. PMID:26733859

  16. Neural populations in human posteromedial cortex display opposing responses during memory and numerical processing

    PubMed Central

    Foster, Brett L.; Dastjerdi, Mohammad; Parvizi, Josef

    2012-01-01

    Our understanding of the human default mode network derives primarily from neuroimaging data but its electrophysiological correlates remain largely unexplored. To address this limitation, we recorded intracranially from the human posteromedial cortex (PMC), a core structure of the default mode network, during various conditions of internally directed (e.g., autobiographical memory) as opposed to externally directed focus (e.g., arithmetic calculation). We observed late-onset (>400 ms) increases in broad high γ-power (70–180 Hz) within PMC subregions during memory retrieval. High γ-power was significantly reduced or absent when subjects retrieved self-referential semantic memories or responded to self-judgment statements, respectively. Conversely, a significant deactivation of high γ-power was observed during arithmetic calculation, the duration of which correlated with reaction time at the signal-trial level. Strikingly, at each recording site, the magnitude of activation during episodic autobiographical memory retrieval predicted the degree of suppression during arithmetic calculation. These findings provide important anatomical and temporal details—at the neural population level—of PMC engagement during autobiographical memory retrieval and address how the same populations are actively suppressed during tasks, such as numerical processing, which require externally directed attention. PMID:22949666

  17. Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays.

    PubMed

    Muthmann, Jens-Oliver; Amin, Hayder; Sernagor, Evelyne; Maccione, Alessandro; Panas, Dagmara; Berdondini, Luca; Bhalla, Upinder S; Hennig, Matthias H

    2015-01-01

    An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.

  18. Thirst Driving and Suppressing Signals Encoded by Distinct Neural Populations in the Brain

    PubMed Central

    Oka, Yuki; Ye, Mingyu; Zuker, Charles S.

    2014-01-01

    Thirst is the basic instinct to drink water. Previously, it was shown that neurons in several circumventricular organs (CVO) of the hypothalamus are activated by thirst-inducing conditions 1. Here, we identify two distinct, genetically-separable neural populations in the subfornical organ (SFO) that trigger or suppress thirst. We show that optogenetic activation of SFO excitatory neurons, marked by the expression of the transcription factor ETV-1, evokes intense drinking behavior, and does so even in fully water-satiated animals. The light-induced response is highly specific for water, immediate, and strictly locked to the laser stimulus. In contrast, activation of a second population of SFO neurons, marked by expression of the vesicular GABA transporter VGAT, drastically suppressed drinking, even in water-craving thirsty animals. These results reveal an innate brain circuit that can turn on and off an animal’s water-drinking behavior, and likely functions as a center for thirst control in the mammalian brain. PMID:25624099

  19. Enhancement of cognitive and neural functions through complex reasoning training: evidence from normal and clinical populations

    PubMed Central

    Chapman, Sandra B.; Mudar, Raksha A.

    2014-01-01

    Public awareness of cognitive health is fairly recent compared to physical health. Growing evidence suggests that cognitive training offers promise in augmenting cognitive brain performance in normal and clinical populations. Targeting higher-order cognitive functions, such as reasoning in particular, may promote generalized cognitive changes necessary for supporting the complexities of daily life. This data-driven perspective highlights cognitive and brain changes measured in randomized clinical trials that trained gist reasoning strategies in populations ranging from teenagers to healthy older adults, individuals with brain injury to those at-risk for Alzheimer's disease. The evidence presented across studies support the potential for Gist reasoning training to strengthen cognitive performance in trained and untrained domains and to engage more efficient communication across widespread neural networks that support higher-order cognition. The meaningful benefits of Gist training provide compelling motivation to examine optimal dose for sustained benefits as well as to explore additive benefits of meditation, physical exercise, and/or improved sleep in future studies. PMID:24808834

  20. Enhancement of cognitive and neural functions through complex reasoning training: evidence from normal and clinical populations.

    PubMed

    Chapman, Sandra B; Mudar, Raksha A

    2014-01-01

    Public awareness of cognitive health is fairly recent compared to physical health. Growing evidence suggests that cognitive training offers promise in augmenting cognitive brain performance in normal and clinical populations. Targeting higher-order cognitive functions, such as reasoning in particular, may promote generalized cognitive changes necessary for supporting the complexities of daily life. This data-driven perspective highlights cognitive and brain changes measured in randomized clinical trials that trained gist reasoning strategies in populations ranging from teenagers to healthy older adults, individuals with brain injury to those at-risk for Alzheimer's disease. The evidence presented across studies support the potential for Gist reasoning training to strengthen cognitive performance in trained and untrained domains and to engage more efficient communication across widespread neural networks that support higher-order cognition. The meaningful benefits of Gist training provide compelling motivation to examine optimal dose for sustained benefits as well as to explore additive benefits of meditation, physical exercise, and/or improved sleep in future studies.

  1. Multi-layered population structure in Island Southeast Asians

    PubMed Central

    Ricaut, Francois-Xavier; Yngvadottir, Bryndis; Harney, Eadaoin; Castillo, Cristina; Hoogervorst, Tom; Antao, Tiago; Kusuma, Pradiptajati; Razafindrazaka, Harilanto; Cardona, Alexia; Pierron, Denis; Letellier, Thierry; Wee, Joseph; Abdullah, Syafiq; Metspalu, Mait; Kivisild, Toomas

    2016-01-01

    The history of human settlement in Southeast Asia has been complex and involved several distinct dispersal events. Here we report the analyses of 1825 individuals from Southeast Asia including new genome-wide genotype data for 146 individuals from three Mainland Southeast Asian (Burmese, Malay and Vietnamese) and four Island Southeast Asian (Dusun, Filipino, Kankanaey and Murut) populations. While confirming the presence of previously recognized major ancestry components in the Southeast Asian population structure, we highlight the Kankanaey Igorots from the highlands of the Philippine Mountain Province as likely the closest living representatives of the source population that may have given rise to the Austronesian expansion. This conclusion rests on independent evidence from various analyses of autosomal data and uniparental markers. Given the extensive presence of trade goods, cultural and linguistic evidence of Indian influence in Southeast Asia starting from 2.5kya we also detect traces of a South Asian signature in different populations in the region dating to the last couple of thousand years. PMID:27302840

  2. Multi-layered population structure in Island Southeast Asians.

    PubMed

    Mörseburg, Alexander; Pagani, Luca; Ricaut, Francois-Xavier; Yngvadottir, Bryndis; Harney, Eadaoin; Castillo, Cristina; Hoogervorst, Tom; Antao, Tiago; Kusuma, Pradiptajati; Brucato, Nicolas; Cardona, Alexia; Pierron, Denis; Letellier, Thierry; Wee, Joseph; Abdullah, Syafiq; Metspalu, Mait; Kivisild, Toomas

    2016-11-01

    The history of human settlement in Southeast Asia has been complex and involved several distinct dispersal events. Here, we report the analyses of 1825 individuals from Southeast Asia including new genome-wide genotype data for 146 individuals from three Mainland Southeast Asian (Burmese, Malay and Vietnamese) and four Island Southeast Asian (Dusun, Filipino, Kankanaey and Murut) populations. While confirming the presence of previously recognised major ancestry components in the Southeast Asian population structure, we highlight the Kankanaey Igorots from the highlands of the Philippine Mountain Province as likely the closest living representatives of the source population that may have given rise to the Austronesian expansion. This conclusion rests on independent evidence from various analyses of autosomal data and uniparental markers. Given the extensive presence of trade goods, cultural and linguistic evidence of Indian influence in Southeast Asia starting from 2.5 kya, we also detect traces of a South Asian signature in different populations in the region dating to the last couple of thousand years.

  3. Adrenomedullary progenitor cells: Isolation and characterization of a multi-potent progenitor cell population.

    PubMed

    Vukicevic, Vladimir; Rubin de Celis, Maria Fernandez; Pellegata, Natalia S; Bornstein, Stefan R; Androutsellis-Theotokis, Andreas; Ehrhart-Bornstein, Monika

    2015-06-15

    The adrenal is a highly plastic organ with the ability to adjust to physiological needs by adapting hormone production but also by generating and regenerating both adrenocortical and adrenomedullary tissue. It is now apparent that many adult tissues maintain stem and progenitor cells that contribute to their maintenance and adaptation. Research from the last years has proven the existence of stem and progenitor cells also in the adult adrenal medulla throughout life. These cells maintain some neural crest properties and have the potential to differentiate to the endocrine and neural lineages. In this article, we discuss the evidence for the existence of adrenomedullary multi potent progenitor cells, their isolation and characterization, their differentiation potential as well as their clinical potential in transplantation therapies but also in pathophysiology.

  4. Breast cancer in multi-ethnic populations: the Hawaii perspective.

    PubMed

    Goodman, M J

    1991-05-01

    The five major ethnic groups in Hawaii's population of 1.1 million are the Japanese, comprising 23%; Caucasians, 23%; ethnic Hawaiians, 19.9%; Filipinos, 11.3%; and Chinese, 4.8%. Only 14% of the population is foreign born. Breast cancer incidences are 29.2 per 100,000 among Filipinos, 51.3 for Japanese, 64.1 for Chinese, 104.3 for Hawaiian, and 105.6 for Caucasian women. The Caucasian incidence is similar to mainland US rates, but the incidence among Hawaii's Japanese is more than twice the rate in Japan. Japanese in Hawaii have less postmenopausal breast cancer than Caucasians, fewer axillary lymph node metastases, and a greater proportion of non-invasive tumors. Late stage at diagnosis is common among Filipino and ethnic Hawaiian woman, and their risk of death is 1.5-1.7 times that of Caucasian, Chinese, and Japanese women with the disease, even after adjustment for age, extent of disease, and socio-economic status. In the BCDDP screening study, only 20% of breast cancers detected in ethnic Hawaiians were not yet palpable and were found by mammography alone. Comparative studies of diet and estrogen levels in the ethnic groups of Hawaii and the parental populations in Japan and the West do not account for the degree of variation observed in breast cancer incidence and tumor pathology. Future research directions are suggested with a view to accounting for these differences.

  5. A multi-laboratory comparison of blood dendritic cell populations.

    PubMed

    Fromm, Phillip Dieter; Kupresanin, Fiona; Brooks, Anna Elizabeth Stella; Dunbar, Peter Rodney; Haniffa, Muzifilla; Hart, Derek Nigel John; Clark, Georgina Jane

    2016-04-01

    HLDA10 collated a panel of monoclonal antibodies (mAbs) that primarily recognised molecules on human myeloid cell and dendritic cell (DC) populations. As part of the studies, we validated a backbone of mAbs to delineate monocyte and DC populations from peripheral blood. The mAb backbone allowed identification of monocyte and DC subsets using fluorochromes that were compatible with most 'off the shelf' or routine flow cytometers. Three laboratories used this mAb backbone to assess the HLDA10 panel on blood monocytes and DCs. Each laboratory was provided with enough mAbs to perform five repeat experiments. The data were collated and analysed using Spanning-tree Progression Analysis of Density-normalised Events (SPADE). The data were interrogated for inter- and intra-laboratory variability. The results highlight the definition of DC populations using current readily available reagents. This collaborative process provides the broader scientific community with an invaluable data set that validates mAbs to leucocyte surface molecules.

  6. Investigations of the polar atmosphere with use of dynamical characteristics of the processes and self -organizing neural networks on ground-based multi-position optical observations

    NASA Astrophysics Data System (ADS)

    Alpatov, V. V.; Matveev, A. A.; Enel, F.; Brandstrom, U.; Gustavsson, B.; Steen, A.

    This article discusses questions connected with investigations of the polar atmosphere via ground-based multi-position optical observations. For this investigation a specially developed method is applied. It use the calculated dynamical parameters of the processes and self-organizing artificial neural networks. With self-organizing neural networks 5-7 classes have been extracted which can be associated with the regions having different physical properties. In particular, have been extracted regions intuitively coincident with polar stratospheric clouds and aurora.

  7. Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space.

    PubMed

    Irwin, Zachary T; Thompson, David E; Schroeder, Karen E; Tat, Derek M; Hassani, Ali; Bullard, Autumn J; Woo, Shoshana L; Urbanchek, Melanie G; Sachs, Adam J; Cederna, Paul S; Stacey, William C; Patil, Parag G; Chestek, Cynthia A

    2016-05-01

    Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

  8. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

    PubMed

    Cang, Zixuan; Wei, Guo-Wei

    2017-07-01

    Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. weilab.math.msu.edu/TDL/.

  9. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

    PubMed Central

    2017-01-01

    Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

  10. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

    PubMed

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-02-21

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

  11. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    PubMed Central

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  12. Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings.

    PubMed

    Whiteway, Matthew R; Butts, Daniel A

    2017-03-01

    The activity of sensory cortical neurons is not only driven by external stimuli but also shaped by other sources of input to the cortex. Unlike external stimuli, these other sources of input are challenging to experimentally control, or even observe, and as a result contribute to variability of neural responses to sensory stimuli. However, such sources of input are likely not "noise" and may play an integral role in sensory cortex function. Here we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs nonnegative (rectified) latent variables and is much less restrictive in the mathematical constraints on solutions because of the use of an autoencoder neural network to initialize model parameters. We show that the RLVM outperforms principal component analysis, factor analysis, and independent component analysis, using simulated data across a range of conditions. We then apply this model to two-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function and demonstrate novel methods for leveraging large population recordings to this end.NEW & NOTEWORTHY The rapid development of neural recording technologies presents new opportunities for understanding patterns of activity across neural populations. Here we show how a latent variable model with appropriate nonlinear form can be used to identify sources of input to a neural population and infer their time courses. Furthermore, we demonstrate how these sources are

  13. Coculture with endothelial cells reduces the population of cycling LeX neural precursors but increases that of quiescent cells with a side population phenotype

    SciTech Connect

    Mathieu, Celine . E-mail: marc-andre.mouthon@cea.fr

    2006-04-01

    Neural stem cell proliferation and differentiation are regulated by external cues from their microenvironment. As endothelial cells are closely associated with neural stem cell in brain germinal zones, we investigated whether endothelial cells may interfere with neurogenesis. Neural precursor cells (NPC) from telencephalon of EGFP mouse embryos were cocultured in direct contact with endothelial cells. Endothelial cells did not modify the overall proliferation and apoptosis of neural cells, albeit they transiently delayed spontaneous apoptosis. These effects appeared to be specific to endothelial cells since a decrease in proliferation and a raise in apoptosis were observed in cocultures with fibroblasts. Endothelial cells stimulated the differentiation of NPC into astrocytes and into neurons, whereas they reduced differentiation into oligodendrocytes in comparison to adherent cultures on polyornithine. Determination of NPC clonogenicity and quantification of LeX expression, a marker for NPC, showed that endothelial cells decreased the number of cycling NPC. On the other hand, the presence of endothelial cells increased the number of neural cells having 'side population' phenotype, another marker reported on NPC, which we have shown to contain quiescent cells. Thus, we show that endothelial cells may regulate neurogenesis by acting at different level of NPC differentiation, proliferation and quiescence.

  14. Automatic Classification of Volcanic Earthquakes Using Multi-Station Waveforms and Dynamic Neural Networks

    NASA Astrophysics Data System (ADS)

    Bruton, C. P.; West, M. E.

    2013-12-01

    Earthquakes and seismicity have long been used to monitor volcanoes. In addition to time, location, and magnitude of an earthquake, the characteristics of the waveform itself are important. For example, low-frequency or hybrid type events could be generated by magma rising toward the surface. A rockfall event could indicate a growing lava dome. Classification of earthquake waveforms is thus a useful tool in volcano monitoring. A procedure to perform such classification automatically could flag certain event types immediately, instead of waiting for a human analyst's review. Inspired by speech recognition techniques, we have developed a procedure to classify earthquake waveforms using artificial neural networks. A neural network can be "trained" with an existing set of input and desired output data; in this case, we use a set of earthquake waveforms (input) that has been classified by a human analyst (desired output). After training the neural network, new waveforms can be classified automatically as they are presented. Our procedure uses waveforms from multiple stations, making it robust to seismic network changes and outages. The use of a dynamic time-delay neural network allows waveforms to be presented without precise alignment in time, and thus could be applied to continuous data or to seismic events without clear start and end times. We have evaluated several different training algorithms and neural network structures to determine their effects on classification performance. We apply this procedure to earthquakes recorded at Mount Spurr and Katmai in Alaska, and Uturuncu Volcano in Bolivia.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    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.

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

    SciTech Connect

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

    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.

  18. The Effect of Inhibitory Neuron on the Evolution Model of Higher-Order Coupling Neural Oscillator Population

    PubMed Central

    Qi, Yi; Wang, Rubin; Jiao, Xianfa; Du, Ying

    2014-01-01

    We proposed a higher-order coupling neural network model including the inhibitory neurons and examined the dynamical evolution of average number density and phase-neural coding under the spontaneous activity and external stimulating condition. The results indicated that increase of inhibitory coupling strength will cause decrease of average number density, whereas increase of excitatory coupling strength will cause increase of stable amplitude of average number density. Whether the neural oscillator population is able to enter the new synchronous oscillation or not is determined by excitatory and inhibitory coupling strength. In the presence of external stimulation, the evolution of the average number density is dependent upon the external stimulation and the coupling term in which the dominator will determine the final evolution. PMID:24516505

  19. Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

    PubMed

    Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

    2015-06-18

    Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

  20. Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Le, Minh Hung; Chen, Jingyu; Wang, Liang; Wang, Zhiwei; Liu, Wenyu; (Tim Cheng, Kwang-Ting; Yang, Xin

    2017-08-01

    Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to ‘see’ the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our

  1. HPV prevalence in a multi-ethnic screening population

    PubMed Central

    Chen, Kang Mei; Stephen, Josena K.; Ghanem, Tamer; Stachler, Robert; Gardner, Glendon; Jones, Lamont; Schweitzer, Vanessa P.; Hall, Francis; Divine, George; Worsham, Maria J.

    2013-01-01

    Objective The goal was to determine prevalence of high-risk HPV16 using saliva in a screening population in Detroit, MI. Materials and Methods Real time quantitative PCR was applied to detect HPV16 in saliva DNA from 349 screening subjects without head and neck cancer (HNC), 156 HNC, and 19 controls. Cut points for HPV positivity were: >0 and >0.001 copy/cell. Proportions were compared between groups using exact chi-square or Fisher’s exact tests (p<0.05). Results At cut point >0, each group had an overall HPV prevalence of over 5%, with a higher prevalence of 30.8% in the HNC patient group. At cut point >0.001, the prevalence was lower, 0% in the control, 1.2% in the screening, and 16.7% in the HNC group. In the latter, for both cut points, HPV prevalence was different across sites (<0.001) and significantly higher in the oropharynx (OP) than larynx or site as other after Hochberg’s adjustment. At >0, females in the screening group had a higher prevalence of HPV than males (p=0.010) and at >0.001, prevalence was higher for males in the HNC group than females (p=0.035). In the screening group, at >0, only AA had higher prevalence than CA (p=0.025). Conclusions In the screening group, a 6.9% and 1.2% screening rate was noted at cut-points >0 and >0.001, respectively. The results provide data to inform public health considerations of the feasibility of saliva as a screening tool in at-risk populations with the long term goal of prophylactic vaccination against oral HPV. PMID:23300223

  2. A neural signal processor for an implantable multi-channel cortical recording microsystem.

    PubMed

    Sodagar, Amir M; Wise, Kensall D; Najafi, Khalil

    2006-01-01

    A 64-channel neural processor has been developed for use in an implantable neural recording microsystem. In the scan mode, the processor is capable of detecting positive, negative, and biphasic spikes with programmable thresholds. It collects action potential information from the input channels, tags the activities with the associated channel address, compresses and finally packs the activity information in a serial digital bit stream to be sent to an external host. In the monitor mode, two channels can be selected and viewed at high resolution for studies where the entire signal is of interest.

  3. Association study of PARD3 gene polymorphisms with neural tube defects in a Chinese Han population.

    PubMed

    Gao, Yonghui; Chen, Xiaoli; Shangguan, Shaofang; Bao, Yihua; Lu, Xiaoli; Zou, Jizhen; Guo, Jin; Dai, Yaohua; Zhang, Ting

    2012-07-01

    Partitioning defective 3 homolog (PARD3) is an attractive candidate gene for screening neural tube defect (NTD) risk. To investigate the role of genetic variants in PARD3 on NTD risk, a case-control study was performed in a region of China with a high prevalence of NTDs. Total 53 single-nucleotide polymorphisms (SNPs) in PARD3 were genotyped in 224 fetuses with NTDs and in 253 normal fetuses. We found that 6 SNPs (rs2496720, rs2252655, rs3851068, rs118153230, rs10827337, and rs12218196) were statistically associated with NTDs (P < .05). After stratifying participants by NTD phenotypes, the significant association only existed in cases with anencephaly rather than spina bifida. Further haplotype analysis confirmed the association between PARD3 polymorphisms and NTD risk (global test P = 3.41e-008). Our results suggested that genetic variants in PARD3 were associated with susceptibility to NTDs in a Chinese Han population, and this association was affected by NTD phenotypes.

  4. The utilization of neural nets in populating an object-oriented database

    NASA Technical Reports Server (NTRS)

    Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

    1989-01-01

    Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms (i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

  5. In silico lineage tracing through single cell transcriptomics identifies a neural stem cell population in planarians.

    PubMed

    Molinaro, Alyssa M; Pearson, Bret J

    2016-04-27

    The planarian Schmidtea mediterranea is a master regenerator with a large adult stem cell compartment. The lack of transgenic labeling techniques in this animal has hindered the study of lineage progression and has made understanding the mechanisms of tissue regeneration a challenge. However, recent advances in single-cell transcriptomics and analysis methods allow for the discovery of novel cell lineages as differentiation progresses from stem cell to terminally differentiated cell. Here we apply pseudotime analysis and single-cell transcriptomics to identify adult stem cells belonging to specific cellular lineages and identify novel candidate genes for future in vivo lineage studies. We purify 168 single stem and progeny cells from the planarian head, which were subjected to single-cell RNA sequencing (scRNAseq). Pseudotime analysis with Waterfall and gene set enrichment analysis predicts a molecularly distinct neoblast sub-population with neural character (νNeoblasts) as well as a novel alternative lineage. Using the predicted νNeoblast markers, we demonstrate that a novel proliferative stem cell population exists adjacent to the brain. scRNAseq coupled with in silico lineage analysis offers a new approach for studying lineage progression in planarians. The lineages identified here are extracted from a highly heterogeneous dataset with minimal prior knowledge of planarian lineages, demonstrating that lineage purification by transgenic labeling is not a prerequisite for this approach. The identification of the νNeoblast lineage demonstrates the usefulness of the planarian system for computationally predicting cellular lineages in an adult context coupled with in vivo verification.

  6. The Sign Rule and Beyond: Boundary Effects, Flexibility, and Noise Correlations in Neural Population Codes

    PubMed Central

    Hu, Yu; Zylberberg, Joel; Shea-Brown, Eric

    2014-01-01

    Over repeat presentations of the same stimulus, sensory neurons show variable responses. This “noise” is typically correlated between pairs of cells, and a question with rich history in neuroscience is how these noise correlations impact the population's ability to encode the stimulus. Here, we consider a very general setting for population coding, investigating how information varies as a function of noise correlations, with all other aspects of the problem – neural tuning curves, etc. – held fixed. This work yields unifying insights into the role of noise correlations. These are summarized in the form of theorems, and illustrated with numerical examples involving neurons with diverse tuning curves. Our main contributions are as follows. (1) We generalize previous results to prove a sign rule (SR) — if noise correlations between pairs of neurons have opposite signs vs. their signal correlations, then coding performance will improve compared to the independent case. This holds for three different metrics of coding performance, and for arbitrary tuning curves and levels of heterogeneity. This generality is true for our other results as well. (2) As also pointed out in the literature, the SR does not provide a necessary condition for good coding. We show that a diverse set of correlation structures can improve coding. Many of these violate the SR, as do experimentally observed correlations. There is structure to this diversity: we prove that the optimal correlation structures must lie on boundaries of the possible set of noise correlations. (3) We provide a novel set of necessary and sufficient conditions, under which the coding performance (in the presence of noise) will be as good as it would be if there were no noise present at all. PMID:24586128

  7. Model Evaluation with Multi-wavelength Satellite Observations Using a Neural Network

    NASA Astrophysics Data System (ADS)

    Kolassa, Jana; Jimenez, Carlos; Aires, Filipe

    2013-04-01

    A methodology has been developed to evaluate fields of modelled parameters against a set of satellite observations. The method employs a Neural Network (NN) to construct a statistical model capturing the relationship between the satellite observations and the parameter from a land surface model, in this case the Soil Moisture (SM). This statistical model is then used to estimate the parameter of interest from the set of satellite observations. These estimates are compared to the modelled parameter in order to detect local deviations indicating a possible problem in the model or in the satellite observations. Several synthetic tests, during which an artificial error was added to the"true" soil moisture fields, showed that the methodology is able to correct the errors (Jimenez et al., submitted, 2012). This evaluation technique is very general and can be applied to any modelled parameter for which sensitive satellite observations are available. The use of NNs simplifies the evaluation of the model against satellite observations and is particularly well-suited to utilize the synergy from the observations at different wavelengths (Aires et al., 2005, 2012). In this study the proposed methodology has been applied to evaluate SM fields from a number of land surface models against a synergy of satellite observations from passive and active microwave, infrared and visible sensors. In an inter-comparison of the performance of several land surface models (ORCHIDEE (de Rosnay et al., 2002), HTESSEL (Balsamo et al., 2009), JULES (Best et al., 2011) ) it was found that the soil moisture fields from JULES, HTESSEL and ORCHIDEE are very consistent with the observations, but ORCHIDEE soil moisture shows larger local deviations close to some river basins (Kolassa et al., in press, 2012; Jimenez et al., submitted, 2012). Differences between all models and the observations could also be observed in the Eastern US and over mountainous regions, however, the errors here are more likely

  8. The variation of glucose tolerance in a multi racial population.

    PubMed

    Ch'ng, S L; Chandrasekharan, N

    1985-04-01

    The pattern of plasma and urine sugar changes after 50g glucose load in 1900 Malaysians (522 males and 1378 females) consisting predominantly of Malays, Chinese and Indians were studied. The data were analysed using Statistical Package for Social Sciences (SPSS). The results show bimodal distribution of 120 min. plasma sugar values in the age groups 21 years and above and trimodal distribution in most groups above 40 years. The mean 120 minutes plasma sugar cut-off values for nondiabetics (ND), impaired glucose tolerance (IGT), and diabetics (DM) of 8.4 and 11.1 mmol/l respectively were close to the values recommended by the National Diabetic Data Group (NDDG). Fifty two percent of all subjects showed peaked plasma sugar values at 60 minutes (14% of them had IGT, 12% DM), 25% peaked at 30 minutes (98% of them were ND). The rest showed peaked values at 90 minutes (17%), 120 minutes (4%) and 150 minutes (2%) and from this group forty two percent were DM and 23% had IGT. Reliance on urine sugar qualitative tests could misclassify 7.3% of subjects (predominantly elderly females) with hyperglycaemia of greater than 11 mmol/l. This study shows that in the 50 g glucose tolerance test, the NDDG criteria for ND, IGT, DM is still applicable to the Malaysian population. The sampling time could be reduced to four points at 0, 60, 90, and 120 minutes. Blood analysis is the preferred method for the diagnosis of hyperglycaemia in elderly females.

  9. Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks

    ERIC Educational Resources Information Center

    Ray, Loye Lynn

    2014-01-01

    The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and…

  10. Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

    NASA Astrophysics Data System (ADS)

    Rußwurm, M.; Körner, M.

    2017-05-01

    Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  11. Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks

    ERIC Educational Resources Information Center

    Ray, Loye Lynn

    2014-01-01

    The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and…

  12. Automatic classification of volcanic earthquakes using multi-station waveforms and dynamic neural networks

    NASA Astrophysics Data System (ADS)

    Bruton, Christopher Patrick

    Earthquakes and seismicity have long been used to monitor volcanoes. In addition to the time, location, and magnitude of an earthquake, the characteristics of the waveform itself are important. For example, low-frequency or hybrid type events could be generated by magma rising toward the surface. A rockfall event could indicate a growing lava dome. Classification of earthquake waveforms is thus a useful tool in volcano monitoring. A procedure to perform such classification automatically could flag certain event types immediately, instead of waiting for a human analyst's review. Inspired by speech recognition techniques, we have developed a procedure to classify earthquake waveforms using artificial neural networks. A neural network can be "trained" with an existing set of input and desired output data; in this case, we use a set of earthquake waveforms (input) that has been classified by a human analyst (desired output). After training the neural network, new sets of waveforms can be classified automatically as they are presented. Our procedure uses waveforms from multiple stations, making it robust to seismic network changes and outages. The use of a dynamic time-delay neural network allows waveforms to be presented without precise alignment in time, and thus could be applied to continuous data or to seismic events without clear start and end times. We have evaluated several different training algorithms and neural network structures to determine their effects on classification performance. We apply this procedure to earthquakes recorded at Mount Spurr and Katmai in Alaska, and Uturuncu Volcano in Bolivia. The procedure can successfully distinguish between slab and volcanic events at Uturuncu, between events from four different volcanoes in the Katmai region, and between volcano-tectonic and long-period events at Spurr. Average recall and overall accuracy were greater than 80% in all three cases.

  13. Use of alternative medicines in a multi-ethnic population.

    PubMed

    Cappuccio, F P; Duneclift, S M; Atkinson, R W; Cook, D G

    2001-01-01

    The prevalence and cost of regular use of non-prescribed alternative medicines are rising around the world, yet, little evidence is available that demonstrates the safety, efficacy, or effectiveness of specific alternative medicine interventions. It is of interest to understand how and why these practices have become so popular in different societies with different health care organizations and provisions, and which factors predict the regular use of alternative medicines. We assessed the prevalence and the predictors of regular use of non-prescribed vitamin supplements, cod liver oil, primrose oil, and garlic in a cross-sectional population-based study in South London of 1,577 men and women, aged 40-59 years (883 women, 523 White, 549 of African origin and 505 of South Asian origin), when allowing for potential confounders. The prevalence of regular users of alternative medicines was 10.4% (164/1,577); 7.4% (116) made regular use of non-prescribed vitamin supplements, whereas 5.3% (84) used either cod liver oil, primrose oil, or garlic preparations. When adjusted for age, ethnicity and social class, women were more likely than men to use at least one alternative medicine (OR 2.09 [95% CI 1.45-3.00]). This was true both for vitamin supplements (1.98 [1.29-3.03]) and for oil or garlic supplements (1.91 [1.17-3.14]). The use of oil or garlic (P<.005) but not vitamin supplements (P=.32) varied by ethnic group. In particular, Black people of African origin were more likely to use alternative medicines than either Whites (1.78 [1.07-2.94]) or South Asians (1.66 [1.07-2.59]), the least common users. People in social classes IV and V were less likely to use alternative medicines (0.53 [0.31-0.90]) than those in social classes I and II, though this was due more to lesser use of non-prescribed vitamin supplements than of cod liver oil, primrose oil or garlic. These associations were not attenuated by further adjustment for body mass index, smoking, marital status and age at

  14. Multi-Stepped Optogenetics: A Novel Strategy to Analyze Neural Network Formation and Animal Behaviors by Photo-Regulation of Local Gene Expression, Fluorescent Color and Neural Excitation

    NASA Astrophysics Data System (ADS)

    Hatta, Kohei; Nakajima, Yohei; Isoda, Erika; Itoh, Mariko; Yamamoto, Tamami

    The brain is one of the most complicated structures in nature. Zebrafish is a useful model to study development of vertebrate brain, because it is transparent at early embryonic stage and it develops rapidly outside of the body. We made a series of transgenic zebrafish expressing green-fluorescent protein related molecules, for example, Kaede and KikGR, whose green fluorescence can be irreversibly converted to red upon irradiation with ultra-violet (UV) or violet light, and Dronpa, whose green fluorescence is eliminated with strong blue light but can be reactivated upon irradiation with UV or violet-light. We have recently shown that infrared laser evoked gene operator (IR-LEGO) which causes a focused heat shock could locally induce these fluorescent proteins and the other genes. Neural cell migration and axonal pattern formation in living brain could be visualized by this technique. We also can express channel rhodopsine 2 (ChR2), a photoactivatable cation channel, or Natronomonas pharaonis halorhodopsin (NpHR), a photoactivatable chloride ion pump, locally in the nervous system by IR. Then, behaviors of these animals can be controlled by activating or silencing the local neurons by light. This novel strategy is useful in discovering neurons and circuits responsible for a wide variety of animal behaviors. We proposed to call this method ‘multi-stepped optogenetics’.

  15. Determination of near-surface structures from multi-channel surface wave data using multi-layer perceptron neural network (MLPNN) algorithm

    NASA Astrophysics Data System (ADS)

    Çaylak, Çağrı; Kaftan, İlknur

    2014-12-01

    This study proposes the use of multi-layer perceptron neural networks (MLPNN) to invert dispersion curves obtained via multi-channel analysis of surface waves (MASW) for shear S-wave velocity profile. The dispersion curve used in inversion includes the fundamental-mode dispersion data. In order to investigate the applicability and performance of the proposed MLPNN algorithm, test studies were performed using both synthetic and field examples. Gaussian random noise with a standard deviation of 4 and 8% was added to the noise-free test data to make the synthetic test more realistic. The model parameters, such as S-wave velocities and thicknesses of the synthetic layered-earth model, were obtained for different S/N ratios and noise-free data. The field survey was performed over the natural gas pipeline, located in the Germencik district of Aydın city, western Turkey. The results show that depth, velocity, and location of the embedded natural gas pipe are successfully estimated with reasonably good approximation.

  16. GRAIL: A multi-agent neural network system for gene identification

    SciTech Connect

    Xu, Y.; Mural, R.J.; Einstein, J.R.; Shah, M.B.; Uberbacher, E.C.

    1996-10-01

    Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. The authors describe a gene localization and modeling system, called GRAIL. GRAIL is a multiple sensor-neural network-based system. It localizes genes in anonymous DNA sequence by recognizing features related to protein-coding regions and the boundaries of coding regions, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene model. Through years of extensive testing, GRAIL consistently localizes about 90% of coding portions of test genes with a false positive rate of about 10%. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1,000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.

  17. Toward a multi-sensor neural net approach to automatic text classification

    SciTech Connect

    Dasigi, V.; Mann, R.

    1996-01-26

    Many automatic text indexing and retrieval methods use a term-document matrix that is automatically derived from the text in question. Latent Semantic Indexing, a recent method for approximating large term-document matrices, appears to be quite useful in the problem of text information retrieval, rather than text classification. Here we outline a method that attempts to combine the strength of the LSI method with that of neural networks, in addressing the problem of text classification. In doing so, we also indicate ways to improve performance by adding additional {open_quotes}logical sensors{close_quotes} to the neural network, something that is hard to do with the LSI method when employed by itself. Preliminary results are summarized, but much work remains to be done.

  18. A wavelet-based neural model to optimize and read out a temporal population code

    PubMed Central

    Luvizotto, Andre; Rennó-Costa, César; Verschure, Paul F. M. J.

    2012-01-01

    It has been proposed that the dense excitatory local connectivity of the neo-cortex plays a specific role in the transformation of spatial stimulus information into a temporal representation or a temporal population code (TPC). TPC provides for a rapid, robust, and high-capacity encoding of salient stimulus features with respect to position, rotation, and distortion. The TPC hypothesis gives a functional interpretation to a core feature of the cortical anatomy: its dense local and sparse long-range connectivity. Thus far, the question of how the TPC encoding can be decoded in downstream areas has not been addressed. Here, we present a neural circuit that decodes the spectral properties of the TPC using a biologically plausible implementation of a Haar transform. We perform a systematic investigation of our model in a recognition task using a standardized stimulus set. We consider alternative implementations using either regular spiking or bursting neurons and a range of spectral bands. Our results show that our wavelet readout circuit provides for the robust decoding of the TPC and further compresses the code without loosing speed or quality of decoding. We show that in the TPC signal the relevant stimulus information is present in the frequencies around 100 Hz. Our results show that the TPC is constructed around a small number of coding components that can be well decoded by wavelet coefficients in a neuronal implementation. The solution to the TPC decoding problem proposed here suggests that cortical processing streams might well consist of sequential operations where spatio-temporal transformations at lower levels forming a compact stimulus encoding using TPC that are subsequently decoded back to a spatial representation using wavelet transforms. In addition, the results presented here show that different properties of the stimulus might be transmitted to further processing stages using different frequency components that are captured by appropriately tuned

  19. The advantage of ambiguity? Enhanced neural responses to multi-stable percepts correlate with the degree of perceived instability.

