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Sample records for multi population neural

  1. 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.

  2. 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

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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

  14. 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.

  15. 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

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  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. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Recognition of In-Ear Microphone Speech Data Using Multi-Layer Neural Networks

    DTIC Science & Technology

    2006-03-01

    types, namely, the recurrent network [ Hopfield , 1982], and the backpropagation algorithm [Rumelhart, McClelland, 1986]. In particular, the discovery... Network Design, Campus Publishing Service, University of Colorado, Boulder, Colorado, 1996. Hopfield , J. J., “Neural networks and physical systems...MICROPHONE SPEECH DATA USING MULTI-LAYER NEURAL NETWORKS by Gokhan Bulbuller March 2006 Thesis Advisor: Monique P. Fargues Co

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Genomic selection in multi-breed dairy cattle populations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  7. Neural Population Tuning Links Visual Cortical Anatomy to Human Visual Perception

    PubMed Central

    Song, Chen; Schwarzkopf, Dietrich Samuel; Kanai, Ryota; Rees, Geraint

    2015-01-01

    Summary The anatomy of cerebral cortex is characterized by two genetically independent variables, cortical thickness and cortical surface area, that jointly determine cortical volume. It remains unclear how cortical anatomy might influence neural response properties and whether such influences would have behavioral consequences. Here, we report that thickness and surface area of human early visual cortices exert opposite influences on neural population tuning with behavioral consequences for perceptual acuity. We found that visual cortical thickness correlated negatively with the sharpness of neural population tuning and the accuracy of perceptual discrimination at different visual field positions. In contrast, visual cortical surface area correlated positively with neural population tuning sharpness and perceptual discrimination accuracy. Our findings reveal a central role for neural population tuning in linking visual cortical anatomy to visual perception and suggest that a perceptually advantageous visual cortex is a thinned one with an enlarged surface area. PMID:25619658

  8. 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.

  9. 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…

  10. 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

  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. 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.

  13. 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.

  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. 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.

  16. 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.

  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.

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  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-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.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  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. 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.

  14. 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.

  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. 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.

  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. 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

  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. 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.

  3. 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.

  4. 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.

  5. 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

  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. 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.

  8. 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.

  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. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Assessing survival in a multi-population system: a case study on bat populations.

    PubMed

    Papadatou, Eleni; Ibáñez, Carlos; Pradel, Roger; Juste, Javier; Gimenez, Olivier

    2011-04-01

    In long-lived animals, adult survival is among the most important determinants of population dynamics. Although it may show considerable variation both in time and among populations and sites, a single survival estimate per species is often used in comparative evolutionary studies or in conservation management to identify threatened populations. We estimated adult survival of the isabelline serotine bat Eptesicus isabellinus using capture-recapture data collected on six maternity colonies scattered over a large area (distance 8-103 km) during periods varying from 8 to 26 years. We modelled temporal and inter-colony variations as random effects in a Bayesian framework and estimated mean annual adult survival of females on two scales and a single survival value across all colonies. On a coarse scale, we grouped colonies according to two different habitat types and investigated the effect on survival. A difference in adult survival was detected between the two habitat types [posterior mean of annual survival probability 0.71; 95% credible interval (CI) 0.51-0.86 vs. 0.60; 0.28-0.89], but it was not statistically supported. On a fine scale, survival of the six colonies ranged between 0.58 (95% CI 0.23-0.92) and 0.81 (0.73-0.88), with variation between only two colonies being statistically supported. Overall survival was 0.72 (95% CI 0.57-0.93) with important inter-colony variability (on a logit scale 0.98; 95% CI 0.00-8.16). Survival varied temporally in a random fashion across colonies. Our results show that inference based solely on single colonies should be treated with caution and that a representative unbiased estimate of survival for any species should ideally be based on multiple populations.

  12. 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

  13. 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.

  14. 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

  15. 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

  16. 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…

  17. 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.

  18. 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.

  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. 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’.

  1. 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.

  2. 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.

  3. 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.

  4. 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).

  5. 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

  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 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

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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.

  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. 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

  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. 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.

  17. 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.

  18. 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

  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. 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

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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

  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. 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.

  20. 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.

  1. Identification of genetic variants associated with maize flowering time using an extremely large multi-genetic background population

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. [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.

  10. Multi-infections of feminizing Wolbachia strains in natural populations of the terrestrial isopod Armadillidium vulgare.

    PubMed

    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 occurring 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.

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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

  18. Fumonisin as a possible contributing factor to neural tube defects in populations consuming large amounts of maize

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 ...

  19. 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

  20. 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.

  1. Mortality and Population Dynamics of Bemisia tabaci within a Multi-Crop System

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. Population.

    ERIC Educational Resources Information Center

    King, Pat; Landahl, John

    This pamphlet has been prepared in response to a new problem, a rapidly increasing population, and a new need, population education. It is designed to help teachers provide their students with some basic population concepts with stress placed on the elements of decision making. In the first section of the pamphlet, some of the basic concepts of…

  7. [Population].

    PubMed

    1979-01-01

    Data on the population of Venezuela between 1975 and 1977 are presented in descriptive tables and graphs. Information is included on the employed population according to category, sex, and type of economic activity, and by sex, age, and area on the employment rate and the total, the economically active, and the unemployed population.

  8. 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.

  9. 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...

  10. 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.

  11. 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.

  12. 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…

  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. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Population.

    ERIC Educational Resources Information Center

    International Planned Parenthood Federation, London (England).

    In an effort to help meet the growing interest and concern about the problems created by the rapid growth of population, The International Planned Parenthood Federation has prepared this booklet with the aim of assisting the study of the history and future trends of population growth and its impact on individual and family welfare, national,…

  2. 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.

  3. Assessment of population genetic structure in the arbovirus vector midge, Culicoides brevitarsis (Diptera: Ceratopogonidae), using multi-locus DNA microsatellites.

    PubMed

    Onyango, Maria G; Beebe, Nigel W; Gopurenko, David; Bellis, Glenn; Nicholas, Adrian; Ogugo, Moses; Djikeng, Appolinaire; Kemp, Steve; Walker, Peter J; Duchemin, Jean-Bernard

    2015-09-25

    Bluetongue virus (BTV) is a major pathogen of ruminants that is transmitted by biting midges (Culicoides spp.). Australian BTV serotypes have origins in Asia and are distributed across the continent into two distinct episystems, one in the north and another in the east. Culicoides brevitarsis is the major vector of BTV in Australia and is distributed across the entire geographic range of the virus. Here, we describe the isolation and use of DNA microsatellites and gauge their ability to determine population genetic connectivity of C. brevitarsis within Australia and with countries to the north. Eleven DNA microsatellite markers were isolated using a novel genomic enrichment method and identified as useful for genetic analyses of sampled populations in Australia, northern Papua New Guinea (PNG) and Timor-Leste. Significant (P < 0.05) population genetic subdivision was observed between all paired regions, though the highest levels of genetic sub-division involved pair-wise tests with PNG (PNG vs. Australia (FST = 0.120) and PNG vs. Timor-Leste (FST = 0.095)). Analysis of multi-locus allelic distributions using STRUCTURE identified a most probable two-cluster population model, which separated PNG specimens from a cluster containing specimens from Timor-Leste and Australia. The source of incursions of this species in Australia is more likely to be Timor-Leste than PNG. Future incursions of BTV positive C. brevitarsis into Australia may be genetically identified to their source populations using these microsatellite loci. The vector's panmictic genetic structure within Australia cannot explain the differential geographic distribution of BTV serotypes.

  4. 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.

  5. Multi-Wavelength Observations of the Supernova Remnant Populations in the Nearby Spiral Galaxies IC 342 and NGC 4258

    NASA Astrophysics Data System (ADS)

    Pannuti, Thomas; Chomiuk, L.; Grimes, C. K.; Staggs, W. D.; Tussey, J. M.; Laine, S.; Schlegel, E.

    2011-01-01

    Supernova remnants (SNRs) are intimately tied to many crucial processes associated with the interstellar medium of galaxies, such as the acceleration of cosmic-ray particles and the deposition of vast amounts of kinetic energy and chemically-enriched material. Well-known observational challenges in the study of SNRs located in the Milky Way Galaxy (for example, formidable extinction along Galactic lines of sight and considerable uncertainties in the distances to these sources) have motivated searches for SNRs in nearby galaxies at such characteristic wavelengths as X-ray, optical and radio. These searches have revealed a considerable number of SNRs and led to new insights into their properties, but the SNR populations in only a handful of nearby galaxies have been adequately surveyed at multiple wavelengths. To help remedy this situation, we are conducting a multi-wavelength study of the SNR population of selected nearby galaxies. To illustrate our work, we present the results of studies of the SNR population in two nearby spiral galaxies, IC 342 and NGC 4258. Our results draw upon the analysis of pointed archival radio and X-ray observations of these two galaxies. Initial results will be presented and discussed.

  6. 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

  7. Multi-source self-calibration: Unveiling the microJy population of compact radio sources

    NASA Astrophysics Data System (ADS)

    Radcliffe, J. F.; Garrett, M. A.; Beswick, R. J.; Muxlow, T. W. B.; Barthel, P. D.; Deller, A. T.; Middelberg, E.