    PubMed

    Dyson, Benjamin J

    2011-01-01

    Artwork can often pique the interest of the viewer or listener as a result of the ambiguity or instability contained within it. Our engagement with uncertain sensory experiences might have its origins in early cortical responses, in that perceptually unstable stimuli might preclude neural habituation and maintain activity in early sensory areas. To assess this idea, participants engaged with an ambiguous visual stimulus wherein two squares alternated with one another, in terms of simultaneously opposing vertical and horizontal locations relative to fixation (i.e., stroboscopic alternating motion; von Schiller, 1933). At each trial, participants were invited to interpret the movement of the squares in one of five ways: traditional vertical or horizontal motion, novel clockwise or counter-clockwise motion, and, a free-view condition in which participants were encouraged to switch the direction of motion as often as possible. Behavioral reports of perceptual stability showed clockwise and counter-clockwise motion to possess an intermediate level of stability compared to relatively stable vertical and horizontal motion, and, relatively unstable motion perceived during free-view conditions. Early visual evoked components recorded at parietal-occipital sites such as C1, P1, and N1 modulated as a function of visual intention. Both at a group and individual level, increased perceptual instability was related to increased negativity in all three of these early visual neural responses. Engagement with increasingly ambiguous input may partly result from the underlying exaggerated neural response to it. The study underscores the utility of combining neuroelectric recording with the presentation of perceptually multi-stable yet physically identical stimuli, in revealing brain activity associated with the purely internal process of interpreting and appreciating the sensory world that surrounds us.

  20. Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI

    PubMed Central

    Manor, Ran; Geva, Amir B.

    2015-01-01

    Brain computer interfaces rely on machine learning (ML) algorithms to decode the brain's electrical activity into decisions. For example, in rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. Here, we continue our previous work, presenting a deep neural network model for the use of single trial EEG classification in RSVP tasks. Deep neural networks have shown state of the art performance in computer vision and speech recognition and thus have great promise for other learning tasks, like classification of EEG samples. In our model, we introduce a novel spatio-temporal regularization for EEG data to reduce overfitting. We show improved classification performance compared to our earlier work on a five categories RSVP experiment. In addition, we compare performance on data from different sessions and validate the model on a public benchmark data set of a P300 speller task. Finally, we discuss the advantages of using neural network models compared to manually designing feature extraction algorithms. PMID:26696875

  1. A multi-channel analog IC for in vitro neural recording

    NASA Astrophysics Data System (ADS)

    Feng, Yuan; Zhigong, Wang; Xiaoying, Lü

    2016-02-01

    Recent work in the field of neurophysiology has demonstrated that, by observing the firing characteristic of action potentials (AP) and the exchange pattern of signals between neurons, it is possible to reveal the nature of “memory” and “thinking” and help humans to understand how the brain works. To address these needs, we developed a prototype fully integrated circuit (IC) with micro-electrode array (MEA) for neural recording. In this scheme, the microelectrode array is composed by 64 detection electrodes and 2 reference electrodes. The proposed IC consists of 8 recording channels with an area of 5 × 5 mm2. Each channel can operate independently to process the neural signal by amplifying, filtering, etc. The chip is fabricated in 0.5-μm CMOS technology. The simulated and measured results show the system provides an effective device for recording feeble signal such as neural signals. Project supported by the National Natural Science Foundation of China (No. 61076118).

  2. Cropping Pattern Detection and Change Analysis in Central Luzon, Philippines Using Multi-Temporal MODIS Imagery and Artificial Neural Network Classifier

    NASA Astrophysics Data System (ADS)

    dela Torre, D. M.; Perez, G. J. P.

    2016-12-01

    Cropping practices in the Philippines has been intensifying with greater demand for food and agricultural supplies in view of an increasing population and advanced technologies for farming. This has not been monitored regularly using traditional methods but alternative methods using remote sensing has been promising yet underutilized. This study employed multi-temporal data from MODIS and neural network classifier to map annual land use in agricultural areas from 2001-2014 in Central Luzon, the primary rice growing area of the Philippines. Land use statistics derived from these maps were compared with historical El Nino events to examine how land area is affected by drought events. Fourteen maps of agricultural land use was produced, with the primary classes being single-cropping, double-cropping and perennial crops with secondary classes of forests, urban, bare, water and other classes. Primary classes were produced from the neural network classifier while secondary classes were derived from NDVI threshold masks. The overall accuracy for the 2014 map was 62.05% and a kappa statistic of 0.45. 155.56% increase in single-cropping systems from 2001 to 2014 was observed while double cropping systems decreased by 14.83%. Perennials increased by 76.21% while built-up areas decreased by 12.22% within the 14-year interval. There are several sources of error including mixed-pixels, scale-conversion problems and limited ground reference data. An analysis including El Niño events in 2004 and 2010 demonstrated that marginally irrigated areas that usually planted twice in a year resorted to single cropping, indicating that scarcity of water limited the intensification allowable in the area. Findings from this study can be used to predict future use of agricultural land in the country and also examine how farmlands have responded to climatic factors and stressors.

  3. Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network.

    PubMed

    Kuo, R J.; Cohen, P H.

    1999-03-01

    On-line tool wear estimation plays a very critical role in industry automation for higher productivity and product quality. In addition, appropriate and timely decision for tool change is significantly required in the machining systems. Thus, this paper is dedicated to develop an estimation system through integration of two promising technologies, artificial neural networks (ANN) and fuzzy logic. An on-line estimation system consisting of five components: (1) data collection; (2) feature extraction; (3) pattern recognition; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis function (RBF) network is employed to recognize the extracted features. Thereafter, the decisions from multiple sensors are integrated through a proposed fuzzy neural network (FNN) model. Such a model is self-organizing and self-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed system. The results show that the proposed system can significantly increase the accuracy of the product profile.

  4. An application of artificial neural intelligence for personal dose assessment using a multi-area OSL dosimetry system.

    PubMed

    Lee, S Y; Kim, B H; Lee, K J

    2001-06-01

    Significant advances have been made in recent years to improve measurement technology and performance of phosphor materials in the fields of optically stimulated luminescence (OSL) dosimetry. Pulsed and continuous wave OSL studies recently carried out on alpha-Al2O3:C have shown that the material seems to be the most promising for routine application of OSL for dosimetric purposes. The main objective of the study is to propose a new personal dosimetry system using alpha-Al2O3:C by taking advantage of its optical properties and energy dependencies. In the process of the study, a new dose assessment algorithm was developed using artificial neural networks in hopes of achieving a higher degree of accuracy and precision in personal OSL dosimetry system. The original hypothesis of this work is that the spectral information of X- and gamma-ray fields may be obtained by the analysis of the response of a multi-element system. In this study, a feedforward neural network using the error back-propagation method with Bayesian optimization was applied for the response unfolding procedure. The validation of the proposed algorithm was investigated by unfolding the 10 measured responses of alpha-Al2O3:C for arbitrarily mixed photon fields which range from 20 to 662 keV. c2001 Elsevier Science Ltd. All rights reserved.

  5. The effects of selective attention and speech acoustics on neural speech-tracking in a multi-talker scene

    PubMed Central

    Rimmele, Johanna M.; Golumbic, Elana Zion; Schröger, Erich; Poeppel, David

    2015-01-01

    Attending to one speaker in multi-speaker situations is challenging. One neural mechanism proposed to underlie the ability to attend to a particular speaker is phase-locking of low-frequency activity in auditory cortex to speech’s temporal envelope (“speech-tracking”), which is more precise for attended speech. However, it is not known what brings about this attentional effect, and specifically if it reflects enhanced processing of the fine structure of attended speech. To investigate this question we compared attentional effects on speech-tracking of natural vs. vocoded speech which preserves the temporal envelope but removes the fine-structure of speech. Pairs of natural and vocoded speech stimuli were presented concurrently and participants attended to one stimulus and performed a detection task while ignoring the other stimulus. We recorded magnetoencephalography (MEG) and compared attentional effects on the speech-tracking response in auditory cortex. Speech-tracking of natural, but not vocoded, speech was enhanced by attention, whereas neural tracking of ignored speech was similar for natural and vocoded speech. These findings suggest that the more precise speech tracking of attended natural speech is related to processing its fine structure, possibly reflecting the application of higher-order linguistic processes. In contrast, when speech is unattended its fine structure is not processed to the same degree and thus elicits less precise speech tracking more similar to vocoded speech. PMID:25650107

  6. Determination of multi-component flow process parameters based on electrical capacitance tomography data using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Mohamad-Saleh, J.; Hoyle, B. S.

    2002-12-01

    Artificial neural networks (ANNs) have been used to investigate their capabilities at estimating key parameters for the characterization of flow processes, based on electrical capacitance-sensed tomographic (ECT) data. The estimations of the parameters are made directly, without recourse to tomographic images. The parameters of interest include component height and interface orientation of two-component flows, and component fractions of two-component and three-component flows. Separate multi-layer perceptron networks were trained with patterns consisting of pairs of simulated ECT data and the corresponding component heights, interface orientations and component fractions. The networks were then tested with patterns consisting of unlearned simulated ECT data of various flows and with real ECT data of gas-water flows. The neural systems provided estimations having mean absolute errors of less than 1% for oil and water heights and fractions and less than 10° for interface orientations. When tested with real plant ECT data, the mean absolute errors were less than 4% for water height, less than 15° for gas-water interface orientation and less than 3% for water fraction, respectively. The results demonstrate the feasibility of the application of ANNs for flow process parameter estimations based upon tomography data.

  7. The effects of selective attention and speech acoustics on neural speech-tracking in a multi-talker scene.

    PubMed

    Rimmele, Johanna M; Zion Golumbic, Elana; Schröger, Erich; Poeppel, David

    2015-07-01

    Attending to one speaker in multi-speaker situations is challenging. One neural mechanism proposed to underlie the ability to attend to a particular speaker is phase-locking of low-frequency activity in auditory cortex to speech's temporal envelope ("speech-tracking"), which is more precise for attended speech. However, it is not known what brings about this attentional effect, and specifically if it reflects enhanced processing of the fine structure of attended speech. To investigate this question we compared attentional effects on speech-tracking of natural versus vocoded speech which preserves the temporal envelope but removes the fine structure of speech. Pairs of natural and vocoded speech stimuli were presented concurrently and participants attended to one stimulus and performed a detection task while ignoring the other stimulus. We recorded magnetoencephalography (MEG) and compared attentional effects on the speech-tracking response in auditory cortex. Speech-tracking of natural, but not vocoded, speech was enhanced by attention, whereas neural tracking of ignored speech was similar for natural and vocoded speech. These findings suggest that the more precise speech-tracking of attended natural speech is related to processing its fine structure, possibly reflecting the application of higher-order linguistic processes. In contrast, when speech is unattended its fine structure is not processed to the same degree and thus elicits less precise speech-tracking more similar to vocoded speech.

  8. Application of neural adaptive power system stabilizer in a multi-machine power system

    SciTech Connect

    Shamsollahi, P.; Malik, O.P.

    1999-09-01

    Application of a neural adaptive power system stabilizer (NAPSS) to a five-machine power system is described in this paper. The proposed NAPSS comprises two subnetworks. The adaptive neuro-identifier (ANI) to dynamically identify the non-linear plant, and the adaptive neuro-controller (ANC) to damp output oscillations. The back-propagation training method is used on-line to train these subnetworks. The effectiveness of the proposed NAPSS in damping both local and inter-area modes of oscillations and its self-coordination ability are demonstrated.

  9. Dissociation between neural signatures of stimulus and choice in population activity of human V1 during perceptual decision-making.

    PubMed

    Choe, Kyoung Whan; Blake, Randolph; Lee, Sang-Hun

    2014-02-12

    Primary visual cortex (V1) forms the initial cortical representation of objects and events in our visual environment, and it distributes information about that representation to higher cortical areas within the visual hierarchy. Decades of work have established tight linkages between neural activity occurring in V1 and features comprising the retinal image, but it remains debatable how that activity relates to perceptual decisions. An actively debated question is the extent to which V1 responses determine, on a trial-by-trial basis, perceptual choices made by observers. By inspecting the population activity of V1 from human observers engaged in a difficult visual discrimination task, we tested one essential prediction of the deterministic view: choice-related activity, if it exists in V1, and stimulus-related activity should occur in the same neural ensemble of neurons at the same time. Our findings do not support this prediction: while cortical activity signifying the variability in choice behavior was indeed found in V1, that activity was dissociated from activity representing stimulus differences relevant to the task, being advanced in time and carried by a different neural ensemble. The spatiotemporal dynamics of population responses suggest that short-term priors, perhaps formed in higher cortical areas involved in perceptual inference, act to modulate V1 activity prior to stimulus onset without modifying subsequent activity that actually represents stimulus features within V1.

  10. Dissociation between Neural Signatures of Stimulus and Choice in Population Activity of Human V1 during Perceptual Decision-Making

    PubMed Central

    Choe, Kyoung Whan; Blake, Randolph

    2014-01-01

    Primary visual cortex (V1) forms the initial cortical representation of objects and events in our visual environment, and it distributes information about that representation to higher cortical areas within the visual hierarchy. Decades of work have established tight linkages between neural activity occurring in V1 and features comprising the retinal image, but it remains debatable how that activity relates to perceptual decisions. An actively debated question is the extent to which V1 responses determine, on a trial-by-trial basis, perceptual choices made by observers. By inspecting the population activity of V1 from human observers engaged in a difficult visual discrimination task, we tested one essential prediction of the deterministic view: choice-related activity, if it exists in V1, and stimulus-related activity should occur in the same neural ensemble of neurons at the same time. Our findings do not support this prediction: while cortical activity signifying the variability in choice behavior was indeed found in V1, that activity was dissociated from activity representing stimulus differences relevant to the task, being advanced in time and carried by a different neural ensemble. The spatiotemporal dynamics of population responses suggest that short-term priors, perhaps formed in higher cortical areas involved in perceptual inference, act to modulate V1 activity prior to stimulus onset without modifying subsequent activity that actually represents stimulus features within V1. PMID:24523561

  11. Neural crest stem cell population in craniomaxillofacial development and tissue repair.

    PubMed

    La Noce, M; Mele, L; Tirino, V; Paino, F; De Rosa, A; Naddeo, P; Papagerakis, P; Papaccio, G; Desiderio, V

    2014-10-28

    Neural crest cells, delaminating from the neural tube during migration, undergo an epithelial-mesenchymal transition and differentiate into several cell types strongly reinforcing the mesoderm of the craniofacial body area - giving rise to bone, cartilage and other tissues and cells of this human body area. Recent studies on craniomaxillofacial neural crest-derived cells have provided evidence for the tremendous plasticity of these cells. Actually, neural crest cells can respond and adapt to the environment in which they migrate and the cranial mesoderm plays an important role toward patterning the identity of the migrating neural crest cells. In our experience, neural crest-derived stem cells, such as dental pulp stem cells, can actively proliferate, repair bone and give rise to other tissues and cytotypes, including blood vessels, smooth muscle, adipocytes and melanocytes, highlighting that their use in tissue engineering is successful. In this review, we provide an overview of the main pathways involved in neural crest formation, delamination, migration and differentiation; and, in particular, we concentrate our attention on the translatability of the latest scientific progress. Here we try to suggest new ideas and strategies that are needed to fully develop the clinical use of these cells. This effort should involve both researchers/clinicians and improvements in good manufacturing practice procedures. It is important to address studies towards clinical application or take into consideration that studies must have an effective therapeutic prospect for humans. New approaches and ideas must be concentrated also toward stem cell recruitment and activation within the human body, overcoming the classical grafting.

  12. ABC inference of multi-population divergence with admixture from unphased population genomic data.

    PubMed

    Robinson, John D; Bunnefeld, Lynsey; Hearn, Jack; Stone, Graham N; Hickerson, Michael J

    2014-09-01

    Rapidly developing sequencing technologies and declining costs have made it possible to collect genome-scale data from population-level samples in nonmodel systems. Inferential tools for historical demography given these data sets are, at present, underdeveloped. In particular, approximate Bayesian computation (ABC) has yet to be widely embraced by researchers generating these data. Here, we demonstrate the promise of ABC for analysis of the large data sets that are now attainable from nonmodel taxa through current genomic sequencing technologies. We develop and test an ABC framework for model selection and parameter estimation, given histories of three-population divergence with admixture. We then explore different sampling regimes to illustrate how sampling more loci, longer loci or more individuals affects the quality of model selection and parameter estimation in this ABC framework. Our results show that inferences improved substantially with increases in the number and/or length of sequenced loci, while less benefit was gained by sampling large numbers of individuals. Optimal sampling strategies given our inferential models included at least 2000 loci, each approximately 2 kb in length, sampled from five diploid individuals per population, although specific strategies are model and question dependent. We tested our ABC approach through simulation-based cross-validations and illustrate its application using previously analysed data from the oak gall wasp, Biorhiza pallida. © 2014 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.

  13. ABC inference of multi-population divergence with admixture from unphased population genomic data

    PubMed Central

    Robinson, John D; Bunnefeld, Lynsey; Hearn, Jack; Stone, Graham N; Hickerson, Michael J

    2014-01-01

    Rapidly developing sequencing technologies and declining costs have made it possible to collect genome-scale data from population-level samples in nonmodel systems. Inferential tools for historical demography given these data sets are, at present, underdeveloped. In particular, approximate Bayesian computation (ABC) has yet to be widely embraced by researchers generating these data. Here, we demonstrate the promise of ABC for analysis of the large data sets that are now attainable from nonmodel taxa through current genomic sequencing technologies. We develop and test an ABC framework for model selection and parameter estimation, given histories of three-population divergence with admixture. We then explore different sampling regimes to illustrate how sampling more loci, longer loci or more individuals affects the quality of model selection and parameter estimation in this ABC framework. Our results show that inferences improved substantially with increases in the number and/or length of sequenced loci, while less benefit was gained by sampling large numbers of individuals. Optimal sampling strategies given our inferential models included at least 2000 loci, each approximately 2 kb in length, sampled from five diploid individuals per population, although specific strategies are model and question dependent. We tested our ABC approach through simulation-based cross-validations and illustrate its application using previously analysed data from the oak gall wasp, Biorhiza pallida. PMID:25113024

  14. Multi-locus estimates of population structure and migration in a fence lizard hybrid zone.

    PubMed

    Leaché, Adam D

    2011-01-01

    A hybrid zone between two species of lizards in the genus Sceloporus (S. cowlesi and S. tristichus) on the Mogollon Rim in Arizona provides a unique opportunity to study the processes of lineage divergence and merging. This hybrid zone involves complex interactions between 2 morphologically and ecologically divergent subspecies, 3 chromosomal groups, and 4 mitochondrial DNA (mtDNA) clades. The spatial patterns of divergence between morphology, chromosomes and mtDNA are discordant, and determining which of these character types (if any) reflects the underlying population-level lineages that are of interest has remained impeded by character conflict. The focus of this study is to estimate the number of populations interacting in the hybrid zone using multi-locus nuclear data, and to then estimate the migration rates and divergence time between the inferred populations. Multi-locus estimates of population structure and gene flow were obtained from 12 anonymous nuclear loci sequenced for 93 specimens of Sceloporus. Population structure estimates support two populations, and this result is robust to changes to the prior probability distribution used in the Bayesian analysis and the use of spatially-explicit or non-spatial models. A coalescent analysis of population divergence suggests that gene flow is high between the two populations, and that the timing of divergence is restricted to the Pleistocene. The hybrid zone is more accurately described as involving two populations belonging to S. tristichus, and the presence of S. cowlesi mtDNA haplotypes in the hybrid zone is an anomaly resulting from mitochondrial introgression.

  15. Accurate and representative decoding of the neural drive to muscles in humans with multi-channel intramuscular thin-film electrodes.

    PubMed

    Muceli, Silvia; Poppendieck, Wigand; Negro, Francesco; Yoshida, Ken; Hoffmann, Klaus P; Butler, Jane E; Gandevia, Simon C; Farina, Dario

    2015-09-01

    Intramuscular electrodes developed over the past 80 years can record the concurrent activity of only a few motor units active during a muscle contraction. We designed, produced and tested a novel multi-channel intramuscular wire electrode that allows in vivo concurrent recordings of a substantially greater number of motor units than with conventional methods. The electrode has been extensively tested in deep and superficial human muscles. The performed tests indicate the applicability of the proposed technology in a variety of conditions. The electrode represents an important novel technology that opens new avenues in the study of the neural control of muscles in humans. We describe the design, fabrication and testing of a novel multi-channel thin-film electrode for detection of the output of motoneurones in vivo and in humans, through muscle signals. The structure includes a linear array of 16 detection sites that can sample intramuscular electromyographic activity from the entire muscle cross-section. The structure was tested in two superficial muscles (the abductor digiti minimi (ADM) and the tibialis anterior (TA)) and a deep muscle (the genioglossus (GG)) during contractions at various forces. Moreover, surface electromyogram (EMG) signals were concurrently detected from the TA muscle with a grid of 64 electrodes. Surface and intramuscular signals were decomposed into the constituent motor unit (MU) action potential trains. With the intramuscular electrode, up to 31 MUs were identified from the ADM muscle during an isometric contraction at 15% of the maximal force (MVC) and 50 MUs were identified for a 30% MVC contraction of TA. The new electrode detects different sources from a surface EMG system, as only one MU spike train was found to be common in the decomposition of the intramuscular and surface signals acquired from the TA. The system also allowed access to the GG muscle, which cannot be analysed with surface EMG, with successful identification of MU

  16. Regional application of multi-layer artificial neural networks in 3-D ionosphere tomography

    NASA Astrophysics Data System (ADS)

    Ghaffari Razin, Mir Reza; Voosoghi, Behzad

    2016-08-01

    Tomography is a very cost-effective method to study physical properties of the ionosphere. In this paper, residual minimization training neural network (RMTNN) is used in voxel-based tomography to reconstruct of 3-D ionosphere electron density with high spatial resolution. For numerical experiments, observations collected at 37 GPS stations from Iranian permanent GPS network (IPGN) are used. A smoothed TEC approach was used for absolute STEC recovery. To improve the vertical resolution, empirical orthogonal functions (EOFs) obtained from international reference ionosphere 2012 (IRI-2012) used as object function in training neural network. Ionosonde observations is used for validate reliability of the proposed method. Minimum relative error for RMTNN is 1.64% and maximum relative error is 15.61%. Also root mean square error (RMSE) of 0.17 × 1011 (electrons/m3) is computed for RMTNN which is less than RMSE of IRI2012. The results show that RMTNN has higher accuracy and compiles speed than other ionosphere reconstruction methods.

  17. Imaging neuronal populations in behaving rodents: paradigms for studying neural circuits underlying behavior in the mammalian cortex.

    PubMed

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

    2013-11-06

    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.

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

    PubMed Central

    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

  19. Pregnancy outcomes in severe hyperemesis gravidarum in a multi-ethnic population.

    PubMed

    Vlachodimitropoulou Koumoutsea, E; Vlachodimitropoulou-Koumoutsea, E; Gosh, S; Manmatharajah, B; Ray, A; Igwe-Omoke, N; Yoong, W

    2013-07-01

    We evaluated pregnancy outcomes among women with hyperemesis gravidarum (HG) in a North London multi-ethnic population. A retrospective case-control study was performed on records of obstetric admissions during a 4-year period, at North Middlesex University Hospital in North London. A total of 208 women with HG were identified according to Fairweather's criteria occurring in the first 20 weeks of pregnancy, which is severe enough to require admission to hospital. The control study group consisted of 208 women without HG, matched for age, ethnicity and parity. Maternal characteristics as well as pregnancy outcomes were compared in the two groups. The incidence of a delivery of a small-for-gestational-age (SGA) neonate below the 10th per centile was significantly higher in the HG group compared with unaffected pregnancies (8.7% vs 16.8%, p = 0.01). Hyperemesis gravidarum in a multi-ethnic population in North London is associated with SGA neonates.

  20. Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Fieuzal, R.; Marais Sicre, C.; Baup, F.

    2017-05-01

    The yield forecasting of corn constitutes a key issue in agricultural management, particularly in the context of demographic pressure and climate change. This study presents two methods to estimate yields using artificial neural networks: a diagnostic approach based on all the satellite data acquired throughout the agricultural season, and a real-time approach, where estimates are updated after each image was acquired in the microwave and optical domains (Formosat-2, Spot-4/5, TerraSAR-X, and Radarsat-2) throughout the crop cycle. The results are based on the Multispectral Crop Monitoring experimental campaign conducted by the CESBIO (Centre d'Études de la BIOsphère) laboratory in 2010 over an agricultural region in southwestern France. Among the tested sensor configurations (multi-frequency, multi-polarization or multi-source data), the best yield estimation performance (using the diagnostic approach) is obtained with reflectance acquired in the red wavelength region, with a coefficient of determination of 0.77 and an RMSE of 6.6 q ha-1. In the real-time approach the combination of red reflectance and CHH backscattering coefficients provides the best compromise between the accuracy and earliness of the yield estimate (more than 3 months before the harvest), with an R2 of 0.69 and an RMSE of 7.0 q ha-1 during the development of the central stem. The two best yield estimates are similar in most cases (for more than 80% of the monitored fields), and the differences are related to discrepancies in the crop growth cycle and/or the consequences of pests.

  1. Using artificial neural networks to retrieve the aerosol type from multi-spectral lidar data

    NASA Astrophysics Data System (ADS)

    Nicolae, Doina; Belegante, Livio; Talianu, Camelia; Vasilescu, Jeni

    2015-04-01

    Aerosols can influence the microphysical and macrophysical properties of clouds and hence impact the energy balance, precipitation and the hydrological cycle. They have different scattering and absorption properties depending on their origin, therefore measured optical properties can be used to retrieve their physical properties, as well as to estimate their chemical composition. Due to the measurement limitations (spectral, uncertainties, range) and high variability of the aerosol properties with environmental conditions (including mixing during transport), the identification of the aerosol type from lidar data is still not solved. However, ground, airborne and space-based lidars provide more and more observations to be exploited. Since 2000, EARLINET collected more than 20,000 aerosol vertical profiles under various meteorological conditions, concerning local or long-range transport of aerosols in the free troposphere. This paper describes the basic algorithm for aerosol typing from optical data using the benefits of artificial neural networks. A relevant database was built to provide sufficient training cases for the neural network, consisting of synthetic and measured aerosol properties. Synthetic aerosols were simulated starting from the microphysical properties of basic components, internally mixed in various proportions. The algorithm combines the GADS database (Global Aerosol DataSet) to OPAC model (Optical Properties of Aerosol and Clouds) and T-Matrix code in order to compute, in an iterative way, the intensive optical properties of each aerosol type. Both pure and mixed aerosol types were considered, as well as their particular non-sphericity and hygroscopicity. Real aerosol cases were picked up from the ESA-CALIPSO database, as well as EARLINET datasets. Specific selection criteria were applied to identify cases with accurate optical data and validated sources. Cross-check of the synthetic versus measured aerosol intensive parameters was performed in

  2. Multi-parameter prediction of drivers' lane-changing behaviour with neural network model.

    PubMed

    Peng, Jinshuan; Guo, Yingshi; Fu, Rui; Yuan, Wei; Wang, Chang

    2015-09-01

    Accurate prediction of driving behaviour is essential for an active safety system to ensure driver safety. A model for predicting lane-changing behaviour is developed from the results of naturalistic on-road experiment for use in a lane-changing assistance system. Lane changing intent time window is determined via visual characteristics extraction of rearview mirrors. A prediction index system for left lane changes was constructed by considering drivers' visual search behaviours, vehicle operation behaviours, vehicle motion states, and driving conditions. A back-propagation neural network model was developed to predict lane-changing behaviour. The lane-change-intent time window is approximately 5 s long, depending on the subjects. The proposed model can accurately predict drivers' lane changing behaviour for at least 1.5 s in advance. The accuracy and time series characteristics of the model are superior to the use of turn signals in predicting lane-changing behaviour.

  3. Multi-Temporal Land Use Analysis of AN Ephemeral River Area Using AN Artificial Neural Network Approach on Landsat Imagery

    NASA Astrophysics Data System (ADS)

    Aquilino, M.; Tarantino, E.; Fratino, U.

    2013-01-01

    This paper proposes a change detection analysis method based on multitemporal LANDSAT satellite data, presenting a study performed on the Lama San Giorgio (Bari, Italy) river basin area. Based on its geological and hydrological characteristics, as well as on the number of recent and remote flooding events already occurred, this area seems to be naturally prone to flooding. The historical archive of LANDSAT imagery dating back to the launch of ERTS in 1972 provides a comprehensive and permanent data source for tracking change on the planet‟s land surface. In this study case the imagery acquisition dates of 1987, 2002 and 2011 were selected to cover a time trend of 24 years. Land cover categories were based on classes outlined by the Curve Number method with the aim of characterizing land use according to the level of surface imperviousness. After comparing two land use classification methods, i.e. Maximum Likelihood Classifier (MLC) and Multi-Layer Perceptron (MLP) neural network, the Artificial Neural Networks (ANN) approach was found the best reliable and efficient method in the absence of ground reference data. The ANN approach has a distinct advantage over statistical classification methods in that it is non-parametric and requires little or no a priori knowledge on the distribution model of input data. The results quantify land cover change patterns in the river basin area under study and demonstrate the potential of multitemporal LANDSAT data to provide an accurate and cost-effective means to map and analyse land cover changes over time that can be used as input in land management and policy decision-making.

  4. An Improved Cloud Classification Algorithm for China’s FY-2C Multi-Channel Images Using Artificial Neural Network

    PubMed Central

    Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

    2009-01-01

    The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China’s first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3–11.3 μm; IR2, 11.5–12.5 μm and WV 6.3–7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products. PMID:22346714

  5. An Improved Cloud Classification Algorithm for China's FY-2C Multi-Channel Images Using Artificial Neural Network.

    PubMed

    Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

    2009-01-01

    The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3-11.3 μm; IR2, 11.5-12.5 μm and WV 6.3-7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products.

  6. A multi-scale hybrid neural network retrieval model for dust storm detection, a study in Asia

    NASA Astrophysics Data System (ADS)

    Wong, Man Sing; Xiao, Fei; Nichol, Janet; Fung, Jimmy; Kim, Jhoon; Campbell, James; Chan, P. W.

    2015-05-01

    Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.

  7. Associations between life conditions and multi-morbidity in marginalized populations: the case of Palestinian refugees

    PubMed Central

    Hojeij, Safa; Elzein, Kareem; Chaaban, Jad; Seyfert, Karin

    2014-01-01

    Background: Evidence suggests that higher multi-morbidity rates among people with low socioeconomic status produces and maintains poverty. Our research explores the relationship between socioeconomic deprivation and multi-morbidity among Palestinian refugees in Lebanon, a marginalized and impoverished population. Methods: A representative sample of Palestinian refugees in Lebanon was surveyed, interviewing 2501 respondents (97% response rate). Multi-morbidity was measured by mental health, chronic and acute illnesses and disability. Multinomial logistic regression models assessed the association between indicators of poverty and multi-morbidities. Results: Findings showed that 14% of respondents never went to school, 41% of households reported water leakage and 10% suffered from severe food insecurity. Participants with an elementary education or less and those completing intermediate school were more than twice as likely to report two health problems than those with secondary education or more (OR: 2.60, CI: 1.73–3.91; OR: 2.47, CI: 1.62–3.77, respectively). Those living in households with water leakage were nearly twice as likely to have three or more health reports (OR = 1.88, CI = 1.45–2.44); this pattern was more pronounced for severely food insecure households (OR = 3.41, CI = 1.83–6.35). Conclusion: We identified a positive gradient between socioeconomic status and multi-morbidity within a refugee population. These findings reflect inequalities produced by the health and social systems in Lebanon, a problem expected to worsen following the massive influx of refugees from Syria. Ending legal discrimination and funding infrastructural, housing and health service improvements may counteract the effects of deprivation. Addressing this problem requires providing a decent livelihood for refugees in Lebanon. PMID:24994504

  8. Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks

    NASA Astrophysics Data System (ADS)

    Honegger, Thibault; Thielen, Moritz I.; Feizi, Soheil; Sanjana, Neville E.; Voldman, Joel

    2016-06-01

    The central nervous system is a dense, layered, 3D interconnected network of populations of neurons, and thus recapitulating that complexity for in vitro CNS models requires methods that can create defined topologically-complex neuronal networks. Several three-dimensional patterning approaches have been developed but none have demonstrated the ability to control the connections between populations of neurons. Here we report a method using AC electrokinetic forces that can guide, accelerate, slow down and push up neurites in un-modified collagen scaffolds. We present a means to create in vitro neural networks of arbitrary complexity by using such forces to create 3D intersections of primary neuronal populations that are plated in a 2D plane. We report for the first time in vitro basic brain motifs that have been previously observed in vivo and show that their functional network is highly decorrelated to their structure. This platform can provide building blocks to reproduce in vitro the complexity of neural circuits and provide a minimalistic environment to study the structure-function relationship of the brain circuitry.

  9. Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks

    PubMed Central

    Honegger, Thibault; Thielen, Moritz I.; Feizi, Soheil; Sanjana, Neville E.; Voldman, Joel

    2016-01-01

    The central nervous system is a dense, layered, 3D interconnected network of populations of neurons, and thus recapitulating that complexity for in vitro CNS models requires methods that can create defined topologically-complex neuronal networks. Several three-dimensional patterning approaches have been developed but none have demonstrated the ability to control the connections between populations of neurons. Here we report a method using AC electrokinetic forces that can guide, accelerate, slow down and push up neurites in un-modified collagen scaffolds. We present a means to create in vitro neural networks of arbitrary complexity by using such forces to create 3D intersections of primary neuronal populations that are plated in a 2D plane. We report for the first time in vitro basic brain motifs that have been previously observed in vivo and show that their functional network is highly decorrelated to their structure. This platform can provide building blocks to reproduce in vitro the complexity of neural circuits and provide a minimalistic environment to study the structure-function relationship of the brain circuitry. PMID:27328705

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

    SciTech Connect

    Sun, W; Jiang, M; Yin, F

    2016-06-15

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

  11. Neural crest induction at the neural plate border in vertebrates.

    PubMed

    Milet, Cécile; Monsoro-Burq, Anne H

    2012-06-01

    The neural crest is a transient and multipotent cell population arising at the edge of the neural plate in vertebrates. Recent findings highlight that neural crest patterning is initiated during gastrulation, i.e. earlier than classically described, in a progenitor domain named the neural border. This chapter reviews the dynamic and complex molecular interactions underlying neural border formation and neural crest emergence.

  12. Multi-Agent Inference in Social Networks: A Finite Population Learning Approach

    PubMed Central

    Tong, Xin; Zeng, Yao

    2016-01-01

    When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people’s incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning, to address whether with high probability, a large fraction of people in a given finite population network can make “good” inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows. PMID:27076691

  13. Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

    PubMed

    Fan, Jianqing; Tong, Xin; Zeng, Yao

    When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning, to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

  14. An overview of population-based algorithms for multi-objective optimisation

    NASA Astrophysics Data System (ADS)

    Giagkiozis, Ioannis; Purshouse, Robin C.; Fleming, Peter J.

    2015-07-01

    In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided.

  15. Simulated village locations in Thailand: A multi-scale model including a neural network approach

    PubMed Central

    Malanson, George P.; Entwisle, Barbara

    2010-01-01

    The simulation of rural land use systems, in general, and rural settlement dynamics in particular has developed with synergies of theory and methods for decades. Three current issues are: linking spatial patterns and processes, representing hierarchical relations across scales, and considering nonlinearity to address complex non-stationary settlement dynamics. We present a hierarchical simulation model to investigate complex rural settlement dynamics in Nang Rong, Thailand. This simulation uses sub-models to allocate new villages at three spatial scales. Regional and sub-regional models, which involve a nonlinear space-time autoregressive model implemented in a neural network approach, determine the number of new villages to be established. A dynamic village niche model, establishing pattern-process link, was designed to enable the allocation of villages into specific locations. Spatiotemporal variability in model performance indicates the pattern of village location changes as a settlement frontier advances from rice-growing lowlands to higher elevations. Experiments results demonstrate this simulation model can enhance our understanding of settlement development in Nang Rong and thus gain insight into complex land use systems in this area. PMID:21399748

  16. Speech recognition using Kohonen neural networks, dynamic programming, and multi-feature fusion

    NASA Astrophysics Data System (ADS)

    Stowe, Francis S.

    1990-12-01

    The purpose of this thesis was to develop and evaluate the performance of a three-feature speech recognition system. The three features used were LPC spectrum, formants (F1/F2), and cepstrum. The system uses Kohonen neural networks, dynamic programming, and a rule-based, feature-fusion process which integrates the three input features into one output result. The first half of this research involved evaluating the system in a speaker-dependent atmosphere. For this, the 70 word F-16 cockpit command vocabulary was used and both isolated and connected speech was tested. Results obtained are compared to a two-feature system with the same system configuration. Isolated-speech testing yielded 98.7 percent accuracy. Connected-speech testing yielded 75/0 percent accuracy. The three-feature system performed an average of 1.7 percent better than the two-feature system for isolated-speech. The second half of this research was concerned with the speaker-independent performance of the system. First, cross-speaker testing was performed using an updated 86 word library. In general, this testing yielded less than 50 percent accuracy. Then, testing was performed using averaged templates. This testing yielded an overall average in-template recognition rate of approximately 90 percent and an out-of-template recognition rate of approximately 75 percent.

  17. Simulated village locations in Thailand: A multi-scale model including a neural network approach.

    PubMed

    Tang, Wenwu; Malanson, George P; Entwisle, Barbara

    2009-04-01

    The simulation of rural land use systems, in general, and rural settlement dynamics in particular has developed with synergies of theory and methods for decades. Three current issues are: linking spatial patterns and processes, representing hierarchical relations across scales, and considering nonlinearity to address complex non-stationary settlement dynamics. We present a hierarchical simulation model to investigate complex rural settlement dynamics in Nang Rong, Thailand. This simulation uses sub-models to allocate new villages at three spatial scales. Regional and sub-regional models, which involve a nonlinear space-time autoregressive model implemented in a neural network approach, determine the number of new villages to be established. A dynamic village niche model, establishing pattern-process link, was designed to enable the allocation of villages into specific locations. Spatiotemporal variability in model performance indicates the pattern of village location changes as a settlement frontier advances from rice-growing lowlands to higher elevations. Experiments results demonstrate this simulation model can enhance our understanding of settlement development in Nang Rong and thus gain insight into complex land use systems in this area.