    2016-03-01

    Context. Very long baseline interferometry (VLBI) data are extremely sensitive to the phase stability of the VLBI array. This is especially important when we reach μJy rms sensitivities. Calibration using standard phase-referencing techniques is often used to improve the phase stability of VLBI data, but the results are often not optimal. This is evident in blank fields that do not have in-beam calibrators. Aims: We present a calibration algorithm termed multi-source self-calibration (MSSC) which can be used after standard phase referencing on wide-field VLBI observations. This is tested on a 1.6 GHz wide-field VLBI data set of the Hubble Deep Field North and the Hubble Flanking Fields. Methods: MSSC uses multiple target sources that are detected in the field via standard phase referencing techniques and modifies the visibilities so that each data set approximates to a point source. These are combined to increase the signal to noise and permit self-calibration. In principle, this should allow residual phase changes caused by the troposphere and ionosphere to be corrected. By means of faceting, the technique can also be used for direction-dependent calibration. Results: Phase corrections, derived using MSSC, were applied to a wide-field VLBI data set of the HDF-N, which comprises of 699 phase centres. MSSC was found to perform considerably better than standard phase referencing and single source self-calibration. All detected sources exhibited dramatic improvements in dynamic range. Using MSSC, one source reached the detection threshold, taking the total detected sources to twenty. This means 60% of these sources can now be imaged with uniform weighting, compared to just 45% with standard phase referencing. In principle, this technique can be applied to any future VLBI observations. The Parseltongue code, which implements MSSC, has been released and made publicly available to the astronomical community (http://https://github.com/jradcliffe5/multi_self_cal).

  8. Diversity of multi-drug resistant Acinetobacter baumannii population in a major hospital in Kuwait

    PubMed Central

    Vali, Leila; Dashti, Khadija; Opazo-Capurro, Andrés F.; Dashti, Ali A.; Al Obaid, Khaled; Evans, Benjamin A.

    2015-01-01

    Acinetobacter baumannii is one of the most important opportunistic pathogens that causes serious health care associated complications in critically ill patients. In the current study we report on the diversity of the clinical multi-drug resistant (MDR) A. baumannii in Kuwait by molecular characterization. One hundred A. baumannii were isolated from one of the largest governmental hospitals in Kuwait. Following the identification of the isolates by molecular methods, the amplified blaOXA-51-like gene product of one isolate (KO-12) recovered from blood showed the insertion of the ISAba19 at position 379 in blaOXA-78. Of the 33 MDR isolates, 28 (85%) contained blaOXA-23, 2 (6%) blaOXA-24 and 6 (18%) blaPER-1 gene. We did not detect blaOXA-58, blaVIM, blaIMP, blaGES, blaVEB, and blaNDM genes in any of the tested isolates. In three blaPER-1 positive isolates the genetic environment of blaPER-1 consisted of two copies of ISPa12 (tnpiA1) surrounding the blaPER-1 gene on a highly stable plasmid of ca. 140-kb. Multilocus-sequence typing (MLST) analysis of the 33 A. baumannii isolates identified 20 different STs, of which six (ST-607, ST-608, ST-609, ST-610, ST-611, and ST-612) were novel. Emerging STs such as ST15 (identified for the first time in the Middle East), ST78 and ST25 were also detected. The predominant clonal complex was CC2. Pulsed-field gel electrophoresis and MLST defined the MDR isolates as multi-clonal with diverse lineages. Our results lead us to believe that A. baumannii is diverse in clonal origins and/or is undergoing clonal expansion continuously while multiple lineages of MDR A. baumannii circulate in hospital ward simultaneously. PMID:26257720

  9. 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

  10. Phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics

    PubMed Central

    Jiang, Da-Quan; Zhou, Da

    2017-01-01

    We consider the cell population dynamics with n different phenotypes. Both the Markovian branching process model (stochastic model) and the ordinary differential equation (ODE) system model (deterministic model) are presented, and exploited to investigate the dynamics of the phenotypic proportions. We will prove that in both models, these proportions will tend to constants regardless of initial population states (“phenotypic equilibrium”) under weak conditions, which explains the experimental phenomenon in Gupta et al.’s paper. We also prove that Gupta et al.’s explanation is the ODE model under a special assumption. As an application, we will give sufficient and necessary conditions under which the proportion of one phenotype tends to 0 (die out) or 1 (dominate). We also extend our results to non-Markovian cases. PMID:28182672

  11. MULTI-WAVELENGTH HUBBLE SPACE TELESCOPE PHOTOMETRY OF STELLAR POPULATIONS IN NGC 288

    SciTech Connect

    Piotto, G.; Milone, A. P.; Marino, A. F.; Jerjen, H.; Bedin, L. R.; Anderson, J.; Bellini, A.; Cassisi, S. E-mail: luigi.bedin@oapd.inaf.it E-mail: amarino@mso.anu.edu.au E-mail: jayander@stsci.edu E-mail: cassisi@oa-teramo.inaf.it

    2013-09-20

    We present new UV observations for NGC 288, taken with the WFC3 detector on board the Hubble Space Telescope, and combine them with existing optical data from the archive to explore the multiple-population phenomenon in this globular cluster (GC). The WFC3's UV filters have demonstrated an uncanny ability to distinguish multiple populations along all photometric sequences in GCs thanks to their exquisite sensitivity to the atmospheric changes that are telltale signs of second-generation enrichment. Optical filters, on the other hand, are more sensitive to stellar-structure changes related to helium enhancement. By combining both UV and optical data, we can measure the helium variation. We quantify this enhancement for NGC 288 and find that the variation is typical of what we have come to expect in other clusters.

  12. Travel determinants and multi-scale transferability of national activity patterns to local populations

    SciTech Connect

    Henson, Kriste M; Gou; ias, Konstadinos G

    2010-11-30

    The ability to transfer national travel patterns to a local population is of interest when attempting to model megaregions or areas that exceed metropolitan planning organization (MPO) boundaries. At the core of this research are questions about the connection between travel behavior and land use, urban form, and accessibility. As a part of this process, a group of land use variables have been identified to define activity and travel patterns for individuals and households. The 2001 National Household Travel Survey (NHTS) participants are divided into categories comprised of a set of latent cluster models representing persons, travel, and land use. These are compared to two sets of cluster models constructed for two local travel surveys. Comparison of means statistical tests are used to assess differences among sociodemographic groups residing in localities with similar land uses. The results show that the NHTS and the local surveys share mean population activity and travel characteristics. However, these similarities mask behavioral heterogeneity that are shown when distributions of activity and travel behavior are examined. Therefore, data from a national household travel survey cannot be used to model local population travel characteristics if the goal to model the actual distributions and not mean travel behavior characteristics.

  13. TRIM28 multi-domain protein regulates cancer stem cell population in breast tumor development

    PubMed Central

    Czerwińska, Patrycja; Shah, Parantu K.; Tomczak, Katarzyna; Klimczak, Marta; Mazurek, Sylwia; Sozańska, Barbara; Biecek, Przemysław; Korski, Konstanty; Filas, Violetta; Mackiewicz, Andrzej; Andersen, Jannik N.; Wiznerowicz, Maciej

    2017-01-01

    The expression of Tripartite motif-containing protein 28 (TRIM28)/Krüppel-associated box (KRAB)-associated protein 1 (KAP1), is elevated in at least 14 tumor types, including solid and hematopoietic tumors. High level of TRIM28 is associated with triple-negative subtype of breast cancer (TNBC), which shows higher aggressiveness and lower survival rates. Interestingly, TRIM28 is essential for maintaining the pluripotent phenotype in embryonic stem cells. Following on that finding, we evaluated the role of TRIM28 protein in the regulation of breast cancer stem cells (CSC) populations and tumorigenesis in vitro and in vivo. Downregulation of TRIM28 expression in xenografts led to deceased expression of pluripotency and mesenchymal markers, as well as inhibition of signaling pathways involved in the complex mechanism of CSC maintenance. Moreover, TRIM28 depletion reduced the ability of cancer cells to induce tumor growth when subcutaneously injected in limiting dilutions. Our data demonstrate that the downregulation of TRIM28 gene expression reduced the ability of CSCs to self-renew that resulted in significant reduction of tumor growth. Loss of function of TRIM28 leads to dysregulation of cell cycle, cellular response to stress, cancer cell metabolism, and inhibition of oxidative phosphorylation. All these mechanisms directly regulate maintenance of CSC population. Our original results revealed the role of the TRIM28 in regulating the CSC population in breast cancer. These findings may pave the way to novel and more effective therapies targeting cancer stem cells in breast tumors. PMID:27845900

  14. 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.