  18. Estimating Photometric Redshifts with Artificial Neural Networks and Multi-Parameters

    NASA Astrophysics Data System (ADS)

    Li, Li-Li; Zhang, Yan-Xia; Zhao, Yong-Heng; Yang, Da-Wei

    2007-06-01

    We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 (SDSS DR2) Galaxy Sample using artificial neural networks (ANNs). Different input sets based on various parameters (e.g. magnitude, color index, flux information) are explored. Mainly, parameters from broadband photometry are utilized and their performances in redshift prediction are compared. While any parameter may be easily incorporated in the input, our results indicate that using the dereddened magnitudes often produces more accurate photometric redshifts than using the Petrosian magnitudes or model magnitudes as input, but the model magnitudes are superior to the Petrosian magnitudes. Also, better performance results when more effective parameters are used in the training set. The method is tested on a sample of 79 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddened magnitudes, the rms error in redshift estimation is σz = 0.020184. The ANN is highly competitive tool compared to the traditional template-fitting methods when a large and representative training set is available.

  19. Neural population dynamics in human motor cortex during movements in people with ALS.

    PubMed

    Pandarinath, Chethan; Gilja, Vikash; Blabe, Christine H; Nuyujukian, Paul; Sarma, Anish A; Sorice, Brittany L; Eskandar, Emad N; Hochberg, Leigh R; Henderson, Jaimie M; Shenoy, Krishna V

    2015-06-23

    The prevailing view of motor cortex holds that motor cortical neural activity represents muscle or movement parameters. However, recent studies in non-human primates have shown that neural activity does not simply represent muscle or movement parameters; instead, its temporal structure is well-described by a dynamical system where activity during movement evolves lawfully from an initial pre-movement state. In this study, we analyze neuronal ensemble activity in motor cortex in two clinical trial participants diagnosed with Amyotrophic Lateral Sclerosis (ALS). We find that activity in human motor cortex has similar dynamical structure to that of non-human primates, indicating that human motor cortex contains a similar underlying dynamical system for movement generation.

  20. Are great apes able to reason from multi-item samples to populations of food items?

    PubMed

    Eckert, Johanna; Rakoczy, Hannes; Call, Josep

    2017-10-01

    Inductive learning from limited observations is a cognitive capacity of fundamental importance. In humans, it is underwritten by our intuitive statistics, the ability to draw systematic inferences from populations to randomly drawn samples and vice versa. According to recent research in cognitive development, human intuitive statistics develops early in infancy. Recent work in comparative psychology has produced first evidence for analogous cognitive capacities in great apes who flexibly drew inferences from populations to samples. In the present study, we investigated whether great apes (Pongo abelii, Pan troglodytes, Pan paniscus, Gorilla gorilla) also draw inductive inferences in the opposite direction, from samples to populations. In two experiments, apes saw an experimenter randomly drawing one multi-item sample from each of two populations of food items. The populations differed in their proportion of preferred to neutral items (24:6 vs. 6:24) but apes saw only the distribution of food items in the samples that reflected the distribution of the respective populations (e.g., 4:1 vs. 1:4). Based on this observation they were then allowed to choose between the two populations. Results show that apes seemed to make inferences from samples to populations and thus chose the population from which the more favorable (4:1) sample was drawn in Experiment 1. In this experiment, the more attractive sample not only contained proportionally but also absolutely more preferred food items than the less attractive sample. Experiment 2, however, revealed that when absolute and relative frequencies were disentangled, apes performed at chance level. Whether these limitations in apes' performance reflect true limits of cognitive competence or merely performance limitations due to accessory task demands is still an open question. © 2017 Wiley Periodicals, Inc.

  1. Neural network based multi-criterion optimization image reconstruction technique for imaging two- and three-phase flow systems using electrical capacitance tomography

    NASA Astrophysics Data System (ADS)

    Warsito, W.; Fan, L.-S.

    2001-12-01

    In this work a new image reconstruction technique for imaging two- and three-phase flows using electrical capacitance tomography (ECT) has been developed. A brief review on reconstruction techniques developed recently for ECT is also given. The reconstruction technique proposed here is based on multi-criterion optimization using an analogue neural network, hereafter referred to as neural network multi-criteria optimization image reconstruction (NN-MOIRT). The reconstruction technique is a combination between a multi-criterion optimization image reconstruction technique for linear tomography, and the so-called linear back projection (LBP) technique commonly used for capacitance tomography. The multi-criterion optimization image reconstruction problem is solved using Hopfield model dynamic neural network computing. For three-component imaging, the single-step sigmoid function in the Hopfield networks is replaced by a double-step sigmoid function, allowing the neural computation to converge to three distinct stable regions in the output space corresponding to the three components, enabling differentiation among the single phases. The technique has been tested on a capacitance data set obtained from simulated measurement as well as experiment using a 12-electrode sensor. The performance of the technique has been compared with other commonly used iterative techniques, i.e. iterative linear back projection technique (ILBP) and the simultaneous image reconstruction technique (SIRT) for two-phase system imaging, and has shown great improvements in the accuracy and the consistency as compared with those techniques. The technique has also shown the capability of three-phase image reconstruction with high accuracy.

  2. Transplantation of Defined Populations of Differentiated Human Neural Stem Cell Progeny

    PubMed Central

    Fortin, Jeff M.; Azari, Hassan; Zheng, Tong; Darioosh, Roya P.; Schmoll, Michael E.; Vedam-Mai, Vinata; Deleyrolle, Loic P.; Reynolds, Brent A.

    2016-01-01

    Many neurological injuries are likely too extensive for the limited repair capacity of endogenous neural stem cells (NSCs). An alternative is to isolate NSCs from a donor, and expand them in vitro as transplantation material. Numerous groups have already transplanted neural stem and precursor cells. A caveat to this approach is the undefined phenotypic distribution of the donor cells, which has three principle drawbacks: (1) Stem-like cells retain the capacity to proliferate in vivo. (2) There is little control over the cells’ terminal differentiation, e.g., a graft intended to replace neurons might choose a predominantly glial fate. (3) There is limited ability of researchers to alter the combination of cell types in pursuit of a precise treatment. We demonstrate a procedure for differentiating human neural precursor cells (hNPCs) in vitro, followed by isolation of the neuronal progeny. We transplanted undifferentiated hNPCs or a defined concentration of hNPC-derived neurons into mice, then compared these two groups with regard to their survival, proliferation and phenotypic fate. We present evidence suggesting that in vitro-differentiated-and-purified neurons survive as well in vivo as their undifferentiated progenitors, and undergo less proliferation and less astrocytic differentiation. We also describe techniques for optimizing low-temperature cell preservation and portability. PMID:27030542

  3. Neural substrates of child irritability in typically-developing and psychiatric populations

    PubMed Central

    Perlman, Susan B.; Jones, Brianna M.; Wakschlag, Lauren S.; Axelson, David; Birmaher, Boris; Phillips, Mary L.

    2015-01-01

    Irritability is an aspect of the negative affectivity domain of temperament, but in severe and dysregulated forms is a symptom of a range of psychopathologies. Better understanding of the neural underpinnings of irritability, outside the context of specific disorders, can help to understand normative variation but also characterize its clinical salience in psychopathology diagnosis. This study assessed brain activation during reward and frustration, domains of behavioral deficits in childhood irritability. Children (age 6–9) presenting in mental health clinics for extreme and impairing irritability (n=26) were compared to healthy children (n=28). Using developmentally-sensitive methods, neural activation was measured via a negative mood induction paradigm during fMRI scanning. The clinical group displayed more activation of the anterior cingulate and middle frontal gyrus during reward, but less activation during frustration, than healthy comparison children. The opposite pattern was found in the posterior cingulate. Further, in clinical subjects, parent report of irritability was dimensionally related to decreased activation of the anterior cingulate and striatum during frustration. The results of this study indicate neural dysfunction within brain regions related to reward processing, error monitoring, and emotion regulation underlying clinically impairing irritability. Results are discussed in the context of a growing field of neuroimaging research investigating irritable children. PMID:26218424

  4. A Multi-channel Semicircular Canal Neural Prosthesis Using Electrical Stimulation to Restore 3D Vestibular Sensation

    PubMed Central

    Della Santina, Charles C.; Migliaccio, Americo A.; Patel, Amit H.

    2009-01-01

    Bilateral loss of vestibular sensation can be disabling. Those afflicted suffer illusory visual field movement during head movements, chronic disequilibrium and postural instability due to failure of vestibulo-ocular and vestibulo-spinal reflexes. A neural prosthesis that emulates the normal transduction of head rotation by semicircular canals could significantly improve quality of life for these patients. Like the 3 semicircular canals in a normal ear, such a device should at least transduce 3 orthogonal (or linearly separable) components of head rotation into activity on corresponding ampullary branches of the vestibular nerve. We describe the design, circuit performance and in vivo application of a head-mounted, semi-implantable multi-channel vestibular prosthesis that encodes head movement in 3 dimensions as pulse-frequency-modulated electrical stimulation of 3 or more ampullary nerves. In chinchillas treated with intratympanic gentamicin to ablate vestibular sensation bilaterally, prosthetic stimuli elicited a partly compensatory angular vestibulo-ocular reflex in multiple planes. Minimizing misalignment between the axis of eye and head rotation, apparently caused by current spread beyond each electrode’s targeted nerve branch, emerged as a key challenge. Increasing stimulation selectivity via improvements in electrode design, surgical technique and stimulus protocol will likely be required to restore AVOR function over the full range of normal behavior. PMID:17554821

  5. Evaluation of performance of multi-sensors hot-wire probes using Neural-Networks in-situ calibration

    NASA Astrophysics Data System (ADS)

    Liberzon, Dan; Kit, Eliezer

    2015-11-01

    Neural Networks (NN) based in-situ calibration of hot-wire anemometers was recently successfully implemented in field measurements. Although proving feasibility of field measurements using this, relatively new, calibration method the acquired field data also revealed some significant ambiguities in use of combined two- or three-sensor probes. A clearly better behavior of the probe comprised of four sensors (a pair of X shaped probes) has motivated the presented here work, aimed to investigate the NN based procedure performance dependence on the number of wires in the probe. Hypothesizing that the main reason for performance differences is in the fact that a 3-wire probe lacks any special features to withstand the noise in the signal due to temperature fluctuations and sensors' contamination, series of wind tunnel experiments with grid generated turbulence were designed and performed. Performance of a various multi-sensor probes' geometries was examined using the NN based method, while standard calibration data sets were also obtained prior to each set of measurements serving as a reference and as alternative training sets for the NN. The obtained results clearly indicated an advantage in using a symmetrical geometry, and especially using the four-sensor probe to obtain a reasonable description of the 3D velocity field. This is argued to be a result of redundant information on one or several velocity components present in four-sensor probes and serving as an efficient tool for noise reduction.

  6. Impact of migration on the multi-strategy selection in finite group-structured populations

    PubMed Central

    Zhang, Yanling; Liu, Aizhi; Sun, Changyin

    2016-01-01

    For large quantities of spatial models, the multi-strategy selection under weak selection is the sum of two competition terms: the pairwise competition and the competition of multiple strategies with equal frequency. Two parameters σ1 and σ2 quantify the dependence of the multi-strategy selection on these two terms, respectively. Unlike previous studies, we here do not require large populations for calculating σ1 and σ2, and perform the first quantitative analysis of the effect of migration on them in group-structured populations of any finite sizes. The Moran and the Wright-Fisher process have the following common findings. Compared with well-mixed populations, migration causes σ1 to change with the mutation probability from a decreasing curve to an inverted U-shaped curve and maintains the increase of σ2. Migration (probability and range) leads to a significant change of σ1 but a negligible one of σ2. The way that migration changes σ1 is qualitatively similar to its influence on the single parameter characterizing the two-strategy selection. The Moran process is more effective in increasing σ1 for most migration probabilities and the Wright-Fisher process is always more effective in increasing σ2. Finally, our findings are used to study the evolution of cooperation under direct reciprocity. PMID:27767074

  7. Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks.

    PubMed

    Walsh, Ian; Baù, Davide; Martin, Alberto J M; Mooney, Catherine; Vullo, Alessandro; Pollastri, Gianluca

    2009-01-30

    Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3-4 A from the native structure). Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure. We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that C alpha trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10%) yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 A threshold (as per CASP assessment) from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8 A predictions on the CASP7 targets using a pre-CASP7 PDB

  8. Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

    PubMed Central

    Walsh, Ian; Baù, Davide; Martin, Alberto JM; Mooney, Catherine; Vullo, Alessandro; Pollastri, Gianluca

    2009-01-01

    Background Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure). Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure. Results We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that Cα trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10%) yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment) from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8 Å predictions on the CASP7 targets

  9. Spatially-resolved stellar populations of nearby galaxies in multi-filter surveys

    NASA Astrophysics Data System (ADS)

    San Roman, Izaskun; Cenarro, A. Javier; Díaz-García, Luis A.; López-Sanjuan, Carlos; Varela, Jesús; J-PLUS Team

    2017-03-01

    We have developed a new technique using a novel approach to analyze unresolved stellar populations of spatially-resolved galaxies based on large sky multi-filter surveys. We have successfully applied this technique to 42 early-type galaxies in the ALHAMBRA survey. In agreement with some previous work, we find the gradients of early-type galaxies to be on average slightly positive in age and negative in metallicity at large radii (R > Reff). These mildly negative metallicity gradients support a merging scenario. The positive/flat age gradients could support a more uniformly distributed star formation or even secondary burst triggered by mergers.

  10. The application of the multi-alternative approach in active neural network models

    NASA Astrophysics Data System (ADS)

    Podvalny, S.; Vasiljev, E.

    2017-02-01

    The article refers to the construction of intelligent systems based artificial neuron networks are used. We discuss the basic properties of the non-compliance of artificial neuron networks and their biological prototypes. It is shown here that the main reason for these discrepancies is the structural immutability of the neuron network models in the learning process, that is, their passivity. Based on the modern understanding of the biological nervous system as a structured ensemble of nerve cells, it is proposed to abandon the attempts to simulate its work at the level of the elementary neurons functioning processes and proceed to the reproduction of the information structure of data storage and processing on the basis of the general enough evolutionary principles of multialternativity, i.e. the multi-level structural model, diversity and modularity. The implementation method of these principles is offered, using the faceted memory organization in the neuron network with the rearranging active structure. An example of the implementation of the active facet-type neuron network in the intellectual decision-making system in the conditions of critical events development in the electrical distribution system.

  11. Multi Population Genetic Algorithm to estimate snow properties from GPR data

    NASA Astrophysics Data System (ADS)

    Godio, A.

    2016-08-01

    Multi-population genetic algorithms (DGA or MGA) are based on the partition of the population into several semi-isolated subpopulations (demes). Each sub-population is associated to an independent GA and explores different promising regions of the search space. We evaluate the sensitivity of some parameters to solve a non-linear problem in georadar data analysis. Particularly, we adapt the DGAs to optimize the model parameters of a data set of variable-offset data, collected in variable offset modality with Ground Penetrating Radar, to estimate porosity, saturation and density of snowpack in a glacial environment. The data set comes from investigation on glaciers to estimate the thickness and density of the seasonal snow. The main strategies to select the best parameters of the optimization process are outlined. We analyze the sensitivity on the solution of the optimization problems of some parameters of DGA; we deal with the effects of population and sub-population, and mutation properties. We consider the reflection traveltimes in a layered medium including a relationship between the traveltimes, porosity and saturation of the snow. We solve the problem for the layer thickness and the porosity, saturation and structural exponent of the snow. Reliable results are obtained in the snow density estimating, while the evaluation of free water content into the snow still remains challenging.

  12. A mathematical framework for the registration and analysis of multi-fascicle models for population studies of the brain microstructure.

    PubMed

    Taquet, Maxime; Scherrer, Benoit; Commowick, Olivier; Peters, Jurriaan M; Sahin, Mustafa; Macq, Benoit; Warfield, Simon K

    2014-02-01

    Diffusion tensor imaging (DTI) is unable to represent the diffusion signal arising from multiple crossing fascicles and freely diffusing water molecules. Generative models of the diffusion signal, such as multi-fascicle models, overcome this limitation by providing a parametric representation for the signal contribution of each population of water molecules. These models are of great interest in population studies to characterize and compare the brain microstructural properties. Central to population studies is the construction of an atlas and the registration of all subjects to it. However, the appropriate definition of registration and atlasing methods for multi-fascicle models have proven challenging. This paper proposes a mathematical framework to register and analyze multi-fascicle models. Specifically, we define novel operators to achieve interpolation, smoothing and averaging of multi-fascicle models. We also define a novel similarity metric to spatially align multi-fascicle models. Our framework enables simultaneous comparisons of different microstructural properties that are confounded in conventional DTI. The framework is validated on multi-fascicle models from 24 healthy subjects and 38 patients with tuberous sclerosis complex, 10 of whom have autism. We demonstrate the use of the multi-fascicle models registration and analysis framework in a population study of autism spectrum disorder.

  13. Sub-Volumetric Classification and Visualization of Emphysema Using a Multi-Threshold Method and Neural Network

    NASA Astrophysics Data System (ADS)

    Tan, Kok Liang; Tanaka, Toshiyuki; Nakamura, Hidetoshi; Shirahata, Toru; Sugiura, Hiroaki

    Chronic Obstructive Pulmonary Disease is a disease in which the airways and tiny air sacs (alveoli) inside the lung are partially obstructed or destroyed. Emphysema is what occurs as more and more of the walls between air sacs get destroyed. The goal of this paper is to produce a more practical emphysema-quantification algorithm that has higher correlation with the parameters of pulmonary function tests compared to classical methods. The use of the threshold range from approximately -900 Hounsfield Unit to -990 Hounsfield Unit for extracting emphysema from CT has been reported in many papers. From our experiments, we realize that a threshold which is optimal for a particular CT data set might not be optimal for other CT data sets due to the subtle radiographic variations in the CT images. Consequently, we propose a multi-threshold method that utilizes ten thresholds between and including -900 Hounsfield Unit and -990 Hounsfield Unit for identifying the different potential emphysematous regions in the lung. Subsequently, we divide the lung into eight sub-volumes. From each sub-volume, we calculate the ratio of the voxels with the intensity below a certain threshold. The respective ratios of the voxels below the ten thresholds are employed as the features for classifying the sub-volumes into four emphysema severity classes. Neural network is used as the classifier. The neural network is trained using 80 training sub-volumes. The performance of the classifier is assessed by classifying 248 test sub-volumes of the lung obtained from 31 subjects. Actual diagnoses of the sub-volumes are hand-annotated and consensus-classified by radiologists. The four-class classification accuracy of the proposed method is 89.82%. The sub-volumetric classification results produced in this study encompass not only the information of emphysema severity but also the distribution of emphysema severity from the top to the bottom of the lung. We hypothesize that besides emphysema severity, the

  14. Dynamic population artificial bee colony algorithm for multi-objective optimal power flow.

    PubMed

    Ding, Man; Chen, Hanning; Lin, Na; Jing, Shikai; Liu, Fang; Liang, Xiaodan; Liu, Wei

    2017-03-01

    This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.

  15. Disparities in Early Transitions to Obesity in Contemporary Multi-Ethnic U.S. Populations

    PubMed Central

    Avery, Christy L.; Holliday, Katelyn M.; Chakladar, Sujatro; Engeda, Joseph C.; Hardy, Shakia T.; Reis, Jared P.; Schreiner, Pamela J.; Shay, Christina M.; Daviglus, Martha L.; Heiss, Gerardo; Lin, Dan Yu; Zeng, Donglin

    2016-01-01

    Background Few studies have examined weight transitions in contemporary multi-ethnic populations spanning early childhood through adulthood despite the ability of such research to inform obesity prevention, control, and disparities reduction. Methods and Results We characterized the ages at which African American, Caucasian, and Mexican American populations transitioned to overweight and obesity using contemporary and nationally representative cross-sectional National Health and Nutrition Examination Survey data (n = 21,220; aged 2–80 years). Age-, sex-, and race/ethnic-specific one-year net transition probabilities between body mass index-classified normal weight, overweight, and obesity were estimated using calibrated and validated Markov-type models that accommodated complex sampling. At age two, the obesity prevalence ranged from 7.3% in Caucasian males to 16.1% in Mexican American males. For all populations, estimated one-year overweight to obesity net transition probabilities peaked at age two and were highest for Mexican American males and African American females, for whom a net 12.3% (95% CI: 7.6%-17.0%) and 11.9% (95% CI: 8.5%-15.3%) of the overweight populations transitioned to obesity by age three, respectively. However, extrapolation to the 2010 U.S. population demonstrated that Mexican American males were the only population for whom net increases in obesity peaked during early childhood; age-specific net increases in obesity were approximately constant through the second decade of life for African Americans and Mexican American females and peaked at age 20 for Caucasians. Conclusions African American and Mexican American populations shoulder elevated rates of many obesity-associated chronic diseases and disparities in early transitions to obesity could further increase these inequalities if left unaddressed. PMID:27348868

  16. Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India.

    PubMed

    Sau, Arkaprabha; Bhakta, Ishita

    2017-05-01

    Depression is one of the most important causes of mortality and morbidity among the geriatric population. Although, the aging brain is more vulnerable to depression, it cannot be considered as physiological and an inevitable part of ageing. Various sociodemographic and morbidity factors are responsible for the depression among them. Using Artificial Neural Network (ANN) model depression can be predicted from various sociodemographic variables and co morbid conditions even at community level by the grass root level health care workers. To predict depression among geriatric population from sociodemographic and morbidity attributes using ANN. An observational descriptive study with cross-sectional design was carried out at a slum under the service area of Bagbazar Urban Health and Training Centre (UHTC) in Kolkata. Among 126 elderlies under Bagbazar UHTC, 105 were interviewed using predesigned and pretested schedule. Depression status was assessed using 30 item Geriatric Depression Scale. WEKA 3.8.0 was used to develop the ANN model and test its performance. Prevalence of depression among the study population was 45.7%. Various sociodemographic variables like age, gender, literacy, living spouse, working status, personal income, family type, substance abuse and co morbid conditions like visual problem, mobility problem, hearing problem and sleeping problem were taken into consideration to develop the model. Prediction accuracy of this ANN model was 97.2%. Depression among geriatric population can be predicted accurately using ANN model from sociodemographic and morbidity attributes.

  17. Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India

    PubMed Central

    Bhakta, Ishita

    2017-01-01

    Introduction Depression is one of the most important causes of mortality and morbidity among the geriatric population. Although, the aging brain is more vulnerable to depression, it cannot be considered as physiological and an inevitable part of ageing. Various sociodemographic and morbidity factors are responsible for the depression among them. Using Artificial Neural Network (ANN) model depression can be predicted from various sociodemographic variables and co morbid conditions even at community level by the grass root level health care workers. Aim To predict depression among geriatric population from sociodemographic and morbidity attributes using ANN. Materials and Methods An observational descriptive study with cross-sectional design was carried out at a slum under the service area of Bagbazar Urban Health and Training Centre (UHTC) in Kolkata. Among 126 elderlies under Bagbazar UHTC, 105 were interviewed using predesigned and pretested schedule. Depression status was assessed using 30 item Geriatric Depression Scale. WEKA 3.8.0 was used to develop the ANN model and test its performance. Results Prevalence of depression among the study population was 45.7%. Various sociodemographic variables like age, gender, literacy, living spouse, working status, personal income, family type, substance abuse and co morbid conditions like visual problem, mobility problem, hearing problem and sleeping problem were taken into consideration to develop the model. Prediction accuracy of this ANN model was 97.2%. Conclusion Depression among geriatric population can be predicted accurately using ANN model from sociodemographic and morbidity attributes. PMID:28658883

  18. Development of a Multi-Functional Biopolymer Scaffold for Neural Tissue Engineering

    NASA Astrophysics Data System (ADS)

    Francis, Nicola Louise

    . The present data suggest these multi-functional scaffolds are suitable for use and future testing in vivo as a combination strategy for spinal cord repair due to their ability to promote the directionally oriented growth of neurites and their ability to provide the sustained release of therapeutic bioactive molecules for the stimulation of axonal growth through the glial scar.

  19. Development of a multi-classification neural network model to determine the microbial growth/no growth interface.

    PubMed

    Fernández-Navarro, Francisco; Valero, Antonio; Hervás-Martínez, César; Gutiérrez, Pedro A; García-Gimeno, Rosa M; Zurera-Cosano, Gonzalo

    2010-07-15

    Boundary models have been recognized as useful tools to predict the ability of microorganisms to grow at limiting conditions. However, at these conditions, microbial behaviour can vary, being difficult to distinguish between growth or no growth. In this paper, the data from the study of Valero et al. [Valero, A., Pérez-Rodríguez, F., Carrasco, E., Fuentes-Alventosa, J.M., García-Gimeno, R.M., Zurera, G., 2009. Modelling the growth boundaries of Staphylococcus aureus: Effect of temperature, pH and water activity. International Journal of Food Microbiology 133 (1-2), 186-194] belonging to growth/no growth conditions of Staphylococcus aureus against temperature, pH and a(w) were divided into three categorical classes: growth (G), growth transition (GT) and no growth (NG). Subsequently, they were modelled by using a Radial Basis Function Neural Network (RBFNN) in order to create a multi-classification model that was able to predict the probability of belonging at one of the three mentioned classes. The model was developed through an over sampling procedure using a memetic algorithm (MA) in order to balance in part the size of the classes and to improve the accuracy of the classifier. The multi-classification model, named Smote Memetic Radial Basis Function (SMRBF) provided a quite good adjustment to data observed, being able to correctly classify the 86.30% of training data and the 82.26% of generalization data for the three observed classes in the best model. Besides, the high number of replicates per condition tested (n=30) produced a smooth transition between growth and no growth. At the most stringent conditions, the probability of belonging to class GT was higher, thus justifying the inclusion of the class in the new model. The SMRBF model presented in this study can be used to better define microbial growth/no growth interface and the variability associated to these conditions so as to apply this knowledge to a food safety in a decision-making process. 2010

  20. First Steps in Using Multi-Voxel Pattern Analysis to Disentangle Neural Processes Underlying Generalization of Spider Fear

    PubMed Central

    Visser, Renée M.; Haver, Pia; Zwitser, Robert J.; Scholte, H. Steven; Kindt, Merel

    2016-01-01

    A core symptom of anxiety disorders is the tendency to interpret ambiguous information as threatening. Using electroencephalography and blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), several studies have begun to elucidate brain processes involved in fear-related perceptual biases, but thus far mainly found evidence for general hypervigilance in high fearful individuals. Recently, multi-voxel pattern analysis (MVPA) has become popular for decoding cognitive states from distributed patterns of neural activation. Here, we used this technique to assess whether biased fear generalization, characteristic of clinical fear, is already present during the initial perception and categorization of a stimulus, or emerges during the subsequent interpretation of a stimulus. Individuals with low spider fear (n = 20) and high spider fear (n = 18) underwent functional MRI scanning while viewing series of schematic flowers morphing to spiders. In line with previous studies, individuals with high fear of spiders were behaviorally more likely to classify ambiguous morphs as spiders than individuals with low fear of spiders. Univariate analyses of BOLD-MRI data revealed stronger activation toward spider pictures in high fearful individuals compared to low fearful individuals in numerous areas. Yet, neither average activation, nor support vector machine classification (i.e., a form of MVPA) matched the behavioral results – i.e., a biased response toward ambiguous stimuli – in any of the regions of interest. This may point to limitations of the current design, and to challenges associated with classifying emotional and neutral stimuli in groups that differ in their judgment of emotionality. Improvements for future research are suggested. PMID:27303278

  1. Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions.

    PubMed

    Makris, Georgios-Marios; Pouliakis, Abraham; Siristatidis, Charalampos; Margari, Niki; Terzakis, Emmanouil; Koureas, Nikolaos; Pergialiotis, Vasilios; Papantoniou, Nikolaos; Karakitsos, Petros

    2017-03-01

    This study aims to investigate the efficacy of an Artificial Neural Network based on Multi-Layer Perceptron (ANN-MPL) to discriminate between benign and malignant endometrial nuclei and lesions in cytological specimens. We collected 416 histologically confirmed liquid-based cytological smears from 168 healthy patients, 152 patients with malignancy, 52 with hyperplasia without atypia, 20 with hyperplasia with atypia, and 24 patients with endometrial polyps. The morphometric characteristics of 90 nuclei per case were analyzed using a custom image analysis system; half of them were used to train the MPL-ANN model, which classified each nucleus as benign or malignant. Data from the remaining 50% of cases were used to evaluate the performance and stability of the ANN. The MLP-ANN for the nuclei classification (numeric and percentage classifiers) and the algorithms for the determination of the optimum threshold values were estimated with in-house developed software for the MATLAB v2011b programming environment; the diagnostic accuracy measures were also calculated. The accuracy of the MPL-ANN model for the classification of endometrial nuclei was 81.33%, while specificity was 88.84% and sensitivity 69.38%. For the case classification based on numeric classifier the overall accuracy was 90.87%, the specificity 93.03% and the sensitivity 87.79%; the indices for the percentage classifier were 95.91%, 93.44%, and 99.42%, respectively. Computerized systems based on ANNs can aid the cytological classification of endometrial nuclei and lesions with sufficient sensitivity and specificity. Diagn. Cytopathol. 2017;45:202-211. © 2016 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  2. A first step toward genomic selection in the multi-breed French dairy goat population.

    PubMed

    Carillier, C; Larroque, H; Palhière, I; Clément, V; Rupp, R; Robert-Granié, C

    2013-01-01

    The objectives of this study were to describe, using the goat SNP50 BeadChip (Illumina Inc., San Diego, CA), molecular data for the French dairy goat population and compare the effect of using genomic information on breeding value accuracy in different reference populations. Several multi-breed (Alpine and Saanen) reference population sizes, including or excluding female genotypes (from 67 males to 677 males, and 1,985 females), were used. Genomic evaluations were performed using genomic best linear unbiased predictor for milk production traits, somatic cell score, and some udder type traits. At a marker distance of 50kb, the average r(2) (squared correlation coefficient) value of linkage disequilibrium was 0.14, and persistence of linkage disequilibrium as correlation of r-values among Saanen and Alpine breeds was 0.56. Genomic evaluation accuracies obtained from cross validation ranged from 36 to 53%. Biases of these estimations assessed by regression coefficients (from 0.73 to 0.98) of phenotypes on genomic breeding values were higher for traits such as protein yield than for udder type traits. Using the reference population that included all males and females, accuracies of genomic breeding values derived from prediction error variances (model accuracy) obtained for young buck candidates without phenotypes ranged from 52 to 56%. This was lower than the average pedigree-derived breeding value accuracies obtained at birth for these males from the official genetic evaluation (62%). Adding females to the reference population of 677 males improved accuracy by 5 to 9% depending on the trait considered. Gains in model accuracies of genomic breeding values ranged from 1 to 7%, lower than reported in other studies. The gains in breeding value accuracy obtained using genomic information were not as good as expected because of the limited size (at most 677 males and 1,985 females) and the structure of the reference population. Copyright © 2013 American Dairy Science

  3. Multi-Population Invariance with Dichotomous Measures: Combining Multi-Group and MIMIC Methodologies in Evaluating the General Aptitude Test in the Arabic Language

    ERIC Educational Resources Information Center

    Sideridis, Georgios D.; Tsaousis, Ioannis; Al-harbi, Khaleel A.

    2015-01-01

    The purpose of the present study was to extend the model of measurement invariance by simultaneously estimating invariance across multiple populations in the dichotomous instrument case using multi-group confirmatory factor analytic and multiple indicator multiple causes (MIMIC) methodologies. Using the Arabic version of the General Aptitude Test…

  4. Effects of Spearfishing on Reef Fish Populations in a Multi-Use Conservation Area

    PubMed Central

    Frisch, Ashley J.; Cole, Andrew J.; Hobbs, Jean-Paul A.; Rizzari, Justin R.; Munkres, Katherine P.

    2012-01-01

    Although spearfishing is a popular method of capturing fish, its ecological effects on fish populations are poorly understood, which makes it difficult to assess the legitimacy and desirability of spearfishing in multi-use marine reserves. Recent management changes within the Great Barrier Reef Marine Park (GBRMP) fortuitously created a unique scenario by which to quantify the effects of spearfishing on fish populations. As such, we employed underwater visual surveys and a before-after-control-impact experimental design to investigate the effects of spearfishing on the density and size structure of target and non-target fishes in a multi-use conservation park zone (CPZ) within the GBRMP. Three years after spearfishing was first allowed in the CPZ, there was a 54% reduction in density and a 27% reduction in mean size of coral trout (Plectropomus spp.), the primary target species. These changes were attributed to spearfishing because benthic habitat characteristics and the density of non-target fishes were stable through time, and the density and mean size of coral trout in a nearby control zone (where spearfishing was prohibited) remained unchanged. We conclude that spearfishing, like other forms of fishing, can have rapid and substantial negative effects on target fish populations. Careful management of spearfishing is therefore needed to ensure that conservation obligations are achieved and that fishery resources are harvested sustainably. This is particularly important both for the GBRMP, due to its extraordinarily high conservation value and world heritage status, and for tropical island nations where people depend on spearfishing for food and income. To minimize the effects of spearfishing on target species and to enhance protection of functionally important fishes (herbivores), we recommend that fishery managers adjust output controls such as size- and catch-limits, rather than prohibit spearfishing altogether. This will preserve the cultural and social

  5. A Lasso multi-marker mixed model for association mapping with population structure correction.

    PubMed

    Rakitsch, Barbara; Lippert, Christoph; Stegle, Oliver; Borgwardt, Karsten

    2013-01-15

    Exploring the genetic basis of heritable traits remains one of the central challenges in biomedical research. In traits with simple Mendelian architectures, single polymorphic loci explain a significant fraction of the phenotypic variability. However, many traits of interest seem to be subject to multifactorial control by groups of genetic loci. Accurate detection of such multivariate associations is non-trivial and often compromised by limited statistical power. At the same time, confounding influences, such as population structure, cause spurious association signals that result in false-positive findings. We propose linear mixed models LMM-Lasso, a mixed model that allows for both multi-locus mapping and correction for confounding effects. Our approach is simple and free of tuning parameters; it effectively controls for population structure and scales to genome-wide datasets. LMM-Lasso simultaneously discovers likely causal variants and allows for multi-marker-based phenotype prediction from genotype. We demonstrate the practical use of LMM-Lasso in genome-wide association studies in Arabidopsis thaliana and linkage mapping in mouse, where our method achieves significantly more accurate phenotype prediction for 91% of the considered phenotypes. At the same time, our model dissects the phenotypic variability into components that result from individual single nucleotide polymorphism effects and population structure. Enrichment of known candidate genes suggests that the individual associations retrieved by LMM-Lasso are likely to be genuine. Code available under http://webdav.tuebingen. mpg.de/u/karsten/Forschung/research.html. rakitsch@tuebingen.mpg.de, ippert@microsoft.com or stegle@ebi.ac.uk Supplementary data are available at Bioinformatics online.

  6. Neural Correlates and Mechanisms of Spatial Release From Masking: Single-Unit and Population Responses in the Inferior Colliculus

    PubMed Central

    Lane, Courtney C.; Delgutte, Bertrand

    2007-01-01

    Spatial release from masking (SRM), a factor in listening in noisy environments, is the improvement in auditory signal detection obtained when a signal is separated in space from a masker. To study the neural mechanisms of SRM, we recorded from single units in the inferior colliculus (IC) of barbiturate-anesthetized cats, focusing on low-frequency neurons sensitive to interaural time differences. The stimulus was a broadband chirp train with a 40-Hz repetition rate in continuous broadband noise, and the unit responses were measured for several signal and masker (virtual) locations. Masked thresholds (the lowest signal-to-noise ratio, SNR, for which the signal could be detected for 75% of the stimulus presentations) changed systematically with signal and masker location. Single-unit thresholds did not necessarily improve with signal and masker separation; instead, they tended to reflect the units’ azimuth preference. Both how the signal was detected (through a rate increase or decrease) and how the noise masked the signal response (suppressive or excitatory masking) changed with signal and masker azimuth, consistent with a cross-correlator model of binaural processing. However, additional processing, perhaps related to the signal’s amplitude modulation rate, appeared to influence the units’ responses. The population masked thresholds (the most sensitive unit’s threshold at each signal and masker location) did improve with signal and masker separation as a result of the variety of azimuth preferences in our unit sample. The population thresholds were similar to human behavioral thresholds in both SNR value and shape, indicating that these units may provide a neural substrate for low-frequency SRM. PMID:15857966

  7. Modeling Fall Run Chinook Salmon Populations in the San Joaquin River Basin Using an Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Keyantash, J.; Quinn, N. W.; Hidalgo, H. G.; Dracup, J. A.