  15. Seasonal forcing and multi-year cycles in interacting populations: lessons from a predator-prey model.

    PubMed

    Taylor, Rachel A; Sherratt, Jonathan A; White, Andrew

    2013-12-01

    Many natural systems are subject to seasonal environmental change. As a consequence many species exhibit seasonal changes in their life history parameters--such as a peak in the birth rate in spring. It is important to understand how this seasonal forcing affects the population dynamics. The main way in which seasonal models have been studied is through a two dimensional bifurcation approach. We augment this bifurcation approach with extensive simulation in order to understand the potential solution behaviours for a predator-prey system with a seasonally forced prey growth rate. We consider separately how forcing influences the system when the unforced dynamics have either monotonic decay to the coexistence steady state, or oscillatory decay, or stable limit cycles. The range of behaviour the system can exhibit includes multi-year cycles of different periodicities, parameter ranges with coexisting multi-year cycles of the same or different period as well as quasi-periodicity and chaos. We show that the level of oscillation in the unforced system has a large effect on the range of behaviour when the system is seasonally forced. We discuss how the methods could be extended to understand the dynamics of a wide range of ecological and epidemiological systems that are subject to seasonal changes.

  16. Distinct Neural Stem Cell Populations Give Rise to Disparate Brain Tumors in Response to N-MYC

    PubMed Central

    Swartling, Fredrik J.; Savov, Vasil; Persson, Anders I.; Chen, Justin; Hackett, Christopher S.; Northcott, Paul A.; Grimmer, Matthew R.; Lau, Jasmine; Chesler, Louis; Perry, Arie; Phillips, Joanna J.; Taylor, Michael D.; Weiss, William A.

    2012-01-01

    SUMMARY The proto-oncogene MYCN is mis-expressed in various types of human brain tumors. To clarify how developmental and regional differences influence transformation, we transduced wild-type or mutationally-stabilized murine N-mycT58A into neural stem cells (NSCs) from perinatal murine cerebellum, brain stem and forebrain. Transplantation of N-mycWT NSCs was insufficient for tumor formation. N-mycT58A cerebellar and brain stem NSCs generated medulloblastoma/primitive neuroectodermal tumors, whereas forebrain NSCs developed diffuse glioma. Expression analyses distinguished tumors generated from these different regions, with tumors from embryonic versus postnatal cerebellar NSCs demonstrating SHH-dependence and SHH-independence, respectively. These differences were regulated in-part by the transcription factor SOX9, activated in the SHH subclass of human medulloblastoma. Our results demonstrate context-dependent transformation of NSCs in response to a common oncogenic signal. PMID:22624711

  17. 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

  18. Indoor air pollution from coal combustion and the risk of neural tube defects in a rural population in Shanxi Province, China.

    PubMed

    Li, Zhiwen; Zhang, Le; Ye, Rongwei; Pei, Lijun; Liu, Jianmeng; Zheng, Xiaoying; Ren, Aiguo

    2011-08-15

    The authors evaluated indoor air pollution from coal combustion (IAPCC) as a potential risk factor for neural tube defects (NTDs) in a rural population in Shanxi Province, China. The studied rural population has both high IAPCC exposure and a high prevalence of NTDs. A population-based case-control study was used to identify 610 NTD cases and 837 normal controls between November 2002 and December 2007. Information was collected within 1 week following delivery or pregnancy termination due to prenatal NTD diagnosis. The authors derived an exposure index by integrating a series of IAPCC-related characteristics concerning dwelling and lifestyle. Compared with women with no IAPCC exposure, women with any exposure at all had a 60% increased risk of having a child with an NTD (adjusted odds ratio (OR) = 1.6, 95% confidence interval (CI): 1.1, 2.1). An increased NTD risk was linked to both residential heating (adjusted OR = 1.7, 95% CI: 1.1, 2.4) and cooking (adjusted OR = 1.5, 95% CI: 1.1, 2.1). The risk increased with increases in the exposure index, showing a dose-response trend (P < 0.001). This is the first known study to link IAPCC to NTDs. Additional studies are needed to confirm the link between IAPCC and NTDs.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. Murine whole-organ immune cell populations revealed by multi-epitope-ligand cartography.

    PubMed

    Eckhardt, Jenny; Ostalecki, Christian; Kuczera, Katarzyna; Schuler, Gerold; Pommer, Ansgar J; Lechmann, Matthias

    2013-02-01

    Multi-epitope-ligand cartography (MELC) is an innovative high-throughput fluorescence microscopy-based method. A tissue section is analyzed through a repeated cycling of (1) incubation with a fluorophore-labeled antibody, (2) fluorescence imaging, and (3) soft bleaching. This method allows staining of the same tissue section with up to 100 fluorescent markers and to analyze their toponomic expression using further image processing and pixel-precise overlay of the corresponding images. In this study, we adapted this method to identify a large panel of murine leukocyte subpopulations in a whole frozen section of a peripheral lymph node. Using the resulting antibody library, we examined non-inflamed versus inflamed tissues of brain and spinal cord in the experimental autoimmune encephalomyelitis (EAE) model. The presence and activity of specific leukocyte subpopulations (different T cell subpopulations, dendritic cells, macrophages, etc.) could be assessed and the cellular localizations and the corresponding activation status in situ were investigated. The results were then correlated with quantitative RT-PCR.

  5. Relationship between sleep duration and arterial stiffness in a multi-ethnic population: The HELIUS study

    PubMed Central

    Anujuo, Kenneth; Stronks, Karien; Snijder, Marieke B.; Jean-Louis, Girardin; van den Born, Bert-Jan; Peters, Ron J.; Agyemang, Charles

    2017-01-01

    We examined the relationship between sleep duration and arterial stiffness among a multi-ethnic cohort, and whether the associations differed among ethnic minority groups in the Netherlands. Data were derived from 10 994 participants (aged 18–71 years) of the Healthy Life in an Urban Setting (HELIUS) study. Self-reported sleep duration was categorized into: short (<7 h/night), healthy (7–8 h/night) and long (≥9 h/night). Arterial stiffness was assessed by duplicate pulse-wave velocity (PWV in m/s) measurements using the Arteriograph system. The association of sleep duration with PWV was analysed using linear regression (β) with 95% confidence interval (CI). Results showed that neither short nor long sleep was related to PWV in all ethnic groups, except for long sleep in Dutch men which was associated with higher PWV (indicating stiffer arteries) after adjustment for potential confounders (β = 0.67, 95%CI, 0.23–1.11). Our study showed no convincing evidence that sleep duration was related to arterial stiffness among various ethnic groups. The link between sleep duration and cardiovascular outcomes does not seem to operate through arterial stiffness. Further research is needed to consolidate these findings. PMID:27058653

  6. Use of large-scale, multi-species surveys to monitor gyrfalcon and ptarmigan populations

    USGS Publications Warehouse

    Bart, Jonathan; Fuller, Mark; Smith, Paul; Dunn, Leah; Watson, Richard T.; Cade, Tom J.; Fuller, Mark; Hunt, Grainger; Potapov, Eugene

    2011-01-01

    We evaluated the ability of three large-scale, multi-species surveys in the Arctic to provide information on abundance and habitat relationships of Gyrfalcons (Falco rusticolus) and ptarmigan. The Program for Regional and International Shorebird Monitoring (PRISM) has surveyed birds widely across the arctic regions of Canada and Alaska since 2001. The Arctic Coastal Plain survey has collected abundance information on the North Slope of Alaska using fixed-wing aircraft since 1992. The Northwest Territories-Nunavut Bird Checklist has collected presenceabsence information from little-known locations in northern Canada since 1995. All three surveys provide extensive information on Willow Ptarmigan (Lagopus lagopus) and Rock Ptarmigan (L. muta). For example, they show that ptarmigan are most abundant in western Alaska, next most abundant in northern Alaska and northwest Canada, and least abundant in the Canadian Archipelago. PRISM surveys were less successful in detecting Gyrfalcons, and the Arctic Coastal Plain Survey is largely outside the Gyrfalcon?s breeding range. The Checklist Survey, however, reflects the expansive Gyrfalcon range in Canada. We suggest that collaboration by Gyrfalcon and ptarmigan biologists with the organizers of large scale surveys like the ones we investigated provides an opportunity for obtaining useful information on these species and their environment across large areas.

  7. Multi-locus genetic risk score predicts risk for Crohn’s disease in Slovenian population

    PubMed Central

    Zupančič, Katarina; Skok, Kristijan; Repnik, Katja; Weersma, Rinse K; Potočnik, Uroš; Skok, Pavel

    2016-01-01

    AIM: To develop a risk model for Crohn’s disease (CD) based on homogeneous population. METHODS: In our study were included 160 CD patients and 209 healthy individuals from Slovenia. The association study was performed for 112 single nucleotide polymorphisms (SNPs). We generated genetic risk scores (GRS) based on the number of risk alleles using weighted additive model. Discriminatory accuracy was measured by area under ROC curve (AUC). For risk evaluation, we divided individuals according to positive and negative likelihood ratios (LR) of a test, with LR > 5 for high risk group and LR < 0.20 for low risk group. RESULTS: The highest accuracy, AUC of 0.78 was achieved with GRS combining 33 SNPs with optimal sensitivity and specificity of 75.0% and 72.7%, respectively. Individuals with the highest risk (GRS > 5.54) showed significantly increased odds of developing CD (OR = 26.65, 95%CI: 11.25-63.15) compared to the individuals with the lowest risk (GRS < 4.57) which is a considerably greater risk captured than in one SNP with the highest effect size (OR = 3.24). When more than 33 SNPs were included in GRS, discriminatory ability was not improved significantly; AUC of all 74 SNPs was 0.76. CONCLUSION: The authors proved the possibility of building accurate genetic risk score based on 33 risk variants on Slovenian CD patients which may serve as a screening tool in the targeted population. PMID:27076762