    2002-12-01

    The number of chinook salmon returning to spawn during the fall run (September-November) were separately modeled for three San Joaquin River tributaries-the Stanislaus, Tuolumne, and Merced Rivers-to determine the sensitivity of salmon populations to hydrologic alterations associated with potential climate change. The modeling was accomplished using a feed-forward artificial neural network (ANN) with error backpropagation. Inputs to the ANN included modeled monthly river temperature and streamflow data for each tributary, and were lagged multiple years to include the effects of antecedent environmental conditions upon populations of salmon throughout their life histories. Temperature and streamflow conditions at downstream locations in each tributary were computed using the California Dept. of Water Resources' DSM-2 model. Inputs to the DSM-2 model originated from regional climate modeling under a CO2 doubling scenario. Annual population data for adult chinook salmon (1951-present) were provided by the California Dept. of Fish and Game, and were used for supervised training of the ANN. It was determined that Stanislaus, Tuolumne and Merced River chinook runs could be impacted by alterations to the hydroclimatology of the San Joaquin basin.

  8. Diabetic retinopathy equity profile in a multi-ethnic, deprived population in Northern England

    PubMed Central

    Kliner, M; Fell, G; Gibbons, C; Dhothar, M; Mookhtiar, M; Cassels-Brown, A

    2012-01-01

    Purpose Equity profiles are an established public health tool used to systematically identify and address inequity within health and health services. Our aim was to conduct an equity profile to identify inequity in eye health across Leeds and Bradford. This paper presents results of findings for diabetic retinopathy in Bradford and Airedale. Methods A variety of routine health data were included and sub-analysed by measures of equity, including age, sex, ethnicity, and deprivation to identify inequity in eye health and healthcare. The Spearman Rank Correlation Coefficient was used to determine the association between variables. Results The prevalence of diagnosed diabetes in Bradford and Airedale district is 6.6% compared to 4.3% in nearby Leeds and 5.1% nationally. The age-standardised prevalence of diagnosed diabetic retinopathy within Bradford and Airedale is 2.21% (95% CI 1.54–2.26%), with a disproportionately high prevalence of disease in the Pakistani population and the most deprived parts of the population. There was a poorer uptake of diabetic retinopathy screening in more deprived parts of the district and the proportions with a higher rate of referral to ophthalmology following the screening in Black and Minority Ethnic populations compared with the white population (13.2% vs6.9%). Uptake of secondary care outpatient appointments is much lower in more deprived populations. Conclusion Inequalities are inherent in diabetic retinopathy prevalence, diagnosis, and treatment. The reasons for these inequities are multi-factorial and further investigation of reasons for poor uptake of services is required. Addressing the inequalities in eye health and healthcare requires cross-organisational collaboration. PMID:22302063

  9. Bacterial recombination promotes the evolution of multi-drug-resistance in functionally diverse populations

    PubMed Central

    Perron, Gabriel G.; Lee, Alexander E. G.; Wang, Yun; Huang, Wei E.; Barraclough, Timothy G.

    2012-01-01

    Bacterial recombination is believed to be a major factor explaining the prevalence of multi-drug-resistance (MDR) among pathogenic bacteria. Despite extensive evidence for exchange of resistance genes from retrospective sequence analyses, experimental evidence for the evolutionary benefits of bacterial recombination is scarce. We compared the evolution of MDR between populations of Acinetobacter baylyi in which we manipulated both the recombination rate and the initial diversity of strains with resistance to single drugs. In populations lacking recombination, the initial presence of multiple strains resistant to different antibiotics inhibits the evolution of MDR. However, in populations with recombination, the inhibitory effect of standing diversity is alleviated and MDR evolves rapidly. Moreover, only the presence of DNA harbouring resistance genes promotes the evolution of resistance, ruling out other proposed benefits for recombination. Together, these results provide direct evidence for the fitness benefits of bacterial recombination and show that this occurs by mitigation of functional interference between genotypes resistant to single antibiotics. Although analogous to previously described mechanisms of clonal interference among alternative beneficial mutations, our results actually highlight a different mechanism by which interactions among co-occurring strains determine the benefits of recombination for bacterial evolution. PMID:22048956

  10. The faint radio source population at 15.7 GHz - II. Multi-wavelength properties

    NASA Astrophysics Data System (ADS)

    Whittam, I. H.; Riley, J. M.; Green, D. A.; Jarvis, M. J.; Vaccari, M.

    2015-11-01

    A complete, flux density limited sample of 96 faint (>0.5 mJy) radio sources is selected from the 10C survey at 15.7 GHz in the Lockman Hole. We have matched this sample to a range of multi-wavelength catalogues, including Spitzer Extragalactic Representative Volume Survey, Spitzer Wide-area Infrared Extragalactic survey, United Kingdom Infrared Telescope Infrared Deep Sky Survey and optical data; multi-wavelength counterparts are found for 80 of the 96 sources and spectroscopic redshifts are available for 24 sources. Photometric redshifts are estimated for the sources with multi-wavelength data available; the median redshift of the sample is 0.91 with an interquartile range of 0.84. Radio-to-optical ratios show that at least 94 per cent of the sample are radio loud, indicating that the 10C sample is dominated by radio galaxies. This is in contrast to samples selected at lower frequencies, where radio-quiet AGN and star-forming galaxies are present in significant numbers at these flux density levels. All six radio-quiet sources have rising radio spectra, suggesting that they are dominated by AGN emission. These results confirm the conclusions of Paper I that the faint, flat-spectrum sources which are found to dominate the 10C sample below ˜1 mJy are the cores of radio galaxies. The properties of the 10C sample are compared to the Square Kilometre Array Design Studies Simulated Skies; a population of low-redshift star-forming galaxies predicted by the simulation is not found in the observed sample.

  11. Acoustic experience but not attention modifies neural population phase expressed in human primary auditory cortex.

    PubMed

    Gander, P E; Bosnyak, D J; Roberts, L E

    2010-10-01

    We studied the effect of auditory training on the 40-Hz auditory steady-state response (ASSR) known to localize tonotopically to the region of primary auditory cortex (A1). The stimulus procedure was designed to minimize competitive interactions among frequency representations in A1 and delivered target events at random times in a training window, to increase the likelihood that neuroplastic changes could be detected. Experiment 1 found that repeated exposure to this stimulus advanced the phase of the ASSR (shortened the time delay between the 40-Hz response and stimulus waveforms). The phase advance appeared at the outset of the second of two sessions separated by 24-72 h, did not require active training, and was not accompanied by changes in ASSR amplitude over this time interval. Experiment 2 applied training for 10 sessions to reveal further advances in ASSR phase and also an increase in ASSR amplitude, but the amplitude effect lagged that on phase and did not correlate with perceptual performance while the phase advance did. A control group trained for a single session showed a phase advance but no amplitude enhancement when tested 6 weeks later (retention). In both experiments attention to auditory signals increased ASSR amplitude but had no effect on ASSR phase. Our results reveal a persistent form of neural plasticity expressed in the phase of ASSRs generated from the region of A1, which occurs either in A1 or in subcortical nuclei projecting to this region.

  12. Application of a neural network for gentamicin concentration prediction in a general hospital population.

    PubMed

    Corrigan, B W; Mayo, P R; Jamali, F

    1997-02-01

    Neural network (NN) computation is computer modeling based in part on simulation of the structure and function of the brain. These modeling techniques have been found useful as pattern recognition tools. In the present study, data including age, sex, height, weight, serum creatinine concentration, dose, dosing interval, and time of measurement were collected from 240 patients with various diseases being treated with gentamicin in a general hospital setting. The patient records were randomly divided into two sets: a training set of 220 patients used to develop relationships between input and output variables (peak and trough plasma concentrations) and a testing set (blinded from the NN) of 20 to test the NN. The network model was the back-propagation, feed-forward model. Various networks were tested, and the most accurate networks for peak and trough (calculated as mean percent error, root mean squared error of the testing group, and tau value between observed and predicted values) were reported. The results indicate that NNs can predict gentamicin serum concentrations accurately from various input data over a range of patient ages and renal function and may offer advantages over traditional dose prediction methods for gentamicin.

  13. Population balance modelling and multi-stage optimal control of a pulsed spray fluidized bed granulation.

    PubMed

    Liu, Huolong; Li, Mingzhong

    2014-07-01

    In this work, one-dimensional population balance models (PBMs) have been developed to model a pulsed top-spray fluidized bed granulation. The developed PBMs have linked the key binder solution spray operating factors of the binder spray rate, atomizing air pressure and pulsed frequency of spray with the granule properties to predict granule growth behaviour in the pulsed spray fluidized bed granulation process at different operating conditions with accuracy. A multi-stage open optimal control strategy based on the developed PBMs was proposed to reduce the model mismatch, in which through adjusting the trajectory of the evolution of the granule size distribution at predefined sample intervals, to determine the optimal operating variables related to the binder spray including the spray rate of binding liquid, atomizing air pressure and pulsed frequency of spray. The effectiveness of the proposed modelling and multi-stage open optimal control strategies has been validated by experimental and simulation tests. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Developing nutrition education resources for a multi-ethnic population in New Zealand.

    PubMed

    Eyles, Helen; Mhurchu, Cliona Ni; Wharemate, Laurie; Funaki-Tahifote, Mafi; Lanumata, Tolotea; Rodgers, Anthony

    2009-08-01

    In New Zealand, the burden of nutrition-related disease is greatest among vulnerable and disadvantaged groups, including Maori and Pacific peoples. However, little research is currently available on effective ways to improve nutrition in these communities. This paper describes the development of six paper-based nutrition education resources for multi-ethnic participants in a large supermarket intervention trial. Six focus groups involving 15 Maori, 13 Pacific and 16 non-Maori, non-Pacific participants were held. A general inductive approach was applied to identify common themes around participants' understanding and thoughts on relevance and usefulness of the draft resources. Feedback from focus groups was used to modify resources accordingly. Five themes emerged across all focus groups and guided modification of the resources: (i) perceived higher cost of healthy food, (ii) difficulty in changing food-purchasing habits, (iii) lack of knowledge, understanding and information about healthy food, (iv) desire for personally relevant information that uses ethnically appropriate language and (v) other barriers to healthy eating, including limited availability of healthy food. Many issues affect the likelihood of purchase and consumption of healthy food. These issues should be taken into account when developing nutritional materials for New Zealanders and possibly other multi-ethnic populations worldwide.

  15. Developing nutrition education resources for a multi-ethnic population in New Zealand

    PubMed Central

    Eyles, Helen; Mhurchu, Cliona Ni; Wharemate, Laurie; Funaki-Tahifote, Mafi; Lanumata, Tolotea; Rodgers, Anthony

    2009-01-01

    In New Zealand, the burden of nutrition-related disease is greatest among vulnerable and disadvantaged groups, including Maori and Pacific peoples. However, little research is currently available on effective ways to improve nutrition in these communities. This paper describes the development of six paper-based nutrition education resources for multi-ethnic participants in a large supermarket intervention trial. Six focus groups involving 15 Maori, 13 Pacific and 16 non-Maori, non-Pacific participants were held. A general inductive approach was applied to identify common themes around participants' understanding and thoughts on relevance and usefulness of the draft resources. Feedback from focus groups was used to modify resources accordingly. Five themes emerged across all focus groups and guided modification of the resources: (i) perceived higher cost of healthy food, (ii) difficulty in changing food-purchasing habits, (iii) lack of knowledge, understanding and information about healthy food, (iv) desire for personally relevant information that uses ethnically appropriate language and (v) other barriers to healthy eating, including limited availability of healthy food. Many issues affect the likelihood of purchase and consumption of healthy food. These issues should be taken into account when developing nutritional materials for New Zealanders and possibly other multi-ethnic populations worldwide. PMID:18974069

  16. Competition between atomic coherence and electromagnetically induced population grating in multi-wave mixing

    NASA Astrophysics Data System (ADS)

    Lou, Lin; Sun, Jia; Feng, Weikang; Wu, Zhenkun; Zhang, Yiqi; Zhang, Yanpeng

    2014-12-01

    We study the competition and transfer between atomic coherence and electromagnetically induced population grating of multi-wave mixing (MWM) in four- and five-level atomic systems. The MWM signal falls into a new type electromagnetically induced transparency (EIT) window that depends on propagating directions of the related fields rather than atomic system configuration. By blocking different coupling laser beams, we experimentally distinguish different wave mixing processes. In addition, by changing the detuning of pump beams, we can observe double peaks for both EIT and MWM signals. The results may have potential applications in correlated photon-pair generations in four-wave mixing as well as six-wave mixing and quantum information processing.

  17. Multi-scaling mix and non-universality between population and facility density

    NASA Astrophysics Data System (ADS)

    Qian, Jiang-Hai; Yang, Cheng-Hao; Han, Ding-Ding; Ma, Yu-Gang

    2012-11-01

    The distribution of facilities is closely related to our social economic activities. Recent studies have reported a scaling relation between population and facility density, with the exponent depending on the type of facility. In this paper, we show that generally this exponent is not universal for a specific type of facility. Instead, by using Chinese data, we find that it increases with per capita gross domestic product (GDP). Thus our observed scaling law is actually a mixture of several multi-scaling relations. This result indicates that facilities may change their public or commercial attributes according to the outside environment. We argue that this phenomenon results from an unbalanced regional economic level, and suggest a modification of a previous model by introducing the consuming capacity. The modified model reproduces most of our observed properties.

  18. A Multi-Step Assessment Scheme for Seismic Network Site Selection in Densely Populated Areas

    NASA Astrophysics Data System (ADS)

    Plenkers, Katrin; Husen, Stephan; Kraft, Toni

    2015-10-01

    We developed a multi-step assessment scheme for improved site selection during seismic network installation in densely populated areas. Site selection is a complex process where different aspects (seismic background noise, geology, and financing) have to be taken into account. In order to improve this process, we developed a step-wise approach that allows quantifying the quality of a site by using, in addition to expert judgement and test measurements, two weighting functions as well as reference stations. Our approach ensures that the recording quality aimed for is reached and makes different sites quantitatively comparable to each other. Last but not least, it is an easy way to document the decision process, because all relevant parameters are listed, quantified, and weighted.

  19. Public perceptions and attitudes toward thalassaemia: Influencing factors in a multi-racial population.

    PubMed

    Wong, Li Ping; George, Elizabeth; Tan, Jin-Ai Mary Anne

    2011-03-30

    Thalassaemia is a common public health problem in Malaysia and about 4.5 to 6% of the Malays and Chinese are carriers of this genetic disorder. The major forms of thalassaemia result in death in utero of affected foetuses (α-thalassaemia) or life-long blood transfusions for survival in β-thalassaemia. This study, the first nationwide population based survey of thalassaemia in Malaysia, aimed to determine differences in public awareness, perceptions and attitudes toward thalassaemia in the multi-racial population in Malaysia. A cross-sectional computer-assisted telephone interview survey of a representative sample of multi-racial Malaysians aged 18 years and above was conducted between July and December 2009. Of a total of 3723 responding households, 2846 (76.4%) have heard of thalassaemia. Mean knowledge score was 11.85 (SD ± 4.03), out of a maximum of 21, with higher scores indicating better knowledge. Statistically significant differences (P < 0.05) in total knowledge score by age groups, education attainment, employment status, and average household income were observed. Although the majority expressed very positive attitudes toward screening for thalassaemia, only 13.6% of married participants interviewed have been screened for thalassaemia. The majority (63.4%) were unsupportive of selective termination of foetuses diagnosed with thalassaemia major. Study shows that carrier and premarital screening programs for thalassaemia may be more effective and culturally acceptable in the reduction of pregnancies with thalassaemia major. The findings provide insights into culturally congruent educational interventions to reach out diverse socio-demographic and ethnic communities to increase knowledge and cultivate positive attitudes toward prevention of thalassaemia.

  20. Creatine kinase as a marker of obesity in a multi-ethnic population.

    PubMed

    Haan, Yentl C; Oudman, Inge; Diemer, Frederieke S; Karamat, Fares A; van Valkengoed, Irene G; van Montfrans, Gert A; Brewster, Lizzy M

    2017-02-15

    Creatine kinase (CK), the central regulatory enzyme of energy metabolism, is particularly high in type II skeletal muscle fibers, which are associated with insulin resistance and obesity. As resting plasma CK is mainly derived from skeletal muscle, we assessed whether plasma CK is associated with markers of obesity. In this cross-sectional study, we analyzed a random sample of the multi-ethnic population of Amsterdam, the Netherlands, consisting of 1444 subjects aged 34-60 years. The primary outcome was the independent association between plasma CK after rest and waist circumference. Other outcomes included waist-to-hip ratio and body mass index. Mean waist circumference increased from the first through the third CK tertile, respectively 90.3 (SD 13.4), 93.2 (SD 14.3), and 94.4 (SD 13.3) cm (p < 0.001 for differences between tertiles). The increase in waist circumference was 8.91 (95% CI 5.35 to 12.47) cm per log CK increase after adjustment for age, sex, African ethnicity, educational level, physical activity and plasma creatinine. Similarly, CK was independently associated with waist-to-hip ratio and body mass index, with an increase of respectively 0.05 (95% CI 0.03 to 0.07) and 3.6 (95% CI 2.3 to 5.0) kg/m(2) per log CK increase. Plasma CK is independently associated with measures of obesity in a multi-ethnic population. This is in line with the central role of type II skeletal muscle fibers in energy metabolism and obesity. Prospective studies should assess whether resting plasma CK could be an easy accessible marker of CK rich type II fiber predominance that helps identify individuals at risk for obesity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  1. Public perceptions and attitudes toward thalassaemia: Influencing factors in a multi-racial population

    PubMed Central

    2011-01-01

    Background Thalassaemia is a common public health problem in Malaysia and about 4.5 to 6% of the Malays and Chinese are carriers of this genetic disorder. The major forms of thalassaemia result in death in utero of affected foetuses (α-thalassaemia) or life-long blood transfusions for survival in β-thalassaemia. This study, the first nationwide population based survey of thalassaemia in Malaysia, aimed to determine differences in public awareness, perceptions and attitudes toward thalassaemia in the multi-racial population in Malaysia. Methods A cross-sectional computer-assisted telephone interview survey of a representative sample of multi-racial Malaysians aged 18 years and above was conducted between July and December 2009. Results Of a total of 3723 responding households, 2846 (76.4%) have heard of thalassaemia. Mean knowledge score was 11.85 (SD ± 4.03), out of a maximum of 21, with higher scores indicating better knowledge. Statistically significant differences (P < 0.05) in total knowledge score by age groups, education attainment, employment status, and average household income were observed. Although the majority expressed very positive attitudes toward screening for thalassaemia, only 13.6% of married participants interviewed have been screened for thalassaemia. The majority (63.4%) were unsupportive of selective termination of foetuses diagnosed with thalassaemia major. Conclusion Study shows that carrier and premarital screening programs for thalassaemia may be more effective and culturally acceptable in the reduction of pregnancies with thalassaemia major. The findings provide insights into culturally congruent educational interventions to reach out diverse socio-demographic and ethnic communities to increase knowledge and cultivate positive attitudes toward prevention of thalassaemia. PMID:21447191

  2. Validation study of the Leeds Dyspepsia Questionnaire in a multi-ethnic Asian population.

    PubMed

    Mahadeva, Sanjiv; Chan, Wah-Kheong; Mohazmi, Mohammed; Sujarita, Ramanujam; Goh, Khean-Lee

    2011-11-01

    Outcome measures for clinical trials in dyspepsia require an assessment of symptom response. There is a lack of validated instruments assessing dyspepsia symptoms in the Asian region. We aimed to translate and validate the Leeds Dyspepsia Questionnaire (LDQ) in a multi-ethnic Asian population. A Malay and culturally adapted English version of the LDQ were developed according to established protocols. Psychometric evaluation was performed by assessing the validity, internal consistency, test-retest reliability and responsiveness of the instruments in both primary and secondary care patients. Between April and September 2010, both Malay (n=166) and Malaysian English (n=154) versions were assessed in primary and secondary care patients. Both language versions were found to be reliable (internal consistency was 0.80 and 0.74 (Cronbach's α) for Malay and English, respectively; spearman's correlation coefficient for test-retest reliability was 0.98 for both versions), valid (area under receiver operating curve for accuracy of diagnosing dyspepsia was 0.71 and 0.77 for Malay and English versions, respectively), discriminative (median LDQ score discriminated between primary and secondary care patients in Malay (11.0 vs 20.0, P<0.0001) and English (10.0 vs 14.0, P=0.001), and responsive (median LDQ score reduced after treatment in Malay (17.0 to 14.0, P=0.08) and English (18.0 to 11.0, P=0.008) to dyspepsia. The Malaysian versions of the LDQ are valid, reliable and responsive instruments for assessing symptoms in a multi-ethnic Asian population with dyspepsia. © 2011 Journal of Gastroenterology and Hepatology Foundation and Blackwell Publishing Asia Pty Ltd.

  3. Expression of genes encoding multi-transmembrane proteins in specific primate taste cell populations.

    PubMed

    Moyer, Bryan D; Hevezi, Peter; Gao, Na; Lu, Min; Kalabat, Dalia; Soto, Hortensia; Echeverri, Fernando; Laita, Bianca; Yeh, Shaoyang Anthony; Zoller, Mark; Zlotnik, Albert

    2009-12-04

    Using fungiform (FG) and circumvallate (CV) taste buds isolated by laser capture microdissection and analyzed using gene arrays, we previously constructed a comprehensive database of gene expression in primates, which revealed over 2,300 taste bud-associated genes. Bioinformatics analyses identified hundreds of genes predicted to encode multi-transmembrane domain proteins with no previous association with taste function. A first step in elucidating the roles these gene products play in gustation is to identify the specific taste cell types in which they are expressed. Using double label in situ hybridization analyses, we identified seven new genes expressed in specific taste cell types, including sweet, bitter, and umami cells (TRPM5-positive), sour cells (PKD2L1-positive), as well as other taste cell populations. Transmembrane protein 44 (TMEM44), a protein with seven predicted transmembrane domains with no homology to GPCRs, is expressed in a TRPM5-negative and PKD2L1-negative population that is enriched in the bottom portion of taste buds and may represent developmentally immature taste cells. Calcium homeostasis modulator 1 (CALHM1), a component of a novel calcium channel, along with family members CALHM2 and CALHM3; multiple C2 domains; transmembrane 1 (MCTP1), a calcium-binding transmembrane protein; and anoctamin 7 (ANO7), a member of the recently identified calcium-gated chloride channel family, are all expressed in TRPM5 cells. These proteins may modulate and effect calcium signalling stemming from sweet, bitter, and umami receptor activation. Synaptic vesicle glycoprotein 2B (SV2B), a regulator of synaptic vesicle exocytosis, is expressed in PKD2L1 cells, suggesting that this taste cell population transmits tastant information to gustatory afferent nerve fibers via exocytic neurotransmitter release. Identification of genes encoding multi-transmembrane domain proteins expressed in primate taste buds provides new insights into the processes of taste cell

  4. Expression of Genes Encoding Multi-Transmembrane Proteins in Specific Primate Taste Cell Populations

    PubMed Central

    Gao, Na; Lu, Min; Kalabat, Dalia; Soto, Hortensia; Echeverri, Fernando; Laita, Bianca; Yeh, Shaoyang Anthony; Zoller, Mark; Zlotnik, Albert

    2009-01-01

    Background Using fungiform (FG) and circumvallate (CV) taste buds isolated by laser capture microdissection and analyzed using gene arrays, we previously constructed a comprehensive database of gene expression in primates, which revealed over 2,300 taste bud-associated genes. Bioinformatics analyses identified hundreds of genes predicted to encode multi-transmembrane domain proteins with no previous association with taste function. A first step in elucidating the roles these gene products play in gustation is to identify the specific taste cell types in which they are expressed. Methodology/Principal Findings Using double label in situ hybridization analyses, we identified seven new genes expressed in specific taste cell types, including sweet, bitter, and umami cells (TRPM5-positive), sour cells (PKD2L1-positive), as well as other taste cell populations. Transmembrane protein 44 (TMEM44), a protein with seven predicted transmembrane domains with no homology to GPCRs, is expressed in a TRPM5-negative and PKD2L1-negative population that is enriched in the bottom portion of taste buds and may represent developmentally immature taste cells. Calcium homeostasis modulator 1 (CALHM1), a component of a novel calcium channel, along with family members CALHM2 and CALHM3; multiple C2 domains; transmembrane 1 (MCTP1), a calcium-binding transmembrane protein; and anoctamin 7 (ANO7), a member of the recently identified calcium-gated chloride channel family, are all expressed in TRPM5 cells. These proteins may modulate and effect calcium signalling stemming from sweet, bitter, and umami receptor activation. Synaptic vesicle glycoprotein 2B (SV2B), a regulator of synaptic vesicle exocytosis, is expressed in PKD2L1 cells, suggesting that this taste cell population transmits tastant information to gustatory afferent nerve fibers via exocytic neurotransmitter release. Conclusions/Significance Identification of genes encoding multi-transmembrane domain proteins expressed in primate

  5. Population Structure of Hispanics in the United States: The Multi-Ethnic Study of Atherosclerosis

    PubMed Central

    Manichaikul, Ani; Palmas, Walter; Rodriguez, Carlos J.; Peralta, Carmen A.; Divers, Jasmin; Guo, Xiuqing; Chen, Wei-Min; Wong, Quenna; Williams, Kayleen; Kerr, Kathleen F.; Taylor, Kent D.; Tsai, Michael Y.; Goodarzi, Mark O.; Sale, Michèle M.; Diez-Roux, Ana V.; Rich, Stephen S.; Rotter, Jerome I.; Mychaleckyj, Josyf C.

    2012-01-01

    Using ∼60,000 SNPs selected for minimal linkage disequilibrium, we perform population structure analysis of 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By projection of principal components (PCs) of ancestry to samples from the HapMap phase III and the Human Genome Diversity Panel (HGDP), we show the first two PCs quantify the Caucasian, African, and Native American origins, while the third and fourth PCs bring out an axis that aligns with known South-to-North geographic location of HGDP Native American samples and further separates MESA Mexican versus Central/South American samples along the same axis. Using k-means clustering computed from the first four PCs, we define four subgroups of the MESA Hispanic cohort that show close agreement with self-identification, labeling the clusters as primarily Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. To demonstrate our recommendations for genetic analysis in the MESA Hispanic cohort, we present pooled and stratified association analysis of triglycerides for selected SNPs in the LPL and TRIB1 gene regions, previously reported in GWAS of triglycerides in Caucasians but as yet unconfirmed in Hispanic populations. We report statistically significant evidence for genetic association in both genes, and we further demonstrate the importance of considering population substructure and genetic heterogeneity in genetic association studies performed in the United States Hispanic population. PMID:22511882

  6. Population structure of Hispanics in the United States: the multi-ethnic study of atherosclerosis.

    PubMed

    Manichaikul, Ani; Palmas, Walter; Rodriguez, Carlos J; Peralta, Carmen A; Divers, Jasmin; Guo, Xiuqing; Chen, Wei-Min; Wong, Quenna; Williams, Kayleen; Kerr, Kathleen F; Taylor, Kent D; Tsai, Michael Y; Goodarzi, Mark O; Sale, Michèle M; Diez-Roux, Ana V; Rich, Stephen S; Rotter, Jerome I; Mychaleckyj, Josyf C

    2012-01-01

    Using ~60,000 SNPs selected for minimal linkage disequilibrium, we perform population structure analysis of 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By projection of principal components (PCs) of ancestry to samples from the HapMap phase III and the Human Genome Diversity Panel (HGDP), we show the first two PCs quantify the Caucasian, African, and Native American origins, while the third and fourth PCs bring out an axis that aligns with known South-to-North geographic location of HGDP Native American samples and further separates MESA Mexican versus Central/South American samples along the same axis. Using k-means clustering computed from the first four PCs, we define four subgroups of the MESA Hispanic cohort that show close agreement with self-identification, labeling the clusters as primarily Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. To demonstrate our recommendations for genetic analysis in the MESA Hispanic cohort, we present pooled and stratified association analysis of triglycerides for selected SNPs in the LPL and TRIB1 gene regions, previously reported in GWAS of triglycerides in Caucasians but as yet unconfirmed in Hispanic populations. We report statistically significant evidence for genetic association in both genes, and we further demonstrate the importance of considering population substructure and genetic heterogeneity in genetic association studies performed in the United States Hispanic population.

  7. Multi-Locus Assortment (MLA) for Transgene Dispersal and Elimination in Mosquito Populations

    PubMed Central

    Rasgon, Jason L.

    2009-01-01

    Background Replacement of wild-type mosquito populations with genetically modified versions is being explored as a potential strategy to control vector-borne diseases. Due to lower expected relative fitness of transgenic individuals, transgenes must be driven into populations for these scenarios to be successful. Several gene drive mechanisms exist in a theoretical sense but none are currently workable in mosquitoes. Even if strategies were workable, it would be very difficult to recall released transgenes in the event of unforeseen consequences. What is needed is a way to test transgenes in the field for feasibility, efficacy and safety prior to releasing an active drive mechanism. Methodology/Principal Findings We outline a method, termed Multi-locus assortment (MLA), to spread transgenes into vector populations by the release of genetically-modified mosquitoes carrying multiple stable transgene inserts. Simulations indicate that [1] insects do not have to carry transgenes at more than 4 loci, [2] transgenes can be maintained at high levels by sequential small releases, the frequency of which depends on the construct fitness cost, and [3] in the case of unforeseen negative non-target effects, transgenes can be eliminated from the population by halting transgenic releases and/or mass releases of wild-type insects. We also discuss potential methods to create MLA mosquito strains in the laboratory. Conclusions/Significance While not as efficient as active drive mechanisms, MLA has other advantages: [1] MLA strains can be constructed for some mosquito species with currently-available technology, [2] MLA will allow the ecological components of transgenic mosquito releases to be tested before actual gene drive mechanisms are ready to be deployed, [3] since MLA is not self-propagating, the risk of an accidental premature release into nature is minimized, and [4] in the case that active gene drive mechanisms prove impossible to develop, the MLA approach can be used as a

  8. Application of convolutional artificial neural networks to echocardiograms for differentiating congenital heart diseases in a pediatric population

    NASA Astrophysics Data System (ADS)

    Perrin, Douglas P.; Bueno, Alejandra; Rodriguez, Andrea; Marx, Gerald R.; del Nido, Pedro J.

    2017-03-01

    In this paper we describe a pilot study, where machine learning methods are used to differentiate between congenital heart diseases. Our approach was to apply convolutional neural networks (CNNs) to echocardiographic images from five different pediatric populations: normal, coarctation of the aorta (CoA), hypoplastic left heart syndrome (HLHS), transposition of the great arteries (TGA), and single ventricle (SV). We used a single network topology that was trained in a pairwise fashion in order to evaluate the potential to differentiate between patient populations. In total we used 59,151 echo frames drawn from 1,666 clinical sequences. Approximately 80% of the data was used for training, and the remainder for validation. Data was split at sequence boundaries to avoid having related images in the training and validation sets. While training was done with echo images/frames, evaluation was performed for both single frame discrimination as well as sequence discrimination (by majority voting). In total 10 networks were generated and evaluated. Unlike other domains where this network topology has been used, in ultrasound there is low visual variation between classes. This work shows the potential for CNNs to be applied to this low-variation domain of medical imaging for disease discrimination.

  9. Prevalence of neural tube defects in a rural area of north india from 2001 to 2014: A population-based survey.

    PubMed

    Kant, Shashi; Malhotra, Sumit; Singh, Arvind Kumar; Haldar, Partha; Kaur, Ravneet; Misra, Puneet; Gupta, Neerja

    2017-02-15

    Neural tube defects (NTDs) are one of the commonest birth defects. There was paucity of community-based data on occurrence of NTDs in India, especially from rural parts of the country. Against this background, the current study was carried out with main objectives to determine the prevalence of NTDs and its specific types (anencephaly, spina bifida and encephalocele) in a rural community setting over the time period 2001 to 2014. This was a community-based cross-sectional study carried out in 28 villages of Ballabgarh Tehsil of Faridabad district in north India (population ∼ 96,000). A household survey was undertaken by trained multi-purpose workers who enquired ever-married women about history of conception with outcome as NTD during the study period. The probable case of NTD was determined using a colored pictorial card with photographs of different types of NTDs. These cases were confirmed by doctors. A total of 26,946 live births occurred during the years 2001 to 2014. A total of 140 confirmed cases of NTDs were identified. The live birth prevalence of NTDs was 24.1 per 10,000 live births (95% confidence interval, 18.8-30.6). The birth prevalence of NTDs for the years 2008 to 2014 was 50.8 (95% confidence interval, 39.9-63.8) per 10,000 live and stillbirths. The most common type of NTD was found to be spina bifida followed by anencephaly and encephalocele. We found high prevalence of NTDs in rural community settings from north India for years 2001 to 2014.Birth Defects Research 109:203-210, 2017.© 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  10. Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks

    NASA Astrophysics Data System (ADS)

    Torcini, Alessandro; Luccioli, Stefano; Bonifazi, Paolo; Ben-Jacob, Eshel; Barzilai, Ari

    2015-03-01

    It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in developing neuronal circuits, typically composed of only excitatory cells, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally regulated constraints on single neuron excitability and connectivity leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity. This work is part of the activity of the Joint Italian-Israeli Laboratory on Integrative Network Neuroscience supported by the Italian Ministry of Foreign Affairs.

  11. Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks.

    PubMed

    Luccioli, Stefano; Ben-Jacob, Eshel; Barzilai, Ari; Bonifazi, Paolo; Torcini, Alessandro

    2014-09-01

    It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity.

  12. Based-on generalized neural network multi-wavelength conversion technique of laser and its influence study on target detection ranges

    NASA Astrophysics Data System (ADS)

    Liu, Zhonghua; Wu, Ronghua; Huang, Gaoming

    2017-02-01

    The equation of laser detection system range and multi-wavelength conversion technique were used to calculate the maximum detection range under different meteorological conditions. A new algorithm was proposed to solve the detection range implicit function equation, by which the accurate arithmetic solution could be got quickly. When solving the detection range implicit function equation, the atmospheric transmittance is the function of range, the multiwavelength atmospheric extinction coefficients were calculated with neural network conversion model. Then the equation was solved by linear interpolation and iteration methods, the iteration initial value was estimated with numeric method. This algorithm is valuable for simulating, model building and designing of multi-wavelength laser system under different seasons and atmospheric circumstance.

  13. QTL detection power of multi-parental RIL populations in Arabidopsis thaliana.

    PubMed

    Klasen, J R; Piepho, H-P; Stich, B

    2012-06-01

    A major goal of today's biology is to understand the genetic basis of quantitative traits. This can be achieved by statistical methods that evaluate the association between molecular marker variation and phenotypic variation in different types of mapping populations. The objective of this work was to evaluate the statistical power of quantitative trait loci (QTL) detection of various multi-parental mating designs, as well as to assess the reasons for the observed differences. Our study was based on an empirical data of 20 Arabidopsis thaliana accessions, which have been selected to capture the maximum genetic diversity. The examined mating designs differed strongly with respect to the statistical power to detect QTL. We observed the highest power to detect QTL for the diallel cross with random mating design. The results of our study suggested that performing sibling mating within subpopulations of joint-linkage mapping populations has the potential to considerably increase the power for QTL detection. Our results, however, revealed that using designs in which more than two parental alleles segregate in each subpopulation increases the power even more.

  14. A multi-scenario genome-wide medical population genetics simulation framework.

    PubMed

    Mugo, Jacquiline W; Geza, Ephifania; Defo, Joel; Elsheikh, Samar S M; Mazandu, Gaston K; Mulder, Nicola J; Chimusa, Emile R

    2017-10-01

    Recent technological advances in high-throughput sequencing and genotyping have facilitated an improved understanding of genomic structure and disease-associated genetic factors. In this context, simulation models can play a critical role in revealing various evolutionary and demographic effects on genomic variation, enabling researchers to assess existing and design novel analytical approaches. Although various simulation frameworks have been suggested, they do not account for natural selection in admixture processes. Most are tailored to a single chromosome or a genomic region, very few capture large-scale genomic data, and most are not accessible for genomic communities. Here we develop a multi-scenario genome-wide medical population genetics simulation framework called 'FractalSIM'. FractalSIM has the capability to accurately mimic and generate genome-wide data under various genetic models on genetic diversity, genomic variation affecting diseases and DNA sequence patterns of admixed and/or homogeneous populations. Moreover, the framework accounts for natural selection in both homogeneous and admixture processes. The outputs of FractalSIM have been assessed using popular tools, and the results demonstrated its capability to accurately mimic real scenarios. They can be used to evaluate the performance of a range of genomic tools from ancestry inference to genome-wide association studies. The FractalSIM package is available at http://www.cbio.uct.ac.za/FractalSIM. emile.chimusa@uct.ac.za. Supplementary data are available at Bioinformatics online.

  15. Localized states in an unbounded neural field equation with smooth firing rate function: a multi-parameter analysis.

    PubMed

    Faye, Grégory; Rankin, James; Chossat, Pascal

    2013-05-01

    The existence of spatially localized solutions in neural networks is an important topic in neuroscience as these solutions are considered to characterize working (short-term) memory. We work with an unbounded neural network represented by the neural field equation with smooth firing rate function and a wizard hat spatial connectivity. Noting that stationary solutions of our neural field equation are equivalent to homoclinic orbits in a related fourth order ordinary differential equation, we apply normal form theory for a reversible Hopf bifurcation to prove the existence of localized solutions; further, we present results concerning their stability. Numerical continuation is used to compute branches of localized solution that exhibit snaking-type behaviour. We describe in terms of three parameters the exact regions for which localized solutions persist.

  16. Neural responses from the wind-sensitive interneuron population in four cockroach species

    PubMed Central

    McGorry, Clare A.; Newman, Caroline N.; Triblehorn, Jeffrey D.

    2014-01-01

    The wind-sensitive insect cercal sensory system is involved in important behaviors including predator detection and initiating terrestrial escape responses as well as flight maintenance. However, not all insects possessing a cercal system exhibit these behaviors. In cockroaches, wind evokes strong terrestrial escape responses in Periplaneta americana and Blattella germanica, but only weak escape responses in Blaberus craniifer and no escape responses in Gromphadorhina portentosa. Both P. americana and Blab. craniifer possesses pink flight muscles correlated with flight ability while Blat. germanica possesses white flight muscles that cannot support flight and G. portentosa lacks wings. These different behavioral combinations could correlate with differences in sensory processing of wind information by the cercal system. In this study, we focused on the wind-sensitive interneurons (WSIs) since they provide input to the premotor/motor neurons that influence terrestrial escape and flight behavior. Using extracellular recordings, we characterized the responses from the WSI population by generating stimulus-response (S-R) curves and examining spike firing rates. Using cluster analysis, we also examined the activity of individual units (four per species, though not necessarily homologous) comprising the population response in each species. Our main results were: 1) all four species possessed ascending WSIs in the abdominal connectives; 2) wind elicited the weakest WSI responses (lowest spike counts and spike rates) in G. portentosa; 3) wind elicited WSI responses in Blab. craniifer that were greater than P. americana or Blat. germanica; 4) the activity of four individual units comprising the WSI population response in each species was similar across species. PMID:24879967

  17. Neural responses from the wind-sensitive interneuron population in four cockroach species.

    PubMed

    McGorry, Clare A; Newman, Caroline N; Triblehorn, Jeffrey D

    2014-07-01

    The wind-sensitive insect cercal sensory system is involved in important behaviors including predator detection and initiating terrestrial escape responses as well as flight maintenance. However, not all insects possessing a cercal system exhibit these behaviors. In cockroaches, wind evokes strong terrestrial escape responses in Periplaneta americana and Blattella germanica, but only weak escape responses in Blaberus craniifer and no escape responses in Gromphadorhina portentosa. Both P. americana and B. craniifer possesses pink flight muscles correlated with flight ability while B. germanica possesses white flight muscles that cannot support flight and G. portentosa lacks wings. These different behavioral combinations could correlate with differences in sensory processing of wind information by the cercal system. In this study, we focused on the wind-sensitive interneurons (WSIs) since they provide input to the premotor/motor neurons that influence terrestrial escape and flight behavior. Using extracellular recordings, we characterized the responses from the WSI population by generating stimulus-response (S-R) curves and examining spike firing rates. Using cluster analysis, we also examined the activity of individual units (four per species, though not necessarily homologous) comprising the population response in each species. Our main results were: (1) all four species possessed ascending WSIs in the abdominal connectives; (2) wind elicited the weakest WSI responses (lowest spike counts and spike rates) in G. portentosa; (3) wind elicited WSI responses in B. craniifer that were greater than P. americana or B. germanica; (4) the activity of four individual units comprising the WSI population response in each species was similar across species.

  18. Distribution of cytokine gene single nucleotide polymorphisms among a multi-ethnic Iranian population

    PubMed Central

    Kurdistani, Zana Karimi; Saberi, Samaneh; Talebkhan, Yeganeh; Oghalaie, Akbar; Esmaeili, Maryam; Mohajerani, Nazanin; Bababeik, Maryam; Hassanpour, Parisa; Barani, Shaghik; Farjaddoost, Ameneh; Ebrahimzadeh, Fatemeh; Trejaut, Jean; Mohammadi, Marjan

    2015-01-01

    Background: Cytokine gene single nucleotide polymorphisms (SNPs) are widely used to study susceptibility to complex diseases and as a tool for anthropological studies. Materials and Methods: To investigate cytokine SNPs in an Iranian multi-ethnic population, we have investigated 10 interleukin (IL) SNPs (IL-1β (C-511T, T-31C), IL-2 (G-384T), IL-4 (C-590T), IL-6 (G-174C), IL-8 (T-251A), IL-10 (G-1082A, C-819T, C-592A) and tumor necrosis factor-alpha (TNF-α) (G-308A) in 415 Iranian subjects comprising of 6 different ethnicities. Allelic and genotypic frequencies as well as Hardy-Weinberg equilibrium (HWE) were calculated by PyPop software. Population genetic indices including observed heterozygosity (Ho), expected heterozygosity (He), fixation index (FIS), the effective number of alleles (Ne) and polymorphism information content (PIC) were derived using Popgene 32 software. Multidimensional scaling (MDS) was constructed using Reynold's genetic distance obtained from the frequencies of cytokine gene polymorphism. Results: Genotypic distributions were consistent with the HWE assumptions, except for 3 loci (IL-4-590, IL-8-251 and IL-10-819) in Fars and 4 loci (IL-4-590, IL-6-174, IL-10-1082 and TNF-α-308) in Turks. Pairwise assessment of allelic frequencies, detected differences at the IL-4-590 locus in Gilakis versus Kurds (P = 0.028) and Lurs (P = 0.022). Mazanis and Gilakis displayed the highest (Ho= 0.50 ± 0.24) and lowest (Ho= 0.34 ± 0.16) mean observed heterozygosity, respectively. Conclusions: MDS analysis of our study population, in comparison with others, revealed that Iranian ethnicities except Kurds and Mazanis were tightly located within a single cluster with closest genetic affinity to Europeans. PMID:26436076

  19. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.

    PubMed

    Zhang, Zhen; Ma, Cheng; Zhu, Rong

    2016-10-14

    High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

  20. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules

    PubMed Central

    Zhang, Zhen; Ma, Cheng; Zhu, Rong

    2016-01-01

    High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial–temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional–integral–derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions. PMID:27754436

  1. A multi-channel low-power system-on-chip for single-unit recording and narrowband wireless transmission of neural signal.

    PubMed

    Bonfanti, A; Ceravolo, M; Zambra, G; Gusmeroli, R; Spinelli, A S; Lacaita, A L; Angotzi, G N; Baranauskas, G; Fadiga, L

    2010-01-01

    This paper reports a multi-channel neural recording system-on-chip (SoC) with digital data compression and wireless telemetry. The circuit consists of a 16 amplifiers, an analog time division multiplexer, an 8-bit SAR AD converter, a digital signal processor (DSP) and a wireless narrowband 400-MHz binary FSK transmitter. Even though only 16 amplifiers are present in our current die version, the whole system is designed to work with 64 channels demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. A digital data compression, based on the detection of action potentials and storage of correspondent waveforms, allows the use of a 1.25-Mbit/s binary FSK wireless transmission. This moderate bit-rate and a low frequency deviation, Manchester-coded modulation are crucial for exploiting a narrowband wireless link and an efficient embeddable antenna. The chip is realized in a 0.35- εm CMOS process with a power consumption of 105 εW per channel (269 εW per channel with an extended transmission range of 4 m) and an area of 3.1 × 2.7 mm(2). The transmitted signal is captured by a digital TV tuner and demodulated by a wideband phase-locked loop (PLL), and then sent to a PC via an FPGA module. The system has been tested for electrical specifications and its functionality verified in in-vivo neural recording experiments.

  2. Dietary patterns, insulin sensitivity and adiposity in the multi-ethnic Insulin Resistance Atherosclerosis Study population.

    PubMed

    Liese, Angela D; Schulz, Mandy; Moore, Charity G; Mayer-Davis, Elizabeth J

    2004-12-01

    Epidemiological investigations increasingly employ dietary-pattern techniques to fully integrate dietary data. The present study evaluated the relationship of dietary patterns identified by cluster analysis with measures of insulin sensitivity (SI) and adiposity in the multi-ethnic, multi-centre Insulin Resistance Atherosclerosis Study (IRAS, 1992-94). Cross-sectional data from 980 middle-aged adults, of whom 67 % had normal and 33 % had impaired glucose tolerance, were analysed. Usual dietary intake was obtained by an interviewer-administered, validated food-frequency questionnaire. Outcomes included SI, fasting insulin (FI), BMI and waist circumference. The relationship of dietary patterns to log(SI+1), log(FI), BMI and waist circumference was modelled with multivariable linear regressions. Cluster analysis identified six distinct diet patterns--'dark bread', 'wine', 'fruits', 'low-frequency eaters', 'fries' and 'white bread'. The 'white bread' and the 'fries' patterns over-represented the Hispanic IRAS population predominantly from two centres, while the 'wine' and 'dark bread' groups were dominated by non-Hispanic whites. The dietary patterns were associated significantly with each of the outcomes first at the crude, clinical level (P<0.001). Furthermore, they were significantly associated with FI, BMI and waist circumference independent of age, sex, race or ethnicity, clinic, family history of diabetes, smoking and activity (P<0.004), whereas significance was lost for SI. Studying the total dietary behaviour via a pattern approach allowed us to focus both on the qualitative and quantitative dimensions of diet. The present study identified highly consistent associations of distinct dietary patterns with measures of insulin resistance and adiposity, which are risk factors for diabetes and heart disease.

  3. Prevalence and Risk Factors for Epiretinal Membranes in a Multi-Ethnic United States Population

    PubMed Central

    Ng, Ching Hui; Cheung, Ning; Wang, Jie Jin; Islam, Amirul FM; Kawasaki, Ryo; Meuer, Stacy M; Cotch, Mary Frances; Klein, Barbara EK; Klein, Ronald; Wong, Tien Yin

    2010-01-01

    Purpose To describe the prevalence of and risk factors for epiretinal membrane (ERM) in a multi-ethnic population and to evaluate possible racial/ethnic differences. Design Cross-sectional study. Participants Participants of the Multi-Ethnic Study of Atherosclerosis (MESA), examined at the second visit of the MESA when retinal photography was performed. Methods Data on 5960 participants aged 45 to 84 years from MESA, including white, blacks, Hispanic and Chinese from six United States communities, were analysed. ERM was assessed from digital non-stereoscopic fundus photographs and defined as cellophane macular reflex (CMR) without retinal folds or pre-retinal macular fibrosis (PMF) with retinal folds. Risk factors were assessed from standardized interviews, clinical examinations, and laboratory investigations. Main outcome measures ERM prevalence by ethnic/racial group, and risk factors associated with ERM. Results The prevalence of any ERM was 28.9%, of which 25.1% were CMR and 3.8% were PMF. The prevalence of ERM was significantly higher in Chinese (39.0%), compared to Hispanics (29.3%), whites (27.5%), and blacks (26.2%), p<0.001. In multivariable models, increasing age (odds ratio [OR] 1.19, 95% confidence intervals [CI], 1.06, 1.34, per year increase in age), diabetes (OR 1.92, 95% CI, 1.39, 2.65) and hypercholesterolemia (OR 1.33, 95% CI, 1.04, 1.69) were significantly associated with CMR. Conclusions This study showed that ERM was significantly more common in Chinese persons compared to whites, blacks and Hispanics. Risk factors for epiretinal membrane were increasing age, presence of diabetes and hypercholesterolemia. PMID:21035863

  4. Association of interleukins genes polymorphisms with multi-drug resistant tuberculosis in Ukrainian population.

    PubMed

    Butov, Dmytro O; Kuzhko, Mykhaylo M; Makeeva, Natalia I; Butova, Tetyana S; Stepanenko, Hanna L; Dudnyk, Andrii B

    2016-01-01

    Multi-drug resistant tuberculosis (MDR TB) is a significant health problem in some parts of the world. Three major cytokines involved in TB immunopathogenesis include IL-2, IL-4 and IL-10. The susceptibility to MDR TB may be genetically determined. The aim of the study was to assess the association of IL-2, IL-4, IL-10 gene polymorphisms with multi-drug resistant tuberculosis (MDR TB) in Ukrainian population. We observed 140 patients suffering from infiltrative pulmonary tuberculosis (PT) and 30 apparently healthy subjects. The patients were assigned to two groups whether they suffer or do not suffer from pulmonary MDR TB. Interleukin gene (IL) polymorphisms, particularly T330G polymorphism in the IL-2 gene, C589T polymorphism in the IL-4 gene and G1082A polymorphism in the IL-10 gene were studied through polymerase chain reaction. Circulating levels of IL-2, IL-4 and IL-10 in venous blood were estimated using ELISA. Prior to treatment, patients with PT showed significant increase of IL-2 levels and decrease of IL-4 and IL-10 levels compared to apparently healthy subjects. Circulating IL-4 and IL-10 levels were significantly decreased whilst serum IL-2 level was significantly increased in patients with MDR TB compared to non-MDR TB. Low IL-4 and IL-10 secretion and considerable IL-2 alterations were shown to be significantly associated with mutations of homozygous and heterozygous genotypes affecting C589T polymorphism in the IL-4 gene, G1082A polymorphism in the IL-10 gene and T330G polymorphism in the IL-2 gene in patients with PT. Heterozygous genotype and mutations homozygous genotypes gene in polymorphisms determining specified cytokines' production is a PT risk factor and may lead to disease progression into chronic phase. Heterozygous genotype of aforementioned cytokine genetic polymorphisms was significantly the most frequent in patients with MDR TB.

  5. L-type calcium channels refine the neural population code of sound level.

    PubMed

    Grimsley, Calum Alex; Green, David Brian; Sivaramakrishnan, Shobhana

    2016-12-01

    The coding of sound level by ensembles of neurons improves the accuracy with which listeners identify how loud a sound is. In the auditory system, the rate at which neurons fire in response to changes in sound level is shaped by local networks. Voltage-gated conductances alter local output by regulating neuronal firing, but their role in modulating responses to sound level is unclear. We tested the effects of L-type calcium channels (CaL: CaV1.1-1.4) on sound-level coding in the central nucleus of the inferior colliculus (ICC) in the auditory midbrain. We characterized the contribution of CaL to the total calcium current in brain slices and then examined its effects on rate-level functions (RLFs) in vivo using single-unit recordings in awake mice. CaL is a high-threshold current and comprises ∼50% of the total calcium current in ICC neurons. In vivo, CaL activates at sound levels that evoke high firing rates. In RLFs that increase monotonically with sound level, CaL boosts spike rates at high sound levels and increases the maximum firing rate achieved. In different populations of RLFs that change nonmonotonically with sound level, CaL either suppresses or enhances firing at sound levels that evoke maximum firing. CaL multiplies the gain of monotonic RLFs with dynamic range and divides the gain of nonmonotonic RLFs with the width of the RLF. These results suggest that a single broad class of calcium channels activates enhancing and suppressing local circuits to regulate the sensitivity of neuronal populations to sound level. Copyright © 2016 the American Physiological Society.

  6. Identification of genetic variants associated with maize flowering time using an extremely large multi-genetic background population

    USDA-ARS?s Scientific Manuscript database

    Flowering time is one of the major adaptive traits in domestication of maize and an important selection criterion in breeding. To detect more maize flowering time variants we evaluated flowering time traits using an extremely large multi- genetic background population that contained more than 8000 l...

  7. Application of artificial neural network for modeling of phenol mineralization by photo-Fenton process using a multi-lamp reactor.

    PubMed

    Mota, André L N; Chiavone-Filho, Osvaldo; da Silva, Syllos S; Foletto, Edson L; Moraes, José E F; Nascimento, Cláudio A O

    2014-01-01

    An artificial neural network (ANN) was implemented for modeling phenol mineralization in aqueous solution using the photo-Fenton process. The experiments were conducted in a photochemical multi-lamp reactor equipped with twelve fluorescent black light lamps (40 W each) irradiating UV light. A three-layer neural network was optimized in order to model the behavior of the process. The concentrations of ferrous ions and hydrogen peroxide, and the reaction time were introduced as inputs of the network and the efficiency of phenol mineralization was expressed in terms of dissolved organic carbon (DOC) as an output. Both concentrations of Fe(2+) and H2O2 were shown to be significant parameters on the phenol mineralization process. The ANN model provided the best result through the application of six neurons in the hidden layer, resulting in a high determination coefficient. The ANN model was shown to be efficient in the simulation of phenol mineralization through the photo-Fenton process using a multi-lamp reactor.

  8. Neural Networks

    NASA Astrophysics Data System (ADS)

    Schwindling, Jerome

    2010-04-01

    This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p.) corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  9. Mediterranean diet and leukocyte telomere length in a multi-ethnic elderly population.

    PubMed

    Gu, Yian; Honig, Lawrence S; Schupf, Nicole; Lee, Joseph H; Luchsinger, Jose A; Stern, Yaakov; Scarmeas, Nikolaos

    2015-01-01

    Leukocyte telomere length (LTL) is considered as the marker of biological aging and may be related to environmental factors. The current study aimed to examine the relation between Mediterranean-type diet and LTL. We used a cross-sectional study of 1743 multi-ethnic community residents of New York aged 65 years or older. Mediterranean-type diet (MeDi) was calculated from dietary information collected using a food frequency questionnaire. LTL was measured from leukocyte DNA using a real-time PCR method to measure T/S ratio, the ratio of telomere (T) to single-copy gene (S) sequence. Regression analysis showed that the MeDi score was not associated with LTL in the overall study population (β = 12.5; p = 0.32) after adjusting for age, sex, education, ethnicity, caloric intake, smoking, and physical and leisure activities. However, we found a significant association between MeDi and LTL among non-Hispanic whites (β = 48.3; p = 0.05), and the results held after excluding dementia subjects (β = 49.6; p = 0.05). We further found that, in the whole population, vegetable and cereal consumption above the sex-specific population median was associated with longer LTL (β = 89.1, p = 0.04) and shorter LTL (β = -93.5; p = 0.03), respectively. Among non-Hispanic whites, intake of meat or dairy below sex-specific population medians was associated with longer LTL (β = 154.7, p = 0.05; β = 240.5, p < 0.001, respectively). We found that higher adherence to a MeDi was associated with longer LTL among whites but not among African Americans and Hispanics. Additionally, a diet high in vegetables but low in cereal, meat, and dairy might be associated with longer LTL among healthy elderly.

  10. Muscle-derived stem cells isolated as non-adherent population give rise to cardiac, skeletal muscle and neural lineages

    SciTech Connect

    Arsic, Nikola; Mamaeva, Daria; Lamb, Ned J.; Fernandez, Anne

    2008-04-01

    Stem cells with the ability to differentiate in specialized cell types can be extracted from a wide array of adult tissues including skeletal muscle. Here we have analyzed a population of cells isolated from skeletal muscle on the basis of their poor adherence on uncoated or collagen-coated dishes that show multi-lineage differentiation in vitro. When analysed under proliferative conditions, these cells express stem cell surface markers Sca-1 (65%) and Bcrp-1 (80%) but also MyoD (15%), Neuronal {beta} III-tubulin (25%), GFAP (30%) or Nkx2.5 (1%). Although capable of growing as non-attached spheres for months, when given an appropriate matrix, these cells adhere giving rise to skeletal muscle, neuronal and cardiac muscle cell lineages. A similar cell population could not be isolated from either bone marrow or cardiac tissue suggesting their specificity to skeletal muscle. When injected into damaged muscle, these non-adherent muscle-derived cells are retrieved expressing Pax7, in a sublaminar position characterizing satellite cells and participate in forming new myofibers. These data show that a non-adherent stem cell population can be specifically isolated and expanded from skeletal muscle and upon attachment to a matrix spontaneously differentiate into muscle, cardiac and neuronal lineages in vitro. Although competing with resident satellite cells, these cells are shown to significantly contribute to repair of injured muscle in vivo supporting that a similar muscle-derived non-adherent cell population from human muscle may be useful in treatment of neuromuscular disorders.

  11. The Relation Between Inflation in Type-I and Type-II Error Rate and Population Divergence in Genome-Wide Association Analysis of Multi-Ethnic Populations.

    PubMed

    Derks, E M; Zwinderman, A H; Gamazon, E R

    2017-02-10

    Population divergence impacts the degree of population stratification in Genome Wide Association Studies. We aim to: (i) investigate type-I error rate as a function of population divergence (FST) in multi-ethnic (admixed) populations; (ii) evaluate the statistical power and effect size estimates; and (iii) investigate the impact of population stratification on the results of gene-based analyses. Quantitative phenotypes were simulated. Type-I error rate was investigated for Single Nucleotide Polymorphisms (SNPs) with varying levels of FST between the ancestral European and African populations. Type-II error rate was investigated for a SNP characterized by a high value of FST. In all tests, genomic MDS components were included to correct for population stratification. Type-I and type-II error rate was adequately controlled in a population that included two distinct ethnic populations but not in admixed samples. Statistical power was reduced in the admixed samples. Gene-based tests showed no residual inflation in type-I error rate.

  12. Reward System Dysfunction as a Neural Substrate of Symptom Expression Across the General Population and Patients With Schizophrenia

    PubMed Central

    Simon, Joe J.; Cordeiro, Sheila A.; Weber, Marc-André; Friederich, Hans-Christoph; Wolf, Robert C.; Weisbrod, Matthias; Kaiser, Stefan

    2015-01-01

    Dysfunctional patterns of activation in brain reward networks have been suggested as a core element in the pathophysiology of schizophrenia. However, it remains unclear whether this dysfunction is specific to schizophrenia or can be continuously observed across persons with different levels of nonclinical and clinical symptom expression. Therefore, we sought to investigate whether the pattern of reward system dysfunction is consistent with a dimensional or categorical model of psychosis-like symptom expression. 23 patients with schizophrenia and 37 healthy control participants with varying levels of psychosis-like symptoms, separated into 3 groups of low, medium, and high symptom expression underwent event-related functional magnetic resonance imaging while performing a Cued Reinforcement Reaction Time task. We observed lower activation in the ventral striatum during the expectation of high vs no reward to be associated with higher symptom expression across all participants. No significant difference between patients with schizophrenia and healthy participants with high symptom expression was found. However, connectivity between the ventral striatum and the medial orbitofrontal cortex was specifically reduced in patients with schizophrenia. Dysfunctional local activation of the ventral striatum depends less on diagnostic category than on the degree of symptom expression, therefore showing a pattern consistent with a psychosis continuum. In contrast, aberrant connectivity in the reward system is specific to patients with schizophrenia, thereby supporting a categorical view. Thus, the results of the present study provide evidence for both continuous and discontinuous neural substrates of symptom expression across patients with schizophrenia and the general population. PMID:26006262

  13. Enhanced viability and neural differential potential in poor post-thaw hADSCs by agarose multi-well dishes and spheroid culture.

    PubMed

    Guo, Xiaoling; Li, Shanyi; Ji, Qingshan; Lian, Ruiling; Chen, Jiansu

    2015-10-01

    Human adipose-derived stem cells (hADSCs) are potential adult stem cells source for cell therapy. But hADSCs with multi-passage or cryopreservation often revealed poor growth performance. The aim of our work was to improve the activity of poor post-thaw hADSCs by simple and effective means. We describe here a simple method based on commercially available silicone micro-wells for creating hADSCs spheroids to improve viability and neural differentiation potential on poor post-thaw hADSCs. The isolated hADSCs positively expresse d CD29, CD44, CD105, and negatively expressed CD34, CD45, HLA-DR by flow cytometry. Meanwhile, they had adipogenic and osteogenic differentiation capacity. The post-thaw and post-spheroid hADSCs from poor growth status hADSCs showed a marked increase in cell proliferation by CKK-8 analysis, cell cycle analysis and Ki67/P27 quantitative polymerase chain reaction (qPCR) analysis. They also displayed an increase viability of anti-apoptosis by annexin v and propidium iodide assays and mitochondrial membrane potential assays. After 3 days of neural induction, the neural differentiation potential of post-thaw and post-spheroid hADSCs could be enhanced by qPCR analysis and western blotting analysis. These results suggested that the spheroid formation could improve the viability and neural differentiation potential of bad growth status hADSCs, which is conducive to ADSCs research and cell therapy.

  14. Polymorphisms in FZD3 and FZD6 genes and risk of neural tube defects in a northern Han Chinese population.

    PubMed

    Shi, Ou-Yan; Yang, Hui-Yun; Shen, Yong-Ming; Sun, Wei; Cai, Chun-You; Cai, Chun-Quan

    2014-11-01

    Neural tube defects (NTDs) are the most common and severe malformations of the central nervous system. The association of single nucleotide polymorphisms (SNPs) of the Frizzled 3 (FZD3) and Frizzled 6 (FZD6) genes and NTDs in the Han population of northern China was principally studied. One synonymous SNP (rs2241802) in FZD3 gene and three nonsynonymous SNPs (rs827528, rs3808553 and rs12549394) in FZD6 gene were analyzed by polymerase chain reaction (PCR) and sequencing methods in 135 NTD patients and 135 normal controls. The allele, genotype and haplotype frequencies were calculated and analyzed to examine the relationship between FZD3/FZD6 SNPs and NTDs. Both T allele and TT genotype frequencies of the FZD6 rs3808553 loci in the NTDs group were significantly higher than those in the controls, and children with T allele and TT genotype were associated with increased NTDs risk (OR = 1.575, 95 % CI 1.112-2.230, P = 0.010 and OR = 2.811, 95 % CI 1.325-5.967, P = 0.023, respectively). There were no differences among different genotypes or alleles in other three SNPs. Haplotypes A-G-C and A-T-C in FZD6 were found associated with NTDs in the case-control study (OR = 0.560, 95 % CI 0.378-0.830, P = 0.004 and OR = 1.670, 95 % CI 1.126-2.475, P = 0.011, respectively). The rs3808553 of FZD6 is obviously associated with NTDs in Han population of northern China. The TT genotype may increase risk for NTDs.

  15. Association between ALDH1L1 gene polymorphism and neural tube defects in the Chinese Han population.

    PubMed

    Wu, Lihua; Lu, Xiaolin; Guo, Jin; Zhang, Ting; Wang, Fang; Bao, Yihua

    2016-07-01

    We investigated single-nucleotide polymorphisms (SNPs) in the aldehyde dehydrogenase family1 L1 gene (ALDH1L1) and their association with neural tube defects (NTDs) in the Chinese population. A total of 271 NTDs cases and 192 healthy controls were used in this study. A total of 112 selected SNPs in the ALDH1L1 gene were analyzed using the next-generation sequencing method. Statistical analysis was carried out to investigate the correlation between SNPs and patient susceptibility to NTDs. Statistical analysis revealed a significant correlation between the SNP sites rs4646733, rs2305225, and rs2276731 in the ALDH1L1 gene and NTDs. The TT genotype and T allele of rs4646733 in ALDH1L1 were associated with a significantly increased incidence of NTDs [odds ratio (OR) = 2.16, 95 % confidence interval (CI) 1.199-3.896 for genotype; and OR = 1.46, 95 % CI 1.092-1.971 for allele]. The AA genotype and A allele of rs2305225 in ALDH1L1 were associated with a significantly increased incidence of NTDs (OR = 2.03, 95 % CI 1.202-3.646 for genotype, and OR = 1.44, 95 % CI 1.096-1.905 for allele). The CT genotype and C allele of rs2276731 in ALDH1L1 significantly were associated with an increased incidence of NTDs (OR = 1.67, 95 % CI 1.129-2.491 with genotype, and OR = 1.32, 95 % CI 0.956-1.816 with allele).The polymorphic loci rs4646733, rs2305225, and rs2276731 in the ALDH1L1 gene maybe potential risk factors for NTDs in the Chinese population.

  16. Differential Subcellular Localization of the Glucocorticoid Receptor in Distinct Neural Stem and Progenitor Populations of the Mouse Telencephalon In Vivo

    PubMed Central

    Tsiarli, Maria A.; Monaghan, A. Paula; DeFranco, Donald B.

    2013-01-01

    Glucocorticoids are given to pregnant women at risk for premature delivery to promote lung maturation. Despite reports of detrimental effects of glucocorticoids on telencephalic neural stem/progenitor cells (NSPCs), the regional and cellular expression of the glucocorticoid receptor (GR) in various NSPC populations in the intact brain has not been thoroughly assessed. Therefore in this study we performed a detailed analysis of GR protein expression in the developing mouse ventral and dorsal telencephalon in vivo. At embryonic day 11.5 (E11.5), the majority of Pax6-positive radial glial cells (RGCs) and Tbr2-positive intermediate progenitor cells (IPCs) expressed nuclear GR, while a small number of RGCs on the apical ventricular zone (aVZ), expressed cytoplasmic GR. However, on E13.5, the latter population of RGCs increased in size, whereas abventricular NSPCs and especially neurons of the cortical plate, expressed nuclear GR. In IPCs, GR was always nuclear. A similar expression profile was observed throughout the ventral telencephalon, hippocampus and olfactory bulb, with NSPCs of the aVZ primarily expressing cytoplasmic GR, while abventricular NSPCs and mature cells primarily expressed nuclear GR. Close to birth, nuclear GR accumulated within specific cortical areas such as layer V, the subplate and CA1 area of the hippocampus. In summary, our data show that GR protein is present in early NSPCs of the dorsal and ventral telencephalon at E11.5 and primarily occupies the nucleus. Moreover, our study suggests that the subcellular localization of the receptor may be subjected to region and neurodevelopmental stage-specific regulation. PMID:23751362

  17. Effect of blood pressure on the retinal vasculature in a multi-ethnic Asian population.

    PubMed

    Jeganathan, V Swetha E; Sabanayagam, Charumathi; Tai, E Shyong; Lee, Jeannette; Sun, Cong; Kawasaki, Ryo; Nagarajan, Sangeetha; Huey-Shi, Maisie Ho; Sandar, Mya; Wong, Tien Yin

    2009-11-01

    Blood pressure has a significant effect on retinal arterioles. There are few data on whether this effect varies by race/ethnicity. We examined the relationship of blood pressure and retinal vascular caliber in a multi-ethnic Asian population. The study is population-based and cross sectional in design. A total of 3749 Chinese, Malay and Indian participants aged > or =24 years residing in Singapore were included in the study. Retinal vascular caliber was measured using a computer program from digital retinal photographs. The associations of retinal vascular caliber with blood pressure and hypertension in each racial/ethnic group were analyzed. The main outcome measures are retinal arteriolar caliber and venular caliber. The results show that retinal arterioles were narrower in persons with uncontrolled/untreated hypertension (140.0 microm) as compared with persons with controlled hypertension (142.1 microm, P=0.0001) and those with no hypertension (146.0 microm, P<0.0001). On controlling for age, gender, body mass index, lipids and smoking, each 10 mm Hg increase in mean arterial blood pressure was associated with a 3.1 microm decrease in arteriolar caliber (P<0.0001), with a similar magnitude seen in all three racial/ethnic groups: 3.1 microm in Chinese, 2.8 microm in Malays and 3.2 microm in Indians (P<0.0001 for all). Each 10 mm Hg increase in mean arterial blood pressure was associated with a 1.8 microm increase in venular caliber (P<0.0001); furthermore, the magnitude of this effect was similar across the three racial/ethnic groups. The effect of blood pressure on the retinal vasculature was similar across three major racial/ethnic groups in Asia.

  18. Validation of the SQUASH Physical Activity Questionnaire in a Multi-Ethnic Population: The HELIUS Study

    PubMed Central

    Gademan, M. G. J.; Snijder, M. B.; Engelbert, R. H. H.; Dijkshoorn, H.; Terwee, C. B.; Stronks, K.

    2016-01-01

    Purpose To investigate the reliability and validity of the SQUASH physical activity (PA) questionnaire in a multi-ethnic population living in the Netherlands. Methods We included participants from the HELIUS study, a population-based cohort study. In this study we included Dutch (n = 114), Turkish (n = 88), Moroccan (n = 74), South-Asian Surinamese (n = 98) and African Surinamese (n = 91) adults, aged 18–70 years. The SQUASH was self-administered twice to assess test-re-test reliability (mean interval 6–7 weeks) and participants wore an accelerometer and heart rate monitor (Actiheart) to enable assessment of construct validity. Results We observed low test-re-test reliability; Intra class correlation coefficients ranged from low (0.05 for moderate/high intensity PA in African Surinamese women) to acceptable (0.78 for light intensity PA in Moroccan women). The discrepancy between self-reported and measured PA differed on the basis of the intensity of activity: self-reported light intensity PA was lower than measured but self-reported moderate/high intensity PA was higher than measured, with wide limits of agreement. The discrepancy between questionnaire and Actiheart measures of moderate intensity PA did not differ between ethnic minority and Dutch participants with correction for relevant confounders. Additionally, the SQUASH overestimated the number of participants meeting the Dutch PA norm; Cohen’s kappas for the agreement were poor, the highest being 0.30 in Dutch women. Conclusion We found considerable variation in the test-re-test reliability and validity of self-reported PA with no consistency based on ethnic origin. Our findings imply that the SQUASH does not provide a valid basis for comparison of PA between ethnic groups. PMID:27575490

  19. Estimating ancestral proportions in a multi-ethnic US sample: implications for studies of admixed populations

    PubMed Central

    2012-01-01

    This study was designed to determine the ancestral composition of a multi-ethnic sample collected for studies of drug addictions in New York City and Las Vegas, and to examine the reliability of self-identified ethnicity and three-generation family history data. Ancestry biographical scores for seven clusters corresponding to world major geographical regions were obtained using STRUCTURE, based on genotypes of 168 ancestry informative markers (AIMs), for a sample of 1,291 African Americans (AA), European Americans (EA), and Hispanic Americans (HA) along with data from 1,051 HGDP-CEPH ‘diversity panel’ as a reference. Self-identified ethnicity and family history data, obtained in an interview, were accurate in identifying the individual major ancestry in the AA and the EA samples (approximately 99% and 95%, respectively) but were not useful for the HA sample and could not predict the extent of admixture in any group. The mean proportions of the combined clusters corresponding to European and Middle Eastern populations in the AA sample, revealed by AIMs analysis, were 0.13. The HA subjects, predominantly Puerto Ricans, showed a highly variable hybrid contribution pattern of clusters corresponding to Europe (0.27), Middle East (0.27), Africa (0.20), and Central Asia (0.14). The effect of admixture on allele frequencies is demonstrated for two single-nucleotide polymorphisms (118A > G, 17 C > T) of the mu opioid receptor gene (OPRM1). This study reiterates the importance of AIMs in defining ancestry, especially in admixed populations. PMID:23244743

  20. Effect of Population Heterogenization on the Reproducibility of Mouse Behavior: A Multi-Laboratory Study

    PubMed Central

    Richter, S. Helene; Garner, Joseph P.; Zipser, Benjamin; Lewejohann, Lars; Sachser, Norbert; Touma, Chadi; Schindler, Britta; Chourbaji, Sabine; Brandwein, Christiane; Gass, Peter; van Stipdonk, Niek; van der Harst, Johanneke; Spruijt, Berry; Võikar, Vootele; Wolfer, David P.; Würbel, Hanno

    2011-01-01

    In animal experiments, animals, husbandry and test procedures are traditionally standardized to maximize test sensitivity and minimize animal use, assuming that this will also guarantee reproducibility. However, by reducing within-experiment variation, standardization may limit inference to the specific experimental conditions. Indeed, we have recently shown in mice that standardization may generate spurious results in behavioral tests, accounting for poor reproducibility, and that this can be avoided by population heterogenization through systematic variation of experimental conditions. Here, we examined whether a simple form of heterogenization effectively improves reproducibility of test results in a multi-laboratory situation. Each of six laboratories independently ordered 64 female mice of two inbred strains (C57BL/6NCrl, DBA/2NCrl) and examined them for strain differences in five commonly used behavioral tests under two different experimental designs. In the standardized design, experimental conditions were standardized as much as possible in each laboratory, while they were systematically varied with respect to the animals' test age and cage enrichment in the heterogenized design. Although heterogenization tended to improve reproducibility by increasing within-experiment variation relative to between-experiment variation, the effect was too weak to account for the large variation between laboratories. However, our findings confirm the potential of systematic heterogenization for improving reproducibility of animal experiments and highlight the need for effective and practicable heterogenization strategies. PMID:21305027

  1. [Multi-population elitists shared genetic algorithm for outlier detection of spectroscopy analysis].

    PubMed

    Cao, Hui; Zhou, Yan

    2011-07-01

    The present paper proposed an outlier detection method for spectral analysis based on multi-population elitists shared genetic algorithm. The method was exploited in the NIR data set analysis to remove the outliers from the data set, and partial least squares (PLS) was combined with the proposed method to build a prediction model. In contrast with Monte Carlo cross validation, leave-one-out cross validation, Mahalanobis-distance and traditional genetic algorithm for outlier detection, the prediction residual error sum of squares (PRESS) for moisture prediction model based on the proposed method decreases in the rate of 72.4%, 39.5%, 39.5% and 14.5%; the PRESS value for fat prediction model decreases in the rate of 86.2%, 75.9%, 84.9% and 19.9%; and the PRESS value for protein prediction model decreases in the rate of 56.5%, 35.7%, 35.7% and 18.2% respectively. Results indicated that the method is applicable for spectral outlier detection for different species, and the model based on the data set without the removed outliers is more accurate and robust.