  8. 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

  9. Effect of antibiotics on bacterial populations: a multi-hierachical selection process

    PubMed Central

    Martínez, José Luis

    2017-01-01

    Antibiotics have been widely used for a number of decades for human therapy and farming production. Since a high percentage of antibiotics are discharged from the human or animal body without degradation, this means that different habitats, from the human body to river water or soils, are polluted with antibiotics. In this situation, it is expected that the variable concentration of this type of microbial inhibitor present in different ecosystems may affect the structure and the productivity of the microbiota colonizing such habitats. This effect can occur at different levels, including changes in the overall structure of the population, selection of resistant organisms, or alterations in bacterial physiology. In this review, I discuss the available information on how the presence of antibiotics may alter the microbiota and the consequences of such alterations for human health and for the activity of microbiota from different habitats. PMID:28163908

  10. Effect of antibiotics on bacterial populations: a multi-hierachical selection process.

    PubMed

    Martínez, José Luis

    2017-01-01

    Antibiotics have been widely used for a number of decades for human therapy and farming production. Since a high percentage of antibiotics are discharged from the human or animal body without degradation, this means that different habitats, from the human body to river water or soils, are polluted with antibiotics. In this situation, it is expected that the variable concentration of this type of microbial inhibitor present in different ecosystems may affect the structure and the productivity of the microbiota colonizing such habitats. This effect can occur at different levels, including changes in the overall structure of the population, selection of resistant organisms, or alterations in bacterial physiology. In this review, I discuss the available information on how the presence of antibiotics may alter the microbiota and the consequences of such alterations for human health and for the activity of microbiota from different habitats.

  11. Filling-In, Spatial Summation, and Radiation of Pain: Evidence for a Neural Population Code in the Nociceptive System

    PubMed Central

    Quevedo, Alexandre S.

    2009-01-01

    The receptive field organization of nociceptive neurons suggests that noxious information may be encoded by population-based mechanisms. Electrophysiological evidence of population coding mechanisms has remained limited. However, psychophysical studies examining interactions between multiple noxious stimuli can provide indirect evidence that neuron population recruitment can contribute to both spatial and intensity-related percepts of pain. In the present study, pairs of thermal stimuli (35°C/49°C or 49°C/49°C) were delivered at different distances on the leg (0, 5, 10, 20, 40 cm) and abdomen (within and across dermatomes) and subjects evaluated pain intensity and perceived spatial attributes of stimuli. Reports of perceived pain spreading to involve areas that were not stimulated (radiation of pain) were most frequent at 5- and 10-cm distances (χ2 = 34.107, P < 0.0001). Perceived connectivity between two noxious stimuli (filling-in) was influenced by the distance between stimuli (χ2 = 16.756, P < 0.01), with the greatest connectivity reported at 5- and 10-cm separation distances. Spatial summation of pain occurred over probe separation distances as large as 40 cm and six dermatomes (P < 0.05), but was maximal at 5- and 10-cm separation distances. Taken together, all three of these phenomena suggest that interactions between recruited populations of neurons may support both spatial and intensity-related dimensions of the pain experience. PMID:19759320

  12. One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

    NASA Astrophysics Data System (ADS)

    Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

    2015-04-01

    Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

  13. Artificial Neural Network Analysis System

    DTIC Science & Technology

    2007-11-02

    Target detection, multi-target tracking and threat identification of ICBM and its warheads by sensor fusion and data fusion of sensors in a fuzzy neural network system based on the compound eye of a fly.

  14. Neural Networks For Robot Control

    DTIC Science & Technology

    2001-04-17

    following: (a) Application of artificial neural networks (multi-layer perceptrons, MLPs) for 2D planar robot arm by using the dynamic backpropagation...methods for the adjustment of parameters; and optimization of the architecture; (b) Application of artificial neural networks in controlling closed...studies in controlling dynamic robot arms by using neural networks in real-time process; (2) Research of optimal architectures used in closed-loop systems in order to compare with adaptive and robust control.

  15. Typhoidal Salmonellae: Use of Multi-Locus Sequence Typing to Determine Population Structure.

    PubMed

    Sharma, Priyanka; Dahiya, Sushila; Balaji, Veeraraghavan; Kanga, Anil; Panda, Preetilata; Das, Rashna; Dhanraju, Anbumani; Mendiratta, Deepak Kumar; Sood, Seema; Das, Bimal Kumar; Kapil, Arti

    2016-01-01

    Enteric fever is an invasive infection predominantly caused by Salmonella enterica serovars Typhi and Paratyphi A. The pathogens have evolved from other nontyphoidal salmonellaeto become invasive and host restricted. Emergence of antimicrobial resistance in typhoidal salmonellae in some countries is a major therapeutic concern as the travelers returning from endemic countries carry resistant strains to non endemic areas. In order to understand the epidemiology and to design disease control strategies molecular typing of the pathogen is very important. We performed Multilocus Sequence Typing (MLST) of 251 S. Typhi and 18 S. Paratyphi strains isolated from enteric fever patients from seven centers across India during 2010-2013to determine the population structure and prevalence of MLST sequence types in India. MLST analysis revealed the presence of five sequence types (STs) of typhoidal salmonellae in India namely ST1, ST2 and ST3 for S. Typhi and ST85 and ST129 for S. Paratyphi A.S. Typhi strains showed monophyletic lineage and clustered in to 3 Sequence Types-ST1, ST2 and ST3 and S. Paratyphi A isolates segregated in two sequence types ST85 and ST129 respectively. No association was found between antimicrobial susceptibility and sequence types. This study found ST1 as the most prevalent sequence type of S. Typhi in India followed by ST2, which is in concordance with previous studies and MLST database. In addition a rare sequence type ST3 has been found which is reported for the first time from the Indian subcontinent. Amongst S. Paratyphi A, the most common sequence type is ST129 as also reported from other parts of world. This distribution and prevalence suggest the common spread of the sequence types across the globe and these findings can help in understanding the disease distribution.

  16. Typhoidal Salmonellae: Use of Multi-Locus Sequence Typing to Determine Population Structure

    PubMed Central

    Sharma, Priyanka; Dahiya, Sushila; Balaji, Veeraraghavan; Kanga, Anil; Panda, Preetilata; Das, Rashna; Dhanraju, Anbumani; Mendiratta, Deepak Kumar; Sood, Seema; Das, Bimal Kumar; Kapil, Arti

    2016-01-01

    Enteric fever is an invasive infection predominantly caused by Salmonella enterica serovars Typhi and Paratyphi A. The pathogens have evolved from other nontyphoidal salmonellaeto become invasive and host restricted. Emergence of antimicrobial resistance in typhoidal salmonellae in some countries is a major therapeutic concern as the travelers returning from endemic countries carry resistant strains to non endemic areas. In order to understand the epidemiology and to design disease control strategies molecular typing of the pathogen is very important. We performed Multilocus Sequence Typing (MLST) of 251 S. Typhi and 18 S. Paratyphi strains isolated from enteric fever patients from seven centers across India during 2010-2013to determine the population structure and prevalence of MLST sequence types in India. MLST analysis revealed the presence of five sequence types (STs) of typhoidal salmonellae in India namely ST1, ST2 and ST3 for S. Typhi and ST85 and ST129 for S. Paratyphi A.S. Typhi strains showed monophyletic lineage and clustered in to 3 Sequence Types—ST1, ST2 and ST3 and S. Paratyphi A isolates segregated in two sequence types ST85 and ST129 respectively. No association was found between antimicrobial susceptibility and sequence types. This study found ST1 as the most prevalent sequence type of S. Typhi in India followed by ST2, which is in concordance with previous studies and MLST database. In addition a rare sequence type ST3 has been found which is reported for the first time from the Indian subcontinent. Amongst S. Paratyphi A, the most common sequence type is ST129 as also reported from other parts of world. This distribution and prevalence suggest the common spread of the sequence types across the globe and these findings can help in understanding the disease distribution. PMID:27618626

  17. Identifying the Attitudes and Traits of Teachers with an At-Risk Student Population in a Multi-Cultural Urban High School

    ERIC Educational Resources Information Center

    Calabrese, Raymond L.; Goodvin, Sherry; Niles, Rae

    2005-01-01

    Purpose: To identify the attitudes and traits of teachers with an at-risk student population in a multi-cultural urban high school. Design/methodology/approach: A research team consisting of doctoral students and their faculty advisor used an appreciative inquiry model to identify attitudes and traits of teachers who supported effective teaching…

  18. Stimulated Photorefractive Optical Neural Networks

    DTIC Science & Technology

    1992-12-15

    This final report describes research in optical neural networks performed under DARPA sponsorship at Hughes Aircraft Company during the period 1989...in photorefractive crystals. This approach reduces crosstalk and improves the utilization of the optical input device. Successfully implemented neural ... networks include the Perceptron, Bidirectional Associative Memory, and multi-layer backpropagation networks. Up to 104 neurons, 2xl0(7) weights, and

  19. Genetic parameters for lameness, mastitis and dagginess in a multi-breed sheep population.

    PubMed

    O'Brien, A C; McHugh, N; Wall, E; Pabiou, T; McDermott, K; Randles, S; Fair, S; Berry, D P