  2. Multi-Infections of Feminizing Wolbachia Strains in Natural Populations of the Terrestrial Isopod Armadillidium Vulgare

    PubMed Central

    Valette, Victorien; Bitome Essono, Paul-Yannick; Le Clec’h, Winka; Johnson, Monique; Bech, Nicolas; Grandjean, Frédéric

    2013-01-01

    Maternally inherited Wolbachia (α-Proteobacteria) are widespread parasitic reproductive manipulators. A growing number of studies have described the presence of different Wolbachia strains within a same host. To date, no naturally occurring multiple infections have been recorded in terrestrial isopods. This is true for Armadillidium vulgare which is known to harbor non simultaneously three Wolbachia strains. Traditionally, such Wolbachia are detected by PCR amplification of the wsp gene and strains are characterized by sequencing. The presence of nucleotide deletions or insertions within the wsp gene, among these three different strains, provides the opportunity to test a novel genotyping method. Herein, we designed a new primer pair able to amplify products whose lengths are specific to each Wolbachia strain so as to detect the presence of multi-infections in A. vulgare. Experimental injections of Wolbachia strains in Wolbachia-free females were used to validate the methodology. We re-investigated, using this novel method, the infection status of 40 females sampled in 2003 and previously described as mono-infected based on the classical sequencing method. Among these females, 29 were identified as bi-infected. It is the first time that naturally occuring multiple infections of Wolbachia are detected within an individual A. vulgare host. Additionally, we resampled 6 of these populations in 2010 to check the infection status of females. PMID:24324814

  3. Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding.

    PubMed

    Bandillo, Nonoy; Raghavan, Chitra; Muyco, Pauline Andrea; Sevilla, Ma Anna Lynn; Lobina, Irish T; Dilla-Ermita, Christine Jade; Tung, Chih-Wei; McCouch, Susan; Thomson, Michael; Mauleon, Ramil; Singh, Rakesh Kumar; Gregorio, Glenn; Redoña, Edilberto; Leung, Hei

    2013-05-06

    This article describes the development of Multi-parent Advanced Generation Inter-Cross populations (MAGIC) in rice and discusses potential applications for mapping quantitative trait loci (QTLs) and for rice varietal development. We have developed 4 multi-parent populations: indica MAGIC (8 indica parents); MAGIC plus (8 indica parents with two additional rounds of 8-way F1 inter-crossing); japonica MAGIC (8 japonica parents); and Global MAGIC (16 parents - 8 indica and 8 japonica). The parents used in creating these populations are improved varieties with desirable traits for biotic and abiotic stress tolerance, yield, and grain quality. The purpose is to fine map QTLs for multiple traits and to directly and indirectly use the highly recombined lines in breeding programs. These MAGIC populations provide a useful germplasm resource with diverse allelic combinations to be exploited by the rice community. The indica MAGIC population is the most advanced of the MAGIC populations developed thus far and comprises 1328 lines produced by single seed descent (SSD). At the S4 stage of SSD a subset (200 lines) of this population was genotyped using a genotyping-by-sequencing (GBS) approach and was phenotyped for multiple traits, including: blast and bacterial blight resistance, salinity and submergence tolerance, and grain quality. Genome-wide association mapping identified several known major genes and QTLs including Sub1 associated with submergence tolerance and Xa4 and xa5 associated with resistance to bacterial blight. Moreover, the genome-wide association study (GWAS) results also identified potentially novel loci associated with essential traits for rice improvement. The MAGIC populations serve a dual purpose: permanent mapping populations for precise QTL mapping and for direct and indirect use in variety development. Unlike a set of naturally diverse germplasm, this population is tailor-made for breeders with a combination of useful traits derived from multiple elite

  4. Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population.

    PubMed

    Hozé, C; Fritz, S; Phocas, F; Boichard, D; Ducrocq, V; Croiseau, P

    2014-01-01

    Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed

  5. "Proprioceptive signature" of cursive writing in humans: a multi-population coding.

    PubMed

    Roll, Jean-Pierre; Albert, Frédéric; Ribot-Ciscar, Edith; Bergenheim, Mikael

    2004-08-01

    The goal of the present study was to investigate the firing behavior of populations of muscle spindle afferents in all the muscles acting on the ankle while this joint was being subjected to "writing-like" movements. First it was proposed to determine whether the ensemble of muscle spindles give rise to a unique, specific, and reproducible feedback information characterizing each letter, number or short word. Secondly, we analyzed how the proprioceptive feedback on the whole encodes the spatial and temporal characteristics of writing movements using the "vector population model". The unitary activity of 51 primary and secondary muscle spindle afferents was recorded in the tibial and common peroneal nerves at the level of the popliteal fossea, using the microneurographic method. The units recorded from belonged to the tibialis anterior, the extensor digitorum longus, the extensor hallucis longus, the peroneus lateralis, the gastrocnemius-soleus and the tibialis posterior muscles. The "writing-like" movements were randomly imposed at a "natural" velocity via a computer-controlled machine in a two-dimensional space. In general, muscle spindle afferents from any of the six muscles responded according to the tuning properties of the parent muscle, i.e. increasing their discharge rate during the phases where the direction of movement was within the preferred sensory sector of the parent muscle. The whole trajectory of the writing movements was coded in turn by the activity of Ia afferents arising from all the muscles acting on the joint. Both single afferent responses and population responses were found to be highly specific and reproducible with each graphic sign. The complex multi-muscle afferent pattern involved, with its timing and distribution in the muscle space, seems to constitute a true "proprioceptive signature" for each graphic symbol. The ensemble of muscle spindle afferents were therefore found to encode the instantaneous direction and velocity of writing

  6. Stellar populations of galaxies in the ALHAMBRA survey up to z ~ 1. I. MUFFIT: A multi-filter fitting code for stellar population diagnostics

    NASA Astrophysics Data System (ADS)

    Díaz-García, L. A.; Cenarro, A. J.; López-Sanjuan, C.; Ferreras, I.; Varela, J.; Viironen, K.; Cristóbal-Hornillos, D.; Moles, M.; Marín-Franch, A.; Arnalte-Mur, P.; Ascaso, B.; Cerviño, M.; González Delgado, R. M.; Márquez, I.; Masegosa, J.; Molino, A.; Pović, M.; Alfaro, E.; Aparicio-Villegas, T.; Benítez, N.; Broadhurst, T.; Cabrera-Caño, J.; Castander, F. J.; Cepa, J.; Fernández-Soto, A.; Husillos, C.; Infante, L.; Aguerri, J. A. L.; Martínez, V. J.; del Olmo, A.; Perea, J.; Prada, F.; Quintana, J. M.; Gruel, N.

    2015-10-01

    Aims: We present MUFFIT, a new generic code optimized to retrieve the main stellar population parameters of galaxies in photometric multi-filter surveys, and check its reliability and feasibility with real galaxy data from the ALHAMBRA survey. Methods: Making use of an error-weighted χ2-test, we compare the multi-filter fluxes of galaxies with the synthetic photometry of mixtures of two single stellar populations at different redshifts and extinctions, to provide the most likely range of stellar population parameters (mainly ages and metallicities), extinctions, redshifts, and stellar masses. To improve the diagnostic reliability, MUFFIT identifies and removes from the analysis those bands that are significantly affected by emission lines. The final parameters and their uncertainties are derived by a Monte Carlo method, using the individual photometric uncertainties in each band. Finally, we discuss the accuracies, degeneracies, and reliability of MUFFIT using both simulated and real galaxies from ALHAMBRA, comparing with results from the literature. Results: MUFFIT is a precise and reliable code to derive stellar population parameters of galaxies in ALHAMBRA. Using the results from photometric-redshift codes as input, MUFFIT improves the photometric-redshift accuracy by ~10-20%. MUFFIT also detects nebular emissions in galaxies, providing physical information about their strengths. The stellar masses derived from MUFFIT show excellent agreement with the COSMOS and SDSS values. In addition, the retrieved age-metallicity locus for a sample of z ≤ 0.22 early-type galaxies in ALHAMBRA at different stellar mass bins are in very good agreement with the ones from SDSS spectroscopic diagnostics. Moreover, a one-to-one comparison between the redshifts, ages, metallicities, and stellar masses derived spectroscopically for SDSS and by MUFFIT for ALHAMBRA reveals good qualitative agreements in all the parameters, hence reinforcing the strengths of multi-filter galaxy data

  7. Teaching handwashing with soap for schoolchildren in a multi-ethnic population in northern rural Vietnam.

    PubMed

    Xuan, Le Thi Thanh; Rheinländer, Thilde; Hoat, Luu Ngoc; Dalsgaard, Anders; Konradsen, Flemming

    2013-04-24

    In Vietnam, initiatives have been started aimed at increasing the practice of handwashing with soap (HWWS) among primary schoolchildren. However, compliance remains low. This study aims to investigate responses to a teacher-centred participatory HWWS intervention in a multi-ethnic population of primary schoolchildren in northern rural Vietnam. This study was implemented in two phases: a formative research project over 5 months (July-November 2008) and an action research project with a school-based HWWS intervention study in two rural communes during 5 months (May, September-December 2010). Based upon knowledge from the formative research in 2008, schoolteachers from four selected schools in the study communes actively participated in designing and implementing a HWWS intervention. Qualitative data was collected during the intervention to evaluate the responses and reaction to the intervention of teachers, children and parents. This included semi-structured interviews with children (15), and their parents (15), focus group discussions (FGDs) with schoolchildren (32) and school staff (20) and observations during 15 HWWS involving children. Observations and interview data from children demonstrated that children were visibly excited and pleased with HWWS sessions where teachers applied active teaching methods including rewards, games and HWWS demonstrations. All children, schoolteachers and parents also viewed the HWWS intervention as positive and feasible, irrespective of ethnicity, gender of schoolchildren and background of schoolteachers. However, some important barriers were indicated for sustaining and transferring the HWWS practice to the home setting including limited emphasis on hygiene in the standard curriculum of schools, low priority and lack of time given to practical teaching methods and lack of guidance and reminding HWWS on a regular basis at home, in particular by highland parents, who spend most of their time working away from home in the fields

  8. Teaching handwashing with soap for schoolchildren in a multi-ethnic population in northern rural Vietnam

    PubMed Central

    Xuan, Le Thi Thanh; Rheinländer, Thilde; Hoat, Luu Ngoc; Dalsgaard, Anders; Konradsen, Flemming

    2013-01-01

    Background In Vietnam, initiatives have been started aimed at increasing the practice of handwashing with soap (HWWS) among primary schoolchildren. However, compliance remains low. Objective This study aims to investigate responses to a teacher-centred participatory HWWS intervention in a multi-ethnic population of primary schoolchildren in northern rural Vietnam. Design This study was implemented in two phases: a formative research project over 5 months (July–November 2008) and an action research project with a school-based HWWS intervention study in two rural communes during 5 months (May, September–December 2010). Based upon knowledge from the formative research in 2008, schoolteachers from four selected schools in the study communes actively participated in designing and implementing a HWWS intervention. Qualitative data was collected during the intervention to evaluate the responses and reaction to the intervention of teachers, children and parents. This included semi-structured interviews with children (15), and their parents (15), focus group discussions (FGDs) with schoolchildren (32) and school staff (20) and observations during 15 HWWS involving children. Results Observations and interview data from children demonstrated that children were visibly excited and pleased with HWWS sessions where teachers applied active teaching methods including rewards, games and HWWS demonstrations. All children, schoolteachers and parents also viewed the HWWS intervention as positive and feasible, irrespective of ethnicity, gender of schoolchildren and background of schoolteachers. However, some important barriers were indicated for sustaining and transferring the HWWS practice to the home setting including limited emphasis on hygiene in the standard curriculum of schools, low priority and lack of time given to practical teaching methods and lack of guidance and reminding HWWS on a regular basis at home, in particular by highland parents, who spend most of their time

  9. High precision spatial and temporal control of neural circuitry using a semi-automated multi-wavelength nanopatterning system

    NASA Astrophysics Data System (ADS)

    Mitnala, Sandhya; Huebshman, Michael; Herold, Christian; Herz, Joachim; Garner, Harold

    2009-02-01

    It has been one of the most discussed and intriguing topics -the quest to control neural circuitry as a precursor to decoding the operations of the human brain and manipulating its diseased state. Electrophysiology has created a gateway to control this circuitry with high precision. However, it is not practical to apply these techniques to living systems because these techniques are invasive and lack the spatial resolution necessary to properly address various neural cell components, cell assemblies or even tissues. Here we describe a new instrument that has the potential to replace the conventional patch clamping technique, the workhorse of neural physiology. A Digital Light Processing system from Texas Instruments and an Olympus IX71 inverted microscope were combined to achieve neuronal control at a subcellular spatial resolution. Accompanying these two technologies can be almost any light source, and for these experiments a pair of pulsed light sources that produced two pulse trains at different wavelengths tuned to activate or inactivate selectively the ChR2 and NpHR channels that were cloned to express light sensitive versions in neurons. Fura- 2 ratiometric fluorescent dye would be used to read-out calcium activity. The Pulsed light sources and a filter wheel are under computer control using a National Instruments digital control board and a CCD camera used to acquire real time cellular responses to the spatially controlled pulsed light channel activation would be controlled and synchronized using NI LabVIEW software. This will provide for a millisecond precision temporal control of neural circuitry. Thus this technology could provide researchers with an optical tool to control the neural circuitry both spatially and temporally with high precision.

  10. Optical Neural Interfaces

    PubMed Central

    Warden, Melissa R.; Cardin, Jessica A.; Deisseroth, Karl

    2014-01-01

    Genetically encoded optical actuators and indicators have changed the landscape of neuroscience, enabling targetable control and readout of specific components of intact neural circuits in behaving animals. Here, we review the development of optical neural interfaces, focusing on hardware designed for optical control of neural activity, integrated optical control and electrical readout, and optical readout of population and single-cell neural activity in freely moving mammals. PMID:25014785

  11. Portfolio theory as a management tool to guide conservation and restoration of multi-stock fish populations

    USGS Publications Warehouse

    DuFour, Mark R.; May, Cassandra J.; Roseman, Edward F.; Ludsin, Stuart A.; Vandergoot, Christopher S.; Pritt, Jeremy J.; Fraker, Michael E.; Davis, Jeremiah J.; Tyson, Jeffery T.; Miner, Jeffery G.; Marschall, Elizabeth A.; Mayer, Christine M.

    2015-01-01

    Habitat degradation and harvest have upset the natural buffering mechanism (i.e., portfolio effects) of many large-scale multi-stock fisheries by reducing spawning stock diversity that is vital for generating population stability and resilience. The application of portfolio theory offers a means to guide management activities by quantifying the importance of multi-stock dynamics and suggesting conservation and restoration strategies to improve naturally occurring portfolio effects. Our application of portfolio theory to Lake Erie Sander vitreus (walleye), a large population that is supported by riverine and open-lake reef spawning stocks, has shown that portfolio effects generated by annual inter-stock larval fish production are currently suboptimal when compared to potential buffering capacity. Reduced production from riverine stocks has resulted in a single open-lake reef stock dominating larval production, and in turn, high inter-annual recruitment variability during recent years. Our analyses have shown (1) a weak average correlation between annual river and reef larval production (ρ̄ = 0.24), suggesting that a natural buffering capacity exists in the population, and (2) expanded annual production of larvae (potential recruits) from riverine stocks could stabilize the fishery by dampening inter-annual recruitment variation. Ultimately, our results demonstrate how portfolio theory can be used to quantify the importance of spawning stock diversity and guide management on ecologically relevant scales (i.e., spawning stocks) leading to greater stability and resilience of multi-stock populations and fisheries.

  12. Dyslipidemia in pregnancy may contribute to increased risk of neural tube defects -a pilot study in north Indian population.

    PubMed

    Gupta, Supriya; Arora, Sarika; Trivedi, S S; Singh, Ritu

    2009-04-01

    Neural tube defects are congenital structural abnormalities of the brain and vertebral column resulting from improper or non-timely closure of the neural tube. Prevalence of neural tube defects is reported to be higher among women with diabetes mellitus and obesity. This study was designed to investigate the relation between the presence of dyslipidemia in antenatal patients and the risk of fetal neural tube defects. The present study was an observational, cross-sectional study involving 129 pregnant women in 16 to 18 weeks gestation period. Of these, 80 women had normal pregnancies and 49 were clinically high-risk cases for neural tube defects. Fasting blood samples were analyzed for blood sugar and lipid profile by enzymatic assay and alpha-fetoprotein levels using Enzyme Immunoassay. Alpha-fetoprotein (AFP) values were converted to Multiples of Median (MoM) appropriate for the gestational age. Based on AFP values, women were labeled as screen negative (AFP <2 MoM, n= 102) and screen positive (AFP > 2 MoM, n =27). Screen positive women were further evaluated by ultrasound and 21 women were found to carry a neural tube defects positive pregnancy. Statistical analysis was done on SPSS software. Body weight of the women showed a significant positive correlation with serum triglycerides, plasma sugar and AFP MoM values. A significant difference was observed in serum cholesterol levels (p= 0.038), triglycerides (p=0.001) and plasma sugar levels (p=0.002) between normal women and those with neural tube defects positive pregnancy. The Odds ratio for neural tube defects risk in dyslipidemic cases was 24.23 (CI 4.73 - 148.60) with a relative risk of 12.12. Dyslipidemia especially hypertriglyceridemia was found to be significantly associated with fetal neural tube defects.

  13. Levels of Folate Receptor Autoantibodies in Maternal and Cord Blood and Risk of Neural Tube Defects in a Chinese population

    PubMed Central

    Yang, Na; Wang, Linlin; Finnell, Richard H.; Li, Zhiwen; Jin, Lei; Zhang, Le; Cabrera, Robert M.; Ye, Rongwei; Ren, Aiguo

    2016-01-01

    Background After years of periconceptional folic acid supplementation, the prevalence of neural tube defects (NTDs) remains stable following the remarkable reduction observed immediately after the fortification practice. There is accumulating evidence that folate receptor (FR) autoimmunity may play a role in the etiology of folate-sensitive NTDs. Methods From 2011 to 2013, 118 NTD cases and 242 healthy controls were recruited from a population-based birth defects surveillance system in Northern China. Enzyme-linked immunosorbent assay was used to measure FR autoantibodies in maternal and cord blood. Logistic regression models were used to estimate the odds ratios (OR) and 95% confidence intervals (95% CI). Results Plasma FR autoantibodies levels were significantly elevated in mothers of infants with NTDs compared with mothers of healthy controls. Using the lowest tertile as the referent group, 2.20-fold (95% CI, 0.71–6.80) and 5.53-fold increased odds (95% CI, 1.90–16.08) of NTDs were observed for the second and third tertile of immunoglobulin G (IgG), respectively, and the odds of NTDs for each successive tertile of IgM was 0.98 (95% CI, 0.35–2.75) and 3.49 (95% CI, 1.45–8.39), respectively. A dose–response relationship was found between FR autoantibodies levels and risk of NTDs (P < 0.001 for IgG, P = 0.002 for IgM). The same pattern was observed in both subtypes of spina bifida and anencephaly. No significant difference in levels of cord blood FR autoantibodies was observed. Conclusion Higher levels of FR autoimmunity in maternal plasma are associated with elevated risk of NTDs in a dose–response manner. PMID:27166990

  14. A population-based case-control study of risk factors for neural tube defects in Shenyang, China.

    PubMed

    Yin, Zhihua; Xu, Wei; Xu, Changying; Zhang, Shiqi; Zheng, Yajun; Wang, Wei; Zhou, Baosen

    2011-01-01

    To explore the risk factors for neural tube defects (NTD) in Shenyang, we carried out a population-based case-control study. We used chi-square test or Fisher's exact test to evaluate variations in the prevalence by selected covariates. Adjusted odds ratios and 95% confidence intervals were derived from univariate and multivariable conditional logistic models. A history of maternal previous birth defect-affected pregnancy was a risk factor for NTDs (adjusted OR = 4.00, 95%CI = 1.29-12.45). Risks for NTDs were significantly associated with exposure to maternal factors during the periconceptional period such as a history of fever or cold (adjusted OR = 6.36, 95%CI = 3.24-12.52), use of analgesic and antipyretic drugs (adjusted OR = 4.94, 95%CI = 1.79-13.63), oral contraceptive use (adjusted OR 2.06, 95%CI 1.16, 3.68), and passive smoking (adjusted OR = 2.24, 95%CI = 1.04-4.81). Folic acid tablets use and fresh vegetable or fruit consumption ≥6 meals a week in periconception appeared to be protective factors (adjusted OR 0.33, 0.55, and 0.40, and 95%CI 0.13-0.44, 0.30-1.01, and 0.21-0.74, respectively). Differences in risk were found between the two most common phenotypes of NTD, anencephaly, and spina bifida. This study suggests that a history of previous birth defect-affected pregnancy, a history of maternal fever or cold, use of analgesics, antipyretics, and oral contraceptives, exposure to passive smoking, folic acid use, and consumption of fresh vegetable and fruit may be associated with NTD risk.

  15. Closed-loop control of epileptiform activities in a neural population model using a proportional-derivative controller

    NASA Astrophysics Data System (ADS)

    Wang, Jun-Song; Wang, Mei-Li; Li, Xiao-Li; Ernst, Niebur

    2015-03-01

    Epilepsy is believed to be caused by a lack of balance between excitation and inhibitation in the brain. A promising strategy for the control of the disease is closed-loop brain stimulation. How to determine the stimulation control parameters for effective and safe treatment protocols remains, however, an unsolved question. To constrain the complex dynamics of the biological brain, we use a neural population model (NPM). We propose that a proportional-derivative (PD) type closed-loop control can successfully suppress epileptiform activities. First, we determine the stability of root loci, which reveals that the dynamical mechanism underlying epilepsy in the NPM is the loss of homeostatic control caused by the lack of balance between excitation and inhibition. Then, we design a PD type closed-loop controller to stabilize the unstable NPM such that the homeostatic equilibriums are maintained; we show that epileptiform activities are successfully suppressed. A graphical approach is employed to determine the stabilizing region of the PD controller in the parameter space, providing a theoretical guideline for the selection of the PD control parameters. Furthermore, we establish the relationship between the control parameters and the model parameters in the form of stabilizing regions to help understand the mechanism of suppressing epileptiform activities in the NPM. Simulations show that the PD-type closed-loop control strategy can effectively suppress epileptiform activities in the NPM. Project supported by the National Natural Science Foundation of China (Grant Nos. 61473208, 61025019, and 91132722), ONR MURI N000141010278, and NIH grant R01EY016281.

  16. Tcf3 Represses Wnt–β-Catenin Signaling and Maintains Neural Stem Cell Population during Neocortical Development

    PubMed Central

    Itoh, Yasuhiro; Hirabayashi, Yusuke; Gotoh, Yukiko

    2014-01-01

    During mouse neocortical development, the Wnt–β-catenin signaling pathway plays essential roles in various phenomena including neuronal differentiation and proliferation of neural precursor cells (NPCs). Production of the appropriate number of neurons without depletion of the NPC population requires precise regulation of the balance between differentiation and maintenance of NPCs. However, the mechanism that suppresses Wnt signaling to prevent premature neuronal differentiation of NPCs is poorly understood. We now show that the HMG box transcription factor Tcf3 (also known as Tcf7l1) contributes to this mechanism. Tcf3 is highly expressed in undifferentiated NPCs in the mouse neocortex, and its expression is reduced in intermediate neuronal progenitors (INPs) committed to the neuronal fate. We found Tcf3 to be a repressor of Wnt signaling in neocortical NPCs in a reporter gene assay. Tcf3 bound to the promoter of the proneural bHLH gene Neurogenin1 (Neurog1) and repressed its expression. Consistent with this, Tcf3 repressed neuronal differentiation and increased the self-renewal activity of NPCs. We also found that Wnt signal stimulation reduces the level of Tcf3, and increases those of Tcf1 (also known as Tcf7) and Lef1, positive mediators of Wnt signaling, in NPCs. Together, these results suggest that Tcf3 antagonizes Wnt signaling in NPCs, thereby maintaining their undifferentiated state in the neocortex and that Wnt signaling promotes the transition from Tcf3-mediated repression to Tcf1/Lef1-mediated enhancement of Wnt signaling, constituting a positive feedback loop that facilitates neuronal differentiation. PMID:24832538

  17. Predicting symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network in a pediatric population.

    PubMed

    Skoch, Jesse; Tahir, Rizwan; Abruzzo, Todd; Taylor, John M; Zuccarello, Mario; Vadivelu, Sudhakar

    2017-08-29

    Artificial neural networks (ANN) are increasingly applied to complex medical problem solving algorithms because their outcome prediction performance is superior to existing multiple regression models. ANN can successfully identify symptomatic cerebral vasospasm (SCV) in adults presenting after aneurysmal subarachnoid hemorrhage (aSAH). Although SCV is unusual in children with aSAH, the clinical consequences are severe. Consequently, reliable tools to predict patients at greatest risk for SCV may have significant value. We applied ANN modeling to a consecutive cohort of pediatric aSAH cases to assess its ability to predict SCV. A retrospective chart review was conducted to identify patients < 21 years of age who presented with spontaneously ruptured, non-traumatic, non-mycotic, non-flow-related intracranial arterial aneurysms to our institution between January 2002 and January 2015. Demographics, clinical, radiographic, and outcome data were analyzed using an adapted ANN model using learned value nodes from the adult aneurysmal SAH dataset previously reported. The strength of the ANN prediction was measured between - 1 and 1 with - 1 representing no likelihood of SCV and 1 representing high likelihood of SCV. Sixteen patients met study inclusion criteria. The median age for aSAH patients was 15 years. Ten underwent surgical clipping and 6 underwent endovascular coiling for definitive treatment. One patient experienced SCV and 15 did not. The ANN applied here was able to accurately predict all 16 outcomes. The mean strength of prediction for those who did not exhibit SCV was - 0.86. The strength for the one patient who did exhibit SCV was 0.93. Adult-derived aneurysmal SAH value nodes can be applied to a simple AAN model to accurately predict SCV in children presenting with aSAH. Further work is needed to determine if ANN models can prospectively predict SCV in the pediatric aSAH population in toto; adapted to include mycotic, traumatic, and flow

  18. Gender Disparities among Intracerebral Hemorrhage Patients from a Multi-ethnic Population.

    PubMed

    Galati, Alexandra; King, Sage L; Nakagawa, Kazuma

    2015-09-01

    Intracerebral hemorrhage (ICH) is a hemorrhagic stroke with high morbidity and mortality. Recent studies have shown that minorities such as Native Hawaiians and other Pacific Islanders (NHOPI) with ICH are significantly younger compared to whites. However, the interaction of race and gender, and its impact on observed disparities among a multi-ethnic population in Hawai'i, have not been studied. Consecutive ICH patients (whites, Asians or NHOPI), who were hospitalized at a single tertiary center on O'ahu between 2006 and 2013 were retrospectively studied. Clinical characteristics were compared between men and women among the entire cohort, and within the major racial groups. A total of 791 patients (NHOPI 19%, Asians 65%, whites 16%) were studied. Overall, men were younger than women (62±16 years vs 67±18 years respectively, P < .0001). Among whites, ages of men and women were similar (men: 67±14 years vs women: 67±17 years, P = .86). However, among Asians, men were significantly younger than women (men: 63±16 years vs women: 70±17 years, P < .0001). Among NHOPI, ages of men and women were similar (men: 53±15 years vs women: 56±17 years, P = .34), although NHOPI group overall had significantly younger age compared to whites and Asians (NHOPI: 54±16 years vs whites: 67±15 years, P < .0001; vs Asians: 66±17, P < .0001). Overall, men have younger age of ICH presentation than women. However, this observed gender difference was most significant among Asians, but not among whites or NHOPI.

  19. Gender Disparities among Intracerebral Hemorrhage Patients from a Multi-ethnic Population

    PubMed Central

    Galati, Alexandra; King, Sage L

    2015-01-01

    Background: Intracerebral hemorrhage (ICH) is a hemorrhagic stroke with high morbidity and mortality. Recent studies have shown that minorities such as Native Hawaiians and other Pacific Islanders (NHOPI) with ICH are significantly younger compared to whites. However, the interaction of race and gender, and its impact on observed disparities among a multi-ethnic population in Hawai‘i, have not been studied. Methods: Consecutive ICH patients (whites, Asians or NHOPI), who were hospitalized at a single tertiary center on O‘ahu between 2006 and 2013 were retrospectively studied. Clinical characteristics were compared between men and women among the entire cohort, and within the major racial groups. Results: A total of 791 patients (NHOPI 19%, Asians 65%, whites 16%) were studied. Overall, men were younger than women (62±16 years vs 67±18 years respectively, P < .0001). Among whites, ages of men and women were similar (men: 67±14 years vs women: 67±17 years, P = .86). However, among Asians, men were significantly younger than women (men: 63±16 years vs women: 70±17 years, P < .0001). Among NHOPI, ages of men and women were similar (men: 53±15 years vs women: 56±17 years, P = .34), although NHOPI group overall had significantly younger age compared to whites and Asians (NHOPI: 54±16 years vs whites: 67±15 years, P < .0001; vs Asians: 66±17, P < .0001). Conclusions: Overall, men have younger age of ICH presentation than women. However, this observed gender difference was most significant among Asians, but not among whites or NHOPI. PMID:26793409

  20. Ethnic differences in fetal size and growth in a multi-ethnic population.

    PubMed

    Sletner, Line; Rasmussen, Svein; Jenum, Anne Karen; Nakstad, Britt; Jensen, Odd Harald Rognerud; Vangen, Siri

    2015-09-01

    Impaired or excessive fetal growth is associated with adverse short- and long-term health outcomes that differ between ethnic groups. We explored ethnic differences in fetal size and growth from mid pregnancy until birth. Data are from the multi-ethnic STORK-Groruddalen study, a population-based, prospective cohort of 823 pregnant women and their offspring in Oslo, Norway. Measures were z-scores of estimated fetal weight (EFW), head circumference (HC), abdominal circumference (AC) and femur length (FL), in gestational week 24, 32 and 37, measured by ultrasound, and similar measures at birth. Differences in fetal size and growth were assessed using separate Linear Mixed Models including all four time points, with ethnic Europeans as reference. In week 24 South Asian fetuses had smaller AC, but larger FL than Europeans, and slightly lower EFW (-0.17 SD (-0.33, -0.01), p=0.04). Middle East/North African fetuses also had larger FL, but similar AC, and hence slightly higher EFW (0.18 (0.003, 0.36), p=0.05). Both groups had slower growth of AC, FL and EFW from this time until birth, and had -0.61 SD (-0.73, -0.49) and -0.28 SD (-0.41, -0.15) lower birth weight respectively. Ethnic East Asians, on the other hand, were smaller throughout pregnancy and had -0.58 SD (-0.82, -0.34) lower birth weight. Significant ethnic differences remained after adjusting for maternal factors. We observed ethnic differences in fetal size and body proportions already in gestational week 24, and in fetal growth from this time until birth, which were only partly explained by key maternal factors. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  1. Accurate, multi-kb reads resolve complex populations and detect rare microorganisms

    PubMed Central

    Sharon, Itai; Kertesz, Michael; Hug, Laura A.; Pushkarev, Dmitry; Blauwkamp, Timothy A.; Castelle, Cindy J.; Amirebrahimi, Mojgan; Thomas, Brian C.; Burstein, David; Tringe, Susannah G.; Williams, Kenneth H.

    2015-01-01

    Accurate evaluation of microbial communities is essential for understanding global biogeochemical processes and can guide bioremediation and medical treatments. Metagenomics is most commonly used to analyze microbial diversity and metabolic potential, but assemblies of the short reads generated by current sequencing platforms may fail to recover heterogeneous strain populations and rare organisms. Here we used short (150-bp) and long (multi-kb) synthetic reads to evaluate strain heterogeneity and study microorganisms at low abundance in complex microbial communities from terrestrial sediments. The long-read data revealed multiple (probably dozens of) closely related species and strains from previously undescribed Deltaproteobacteria and Aminicenantes (candidate phylum OP8). Notably, these are the most abundant organisms in the communities, yet short-read assemblies achieved only partial genome coverage, mostly in the form of short scaffolds (N50 = ∼2200 bp). Genome architecture and metabolic potential for these lineages were reconstructed using a new synteny-based method. Analysis of long-read data also revealed thousands of species whose abundances were <0.1% in all samples. Most of the organisms in this “long tail” of rare organisms belong to phyla that are also represented by abundant organisms. Genes encoding glycosyl hydrolases are significantly more abundant than expected in rare genomes, suggesting that rare species may augment the capability for carbon turnover and confer resilience to changing environmental conditions. Overall, the study showed that a diversity of closely related strains and rare organisms account for a major portion of the communities. These are probably common features of many microbial communities and can be effectively studied using a combination of long and short reads. PMID:25665577

  2. Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

    PubMed

    Zhang, Jianhua; Li, Sunan; Wang, Rubin

    2017-01-01

    In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

  3. Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression - A quantitative structure property relationship approach.

    PubMed

    Parinet, Julien; Julien, Maxime; Nun, Pierrick; Robins, Richard J; Remaud, Gerald; Höhener, Patrick

    2015-09-01

    We aim at predicting the effect of structure and isotopic substitutions on the equilibrium vapour pressure isotope effect of various organic compounds (alcohols, acids, alkanes, alkenes and aromatics) at intermediate temperatures. We attempt to explore quantitative structure property relationships by using artificial neural networks (ANN); the multi-layer perceptron (MLP) and compare the performances of it with multi-linear regression (MLR). These approaches are based on the relationship between the molecular structure (organic chain, polar functions, type of functions, type of isotope involved) of the organic compounds, and their equilibrium vapour pressure. A data set of 130 equilibrium vapour pressure isotope effects was used: 112 were used in the training set and the remaining 18 were used for the test/validation dataset. Two sets of descriptors were tested, a set with all the descriptors: number of(12)C, (13)C, (16)O, (18)O, (1)H, (2)H, OH functions, OD functions, CO functions, Connolly Solvent Accessible Surface Area (CSA) and temperature and a reduced set of descriptors. The dependent variable (the output) is the natural logarithm of the ratios of vapour pressures (ln R), expressed as light/heavy as in classical literature. Since the database is rather small, the leave-one-out procedure was used to validate both models. Considering higher determination coefficients and lower error values, it is concluded that the multi-layer perceptron provided better results compared to multi-linear regression. The stepwise regression procedure is a useful tool to reduce the number of descriptors. To our knowledge, a Quantitative Structure Property Relationship (QSPR) approach for isotopic studies is novel.

  4. Stochastic multi-scale models of competition within heterogeneous cellular populations: Simulation methods and mean-field analysis.

    PubMed

    Cruz, Roberto de la; Guerrero, Pilar; Spill, Fabian; Alarcón, Tomás

    2016-10-21

    We propose a modelling framework to analyse the stochastic behaviour of heterogeneous, multi-scale cellular populations. We illustrate our methodology with a particular example in which we study a population with an oxygen-regulated proliferation rate. Our formulation is based on an age-dependent stochastic process. Cells within the population are characterised by their age (i.e. time elapsed since they were born). The age-dependent (oxygen-regulated) birth rate is given by a stochastic model of oxygen-dependent cell cycle progression. Once the birth rate is determined, we formulate an age-dependent birth-and-death process, which dictates the time evolution of the cell population. The population is under a feedback loop which controls its steady state size (carrying capacity): cells consume oxygen which in turn fuels cell proliferation. We show that our stochastic model of cell cycle progression allows for heterogeneity within the cell population induced by stochastic effects. Such heterogeneous behaviour is reflected in variations in the proliferation rate. Within this set-up, we have established three main results. First, we have shown that the age to the G1/S transition, which essentially determines the birth rate, exhibits a remarkably simple scaling behaviour. Besides the fact that this simple behaviour emerges from a rather complex model, this allows for a huge simplification of our numerical methodology. A further result is the observation that heterogeneous populations undergo an internal process of quasi-neutral competition. Finally, we investigated the effects of cell-cycle-phase dependent therapies (such as radiation therapy) on heterogeneous populations. In particular, we have studied the case in which the population contains a quiescent sub-population. Our mean-field analysis and numerical simulations confirm that, if the survival fraction of the therapy is too high, rescue of the quiescent population occurs. This gives rise to emergence of resistance

  5. Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes.

    PubMed

    Bojak, I; Oostendorp, Thom F; Reid, Andrew T; Kötter, Rolf

    2011-10-13

    Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.

  6. Desynchronization by Means of a Coordinated Reset of Neural Sub-Populations ---A Novel Technique for Demand-Controlled Deep Brain Stimulation---

    NASA Astrophysics Data System (ADS)

    Tass, P. A.

    The coordinated reset of neural sub-populations is introduced as an effectively desynchronizing stimulation technique. For this, short sequences of high-frequency pulse trains are administered at different sites in a coordinated way, i.e. separated by suitable fixed delays. Desynchronization is effectively maintained by performing a coordinated reset with demand-controlled timing or by periodically administering resetting high-frequency pulse trains of demand-controlled length. Unlike previously developed methods, this novel approach is robust against variations of model parameters, and does not require time-consuming calibration. I suggest the novel technique to be used for demand-controlled deep brain stimulation in patients suffering from Parkinson's disease or essential tremor. Furthermore, the novel stimulation method might even be applicable to diseases with intermittently emerging synchronized neural oscillations like epilepsy.

  7. Multi-dimensional reliability assessment of fractal signature analysis in an outpatient sports medicine population.

    PubMed

    Jarraya, Mohamed; Guermazi, Ali; Niu, Jingbo; Duryea, Jeffrey; Lynch, John A; Roemer, Frank W

    2015-11-01

    The aim of this study has been to test reproducibility of fractal signature analysis (FSA) in a young, active patient population taking into account several parameters including intra- and inter-reader placement of regions of interest (ROIs) as well as various aspects of projection geometry. In total, 685 patients were included (135 athletes and 550 non-athletes, 18-36 years old). Regions of interest (ROI) were situated beneath the medial tibial plateau. The reproducibility of texture parameters was evaluated using intraclass correlation coefficients (ICC). Multi-dimensional assessment included: (1) anterior-posterior (A.P.) vs. posterior-anterior (P.A.) (Lyon-Schuss technique) views on 102 knees; (2) unilateral (single knee) vs. bilateral (both knees) acquisition on 27 knees (acquisition technique otherwise identical; same A.P. or P.A. view); (3) repetition of the same image acquisition on 46 knees (same A.P. or P.A. view, and same unitlateral or bilateral acquisition); and (4) intra- and inter-reader reliability with repeated placement of the ROIs in the subchondral bone area on 99 randomly chosen knees. ICC values on the reproducibility of texture parameters for A.P. vs. P.A. image acquisitions for horizontal and vertical dimensions combined were 0.72 (95% confidence interval (CI) 0.70-0.74) ranging from 0.47 to 0.81 for the different dimensions. For unilateral vs. bilateral image acquisitions, the ICCs were 0.79 (95% CI 0.76-0.82) ranging from 0.55 to 0.88. For the repetition of the identical view, the ICCs were 0.82 (95% CI 0.80-0.84) ranging from 0.67 to 0.85. Intra-reader reliability was 0.93 (95% CI 0.92-0.94) and inter-observer reliability was 0.96 (95% CI 0.88-0.99). A decrease in reliability was observed with increasing voxel sizes. Our study confirms excellent intra- and inter-reader reliability for FSA, however, results seem to be affected by acquisition technique, which has not been previously recognized.

  8. Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

    NASA Technical Reports Server (NTRS)

    Roberts, J. Brent; Robertson, Franklin R.; Clayson, Carol Anne

    2012-01-01

    Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

  9. Design of a Seismic Reflection Multi-Attribute Workflow for Delineating Karst Pore Systems Using Neural Networks and Statistical Dimensionality Reduction Techniques

    NASA Astrophysics Data System (ADS)

    Ebuna, D. R.; Kluesner, J.; Cunningham, K. J.; Edwards, J. H.

    2016-12-01

    An effective method for determining the approximate spatial extent of karst pore systems is critical for hydrological modeling in such environments. When using geophysical techniques, karst features are especially challenging to constrain due to their inherent heterogeneity and complex seismic signatures. We present a method for mapping these systems using three-dimensional seismic reflection data by combining applications of machine learning and modern data science. Supervised neural networks (NN) have been successfully implemented in seismic reflection studies to produce multi-attributes (or meta-attributes) for delineating faults, chimneys, salt domes, and slumps. Using a seismic reflection dataset from southeast Florida, we develop an objective multi-attribute workflow for mapping karst in which potential interpreter bias is minimized by applying linear and non-linear data transformations for dimensionality reduction. This statistical approach yields a reduced set of input seismic attributes to the NN by eliminating irrelevant and overly correlated variables, while still preserving the vast majority of the observed data variance. By initiating the supervised NN from an eigenspace that maximizes the separation between classes, the convergence time and accuracy of the computations are improved since the NN only needs to recognize small perturbations to the provided decision boundaries. We contend that this 3D seismic reflection, data-driven method for defining the spatial bounds of karst pore systems provides great value as a standardized preliminary step for hydrological characterization and modeling in these complex geological environments.

  10. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

    PubMed

    Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

    2016-08-04

    Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

  11. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

    PubMed Central

    Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

    2016-01-01

    Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition. PMID:27527175

  12. Further Evaluation of DNT Hazard Screening using Neural Networks from Rat Cortical Neurons on Multi-well Microelectrode Arrays

    EPA Science Inventory

    Thousands of chemicals have not been characterized for their DNT potential. Due to the need for DNT hazard identification, efforts to develop screening assays for DNT potential is a high priority. Multi-well microelectrode arrays (MEA) measure the spontaneous activity of electr...

  13. Further Evaluation of DNT Hazard Screening using Neural Networks from Rat Cortical Neurons on Multi-well Microelectrode Arrays

    EPA Science Inventory

    Thousands of chemicals have not been characterized for their DNT potential. Due to the need for DNT hazard identification, efforts to develop screening assays for DNT potential is a high priority. Multi-well microelectrode arrays (MEA) measure the spontaneous activity of electr...

  14. Pregnancy outcomes in women greater than 45 years: a cohort control study in a multi-ethnic inner city population.

    PubMed

    Mehta, Shaine; Tran, Kim; Stewart, Laura; Soutter, Eleanor; Nauta, Maud; Yoong, Wai

    2014-05-01

    To examine the pregnancy outcomes of women >45 years in a multi-ethnic population when compared to controls and to reflect on socio-demographic details of the older mothers. A retrospective cohort control study over an 8-year period in an inner city London hospital with multi-ethnic population. The influence of advanced maternal age (>45 years at time of delivery) on fetal and maternal outcomes was assessed by comparing these women to controls (aged 20-30 years) matched for ethnicity, country of origin and parity. Data from 64 cases and 64 controls were compared. Ninety percent of the index group had undergone assisted conception. Mothers >45 years had a fourfold increase in cesarean section (35/64 vs 8/64), a threefold increase in blood loss (669.2 vs 272.4 ml) (both p < 0.001) and were more likely to have preterm birth (12/64 vs 3/64) (p < 0.05). Only 5 % of the 64 women were born in the United Kingdom, 52 % were unemployed and 50 % were not fluent in English. Seventy-five percent of the study population were multiparous, 52 % of the pregnancies were unplanned and 90 % had conceived spontaneously. In an inner city immigrant population, older mothers >45 years were more likely to have cesarean sections, postpartum hemorrhage and premature deliveries. Moreover, social and demographic factors suggest that late child bearing is influenced by cultural factors such as acceptance of large families and lack of contraception.

  15. A multi-laboratory evaluation of microelectrode array-based measurements of neural network activity for acute neurotoxicity testing.

    PubMed

    Vassallo, Andrea; Chiappalone, Michela; De Camargos Lopes, Ricardo; Scelfo, Bibiana; Novellino, Antonio; Defranchi, Enrico; Palosaari, Taina; Weisschu, Timo; Ramirez, Tzutzuy; Martinoia, Sergio; Johnstone, Andrew F M; Mack, Cina M; Landsiedel, Robert; Whelan, Maurice; Bal-Price, Anna; Shafer, Timothy J

    2017-05-01

    There is a need for methods to screen and prioritize chemicals for potential hazard, including neurotoxicity. Microelectrode array (MEA) systems enable simultaneous extracellular recordings from multiple sites in neural networks in real time and thereby provide a robust measure of network activity. In this study, spontaneous activity measurements from primary neuronal cultures treated with three neurotoxic or three non-neurotoxic compounds was evaluated across four different laboratories. All four individual laboratories correctly identifed the neurotoxic compounds chlorpyrifos oxon (an organophosphate insecticide), deltamethrin (a pyrethroid insecticide) and domoic acid (an excitotoxicant). By contrast, the other three compounds (glyphosate, dimethyl phthalate and acetaminophen) considered to be non-neurotoxic ("negative controls"), produced only sporadic changes of the measured parameters. The results were consistent across the different laboratories, as all three neurotoxic compounds caused concentration-dependent inhibition of mean firing rate (MFR). Further, MFR appeared to be the most sensitive parameter for effects of neurotoxic compounds, as changes in electrical activity measured by mean frequency intra burst (MFIB), and mean burst duration (MBD) did not result in concentration-response relationships for some of the positive compounds, or required higher concentrations for an effect to be observed. However, greater numbers of compounds need to be tested to confirm this. The results obtained indicate that measurement of spontaneous electrical activity using MEAs provides a robust assessment of compound effects on neural network function. Published by Elsevier B.V.

  16. Evolvable Neural Software System

    NASA Technical Reports Server (NTRS)

    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.

  17. A multi-marker association method for genome-wide association studies without the need for population structure correction

    PubMed Central

    Klasen, Jonas R.; Barbez, Elke; Meier, Lukas; Meinshausen, Nicolai; Bühlmann, Peter; Koornneef, Maarten; Busch, Wolfgang; Schneeberger, Korbinian

    2016-01-01

    All common genome-wide association (GWA) methods rely on population structure correction, to avoid false genotype-to-phenotype associations. However, population structure correction is a stringent penalization, which also impedes identification of real associations. Using recent statistical advances, we developed a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT), enabling simultaneous multi-marker associations while considering correlations between markers. With this, QTCAT overcomes the need for population structure correction and also reflects the polygenic nature of complex traits better than single-marker methods. Using simulated data, we show that QTCAT clearly outperforms linear mixed model approaches. Moreover, using QTCAT to reanalyse public human, mouse and Arabidopsis GWA data revealed nearly all known and some previously undetected associations. Following up on the most significant novel association in the Arabidopsis data allowed us to identify a so far unknown component of root growth. PMID:27830750

  18. A multi-marker association method for genome-wide association studies without the need for population structure correction.

    PubMed

    Klasen, Jonas R; Barbez, Elke; Meier, Lukas; Meinshausen, Nicolai; Bühlmann, Peter; Koornneef, Maarten; Busch, Wolfgang; Schneeberger, Korbinian

    2016-11-10

    All common genome-wide association (GWA) methods rely on population structure correction, to avoid false genotype-to-phenotype associations. However, population structure correction is a stringent penalization, which also impedes identification of real associations. Using recent statistical advances, we developed a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT), enabling simultaneous multi-marker associations while considering correlations between markers. With this, QTCAT overcomes the need for population structure correction and also reflects the polygenic nature of complex traits better than single-marker methods. Using simulated data, we show that QTCAT clearly outperforms linear mixed model approaches. Moreover, using QTCAT to reanalyse public human, mouse and Arabidopsis GWA data revealed nearly all known and some previously undetected associations. Following up on the most significant novel association in the Arabidopsis data allowed us to identify a so far unknown component of root growth.

  19. Antibiotic resistance and population structure of cystic fibrosis Pseudomonas aeruginosa isolates from a Spanish multi-centre study.

    PubMed

    López-Causapé, Carla; de Dios-Caballero, Juan; Cobo, Marta; Escribano, Amparo; Asensio, Óscar; Oliver, Antonio; Del Campo, Rosa; Cantón, Rafael; Solé, Amparó; Cortell, Isidoro; Asensio, Oscar; García, Gloria; Martínez, María Teresa; Cols, María; Salcedo, Antonio; Vázquez, Carlos; Baranda, Félix; Girón, Rosa; Quintana, Esther; Delgado, Isabel; de Miguel, María Ángeles; García, Marta; Oliva, Concepción; Prados, María Concepción; Barrio, María Isabel; Pastor, María Dolores; Olveira, Casilda; de Gracia, Javier; Álvarez, Antonio; Escribano, Amparo; Castillo, Silvia; Figuerola, Joan; Togores, Bernat; Oliver, Antonio; López, Carla; de Dios Caballero, Juan; Tato, Marta; Máiz, Luis; Suárez, Lucrecia; Cantón, Rafael

    2017-09-01

    The first Spanish multi-centre study on the microbiology of cystic fibrosis (CF) was conducted from 2013 to 2014. The study involved 24 CF units from 17 hospitals, and recruited 341 patients. The aim of this study was to characterise Pseudomonas aeruginosa isolates, 79 of which were recovered from 75 (22%) patients. The study determined the population structure, antibiotic susceptibility profile and genetic background of the strains. Fifty-five percent of the isolates were multi-drug-resistant, and 16% were extensively-drug-resistant. Defective mutS and mutL genes were observed in mutator isolates (15.2%). Considerable genetic diversity was observed by pulsed-field gel electrophoresis (70 patterns) and multi-locus sequence typing (72 sequence types). International epidemic clones were not detected. Fifty-one new and 14 previously described array tube (AT) genotypes were detected by AT technology. This study found a genetically unrelated and highly diverse CF P. aeruginosa population in Spain, not represented by the epidemic clones widely distributed across Europe, with multiple combinations of virulence factors and high antimicrobial resistance rates (except for colistin). Copyright © 2017 Elsevier B.V. and International Society of Chemotherapy. All rights reserved.

  20. Dealing with incomplete and variable detectability in multi-year, multi-site monitoring of ecological populations

    USGS Publications Warehouse

    Converse, Sarah J.; Royle, J. Andrew; Gitzen, Robert A.; Millspaugh, Joshua J.; Cooper, Andrew B.; Licht, Daniel S.

    2012-01-01

    An ecological monitoring program should be viewed as a component of a larger framework designed to advance science and/or management, rather than as a stand-alone activity. Monitoring targets (the ecological variables of interest; e.g. abundance or occurrence of a species) should be set based on the needs of that framework (Nichols and Williams 2006; e.g. Chapters 2–4). Once such monitoring targets are set, the subsequent step in monitoring design involves consideration of the field and analytical methods that will be used to measure monitoring targets with adequate accuracy and precision. Long-term monitoring programs will involve replication of measurements over time, and possibly over space; that is, one location or each of multiple locations will be monitored multiple times, producing a collection of site visits (replicates). Clearly this replication is important for addressing spatial and temporal variability in the ecological resources of interest (Chapters 7–10), but it is worth considering how this replication can further be exploited to increase the effectiveness of monitoring. In particular, defensible monitoring of the majority of animal, and to a lesser degree plant, populations and communities will generally require investigators to account for imperfect detection (Chapters 4, 18). Raw indices of population state variables, such as abundance or occupancy (sensu MacKenzie et al. 2002), are rarely defensible when detection probabilities are < 1, because in those cases detection may vary over time and space in unpredictable ways. Myriad authors have discussed the risks inherent in making inference from monitoring data while failing to correct for differences in detection, resulting in indices that have an unknown relationship to the parameters of interest (e.g. Nichols 1992, Anderson 2001, MacKenzie et al. 2002, Williams et al. 2002, Anderson 2003, White 2005, Kéry and Schmidt 2008). While others have argued that indices may be preferable in some

  1. A multi-scale distribution model for non-equilibrium populations suggests resource limitation in an endangered rodent.

    PubMed

    Bean, William T; Stafford, Robert; Butterfield, H Scott; Brashares, Justin S

    2014-01-01

    Species distributions are known to be limited by biotic and abiotic factors at multiple temporal and spatial scales. Species distribution models, however, frequently assume a population at equilibrium in both time and space. Studies of habitat selection have repeatedly shown the difficulty of estimating resource selection if the scale or extent of analysis is incorrect. Here, we present a multi-step approach to estimate the realized and potential distribution of the endangered giant kangaroo rat. First, we estimate the potential distribution by modeling suitability at a range-wide scale using static bioclimatic variables. We then examine annual changes in extent at a population-level. We define "available" habitat based on the total suitable potential distribution at the range-wide scale. Then, within the available habitat, model changes in population extent driven by multiple measures of resource availability. By modeling distributions for a population with robust estimates of population extent through time, and ecologically relevant predictor variables, we improved the predictive ability of SDMs, as well as revealed an unanticipated relationship between population extent and precipitation at multiple scales. At a range-wide scale, the best model indicated the giant kangaroo rat was limited to areas that received little to no precipitation in the summer months. In contrast, the best model for shorter time scales showed a positive relation with resource abundance, driven by precipitation, in the current and previous year. These results suggest that the distribution of the giant kangaroo rat was limited to the wettest parts of the drier areas within the study region. This multi-step approach reinforces the differing relationship species may have with environmental variables at different scales, provides a novel method for defining "available" habitat in habitat selection studies, and suggests a way to create distribution models at spatial and temporal scales

  2. Multi-decadal responses of a cod (Gadus morhua) population to human-induced trophic changes, fishing, and climate.

    PubMed

    Eero, Margit; MacKenzie, Brian R; Köster, Friedrich W; Gislason, Henrik

    2011-01-01

    Understanding how human impacts have interacted with natural variability to affect populations and ecosystems is required for sustainable management and conservation. The Baltic Sea is one of the few large marine ecosystems worldwide where the relative contribution of several key forcings to changes in fish populations can be analyzed with empirical data. In this study we investigate how climate variability and multiple human impacts (fishing, marine mammal hunting, eutrophication) have affected multi-decadal scale dynamics of cod in the Baltic Sea during the 20th century. We document significant climate-driven variations in cod recruitment production at multi-annual timescales, which had major impacts on population dynamics and the yields to commercial fisheries. We also quantify the roles of marine mammal predation, eutrophication, and exploitation on the development of the cod population using simulation analyses, and show how the intensity of these forcings differed over time. In the early decades of the 20th century, marine mammal predation and nutrient availability were the main limiting factors; exploitation of cod was still relatively low. During the 1940s and subsequent decades, exploitation increased and became a dominant forcing on the population. Eutrophication had a relatively minor positive influence on cod biomass until the 1980s. The largest increase in cod biomass occurred during the late 1970s, following a long period of hydrographically related above-average cod productivity coupled to a temporary reduction in fishing pressure. The Baltic cod example demonstrates how combinations of different forcings can have synergistic effects and consequently dramatic impacts on population dynamics. Our results highlight the potential and limitations of human manipulations to influence predator species and show that sustainable management can only be achieved by considering both anthropogenic and naturally varying processes in a common framework.

  3. A Study in Speech Recognition Using a Kohonen Neural Network Dynamic Programming and Multi-Feature Fusion

    DTIC Science & Technology

    1989-12-01

    example, Air Force pilots could carry a plastic card imprinted with their voice patterns or acoustic sounds that represented all the phonetic sound...recognition system using a Kohonen network and a multi-layer perceptron. Three speakers (two male and one female ) recorded speech digits for both...An attempt should be made to map the vowel sounds on the Kohonen surface. * Add a third feature to the recognition system. Data processed by a Fast

  4. Fumonisin as a possible contributing factor to neural tube defects in populations consuming large amounts of maize

    USDA-ARS?s Scientific Manuscript database

    Fumonisin B1 (FB) is an inhibitor of sphingolipid (SL) biosynthesis and folate transport and can induce neural tube defects (NTD) in mice. NTD incidence is high in countries where maize is a dietary staple and FB exposure is likely. In Guatemala the incidence of FB in maize has been well documented ...

  5. Modelling the Flow Stress of Alloy 316L using a Multi-Layered Feed Forward Neural Network with Bayesian Regularization

    NASA Astrophysics Data System (ADS)

    Abiriand Bhekisipho Twala, Olufunminiyi

    2017-08-01

    In this paper, a multilayer feedforward neural network with Bayesian regularization constitutive model is developed for alloy 316L during high strain rate and high temperature plastic deformation. The input variables are strain rate, temperature and strain while the output value is the flow stress of the material. The results show that the use of Bayesian regularized technique reduces the potential of overfitting and overtraining. The prediction quality of the model is thereby improved. The model predictions are in good agreement with experimental measurements. The measurement data used for the network training and model comparison were taken from relevant literature. The developed model is robust as it can be generalized to deformation conditions slightly below or above the training dataset.

  6. Multi-compartmental biomaterial scaffolds for patterning neural tissue organoids in models of neurodevelopment and tissue regeneration.

    PubMed

    McMurtrey, Richard J

    2016-01-01

    Biomaterials are becoming an essential tool in the study and application of stem cell research. Various types of biomaterials enable three-dimensional culture of stem cells, and, more recently, also enable high-resolution patterning and organization of multicellular architectures. Biomaterials also hold potential to provide many additional advantages over cell transplants alone in regenerative medicine. This article describes novel designs for functionalized biomaterial constructs that guide tissue development to targeted regional identities and structures. Such designs comprise compartmentalized regions in the biomaterial structure that are functionalized with molecular factors that form concentration gradients through the construct and guide stem cell development, axis patterning, and tissue architecture, including rostral/caudal, ventral/dorsal, or medial/lateral identities of the central nervous system. The ability to recapitulate innate developmental processes in a three-dimensional environment and under specific controlled conditions has vital application to advanced models of neurodevelopment and for repair of specific sites of damaged or diseased neural tissue.

  7. Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

    PubMed

    Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

    2016-03-01

    The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin.

    PubMed

    Ghafoorian, Mohsen; Karssemeijer, Nico; Heskes, Tom; Bergkamp, Mayra; Wissink, Joost; Obels, Jiri; Keizer, Karlijn; Leeuw, Frank-Erik de; Ginneken, Bram van; Marchiori, Elena; Platel, Bram

    2017-01-01

    Lacunes of presumed vascular origin (lacunes) are associated with an increased risk of stroke, gait impairment, and dementia and are a primary imaging feature of the small vessel disease. Quantification of lacunes may be of great importance to elucidate the mechanisms behind neuro-degenerative disorders and is recommended as part of study standards for small vessel disease research. However, due to the different appearance of lacunes in various brain regions and the existence of other similar-looking structures, such as perivascular spaces, manual annotation is a difficult, elaborative and subjective task, which can potentially be greatly improved by reliable and consistent computer-aided detection (CAD) routines. In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN). We show that this method has good performance and can considerably benefit readers. We first use a fully convolutional neural network to detect initial candidates. In the second step, we employ a 3D CNN as a false positive reduction tool. As the location information is important to the analysis of candidate structures, we further equip the network with contextual information using multi-scale analysis and integration of explicit location features. We trained, validated and tested our networks on a large dataset of 1075 cases obtained from two different studies. Subsequently, we conducted an observer study with four trained observers and compared our method with them using a free-response operating characteristic analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits performance similar to the trained human observers and achieves a sensitivity of 0.974 with 0.13 false positives per slice. A feasibility study also showed that a trained human observer would considerably benefit once aided by the CAD system.

  9. The Pittsburgh Sleep Quality Index in a multi-ethnic Asian population contains a three-factor structure.

    PubMed

    Koh, Hiromi W L; Lim, Raymond Boon Tar; Chia, Kee Seng; Lim, Wei Yen

    2015-12-01

    The Pittsburgh Sleep Quality Index (PSQI) is a widely used measure for assessing sleep impairment. Although it was developed as a unidimensional instrument, there is much debate that it contains multidimensional latent constructs. We examined the dimensionality of the underlying factor structure of PSQI in Singapore, a rapidly industrialising Asian country with multi-ethnicities representing the Chinese, Malays and Indians. The PSQI was administered through an interviewer-based questionnaire in two separate population-based cross-sectional surveys. An explanatory factor analysis (EFA) was first used to explore the underlying construct of the PSQI in both studies. Then, a confirmatory factor analysis (CFA) was conducted to evaluate an optimal factor model by comparing against other possible models identified in EFA. There are three correlated yet distinguishable factors that account for an individual's sleep experience from the same best-fit model obtained in both studies: perceived sleep quality, daily disturbances and sleep efficiency. Our three-factor structure of PSQI is superior to the originally intended unidimensional model. Our model also shows the best-fit indices when compared to the previously reported single-factor, two-factor and three-factor (by Cole et al.) models in a multi-ethnic Asian population. There is strong evidence that the PSQI contains a three-factor rather than a unidimensional structure in a multi-ethnic Asian population. Scoring the PSQI along their multidimensional perspectives may provide a more accurate understanding of the relationship between sleep impairment and health conditions rather than using a single global score.

  10. Moroccan Leishmania infantum: Genetic Diversity and Population Structure as Revealed by Multi-Locus Microsatellite Typing

    PubMed Central

    Lemrani, Meryem; Mouna, Idrissi; Mohammed, Hida; Mostafa, Sabri; Rhajaoui, Mohamed; Hamarsheh, Omar; Schönian, Gabriele

    2013-01-01

    Leishmania infantum causes Visceral and cutaneous leishmaniasis in northern Morocco. It predominantly affects children under 5 years with incidence of 150 cases/year. Genetic variability and population structure have been investigated for 33 strains isolated from infected dogs and humans in Morocco. A multilocus microsatellite typing (MLMT) approach was used in which a MLMtype based on size variation in 14 independent microsatellite markers was compiled for each strain. MLMT profiles of 10 Tunisian, 10 Algerian and 21 European strains which belonged to zymodeme MON-1 and non-MON-1 according to multilocus enzyme electrophoresis (MLEE) were included for comparison. A Bayesian model-based approach and phylogenetic analysis inferred two L.infantum sub-populations; Sub-population A consists of 13 Moroccan strains grouped with all European strains of MON-1 type; and sub-population B consists of 15 Moroccan strains grouped with the Tunisian and Algerian MON-1 strains. Theses sub-populations were significantly different from each other and from the Tunisian, Algerian and European non MON-1 strains which constructed one separate population. The presence of these two sub-populations co-existing in Moroccan endemics suggests multiple introduction of L. infantum from/to Morocco; (1) Introduction from/to the neighboring North African countries, (2) Introduction from/to the Europe. These scenarios are supported by the presence of sub-population B and sub-population A respectively. Gene flow was noticed between sub-populations A and B. Five strains showed mixed A/B genotypes indicating possible recombination between the two populations. MLMT has proven to be a powerful tool for eco-epidemiological and population genetic investigations of Leishmania. PMID:24147078

  11. Time trends in the prevalence and epidemiological characteristics of neural tube defects in Liaoning Province, China, 2006-2015: A population-based study.

    PubMed

    Zhang, Tie-Ning; Gong, Ting-Ting; Chen, Yan-Ling; Wu, Qi-Jun; Zhang, Yuan; Jiang, Cheng-Zhi; Li, Jing; Li, Li-Li; Zhou, Chen; Huang, Yan-Hong

    2017-02-03

    To evaluate the time trends in the prevalence of neural tube defects and all their subtypes as well as to identify the epidemiological characteristics of these malformations documented in the Liaoning Province of northeast China from 2006 to 2015. This was a population-based observational study using data from 3,248,954 live births as well as from 6217 cases of neural tube defects, 1,600 cases of anencephaly, 2,029 cases of spina bifida, 404 cases of encephalocele, and 3,008 cases of congenital hydrocephalus from 14 cities in Liaoning Province from 2006 to 2015. All analyses were conducted using SPSS software. During the observational period, the prevalence of neural tube defects, anencephaly, spina bifida, encephalocele, and congenital hydrocephalus was 19.1, 4.9, 6.2, 1.2, and 9.3 per 10,000 live births, respectively. Significantly decreasing trends were observed in the prevalence of all these malformations except for encephalocele. Notably, relatively higher prevalence rates were found in isolated compared with non-isolated malformations, with significant differences in selected characteristics (e.g., prognosis status, gestational age, and birth weight) between isolated and non-isolated cases of these malformations. The prevalence of neural tube defects showed a downward trend in Liaoning Province from 2006 to 2015. However, more attention should be focused on non-isolated cases in the future because of the severe clinical manifestations. Future prevention efforts should be strengthened to reduce the risk of these malformations, especially the non-isolated subtype, in areas with high prevalence.

  12. Contribution of common PCSK1 genetic variants to obesity in 8,359 subjects from multi-ethnic American population.

    PubMed

    Choquet, Hélène; Kasberger, Jay; Hamidovic, Ajna; Jorgenson, Eric

    2013-01-01

    Common PCSK1 variants (notably rs6232 and rs6235) have been shown to be associated with obesity in European, Asian and Mexican populations. To determine whether common PCSK1 variants contribute to obesity in American population, we conducted association analyses in 8,359 subjects using two multi-ethnic American studies: the Coronary Artery Risk Development in Young Adults (CARDIA) study and the Multi-Ethnic Study of Atherosclerosis (MESA). By evaluating the contribution of rs6232 and rs6235 in each ethnic group, we found that in European-American subjects from CARDIA, only rs6232 was associated with BMI (P = 0.006) and obesity (P = 0.018) but also increased the obesity incidence during the 20 years of follow-up (HR = 1.53 [1.07-2.19], P = 0.019). Alternatively, in African-American subjects from CARDIA, rs6235 was associated with BMI (P = 0.028) and obesity (P = 0.018). Further, by combining the two case-control ethnic groups from the CARDIA study in a meta-analysis, association between rs6235 and obesity risk remained significant (OR = 1.23 [1.05-1.45], P = 9.5×10(-3)). However, neither rs6232 nor rs6235 was associated with BMI or obesity in the MESA study. Interestingly, rs6232 was associated with BMI (P = 4.2×10(-3)) and obesity (P = 3.4×10(-3)) in the younger European-American group combining samples from the both studies [less than median age (53 years)], but not among the older age group (P = 0.756 and P = 0.935 for BMI and obesity, respectively). By combining all the case-control ethnic groups from CARDIA and MESA in a meta-analysis, we found no significant association for the both variants and obesity risk. Finally, by exploring the full PCSK1 locus, we observed that no variant remained significant after correction for multiple testing. These results indicate that common PCSK1 variants (notably rs6232 and rs6235) contribute modestly to obesity in multi-ethnic American population. Further, these results

  13. Mortality and Population Dynamics of Bemisia tabaci within a Multi-Crop System

    USDA-ARS?s Scientific Manuscript database

    The population dynamics of mobile polyphagous pests is governed by a complex set of interacting factors that involve multiple host-plants, seasonality, movement and demography. Bemisia tabaci is a multivoltine insect with no diapause that maintains population continuity by moving from one host to a...

  14. Control of Neural Daughter Cell Proliferation by Multi-level Notch/Su(H)/E(spl)-HLH Signaling

    PubMed Central

    Bivik, Caroline; MacDonald, Ryan B.; Gunnar, Erika; Mazouni, Khalil; Schweisguth, Francois; Thor, Stefan

    2016-01-01

    The Notch pathway controls proliferation during development and in adulthood, and is frequently affected in many disorders. However, the genetic sensitivity and multi-layered transcriptional properties of the Notch pathway has made its molecular decoding challenging. Here, we address the complexity of Notch signaling with respect to proliferation, using the developing Drosophila CNS as model. We find that a Notch/Su(H)/E(spl)-HLH cascade specifically controls daughter, but not progenitor proliferation. Additionally, we find that different E(spl)-HLH genes are required in different neuroblast lineages. The Notch/Su(H)/E(spl)-HLH cascade alters daughter proliferation by regulating four key cell cycle factors: Cyclin E, String/Cdc25, E2f and Dacapo (mammalian p21CIP1/p27KIP1/p57Kip2). ChIP and DamID analysis of Su(H) and E(spl)-HLH indicates direct transcriptional regulation of the cell cycle genes, and of the Notch pathway itself. These results point to a multi-level signaling model and may help shed light on the dichotomous proliferative role of Notch signaling in many other systems. PMID:27070787

  15. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations.

    PubMed

    Segura, Vincent; Vilhjálmsson, Bjarni J; Platt, Alexander; Korte, Arthur; Seren, Ümit; Long, Quan; Nordborg, Magnus

    2012-06-17

    Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.

  16. Multi-compartmental biomaterial scaffolds for patterning neural tissue organoids in models of neurodevelopment and tissue regeneration

    PubMed Central

    McMurtrey, Richard J

    2016-01-01

    Biomaterials are becoming an essential tool in the study and application of stem cell research. Various types of biomaterials enable three-dimensional culture of stem cells, and, more recently, also enable high-resolution patterning and organization of multicellular architectures. Biomaterials also hold potential to provide many additional advantages over cell transplants alone in regenerative medicine. This article describes novel designs for functionalized biomaterial constructs that guide tissue development to targeted regional identities and structures. Such designs comprise compartmentalized regions in the biomaterial structure that are functionalized with molecular factors that form concentration gradients through the construct and guide stem cell development, axis patterning, and tissue architecture, including rostral/caudal, ventral/dorsal, or medial/lateral identities of the central nervous system. The ability to recapitulate innate developmental processes in a three-dimensional environment and under specific controlled conditions has vital application to advanced models of neurodevelopment and for repair of specific sites of damaged or diseased neural tissue. PMID:27766141

  17. Real-time multi-channel monitoring of burst-suppression using neural network technology during pediatric status epilepticus treatment.

    PubMed

    Papadelis, Christos; Ashkezari, Seyedeh Fatemeh Salimi; Doshi, Chiran; Thome-Souza, Sigride; Pearl, Phillip L; Grant, P Ellen; Tasker, Robert C; Loddenkemper, Tobias

    2016-08-01

    To develop a real-time monitoring system that has the potential to guide the titration of anesthetic agents in the treatment of pediatric status epilepticus (SE). We analyzed stored multichannel electroencephalographic (EEG) data collected from 12 pediatric patients with generalized SE. EEG recordings were initially segmented in 500ms time-windows. Features characterizing the power, frequency, and entropy of the signal were extracted from each segment. The segments were annotated as bursts (B), suppressions (S), or artifacts (A) by two electroencephalographers. The EEG features together with the annotations were inputted in a three-layer feed forward neural network (NN). The sensitivity and specificity of NNs with different architectures and training algorithms to classify segments into B, S, or A were estimated. The maximum sensitivity (95.96% for B, 89.25% for S, and 75% for A) and specificity (89.36 for B, 96.26% for S, and 99.8% for A) was observed for the NN with 10 nodes in the hidden layer. By using this NN, we designed a real-time system that estimates the burst-suppression index (BSI). Our system provides a reliable real-time estimate of multichannel BSI requiring minimal memory and computation time. The system has the potential to assist intensive care unit attendants in the continuous EEG monitoring. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  18. Planar cell polarity gene mutations contribute to the etiology of human neural tube defects in our population.

    PubMed

    De Marco, Patrizia; Merello, Elisa; Piatelli, Gianluca; Cama, Armando; Kibar, Zoha; Capra, Valeria

    2014-08-01

    Neural Tube Defects (NTDs) are congenital malformations that involve failure of the neural tube closure during the early phases of development at any level of the rostro-caudal axis. The planar cell polarity (PCP) pathway is a highly conserved, noncanonical Wnt-Frizzled-Dishevelled signaling cascade, that was first identified in the fruit fly Drosophila. We are here reviewing the role of the PCP pathway genes in the etiology of human NTDs, updating the list of the rare and deleterious mutations identified so far. We report 50 rare nonsynonymous mutations of PCP genes in 54 patients having a pathogenic effect on the protein function. Thirteen mutations that have previously been reported as novel are now reported in public databases, although at very low frequencies. The mutations were private, mostly missense, and transmitted by a healthy parent. To date, no clear genotype-phenotype correlation has been possible to create. Even if PCP pathway genes are involved in the pathogenesis of neural tube defects, future studies will be necessary to better dissect the genetic causes underlying these complex malformations.

  19. Combined inverse-forward artificial neural networks for fast and accurate estimation of the diffusion coefficients of cartilage based on multi-physics models.

    PubMed

    Arbabi, Vahid; Pouran, Behdad; Weinans, Harrie; Zadpoor, Amir A

    2016-09-06

    Analytical and numerical methods have been used to extract essential engineering parameters such as elastic modulus, Poisson׳s ratio, permeability and diffusion coefficient from experimental data in various types of biological tissues. The major limitation associated with analytical techniques is that they are often only applicable to problems with simplified assumptions. Numerical multi-physics methods, on the other hand, enable minimizing the simplified assumptions but require substantial computational expertise, which is not always available. In this paper, we propose a novel approach that combines inverse and forward artificial neural networks (ANNs) which enables fast and accurate estimation of the diffusion coefficient of cartilage without any need for computational modeling. In this approach, an inverse ANN is trained using our multi-zone biphasic-solute finite-bath computational model of diffusion in cartilage to estimate the diffusion coefficient of the various zones of cartilage given the concentration-time curves. Robust estimation of the diffusion coefficients, however, requires introducing certain levels of stochastic variations during the training process. Determining the required level of stochastic variation is performed by coupling the inverse ANN with a forward ANN that receives the diffusion coefficient as input and returns the concentration-time curve as output. Combined together, forward-inverse ANNs enable computationally inexperienced users to obtain accurate and fast estimation of the diffusion coefficients of cartilage zones. The diffusion coefficients estimated using the proposed approach are compared with those determined using direct scanning of the parameter space as the optimization approach. It has been shown that both approaches yield comparable results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Retinal Vein Occlusion in a Multi-Ethnic Asian Population: The Singapore Epidemiology of Eye Disease Study.

    PubMed

    Koh, Victor; Cheung, Carol Y; Li, Xiang; Tian, Dechao; Wang, Jie Jin; Mitchell, Paul; Cheng, Ching-Yu; Wong, Tien Y

    2016-01-01

    To describe the prevalence of retinal vein occlusion (RVO) and its risk factors in a multi-ethnic Asian population. This population-based study of 10,033 participants (75.7% response rate) included Chinese, Indian and Malay persons aged 40 years and older. A comprehensive ophthalmic examination, standardized interviews and laboratory blood tests were performed. Digital fundus photographs were assessed for presence of RVO following the definitions used in the Blue Mountains Eye Study. Regression analysis models were constructed to study the relationship between ocular and systemic factors and RVO. Age-specific prevalence rates of RVO were applied to project the number of people affected in Asia from 2013 to 2040. The overall crude prevalence of RVO was 0.72% (n = 71; 95% confidence interval, CI, 0.54-0.87%). The crude prevalence of RVO was similar in Chinese, Indian and Malay participants (p = 0.865). In multivariable regression models, significant risk factors of RVO included increased age (odds ratio, OR, 1.03, 95% CI 1.01-1.06), hypertension (OR 3.65, 95% CI 1.61-8.31), increased serum creatinine (OR 1.04, 95% CI 1.01-1.06, per 10 mmol/L increase), history of heart attack (OR 2.25, 95% CI 1.11-4.54) and increased total cholesterol (OR 1.31, 95% CI 1.07-1.59, per 1 mmol/L increase). None of the ocular parameters were associated with RVO. RVO is estimated to affect up to 16 and 21 million people in Asia by 2020 and 2040, respectively. RVO was detected in 0.72% of a multi-ethnic Asian population aged 40-80 years in Singapore. The significant systemic risk factors of RVO are consistent with studies in white populations.