    2016-11-24

    The objective of the present study was to quantify the extent of genetic variation in three health-related traits namely dagginess, lameness and mastitis, in an Irish sheep population. Each of the health traits investigated pose substantial welfare implications as well as considerable economic costs to producers. Data were also available on four body-related traits, namely body condition score (BCS), live weight, muscle depth and fat depth. Animals were categorised as lambs (<365 days old) or ewes (⩾365 days old) and were analysed both separately and combined. After edits, 39 315 records from 264 flocks between the years 2009 and 2015 inclusive were analysed. Variance components were estimated using animal linear mixed models. Fixed effects included contemporary group, represented as a three-way interaction between flock, date of inspection and animal type (i.e. lamb, yearling ewe (i.e. females ⩾365 days but <730 days old that have not yet had a recorded lambing) or ewe), animal breed proportion, coefficients of heterosis and recombination, animal gender (lambs only), animal parity (ewes only; lambs were assigned a separate 'parity') and the difference in age of the animal from the median of the respective parity/age group. An additive genetic effect and residual effect were both fitted as random terms with maternal genetic and non-genetic components also considered for traits of the lambs. The direct heritability of dagginess was similar across age groups (0.14 to 0.15), whereas the direct heritability of lameness ranged from 0.06 (ewes) to 0.12 (lambs). The direct heritability of mastitis was 0.04. For dagginess, 13% of the phenotypic variation was explained by dam litter, whereas the maternal heritability of dagginess was 0.05. The genetic correlation between ewe and lamb dagginess was 0.38; the correlation between ewe and lamb lameness was close to zero but was associated with a large standard error. Direct genetic correlations were evident between

  20. Vitamin K Dependent Protection of Renal Function in Multi-ethnic Population Studies

    PubMed Central

    Wei, Fang-Fei; Drummen, Nadja E.A.; Schutte, Aletta E.; Thijs, Lutgarde; Jacobs, Lotte; Petit, Thibaut; Yang, Wen-Yi; Smith, Wayne; Zhang, Zhen-Yu; Gu, Yu-Mei; Kuznetsova, Tatiana; Verhamme, Peter; Allegaert, Karel; Schutte, Rudolph; Lerut, Evelyne; Evenepoel, Pieter; Vermeer, Cees; Staessen, Jan A.

    2016-01-01

    Background Following activation by vitamin K (VK), matrix Gla protein (MGP) inhibits arterial calcification, but its role in preserving renal function remains unknown. Methods In 1166 white Flemish (mean age, 38.2 years) and 714 South Africans (49.2% black; 40.6 years), we correlated estimated glomerular filtration (eGFR [CKD-EPI formula]) and stage of chronic kidney disease (CKD [KDOQI stages 2–3]) with inactive desphospho-uncarboxylated MGP (dp-ucMGP), using multivariable linear and logistic regression. Results Among Flemish and white and black Africans, between-group differences in eGFR (90, 100 and 122 mL/min/1.73 m2), dp-ucMGP (3.7, 6.5 and 3.2 μg/L), and CKD prevalence (53.5, 28.7 and 10.5%) were significant, but associations of eGFR with dp-ucMGP did not differ among ethnicities (P ≥ 0.075). For a doubling of dp-ucMGP, eGFR decreased by 1.5 (P = 0.023), 1.0 (P = 0.56), 2.8 (P = 0.0012) and 2.1 (P < 0.0001) mL/min/1.73 m2 in Flemish, white Africans, black Africans and all participants combined; the odds ratios for moving up one CKD stage were 1.17 (P = 0.033), 1.03 (P = 0.87), 1.29 (P = 0.12) and 1.17 (P = 0.011), respectively. Interpretation In the general population, eGFR decreases and CKD risk increases with higher dp-ucMGP, a marker of VK deficiency. These findings highlight the possibility that VK supplementation might promote renal health. PMID:26981580

  1. The C677T polymorphism of the methylenetetrahydrofolate reductase gene in Mexican mestizo neural-tube defect parents, control mestizo and native populations.

    PubMed

    Dávalos, I P; Olivares, N; Castillo, M T; Cantú, J M; Ibarra, B; Sandoval, L; Morán, M C; Gallegos, M P; Chakraborty, R; Rivas, F

    2000-01-01

    The C677T mutation of the methylenetetrahydrofolate reductase (MTHFR) gene, associated with the thermolabile form of the enzyme, has reportedly been found to be increased in neural-tube defects (NTD), though this association is still unclear. A group of 107 mestizo parents of NTD children and five control populations: 101 mestizo (M), 50 Huichol (H), 38 Tarahumara (T), 21 Purepecha (P) and 20 Caucasian (C) individuals were typed for the MTHFR C677T variant by the PCR/RFLP (HinfI) method. Genotype frequencies were in agreement with the Hardy-Weinberg expectations in all six populations. Allele frequency (%) of the C677T variant was 45 in NTD, 44 in M, 56 in H, 36 in T, 57 in P, 35 in C. Pairwise inter-population comparisons of allele frequency disclosed a very similar distribution between NTD and M groups (exact test, P=0.92). Among controls, differences between M and individual native groups were NS (0.06

  2. Computing with neural synchrony.

    PubMed

    Brette, Romain

    2012-01-01

    Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties.

  3. Computing with Neural Synchrony

    PubMed Central

    Brette, Romain

    2012-01-01

    Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties. PMID:22719243

  4. Understanding barriers and facilitators of fruit and vegetable consumption among a diverse multi-ethnic population in the USA.

    PubMed

    Yeh, Ming-Chin; Ickes, Scott B; Lowenstein, Lisa M; Shuval, Kerem; Ammerman, Alice S; Farris, Rosanne; Katz, David L

    2008-03-01

    A diet high in fruits and vegetables (F&V) has been associated with a decreased risk of certain cancers, reduced morbidity and mortality from heart disease, and enhanced weight management. Yet to date, most of the US population does not consume the recommended amount of F&V despite numerous interventions and government guidelines to promote consumption. Research has found various impediments to F&V consumption, such as high costs, an obesogenic environment and low socio-economic status. However, studies have not sufficiently focused on barriers and enablers to F&V intake among adult multi-ethnic populations. The present qualitative study examines 147 focus group participants' perceptions of impediments and enablers to F&V consumption. Twelve focus groups were conducted among African American, Hispanic and Caucasian men and women in North Carolina and Connecticut. Focus groups were audiotaped, transcribed verbatim and entered into QSR NVivo Software. Text data were systematically analyzed by investigators to identify recurrent themes both within and across groups and states. Focus group results indicate that most participants were aware of the health benefits associated with a diet rich in F&V. Yet many admitted not adhering to the Health and Human Service's recommendations. Individual impediments consisted of the high costs of F&V and a perceived lack of time. Early home food environment was perceived as affecting F&V consumption later in life. Other barriers reported were ethnic-specific. The African American participants reported limited access to fresh produce. This finding is consistent with numerous studies and must be addressed through health promotion intervention. Both the church and primary care clinics were described by African Americans as appropriate settings for health behavior interventions; these findings should be considered. Hispanic participants, mostly immigrants, cited inhibiting factors encountered in their adopted US environment. There is a

  5. The assessment of an inhibited, anxiety-prone temperament in a Dutch multi-ethnic population of preschool children.

    PubMed

    Vreeke, Leonie J; Muris, Peter; Mayer, Birgit; Huijding, Jorg; Bos, Arjan E R; van der Veen, Monique; Raat, Hein; Verheij, Fop

    2012-11-01

    The Behavioral Inhibition Questionnaire-Short Form (BIQ-SF) is a 14-item parent-rating scale for assessing an inhibited, anxiety-prone temperament in preschool children. This study examined the psychometric properties of the BIQ-SF scores in a multi-ethnic community population of Dutch boys and girls aged 2.5-6 years (total N = 2,343, from which various subsamples were derived). Results revealed that the factor structure of the BIQ-SF was as hypothesized: a model with six correlated factors representing children's inhibited behaviors in various social and non-social contexts provided a good fit for the data. The internal consistency of the BIQ-SF was generally satisfactory and scores on the scale were found to be fairly stable over a time period of up to 2 years. Parent-teacher agreement was acceptable, and relations between the BIQ-SF and observations of an inhibited temperament were moderate. Finally, BIQ-SF scores were positively associated with measures of anxiety and internalizing symptoms, whereas no significant links were found with externalizing symptoms. Altogether, these results provide support for the reliability and validity of the BIQ-SF as an economical method for assessing behavioral inhibition and anxiety proneness in young children.

  6. Identifying High-Risk Populations of Tuberculosis Using Environmental Factors and GIS Based Multi-Criteria Decision Making Method

    NASA Astrophysics Data System (ADS)

    Rasam, A. R. Abdul; Shariff, N. M.; Dony, J. F.