  1. Maternal PCMT1 gene polymorphisms and the risk of neural tube defects in a Chinese population of Lvliang high-risk area.

    PubMed

    Zhao, Huizhi; Wang, Fang; Wang, Jianhua; Xie, Hua; Guo, Jin; Liu, Chi; Wang, Li; Lu, Xiaolin; Bao, Yihua; Wang, Guoliang; Zhong, Rugang; Niu, Bo; Zhang, Ting

    2012-09-01

    Protein-L-isoaspartate (D-aspartate) O-methyltransferase 1 (PCMT1) gene encodes for the protein repair enzyme L-isoaspartate (D-aspartate) O-methyltransferase (PIMT), which is known to protect certain neural cells from Bax-induced apoptosis. Previous study has shown that PCMT1 polymorphisms rs4552 and rs4816 of infant are associated with spina bifida in the Californian population. The association between maternal polymorphism and neural tube defects is still uncovered. A case-control study was conducted to investigate a possible association between maternal PCMT1 and NTDs in Lvliang high-risk area of Shanxi Province in China, using a high-resolution DNA melting analysis genotyping method. We found that increased risk for anencephaly in isolated NTDs compared with the normal control group was observed for the G (vs. A) allele (p=0.034, OR=1.896, 95% CI, 1.04-3.45) and genotypes GG+GA (p=0.025, OR=2.237, 95% CI, 1.09-4.57). Although the significance was lost after multiple comparison correction, the results implied that maternal polymorphisms in PCMT1 might be a potential genetic risk factor for isolated anencephaly in this Chinese population.

  2. Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits

    PubMed Central

    Hayworth, Kenneth J.; Morgan, Josh L.; Schalek, Richard; Berger, Daniel R.; Hildebrand, David G. C.; Lichtman, Jeff W.

    2014-01-01

    The automated tape-collecting ultramicrotome (ATUM) makes it possible to collect large numbers of ultrathin sections quickly—the equivalent of a petabyte of high resolution images each day. However, even high throughput image acquisition strategies generate images far more slowly (at present ~1 terabyte per day). We therefore developed WaferMapper, a software package that takes a multi-resolution approach to mapping and imaging select regions within a library of ultrathin sections. This automated method selects and directs imaging of corresponding regions within each section of an ultrathin section library (UTSL) that may contain many thousands of sections. Using WaferMapper, it is possible to map thousands of tissue sections at low resolution and target multiple points of interest for high resolution imaging based on anatomical landmarks. The program can also be used to expand previously imaged regions, acquire data under different imaging conditions, or re-image after additional tissue treatments. PMID:25018701

  3. GMDH-type neural network modeling and genetic algorithm-based multi-objective optimization of thermal and friction characteristics in heat exchanger tubes with wire-rod bundles

    NASA Astrophysics Data System (ADS)

    Rahimi, Masoud; Beigzadeh, Reza; Parvizi, Mehdi; Eiamsa-ard, Smith

    2016-08-01

    The group method of data handling (GMDH) technique was used to predict heat transfer and friction characteristics in heat exchanger tubes equipped with wire-rod bundles. Nusselt number and friction factor were determined as functions of wire-rod bundle geometric parameters and Reynolds number. The performance of the developed GMDH-type neural networks was found to be superior in comparison with the proposed empirical correlations. For optimization, the genetic algorithm-based multi-objective optimization was applied.

  4. Validation of the English version of the Asthma Quality of Life Questionnaire in a multi-ethnic Asian population.

    PubMed

    Tan, Wan C; Tan, Justina W L; Wee, Eric W L; Niti, Matthew; Ng, Tze P

    2004-03-01

    The purpose of study was to assess the validity, reliability and acceptability of the English version of the Asthma Quality of Life Questionnaire in a multi-ethnic Asian population. The English version of the Standardized Asthma Quality of Life Questionnaire (AQLQ-S) and the Asthma Control Questionnaire (ACQ) were self-completed by 119 English-speaking Chinese, Malay and Indian asthmatic subjects, aged 17-78. Spirometric measurements, peak expiratory flow rate, current clinical symptoms and treatment requirements were documented. Reliability and responsiveness were analyzed in a subgroup of 57 patients who were reassessed 6 weeks later. The Cronbach alpha reliability coefficient for internal consistency of the AQLQ-S was 0.97 (0.96-0.98) for the overall score. The intraclass correlation coefficient (ICC) overall score was 0.97 (95% CI: 0.94-0.99) while the responsiveness index was 1.29 with strong longitudinal validity for clinical and spirometric measures of asthma severity and asthma control score (p < 0.001). The results of this study showed that the English version of the AQLQ-S is a sensitive and valid instrument for measuring health-related quality of life in asthmatic subjects from a multi-ethnic Asian population.

  5. The Impact Analysis of Psychological Reliability of Population Pilot Study for Selection of Particular Reliable Multi-Choice Item Test in Foreign Language Research Work

    ERIC Educational Resources Information Center

    Fazeli, Seyed Hossein

    2010-01-01

    The purpose of research described in the current study is the psychological reliability, its importance, application, and more to investigate on the impact analysis of psychological reliability of population pilot study for selection of particular reliable multi-choice item test in foreign language research work. The population for subject…

  6. Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory

    PubMed Central

    Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Xu, Jing; Zheng, Kehong

    2015-01-01

    In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system. PMID:26580620

  7. Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory.

    PubMed

    Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Xu, Jing; Zheng, Kehong

    2015-11-13

    In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system.

  8. Gross parameters prediction of a granular-attached biomass reactor by means of multi-objective genetic-designed artificial neural networks: touristic pressure management case.

    PubMed

    Del Moro, G; Barca, E; De Sanctis, M; Mascolo, G; Di Iaconi, C

    2016-03-01

    The Artificial Neural Networks by Multi-objective Genetic Algorithms (ANN-MOGA) model has been applied to gross parameters data of a Sequencing Batch Biofilter Granular Reactor (SBBGR) with the aim of providing an effective tool for predicting the fluctuations coming from touristic pressure. Six independent multivariate models, which were able to predict the dynamics of raw chemical oxygen demand (COD), soluble chemical oxygen demand (CODsol), total suspended solid (TSS), total nitrogen (TN), ammoniacal nitrogen (N-NH4 (+)) and total phosphorus (Ptot), were developed. The ANN-MOGA software application has shown to be suitable for addressing the SBBGR reactor modelling. The R (2) found are very good, with values equal to 0.94, 0.92, 0.88, 0.88, 0.98 and 0.91 for COD, CODsol, N-NH4 (+), TN, Ptot and TSS, respectively. A comparison was made between SBBGR and traditional activated sludge treatment plant modelling. The results showed the better performance of the ANN-MOGA application with respect to a wide selection of scientific literature cases.

  9. A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network.

    PubMed

    Sun, Yangbo; Chen, Long; Huang, Bisheng; Chen, Keli

    2017-07-01

    As a mineral, the traditional Chinese medicine calamine has a similar shape to many other minerals. Investigations of commercially available calamine samples have shown that there are many fake and inferior calamine goods sold on the market. The conventional identification method for calamine is complicated, therefore as a result of the large scale of calamine samples, a rapid identification method is needed. To establish a qualitative model using near-infrared (NIR) spectroscopy for rapid identification of various calamine samples, large quantities of calamine samples including crude products, counterfeits and processed products were collected and correctly identified using the physicochemical and powder X-ray diffraction method. The NIR spectroscopy method was used to analyze these samples by combining the multi-reference correlation coefficient (MRCC) method and the error back propagation artificial neural network algorithm (BP-ANN), so as to realize the qualitative identification of calamine samples. The accuracy rate of the model based on NIR and MRCC methods was 85%; in addition, the model, which took comprehensive multiple factors into consideration, can be used to identify crude calamine products, its counterfeits and processed products. Furthermore, by in-putting the correlation coefficients of multiple references as the spectral feature data of samples into BP-ANN, a BP-ANN model of qualitative identification was established, of which the accuracy rate was increased to 95%. The MRCC method can be used as a NIR-based method in the process of BP-ANN modeling.

  10. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine

    NASA Astrophysics Data System (ADS)

    Acharya, Nachiketa; Shrivastava, Nitin Anand; Panigrahi, B. K.; Mohanty, U. C.

    2014-09-01

    The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982-2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982-2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson's and Kendal's correlation coefficient and Wilmort's index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.

  11. Multi-objective optimal design of online PID controllers using model predictive control based on the group method of data handling-type neural networks

    NASA Astrophysics Data System (ADS)

    Majdabadi-Farahani, V.; Hanif, M.; Gholaminezhad, I.; Jamali, A.; Nariman-Zadeh, N.

    2014-10-01

    In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of Δ u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.

  12. An obligatory bacterial mutualism in a multi-drug environment exhibits strong oscillatory population dynamics

    NASA Astrophysics Data System (ADS)

    Conwill, Arolyn; Yurtsev, Eugene; Gore, Jeff

    2014-03-01

    A common mechanism of antibiotic resistance in bacteria involves the production of an enzyme that inactivates the antibiotic. By inactivating the antibiotic, resistant cells can protect other cells in the population that would otherwise be sensitive to the drug. In a multidrug environment, an obligatory mutualism arises because populations of different strains rely on each other to breakdown antibiotics in the environment. Here, we experimentally track the population dynamics of two E. coli strains in the presence of two different antibiotics: ampicillin and chloramphenicol. Together the strains are able to grow in antibiotic concentrations that inhibit growth of either one of the strains alone. Although mutualisms are often thought to stabilize population dynamics, we observe strong oscillatory dynamics even when there is long-term coexistence between the two strains. We expect that our results will provide insight into the evolution of antibiotic resistance and, more generally, the evolutionary origin of phenotypic diversity, cooperation, and ecological stability.

  13. CONSTRUCTING, PERTURBATION ANALYSIIS AND TESTING OF A MULTI-HABITAT PERIODIC MATRIX POPULATION MODEL

    EPA Science Inventory

    We present a matrix model that explicitly incorporates spatial habitat structure and seasonality and discuss preliminary results from a landscape level experimental test. Ecological risk to populations is often modeled without explicit treatment of spatially or temporally distri...

  14. CONSTRUCTING, PERTURBATION ANALYSIIS AND TESTING OF A MULTI-HABITAT PERIODIC MATRIX POPULATION MODEL

    EPA Science Inventory

    We present a matrix model that explicitly incorporates spatial habitat structure and seasonality and discuss preliminary results from a landscape level experimental test. Ecological risk to populations is often modeled without explicit treatment of spatially or temporally distri...

  15. Neural Network-Based Retrieval of Surface and Root Zone Soil Moisture using Multi-Frequency Remotely-Sensed Observations

    NASA Astrophysics Data System (ADS)

    Hamed Alemohammad, Seyed; Kolassa, Jana; Prigent, Catherine; Aires, Filipe; Gentine, Pierre

    2017-04-01

    Knowledge of root zone soil moisture is essential in studying plant's response to different stress conditions since plant photosynthetic activity and transpiration rate are constrained by the water available through their roots. Current global root zone soil moisture estimates are based on either outputs from physical models constrained by observations, or assimilation of remotely-sensed microwave-based surface soil moisture estimates with physical model outputs. However, quality of these estimates are limited by the accuracy of the model representations of physical processes (such as radiative transfer, infiltration, percolation, and evapotranspiration) as well as errors in the estimates of the surface parameters. Additionally, statistical approaches provide an alternative efficient platform to develop root zone soil moisture retrieval algorithms from remotely-sensed observations. In this study, we present a new neural network based retrieval algorithm to estimate surface and root zone soil moisture from passive microwave observations of SMAP satellite (L-band) and AMSR2 instrument (X-band). SMAP early morning observations are ideal for surface soil moisture retrieval. AMSR2 mid-night observations are used here as an indicator of plant hydraulic properties that are related to root zone soil moisture. The combined observations from SMAP and AMSR2 together with other ancillary observations including the Solar-Induced Fluorescence (SIF) estimates from GOME-2 instrument provide necessary information to estimate surface and root zone soil moisture. The algorithm is applied to observations from the first 18 months of SMAP mission and retrievals are validated against in-situ observations and other global datasets.

  16. A Single-Chip Full-Duplex High Speed Transceiver for Multi-Site Stimulating and Recording Neural Implants.

    PubMed

    Mirbozorgi, S Abdollah; Bahrami, Hadi; Sawan, Mohamad; Rusch, Leslie A; Gosselin, Benoit

    2016-06-01

    We present a novel, fully-integrated, low-power full-duplex transceiver (FDT) to support high-density and bidirectional neural interfacing applications (high-channel count stimulating and recording) with asymmetric data rates: higher rates are required for recording (uplink signals) than stimulation (downlink signals). The transmitter (TX) and receiver (RX) share a single antenna to reduce implant size and complexity. The TX uses impulse radio ultra-wide band (IR-UWB) based on an edge combining approach, and the RX uses a novel 2.4-GHz on-off keying (OOK) receiver. Proper isolation (>20 dB) between the TX and RX path is implemented 1) by shaping the transmitted pulses to fall within the unregulated UWB spectrum (3.1-7 GHz), and 2) by space-efficient filtering (avoiding a circulator or diplexer) of the downlink OOK spectrum in the RX low-noise amplifier. The UWB 3.1-7 GHz transmitter can use either OOK or binary phase shift keying (BPSK) modulation schemes. The proposed FDT provides dual band 500-Mbps TX uplink data rate and 100 Mbps RX downlink data rate, and it is fully integrated into standard TSMC 0.18- μm CMOS within a total size of 0.8 mm(2). The total measured power consumption is 10.4 mW in full duplex mode (5 mW at 100 Mbps for RX, and 5.4 mW at 500 Mbps or 10.8 pJ/bit for TX). Additionally, a 3-coil inductive link along with on-chip power management circuits allows to powering up the implantable transceiver wirelessly by delivering 25 mW extracted from a 13.56-MHz carrier signal, at a total efficiency of 41.6%.

  17. Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models

    NASA Astrophysics Data System (ADS)

    Chang, Ni-Bin; Yang, Y. Jeffrey; Imen, Sanaz; Mullon, Lee

    2017-05-01

    Global sea surface temperature (SST) anomalies are observed to have a significant effect on terrestrial precipitation patterns throughout the United States. SST variations have been correlated with terrestrial precipitation via ocean-atmospheric interactions known as climate teleconnections. This study demonstrates how the scale effect could affect the forecasting accuracy with or without the inclusion of those newly discovered unknown teleconnection signals between Adirondack precipitation and SST anomaly in the Atlantic and Pacific oceans. Unique SST regions of both known and unknown telecommunication signals were extracted from the wavelet analysis and used as input variables in an artificial neural network (ANN) forecasting model. Monthly and seasonal scales were considered with respect to a host of long-term (30-year) nonlinear and nonstationary teleconnection signals detected locally at the study site of Adirondack. Similar intra-annual time-lag effects of SST on precipitation variability are salient at both time scales. Sensitivity analysis of four scenarios reveals that more improvements of the forecasting accuracy of the ANN model can be observed by including both known and unknown teleconnection patterns at both time scales, although such improvements are not salient. Research findings also highlight the importance of choosing the forecasting model at the seasonal scale to predict more accurate peak values and global trends of terrestrial precipitation in response to teleconnection signals. The scale shift from monthly to seasonal may improve results by 17% and 17 mm/day in terms of R squared and root of mean square error values, respectively, if both known and unknown SST regions are considered for forecasting.

  18. Combination of wearable multi-biosensor platform and resonance frequency training for stress management of the unemployed population.

    PubMed

    Wu, Wanqing; Gil, Yeongjoon; Lee, Jungtae

    2012-09-27

    Currently considerable research is being directed toward developing methodologies for controlling emotion or releasing stress. An applied branch of the basic field of psychophysiology, known as biofeedback, has been developed to fulfill clinical and non-clinical needs related to such control. Wearable medical devices have permitted unobtrusive monitoring of vital signs and emerging biofeedback services in a pervasive manner. With the global recession, unemployment has become one of the most serious social problems; therefore, the combination of biofeedback techniques with wearable technology for stress management of unemployed population is undoubtedly meaningful. This article describes a wearable biofeedback system based on combining integrated multi-biosensor platform with resonance frequency training (RFT) biofeedback strategy for stress management of unemployed population. Compared to commercial system, in situ experiments with multiple subjects indicated that our biofeedback system was discreet, easy to wear, and capable of offering ambulatory RFT biofeedback.Moreover, the comparative studies on the altered autonomic nervous system (ANS) modulation before and after three week RFT biofeedback training was performed in unemployed population with the aid of our wearable biofeedback system. The achieved results suggested that RFT biofeedback in combination with wearable technology was capable of significantly increasingoverall HRV, which indicated by decreasing sympathetic activities, increasing parasympathetic activities, and increasing ANS synchronization. After 3-week RFT-based respiration training, the ANS's regulating function and coping ability of unemployed population have doubled, and tended toward a dynamic balance.

  19. Polymorphism analysis of multi-parent advanced generation inter-cross (MAGIC) populations of upland cotton developed in China.

    PubMed

    Li, D G; Li, Z X; Hu, J S; Lin, Z X; Li, X F

    2016-12-19

    Upland cotton (Gossypium hirsutum L.) is an important cash crop that provides renewable natural fiber worldwide. Currently limited genetic base leads to a decrease in upland cotton genetic diversity. Multi-parent advance generation inter-cross (MAGIC) populations can be used to evaluate complex agronomic traits in crops. In this study, we developed an upland cotton MAGIC population. A total of 258 MAGIC population lines and their twelve founder lines were analyzed, using 432 pairs of simple sequence repeat (SSR) markers. Gene diversity indices and the polymorphism information content were calculated using polymorphism analyses. Our genotype analysis showed that 258 inbred lines could be divided into 158 genotypes. Among these, we identified 17 pairs of specific SSR primers on the A chromosome subgroups and 24 pairs of specific SSR primers on the B chromosome subgroups of upland cotton. These were related to 77 and 128 genotypes, respectively. Our results suggest that the upland cotton MAGIC population contained abundant genetic diversity and may provide enormous resources for future genetic breeding.

  20. Level of colorectal cancer awareness: a cross sectional exploratory study among multi-ethnic rural population in Malaysia

    PubMed Central

    2013-01-01

    Background This paper presents the level of colorectal cancer awareness among multi-ethnic rural population in Malaysia. Methods A rural-based cross sectional survey was carried out in Perak state in Peninsular Malaysia in March 2011. The survey recruited a population-representative sample using multistage sampling. Altogether 2379 participants were included in this study. Validated bowel/colorectal cancer awareness measure questionnaire was used to assess the level of colorectal cancer awareness among study population. Analysis of variance (ANOVA) was done to identify socio-demographic variance of knowledge score on warning signs and risk factors of colorectal cancer. Results Among respondents, 38% and 32% had zero knowledge score for warning signs and risk factors respectively. Mean knowledge score for warning signs and risk factors were 2.89 (SD 2.96) and 3.49 (SD 3.17) respectively. There was a significant positive correlation between the knowledge score of warning signs and level of confidence in detecting a warning sign. Socio-demographic characteristics and having cancer in family and friends play important role in level of awareness. Conclusions Level of awareness on colorectal cancer warning signs and risk factors in the rural population of Malaysia is very low. Therefore, it warrants an extensive health education campaign on colorectal cancer awareness as it is one of the commonest cancer in Malaysia. Health education campaign is urgently needed because respondents would seek medical attention sooner if they are aware of this problem. PMID:23924238

  1. Level of colorectal cancer awareness: a cross sectional exploratory study among multi-ethnic rural population in Malaysia.

    PubMed

    Su, Tin Tin; Goh, Jun Yan; Tan, Jackson; Muhaimah, Abdul Rahim; Pigeneswaren, Yoganathan; Khairun, Nasirin Sallamun; Normazidah, Abdul Wahab; Tharisini, Devi Kunasekaran; Majid, Hazreen Abd

    2013-08-07

    This paper presents the level of colorectal cancer awareness among multi-ethnic rural population in Malaysia. A rural-based cross sectional survey was carried out in Perak state in Peninsular Malaysia in March 2011. The survey recruited a population-representative sample using multistage sampling. Altogether 2379 participants were included in this study. Validated bowel/colorectal cancer awareness measure questionnaire was used to assess the level of colorectal cancer awareness among study population. Analysis of variance (ANOVA) was done to identify socio-demographic variance of knowledge score on warning signs and risk factors of colorectal cancer. Among respondents, 38% and 32% had zero knowledge score for warning signs and risk factors respectively. Mean knowledge score for warning signs and risk factors were 2.89 (SD 2.96) and 3.49 (SD 3.17) respectively. There was a significant positive correlation between the knowledge score of warning signs and level of confidence in detecting a warning sign. Socio-demographic characteristics and having cancer in family and friends play important role in level of awareness. Level of awareness on colorectal cancer warning signs and risk factors in the rural population of Malaysia is very low. Therefore, it warrants an extensive health education campaign on colorectal cancer awareness as it is one of the commonest cancer in Malaysia. Health education campaign is urgently needed because respondents would seek medical attention sooner if they are aware of this problem.

  2. Combination of Wearable Multi-Biosensor Platform and Resonance Frequency Training for Stress Management of the Unemployed Population

    PubMed Central

    Wu, Wanqing; Gil, Yeongjoon; Lee, Jungtae

    2012-01-01

    Currently considerable research is being directed toward developing methodologies for controlling emotion or releasing stress. An applied branch of the basic field of psychophysiology, known as biofeedback, has been developed to fulfill clinical and non-clinical needs related to such control. Wearable medical devices have permitted unobtrusive monitoring of vital signs and emerging biofeedback services in a pervasive manner. With the global recession, unemployment has become one of the most serious social problems; therefore, the combination of biofeedback techniques with wearable technology for stress management of unemployed population is undoubtedly meaningful. This article describes a wearable biofeedback system based on combining integrated multi-biosensor platform with resonance frequency training (RFT) biofeedback strategy for stress management of unemployed population. Compared to commercial system, in situ experiments with multiple subjects indicated that our biofeedback system was discreet, easy to wear, and capable of offering ambulatory RFT biofeedback.Moreover, the comparative studies on the altered autonomic nervous system (ANS) modulation before and after three week RFT biofeedback training was performed in unemployed population with the aid of our wearable biofeedback system. The achieved results suggested that RFT biofeedback in combination with wearable technology was capable of significantly increasingoverall HRV, which indicated by decreasing sympathetic activities, increasing parasympathetic activities, and increasing ANS synchronization. After 3-week RFT-based respiration training, the ANS's regulating function and coping ability of unemployed population have doubled, and tended toward a dynamic balance. PMID:23201994

  3. Chronic kidney disease, cardiovascular disease and mortality: A prospective cohort study in a multi-ethnic Asian population.

    PubMed

    Lim, Cynthia C; Teo, Boon Wee; Ong, Peng Guan; Cheung, Carol Y; Lim, Su Chi; Chow, Khuan Yew; Meng, Chan Choon; Lee, Jeannette; Tai, E Shyong; Wong, Tien Y; Sabanayagam, Charumathi

    2015-08-01

    Few studies have examined the impact of chronic kidney disease (CKD) on adverse cardiovascular outcomes and deaths in Asian populations. We evaluated the associations of CKD with cardiovascular disease (CVD) and all-cause mortality in a multi-ethnic Asian population. Prospective cohort study of 7098 individuals who participated in two independent population-based studies involving Malay adults (n = 3148) and a multi-ethnic cohort of Chinese, Malay and Indian adults (n = 3950). CKD was assessed from CKD-EPI estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR). Incident CVD (myocardial infarction, stroke and CVD mortality) and all-cause mortality were identified by linkage with national disease/death registries. Over a median follow-up of 4.3 years, 4.6% developed CVD and 6.1% died. Risks of both CVD and all-cause mortality increased with decreasing eGFR and increasing albuminuria (all p-trend <0.05). Adjusted hazard ratios (HR (95% confidence interval)) of CVD and all-cause mortality were: 1.54 (1.05-2.27) and 2.21 (1.67-2.92) comparing eGFR <45 vs ≥60; 2.81 (1.49-5.29) and 2.34 (1.28-4.28) comparing UACR ≥300 vs <30. The association between eGFR <60 and all-cause mortality was stronger among those with diabetes (p-interaction = 0.02). PAR of incident CVD was greater among those with UACR ≥300 (12.9%) and that of all-cause mortality greater among those with eGFR <45 (16.5%). In multi-ethnic Asian adults, lower eGFR and higher albuminuria were independently associated with incident CVD and all-cause mortality. These findings extend previously reported similar associations in Western populations to Asians and emphasize the need for early detection of CKD and intervention to prevent adverse outcomes. © The European Society of Cardiology 2014.

  4. Framing air pollution epidemiology in terms of population interventions, with applications to multi-pollutant modeling

    PubMed Central

    Snowden, Jonathan M.; Reid, Colleen E.; Tager, Ira B.

    2015-01-01

    Air pollution epidemiology continues moving toward the study of mixtures and multi-pollutant modeling. Simultaneously, there is a movement in epidemiology to estimate policy-relevant health effects that can be understood in reference to specific interventions. Scaling regression coefficients from a regression model by an interquartile range (IQR) is one common approach to presenting multi-pollutant health effect estimates. We are unaware of guidance on how to interpret these effect estimates as an intervention. To illustrate the issues of interpretability of IQR-scaled air pollution health effects, we analyzed how daily concentration changes in two air pollutants (NO2 and PM2.5; nitrogen dioxide and particulate matter with aerodynamic diameter ≤ 2.5μm) related to one another within two seasons (summer and winter), within three cities with distinct air pollution profiles (Burbank, California; Houston, Texas; and Pittsburgh, Pennsylvania). In each city-season, we examined how realistically IQR-scaling in multipollutant lag-1 time-series studies reflects a hypothetical intervention that is possible given the observed data. We proposed 2 causal conditions to explicitly link IQR-scaled effects to a clearly defined hypothetical intervention. Condition 1 specified that the index pollutant had to experience a daily concentration change of greater than one IQR, reflecting the notion that the IQR is an appropriate measure of variability between consecutive days. Condition 2 specified that the co-pollutant had to remain relatively constant. We found that in some city-seasons, there were very few instances in which these conditions were satisfied (e.g., 1 day in Pittsburgh during summer). We discuss the practical implications of IQR scaling and suggest alternative approaches to presenting multi-pollutant effects that are supported by empirical data. PMID:25643106

  5. Epigenetic profiles in children with a neural tube defect; a case-control study in two populations.

    PubMed

    Stolk, Lisette; Bouwland-Both, Marieke I; van Mil, Nina H; van Mill, Nina H; Verbiest, Michael M P J; Eilers, Paul H C; Zhu, Huiping; Suarez, Lucina; Uitterlinden, André G; Steegers-Theunissen, Régine P M

    2013-01-01

    Folate deficiency is implicated in the causation of neural tube defects (NTDs). The preventive effect of periconceptional folic acid supplement use is partially explained by the treatment of a deranged folate-dependent one carbon metabolism, which provides methyl groups for DNA-methylation as an epigenetic mechanism. Here, we hypothesize that variations in DNA-methylation of genes implicated in the development of NTDs and embryonic growth are part of the underlying mechanism. In 48 children with a neural tube defect and 62 controls from a Dutch case-control study and 34 children with a neural tube defect and 78 controls from a Texan case-control study, we measured the DNA-methylation levels of imprinted candidate genes (IGF2-DMR, H19, KCNQ1OT1) and non-imprinted genes (the LEKR/CCNL gene region associated with birth weight, and MTHFR and VANGL1 associated with NTD). We used the MassARRAY EpiTYPER assay from Sequenom for the assessment of DNA-methylation. Linear mixed model analysis was used to estimate associations between DNA-methylation levels of the genes and a neural tube defect. In the Dutch study group, but not in the Texan study group we found a significant association between the risk of having an NTD and DNA methylation levels of MTHFR (absolute decrease in methylation of -0.33% in cases, P-value = 0.001), and LEKR/CCNL (absolute increase in methylation: 1.36% in cases, P-value = 0.048), and a borderline significant association for VANGL (absolute increase in methylation: 0.17% in cases, P-value = 0.063). Only the association between MTHFR and NTD-risk remained significant after multiple testing correction. The associations in the Dutch study were not replicated in the Texan study. We conclude that the associations between NTDs and the methylation of the MTHFR gene, and maybe VANGL and LEKKR/CNNL, are in line with previous studies showing polymorphisms in the same genes in association with NTDs and embryonic development, respectively.

  6. Pax6 Is Essential for the Maintenance and Multi-Lineage Differentiation of Neural Stem Cells, and for Neuronal Incorporation into the Adult Olfactory Bulb

    PubMed Central

    Curto, Gloria G.; Nieto-Estévez, Vanesa; Hurtado-Chong, Anahí; Valero, Jorge; Gómez, Carmela; Alonso, José R.; Weruaga, Eduardo

    2014-01-01

    The paired type homeobox 6 (Pax6) transcription factor (TF) regulates multiple aspects of neural stem cell (NSC) and neuron development in the embryonic central nervous system. However, less is known about the role of Pax6 in the maintenance and differentiation of adult NSCs and in adult neurogenesis. Using the +/SeyDey mouse, we have analyzed how Pax6 heterozygosis influences the self-renewal and proliferation of adult olfactory bulb stem cells (aOBSCs). In addition, we assessed its influence on neural differentiation, neuronal incorporation, and cell death in the adult OB, both in vivo and in vitro. Our results indicate that the Pax6 mutation alters Nestin+-cell proliferation in vivo, as well as self-renewal, proliferation, and survival of aOBSCs in vitro although a subpopulation of +/SeyDey progenitors is able to expand partially similar to wild-type progenitors. This mutation also impairs aOBSC differentiation into neurons and oligodendrocytes, whereas it increases cell death while preserving astrocyte survival and differentiation. Furthermore, Pax6 heterozygosis causes a reduction in the variety of neurochemical interneuron subtypes generated from aOBSCs in vitro and in the incorporation of newly generated neurons into the OB in vivo. Our findings support an important role of Pax6 in the maintenance of aOBSCs by regulating cell death, self-renewal, and cell fate, as well as in neuronal incorporation into the adult OB. They also suggest that deregulation of the cell cycle machinery and TF expression in aOBSCs which are deficient in Pax6 may be at the origin of the phenotypes observed in this adult NSC population. PMID:25117830

  7. Integration of silicon-based neural probes and micro-drive arrays for chronic recording of large populations of neurons in behaving animals

    NASA Astrophysics Data System (ADS)

    Michon, Frédéric; Aarts, Arno; Holzhammer, Tobias; Ruther, Patrick; Borghs, Gustaaf; McNaughton, Bruce; Kloosterman, Fabian

    2016-08-01

    Objective. Understanding how neuronal assemblies underlie cognitive function is a fundamental question in system neuroscience. It poses the technical challenge to monitor the activity of populations of neurons, potentially widely separated, in relation to behaviour. In this paper, we present a new system which aims at simultaneously recording from a large population of neurons from multiple separated brain regions in freely behaving animals. Approach. The concept of the new device is to combine the benefits of two existing electrophysiological techniques, i.e. the flexibility and modularity of micro-drive arrays and the high sampling ability of electrode-dense silicon probes. Main results. Newly engineered long bendable silicon probes were integrated into a micro-drive array. The resulting device can carry up to 16 independently movable silicon probes, each carrying 16 recording sites. Populations of neurons were recorded simultaneously in multiple cortical and/or hippocampal sites in two freely behaving implanted rats. Significance. Current approaches to monitor neuronal activity either allow to flexibly record from multiple widely separated brain regions (micro-drive arrays) but with a limited sampling density or to provide denser sampling at the expense of a flexible placement in multiple brain regions (neural probes). By combining these two approaches and their benefits, we present an alternative solution for flexible and simultaneous recordings from widely distributed populations of neurons in freely behaving rats.

  8. Estimating multi-factor cumulative watershed effects on fish populations with an individual-based model

    Treesearch

    Bret C. Harvey; Steven F. Railsback

    2007-01-01

    While the concept of cumulative effects is prominent in legislation governing environmental management, the ability to estimate cumulative effects remains limited. One reason for this limitation is that important natural resources such as fish populations may exhibit complex responses to changes in environmental conditions, particularly to alteration of multiple...

  9. A multi-scale, landscape approach to predicting insect populations in agroecosystems.

    PubMed

    O'Rourke, Megan E; Rienzo-Stack, Kaitlin; Power, Alison G

    2011-07-01

    Landscape composition affects ecosystems services, including agricultural pest management. However, relationships between land use and agricultural insects are not well understood, and many complexities remain to be explored. Here we examine whether nonagricultural landscapes can directly suppress agricultural pests, how multiple spatial scales of land use concurrently affect insect populations, and the relationships between regional land use and insect populations. We tracked densities of three specialist corn (Zea mays) pests (Ostrinia nubilalis, European corn borer; Diabrotica virgifera, western corn rootworm; Diabrotica barberi, northern corn rootworm), and two generalist predator lady beetles (Coleomegilla maculata and Propylea quatuordecimpunctata) in field corn and determined their relationships to agricultural land use at three spatial scales (field perimeter, 1-km, and 20-km radius areas). Pest densities were either higher (D. virgifera and D. barberi) or unchanged (O. nubilalis) in landscapes with more corn, while natural enemy densities were either lower (C. maculata) or unchanged (P. quatuordecimpunctata). Results for D. virgifera and D. barberi indicate that decreasing the area of preferred crop in the landscape can directly suppress specialist insect pests. Multiple scales of land use affected populations of D. virgifera and C. maculata, and D. virgifera populations showed strong relationships with regional, 20-km-scale land use. These results suggest that farm planning and government policies aimed at diversifying local and regional agricultural landscapes show promise for increasing biological control and directly suppressing agricultural pests.

  10. Overall multi-media persistence as an indicator of potential for population-level intake of environmental contaminants

    SciTech Connect

    MacLeod, Matthew; McKone, Thomas E.

    2003-06-01

    Although it is intuitively apparent that population-level exposure to contaminants dispersed in the environment must related to the persistence of the contaminant, there has been little effort to formally quantify this link. In this paper we investigate the relationship between overall persistence in a multimedia environment and the population-level exposure as expressed by intake fraction (iF), which is the cumulative fraction of chemical emitted to the environment that is taken up by members of the population. We first confirm that for any given chemical contaminant and emission scenario the definition of iF implies that it is directly proportional to the overall multi-media persistence, P{sub OV}. We show that the proportionality constant has dimensions of time and represents the characteristic time for population intake (CTI) of the chemical from the environment. We then apply the CalTOX fate and exposure model to explore how P{sub OV} and CTI combine to determine the magnitude of iF. We find that CTI has a narrow range of possible values relative to P{sub OV} across multiple chemicals and emissions scenarios. We use data from the Canadian Environmental Protection Act Priority Substance List (PSL1) Assessments to show that exposure assessments based on empirical observation are consistent with interpretations from the model. The characteristic time for intake along different dominant exposure pathways is discussed. Results indicate that P{sub OV} derived from screening-level assessments of persistence, bioaccumulation potential, and toxicity (PBT) is a useful indicator of the potential for population-level exposure.

  11. Genetic Diversity and Population Structure of Leishmania infantum from Southeastern France: Evaluation Using Multi-Locus Microsatellite Typing

    PubMed Central

    Pomares, Christelle; Marty, Pierre; Bañuls, Anne Laure; Lemichez, Emmanuel; Pratlong, Francine; Faucher, Benoît; Jeddi, Fakhri; Moore, Sandy; Michel, Grégory; Aluru, Srikanth; Piarroux, Renaud; Hide, Mallorie

    2016-01-01

    In the south of France, Leishmania infantum is responsible for numerous cases of canine leishmaniasis (CanL), sporadic cases of human visceral leishmaniasis (VL) and rare cases of cutaneous and muco-cutaneous leishmaniasis (CL and MCL, respectively). Several endemic areas have been clearly identified in the south of France including the Pyrénées-Orientales, Cévennes (CE), Provence (P), Alpes-Maritimes (AM) and Corsica (CO). Within these endemic areas, the two cities of Nice (AM) and Marseille (P), which are located 150 km apart, and their surroundings, concentrate the greatest number of French autochthonous leishmaniasis cases. In this study, 270 L. infantum isolates from an extended time period (1978–2011) from four endemic areas, AM, P, CE and CO, were assessed using Multi-Locus Microsatellite Typing (MLMT). MLMT revealed a total of 121 different genotypes with 91 unique genotypes and 30 repeated genotypes. Substantial genetic diversity was found with a strong genetic differentiation between the Leishmania populations from AM and P. However, exchanges were observed between these two endemic areas in which it seems that strains spread from AM to P. The genetic differentiations in these areas suggest strong epidemiological structuring. A model-based analysis using STRUCTURE revealed two main populations: population A (consisting of samples primarily from the P and AM endemic areas with MON-1 and non-MON-1 strains) and population B consisting of only MON-1 strains essentially from the AM endemic area. For four patients, we observed several isolates from different biological samples which provided insight into disease relapse and re-infection. These findings shed light on the transmission dynamics of parasites in humans. However, further data are required to confirm this hypothesis based on a limited sample set. This study represents the most extensive population analysis of L. infantum strains using MLMT conducted in France. PMID:26808522

  12. Assessment of population genetic structure in the arbovirus vector midge, Culicoides brevitarsis (Diptera: Ceratopogonidae), using multi-locus DNA microsatellites.