    2016-09-01

    Development of an innovative method to enhance the detection of tuberculosis (TB) in Malaysia is the latest agenda of the Ministry of Health. Therefore, a geographical information system (GIS) based index model is proposed as an alternative method for defining potential high-risk areas of local TB cases at Section U19, Shah Alam. It is adopted a spatial multi-criteria decision making (MCDM) method for ranking environmental risk factors of the disease in a standardised five-score scale. Scale 1 and 5 illustrate the lowest and the highest risk of the TB spread respectively, while scale from 3 to 5 is included as a potential risk level. These standardised scale values are then combined with expert normalised weights (0 to 1) to calculate the overall index values and produce a TB ranked map using a GIS overlay analysis and weighted linear combination. It is discovered that 71.43% of the Section is potential as TB high risk areas particularly at urban and densely populated settings. This predictive result is also reliable with the current real cases in 2015 by 76.00% accuracy. A GIS based MCDM method has demonstrated analytical capabilities in targeting high-risk spots and TB surveillance monitoring system of the country, but the result could be strengthened by applying other uncertainty assessment method.

  7. Multi-Object Spectroscopy with the James Webb Space Telescope’s Near Infrared Spectrograph: Observing Resolved Stellar Populations

    NASA Astrophysics Data System (ADS)

    Gilbert, Karoline; Karakla, Diane M.; Beck, Tracy

    2015-08-01

    The James Webb Space Telescope’s (JWST) Near Infrared Spectrograph (NIRSpec) will provide a multi-object spectroscopy mode through the four Micro-Shutter Arrays (MSAs). Each MSA is a grid of contiguous shutters that can be configured to form slits on more than 100 astronomical targets simultaneously. The combination of JWST’s sensitivity and superb resolution in the infrared and NIRSpec’s full wavelength coverage from 0.6 to 5 μm will open new parameter space for studies of galaxies and resolved stellar populations alike. We describe a NIRSpec MSA observing scenario for obtaining spectroscopy of individual stars in an external galaxy, and investigate the technical challenges posed by this scenario. We examine the multiplexing capability of the MSA as a function of the possible MSA configuration design choices, and investigate the primary sources of error in velocity measurements and the prospects for minimizing them. We give examples of how this and other use cases are guiding development of the NIRSpec user interfaces, including proposal planning and pipeline calibrations.

  8. A multi-wavelength scattered light analysis of the dust grain population in the GG Tau circumbinary ring

    SciTech Connect

    Duchene, G; McCabe, C; Ghez, A; Macintosh, B

    2004-02-04

    We present the first 3.8 {micro}m image of the dusty ring surrounding the young binary system GG Tau, obtained with the W. M. Keck II 10m telescope's adaptive optics system. THis is the longest wavelength at which the ring has been detected in scattered light so far, allowing a multi-wavelength analysis of the scattering proiperties of the dust grains present in this protoplanetary disk in combination with previous, shorter wavelengths, HST images. We find that the scattering phase function of the dust grains in the disk is only weakly dependent on the wavelength. This is inconsistent with dust models inferred from observations of the interstellar medium or dense molecular clouds. In particular, the strongly forward-throwing scattering phase function observed at 3.8 {micro}m implies a significant increase in the population of large ({approx}> 1 {micro}m) grains, which provides direct evidence for grain growth in the ring. However, the grain size distribution required to match the 3.8 {micro}m image of the ring is incompatible with its published 1 {micro}m polarization map, implying that the dust population is not uniform throughout the ring. We also show that our 3.8 {micro}m image of the ring is incompatible with its published 1 {micro}m polarization map, implying that the dust population is not uniform throughout the ring. We also show that our 3.8 {micro}m scattered light image probes a deeper layer of the ring than previous shorter wavelength images, as demonstrated by a shift in the location of the inner edge of the disk's scattered light distribution between 1 and 3.8 {micro}m. We therefore propose a stratified structure for the ring in which the surface layers, located {approx} 50 AU above the ring midplane, contain dust grains that are very similar to those found in dense molecular clouds, while the region of the ring located {approx} 25 AU from the midplane contains significantly larger grains. This stratified structure is likely the result of vertical

  9. Is neural Darwinism Darwinism?

    PubMed

    van Belle, T

    1997-01-01

    Neural Darwinism is a theory of cognition developed by Gerald Edelman along with George Reeke and Olaf Sporns at Rockefeller University. As its name suggests, neural Darwinism is modeled after biological Darwinism, and its authors assert that the two processes are strongly analogous. both operate on variation in a population, amplifying the more adaptive individuals. However, from a computational perspective, neural Darwinism is quite different from other models of natural selection, such as genetic algorithms. The individuals of neural Darwinism do not replicate, thus robbing the process of the capacity to explore new solutions over time and ultimately reducing it to a random search. Because neural Darwinism does not have the computational power of a truly Darwinian process, it is misleading to label it as such. to illustrate this disparity in adaptive power, one of Edelman's early computer experiments, Darwin I, is revisited, and it is shown that adding replication greatly improves the adaptive power of the system.

  10. 'Stealth' nanoparticles evade neural immune cells but also evade major brain cell populations: Implications for PEG-based neurotherapeutics.

    PubMed

    Jenkins, Stuart I; Weinberg, Daniel; Al-Shakli, Arwa F; Fernandes, Alinda R; Yiu, Humphrey H P; Telling, Neil D; Roach, Paul; Chari, Divya M

    2016-02-28

    Surface engineering to control cell behavior is of high interest across the chemical engineering, drug delivery and biomaterial communities. Defined chemical strategies are necessary to tailor nanoscale protein interactions/adsorption, enabling control of cell behaviors for development of novel therapeutic strategies. Nanoparticle-based therapies benefit from such strategies but particle targeting to sites of neurological injury remains challenging due to circulatory immune clearance. As a strategy to overcome this barrier, the use of stealth coatings can reduce immune clearance and prolong circulatory times, thereby enhancing therapeutic capacity. Polyethylene glycol (PEG) is the most widely-used stealth coating and facilitates particle accumulation in the brain. However, once within the brain, the mode of handling of PEGylated particles by the resident immune cells of the brain itself (the 'microglia') is unknown. This is a critical question as it is well established that microglia avidly sequester nanoparticles, limiting their bioavailability and posing a major translational barrier. If PEGylation can be proved to promote evasion of microglia, then this information will be of high value in developing tailored nanoparticle-based therapies for neurological applications. Here, we have conducted the first comparative study of uptake of PEGylated particles by all the major (immune and non-immune) brain cell types. We prove for the first time that PEGylated nanoparticles evade major brain cell populations - a phenomenon which will enhance extracellular bioavailability. We demonstrate changes in protein coronas around these particles within biological media, and discuss how surface chemistry presentation may affect this process and subsequent cellular interactions.

  11. Understanding the Connection Between Active Galactic Nuclei and Host Star Formation Through Multi-Wavelength Population Synthesis Modeling

    NASA Astrophysics Data System (ADS)

    Draper, Aden R.

    Supermassive black holes, black holes with masses ≳106M⊙ , are found at the centers of all massive galaxies. These massive black holes grew from smaller seed black holes through accretion events. Accreting black holes are very bright in the radio through very hard X-ray spectral regimes. Due to the location of these accreting black holes at the centers of galaxies, they are referred to as active galactic nuclei (AGN). It is understood that AGN are an important phase of galaxy evolution; however, the role of AGN in massive galaxy formation is very poorly constrained. Here, the unique tool of multi-wavelength population synthesis modeling is used to study the average properties of AGN and their host galaxies with a focus on host galaxy star formation and the role of black hole growth in galaxy evolution. Knowledge of the AGN population from deep X-ray surveys is combined with theoretical AGN spectral energy distributions to predict various observables of the AGN population in wavelength regions from the far infrared to very hard X-rays. Comparison of the model predictions to observations constrains the model input parameters and allows for the determination of average properties of the AGN population. Particular attention is paid to a special class of AGN known as Compton thick AGN. These AGN are deeply embedded in gas and dust such that the column density obscuring the line of sight to the central engine of the AGN exceeds 1/sigmaT ˜ 1024 cm -2, where sigmaT is the Thomson cross-section of the electron---a column density comparable to that of the human chest. Theoretical and simulational evidence suggest that these Compton thick AGN may be recently triggered, rapidly accreting AGN, making them of special interest to researchers. I found that Compton thick AGN are likely to contribute ˜20% of the peak of the cosmic X-ray background (XRB) at ˜30 keV and demonstrated that a significant portion of Compton thick AGN may be accreting very rapidly. Moreover, Compton

  12. Neural constraints on learning.

    PubMed

    Sadtler, Patrick T; Quick, Kristin M; Golub, Matthew D; Chase, Steven M; Ryu, Stephen I; Tyler-Kabara, Elizabeth C; Yu, Byron M; Batista, Aaron P

    2014-08-28

    Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already

  13. Development of a Semi-Quantitative Food Frequency Questionnaire to Assess the Dietary Intake of a Multi-Ethnic Urban Asian Population.

    PubMed

    Neelakantan, Nithya; Whitton, Clare; Seah, Sharna; Koh, Hiromi; Rebello, Salome A; Lim, Jia Yi; Chen, Shiqi; Chan, Mei Fen; Chew, Ling; van Dam, Rob M

    2016-08-27

    Assessing habitual food consumption is challenging in multi-ethnic cosmopolitan settings. We systematically developed a semi-quantitative food frequency questionnaire (FFQ) in a multi-ethnic population in Singapore, using data from two 24-h dietary recalls from a nationally representative sample of 805 Singapore residents of Chinese, Malay and Indian ethnicity aged 18-79 years. Key steps included combining reported items on 24-h recalls into standardized food groups, developing a food list for the FFQ, pilot testing of different question formats, and cognitive interviews. Percentage contribution analysis and stepwise regression analysis were used to identify foods contributing cumulatively ≥90% to intakes and individually ≥1% to intake variance of key nutrients, for the total study population and for each ethnic group separately. Differences between ethnic groups were observed in proportions of consumers of certain foods (e.g., lentil stews, 1%-47%; and pork dishes, 0%-50%). The number of foods needed to explain variability in nutrient intakes differed substantially by ethnic groups and was substantially larger for the total population than for separate ethnic groups. A 163-item FFQ covered >95% of total population intake for all key nutrients. The methodological insights provided in this paper may be useful in developing similar FFQs in other multi-ethnic settings.

  14. Development of a Semi-Quantitative Food Frequency Questionnaire to Assess the Dietary Intake of a Multi-Ethnic Urban Asian Population

    PubMed Central

    Neelakantan, Nithya; Whitton, Clare; Seah, Sharna; Koh, Hiromi; Rebello, Salome A.; Lim, Jia Yi; Chen, Shiqi; Chan, Mei Fen; Chew, Ling; van Dam, Rob M.

    2016-01-01

    Assessing habitual food consumption is challenging in multi-ethnic cosmopolitan settings. We systematically developed a semi-quantitative food frequency questionnaire (FFQ) in a multi-ethnic population in Singapore, using data from two 24-h dietary recalls from a nationally representative sample of 805 Singapore residents of Chinese, Malay and Indian ethnicity aged 18–79 years. Key steps included combining reported items on 24-h recalls into standardized food groups, developing a food list for the FFQ, pilot testing of different question formats, and cognitive interviews. Percentage contribution analysis and stepwise regression analysis were used to identify foods contributing cumulatively ≥90% to intakes and individually ≥1% to intake variance of key nutrients, for the total study population and for each ethnic group separately. Differences between ethnic groups were observed in proportions of consumers of certain foods (e.g., lentil stews, 1%–47%; and pork dishes, 0%–50%). The number of foods needed to explain variability in nutrient intakes differed substantially by ethnic groups and was substantially larger for the total population than for separate ethnic groups. A 163-item FFQ covered >95% of total population intake for all key nutrients. The methodological insights provided in this paper may be useful in developing similar FFQs in other multi-ethnic settings. PMID:27618909

  15. Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network

    NASA Astrophysics Data System (ADS)

    Mozumder, Chitrini; Tripathi, Nitin K.

    2014-10-01

    In recent decades, the world has experienced unprecedented urban growth which endangers the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area of Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001-2011, namely simple extrapolation, Markov Chain (MC), and system dynamic (SD) modelling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands.

  16. Analog Very Large Scale Integration (VLSI) Implementations of Artificial Neural Networks

    DTIC Science & Technology

    1992-09-01

    There has been a recent resurgence of interest in the multi- disciplinary field of artificial neural networks . Artificial neural networks , originally...e.g., backpropagation, hopfield, bidirectional associative memories, etc.). Artificial Neural Networks , Analog VLSI.

  17. Multi-wavelength observations of galaxy clusters: Population evolution and scaling relations for intermediate-redshift clusters

    NASA Astrophysics Data System (ADS)

    Connor, Thomas Patrick

    Galaxy clusters are key signatures of the formation of structure in the Universe due to their positions at the nodes of the cosmic web. However, these privileged positions feature significant amounts of activity as a consequence of frequent accretion and collisions with other galaxies and clusters of galaxies. Thus, a rigorous understanding of cluster evolution constrains not only cosmological structure formation but also galaxy dynamics in the most extreme environments. Here, we examine the evolution of clusters in two situations: how the properties of the hot intracluster gas changes with the total masses of the clusters at the observational frontiers of mass and redshift; and how cluster galaxies evolve with redshift in some of the most massive clusters in the Universe. In Chapter 2 we examine a population of moderate-luminosity clusters at intermediate redshifts using the XMM-Newton telescope with well-determined masses from Hubble Space Telescope (HST) observations. We find that these systems do not deviate from scaling relations between mass, luminosity, and temperature derived from more massive clusters, implying that, even at the redshifts and masses probed here, gravitational energetics still dominate over supernovae. In Chapter 3 we utilize new techniques to maximize a multi-wavelength dataset from HST of 25 massive galaxy clusters. We present new methods for detection and photometry of galaxies in the presence of inconsistent, diffuse background. Using these techniques, we construct a photometric catalog down to M* + 4-5 for clusters at redshift z 0:2 to z 0:9, which we validate with comparisons to spectral observations and a similar catalog. We also consider the luminosity function for these clusters; we find a drop-off in the faint-end slope when only selecting red sequence galaxies. Finally, in Chapter 4, we exploit our new photometric catalogs to study the evolution of the red galaxies, the "red sequence of galaxies," in these massive clusters of

  18. Survival of children with trisomy 13 and trisomy 18: A multi-state population-based study.

    PubMed

    Meyer, Robert E; Liu, Gang; Gilboa, Suzanne M; Ethen, Mary K; Aylsworth, Arthur S; Powell, Cynthia M; Flood, Timothy J; Mai, Cara T; Wang, Ying; Canfield, Mark A

    2016-04-01

    Trisomy 13 (T13) and trisomy 18 (T18) are among the most prevalent autosomal trisomies. Both are associated with a very high risk of mortality. Numerous instances, however, of long-term survival of children with T13 or T18 have prompted some clinicians to pursue aggressive treatment instead of the traditional approach of palliative care. The purpose of this study is to assess current mortality data for these conditions. This multi-state, population-based study examined data obtained from birth defect surveillance programs in nine states on live-born infants delivered during 1999-2007 with T13 or T18. Information on children's vital status and selected maternal and infant risk factors were obtained using matched birth and death certificates and other data sources. The Kaplan-Meier method and Cox proportional hazards models were used to estimate age-specific survival probabilities and predictors of survival up to age five. There were 693 children with T13 and 1,113 children with T18 identified from the participating states. Among children with T13, 5-year survival was 9.7%; among children with T18, it was 12.3%. For both trisomies, gestational age was the strongest predictor of mortality. Females and children of non-Hispanic black mothers had the lowest mortality. Omphalocele and congenital heart defects were associated with an increased risk of death for children with T18 but not T13. This study found survival among children with T13 and T18 to be somewhat higher than those previously reported in the literature, consistent with recent studies reporting improved survival following more aggressive medical intervention for these children. © 2015 Wiley Periodicals, Inc.

  19. Time-Location Patterns of a Diverse Population of Older Adults: The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

    PubMed Central

    Spalt, Elizabeth W.; Curl, Cynthia L.; Allen, Ryan W.; Cohen, Martin; Adar, Sara D.; Stukovsky, Karen Hinckley; Avol, Ed; Castro-Diehl, Cecilia; Nunn, Cathy; Mancera-Cuevas, Karen; Kaufman, Joel D.

    2015-01-01

    The primary aim of this analysis was to present and describe questionnaire data characterizing time-location patterns of an older, multi-ethnic population from six American cities. We evaluated consistency of results from repeated administration of this questionnaire and between this questionnaire and other questionnaires collected from participants of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Participants reported spending most of their time inside their homes (average: 121 hours/week or 72%). More than 50% of participants reported spending no time in several of the location options, including at home outdoors, at work/volunteer/school locations indoors or outdoors, or in “other” locations outdoors. We observed consistency between self-reported time-location patterns from repeated administration of the time-location questionnaire and compared with other survey instruments. Comparisons to national cohorts demonstrated differences in time-location patterns in the MESA Air cohort due to differences in demographics, but the data showed similar trends in patterns by age, gender, season, and employment status. This study was the first to explicitly examine time-location patterns in an older, multi-ethnic population and the first to add data on Chinese participants. These data can be used to inform future epidemiological research of MESA Air and other studies that include diverse populations. PMID:25921083

  20. CONSTRAINING SUB-PARSEC BINARY SUPERMASSIVE BLACK HOLES IN QUASARS WITH MULTI-EPOCH SPECTROSCOPY. I. THE GENERAL QUASAR POPULATION

    SciTech Connect

    Shen, Yue; Liu, Xin; Loeb, Abraham; Tremaine, Scott

    2013-09-20

    We perform a systematic search for sub-parsec binary supermassive black holes (BHs) in normal broad-line quasars at z < 0.8, using multi-epoch Sloan Digital Sky Survey (SDSS) spectroscopy of the broad Hβ line. Our working model is that (1) one and only one of the two BHs in the binary is active; (2) the active BH dynamically dominates its own broad-line region (BLR) in the binary system, so that the mean velocity of the BLR reflects the mean velocity of its host BH; (3) the inactive companion BH is orbiting at a distance of a few R{sub BLR}, where R{sub BLR} ∼ 0.01-0.1 pc is the BLR size. We search for the expected line-of-sight acceleration of the broad-line velocity from binary orbital motion by cross-correlating SDSS spectra from two epochs separated by up to several years in the quasar rest frame. Out of ∼700 pairs of spectra for which we have good measurements of the velocity shift between two epochs (1σ error ∼40 km s{sup –1}), we detect 28 systems with significant velocity shifts in broad Hβ, among which 7 are the best candidates for the hypothesized binaries, 4 are most likely due to broad-line variability in single BHs, and the rest are ambiguous. Continued spectroscopic observations of these candidates will easily strengthen or disprove these claims. We use the distribution of the observed accelerations (mostly non-detections) to place constraints on the abundance of such binary systems among the general quasar population. Excess variance in the velocity shift is inferred for observations separated by longer than 0.4 yr (quasar rest frame). Attributing all the excess to binary motion would imply that most of the quasars in this sample must be in binaries, that the inactive BH must be on average more massive than the active one, and that the binary separation is at most a few times the size of the BLR. However, if this excess variance is partly or largely due to long-term broad-line variability, the requirement of a large population of close

  1. Ethnic Differences in the Prevalence of Metabolic Syndrome: Results from a Multi-Ethnic Population-Based Survey in Malaysia

    PubMed Central

    Guallar, Eliseo; Bulgiba, Awang; Mohamed, Rosmawati; Rahmat, Ramlee; Arif, Mohamad Taha; Rampal, Lekhraj

    2012-01-01

    Introduction The prevalence of metabolic syndrome is increasing disproportionately among the different ethnicities in Asia compared to the rest of the world. This study aims to determine the differences in the prevalence of metabolic syndrome across ethnicities in Malaysia, a multi-ethnic country. Methods In 2004, we conducted a national cross-sectional population-based study using a stratified two-stage cluster sampling design (N = 17,211). Metabolic syndrome was defined according to the International Diabetes Federation/National Heart, Lung and Blood Institute/American Heart Association (IDF/NHLBI/AHA-2009) criteria. Multivariate models were used to study the independent association between ethnicity and the prevalence of the metabolic syndrome. Results The overall mean age was 36.9 years, and 50.0% participants were female. The ethnic distribution was 57.0% Malay, 28.5% Chinese, 8.9% Indian and 5.0% Indigenous Sarawakians. The overall prevalence of the metabolic syndrome was 27.5%, with a prevalence of central obesity, raised triglycerides, low high density lipoprotein cholesterol, raised blood pressure and raised fasting glucose of 36.9%, 29.3%, 37.2%, 38.0% and 29.1%, respectively. Among those <40 years, the adjusted prevalence ratios for metabolic syndrome for ethnic Chinese, Indians, and Indigenous Sarawakians compared to ethnic Malay were 0.81 (95% CI 0.67 to 0.96), 1.42 (95% CI 1.19 to 1.69) and 1.37 (95% CI 1.08 to 1.73), respectively. Among those aged ≥40 years, the corresponding prevalence ratios were 0.86 (95% CI 0.79 to 0.92), 1.25 (95% CI 1.15 to 1.36), and 0.94 (95% CI 0.80, 1.11). The P-value for the interaction of ethnicity by age was 0.001. Conclusions The overall prevalence of metabolic syndrome in Malaysia was high, with marked differences across ethnicities. Ethnic Chinese had the lowest prevalence of metabolic syndrome, while ethnic Indians had the highest. Indigenous Sarawakians showed a marked increase in metabolic syndrome at young

  2. Neural control of hand muscles during prehension.

    PubMed

    Johnston, Jamie A; Winges, Sara A; Santello, Marco

    2009-01-01

    In the past two decades a large number of studies have successfully characterized important features of the kinetics and kinematics of object grasping and manipulation, providing significant insight into how the Central Nervous System (CNS) controls the hand, one of the most complex motor systems, in a variety of behaviors. In this chapter we briefly review studies of hand kinematics and kinetics and highlight their major findings and open questions. The major focus of this chapter is on the neural control of the hand, an objective that has been pursued by studies on electromyography (EMG) of hand muscles. Here we review what has been learned through different yet complementary methodological approaches. In particular, the study of single motor unit activity has revealed how the distribution of common neural input within and across hand muscles might reflect a muscle-pair specific organization. Studies of motor unit population have revealed important synergistic patterns of muscle activity while also revealing muscle-pair specific patterns of neural coupling. We conclude the chapter with the results of recent simulation studies aiming at combining advantages of single and multi-unit recordings to maximize the amount of information that can be extracted from EMG signal analysis.

  3. Multi-event capture–recapture modeling of host–pathogen dynamics among European rabbit populations exposed to myxoma and Rabbit Hemorrhagic Disease Viruses: common and heterogeneous patterns

    PubMed Central

    2014-01-01

    Host–pathogen epidemiological processes are often unclear due both to their complexity and over-simplistic approaches used to quantify them. We applied a multi-event capture–recapture procedure on two years of data from three rabbit populations to test hypotheses about the effects on survival of, and the dynamics of host immunity to, both myxoma virus and Rabbit Hemorrhagic Disease Virus (MV and RHDV). Although the populations shared the same climatic and management conditions, MV and RHDV dynamics varied greatly among them; MV and RHDV seroprevalences were positively related to density in one population, but RHDV seroprevalence was negatively related to density in another. In addition, (i) juvenile survival was most often negatively related to seropositivity, (ii) RHDV seropositives never had considerably higher survival, and (iii) seroconversion to seropositivity was more likely than the reverse. We suggest seropositivity affects survival depending on trade-offs among antibody protection, immunosuppression and virus lethality. Negative effects of seropositivity might be greater on juveniles due to their immature immune system. Also, while RHDV directly affects survival through the hemorrhagic syndrome, MV lack of direct lethal effects means that interactions influencing survival are likely to be more complex. Multi-event modeling allowed us to quantify patterns of host–pathogen dynamics otherwise difficult to discern. Such an approach offers a promising tool to shed light on causative mechanisms. PMID:24708296

  4. Effect of advanced intercrossing on genome structure and on the power to detect linked quantitative trait loci in a multi-parent population: a simulation study in rice

    PubMed Central

    2014-01-01

    Background In genetic analysis of agronomic traits, quantitative trait loci (QTLs) that control the same phenotype are often closely linked. Furthermore, many QTLs are localized in specific genomic regions (QTL clusters) that include naturally occurring allelic variations in different genes. Therefore, linkage among QTLs may complicate the detection of each individual QTL. This problem can be resolved by using populations that include many potential recombination sites. Recently, multi-parent populations have been developed and used for QTL analysis. However, their efficiency for detection of linked QTLs has not received attention. By using information on rice, we simulated the construction of a multi-parent population followed by cycles of recurrent crossing and inbreeding, and we investigated the resulting genome structure and its usefulness for detecting linked QTLs as a function of the number of cycles of recurrent crossing. Results The number of non-recombinant genome segments increased linearly with an increasing number of cycles. The mean and median lengths of the non-recombinant genome segments decreased dramatically during the first five to six cycles, then decreased more slowly during subsequent cycles. Without recurrent crossing, we found that there is a risk of missing QTLs that are linked in a repulsion phase, and a risk of identifying linked QTLs in a coupling phase as a single QTL, even when the population was derived from eight parental lines. In our simulation results, using fewer than two cycles of recurrent crossing produced results that differed little from the results with zero cycles, whereas using more than six cycles dramatically improved the power under most of the conditions that we simulated. Conclusion Our results indicated that even with a population derived from eight parental lines, fewer than two cycles of crossing does not improve the power to detect linked QTLs. However, using six cycles dramatically improved the power, suggesting

  5. Spatially Extensive Standardized Surveys Reveal Widespread, Multi-Decadal Increase in East Antarctic Adélie Penguin Populations

    PubMed Central

    Southwell, Colin; Emmerson, Louise; McKinlay, John; Newbery, Kym; Takahashi, Akinori; Kato, Akiko; Barbraud, Christophe; DeLord, Karine; Weimerskirch, Henri

    2015-01-01

    Seabirds are considered to be useful and practical indicators of the state of marine ecosystems because they integrate across changes in the lower trophic levels and the physical environment. Signals from this key group of species can indicate broad scale impacts or response to environmental change. Recent studies of penguin populations, the most commonly abundant Antarctic seabirds in the west Antarctic Peninsula and western Ross Sea, have demonstrated that physical changes in Antarctic marine environments have profound effects on biota at high trophic levels. Large populations of the circumpolar-breeding Adélie penguin occur in East Antarctica, but direct, standardized population data across much of this vast coastline have been more limited than in other Antarctic regions. We combine extensive new population survey data, new population estimation methods, and re-interpreted historical survey data to assess decadal-scale change in East Antarctic Adélie penguin breeding populations. We show that, in contrast to the west Antarctic Peninsula and western Ross Sea where breeding populations have decreased or shown variable trends over the last 30 years, East Antarctic regional populations have almost doubled in abundance since the 1980’s and have been increasing since the earliest counts in the 1960’s. The population changes are associated with five-year lagged changes in the physical environment, suggesting that the changing environment impacts primarily on the pre-breeding age classes. East Antarctic marine ecosystems have been subject to a number of changes over the last 50 years which may have influenced Adélie penguin population growth, including decadal-scale climate variation, an inferred mid-20th century sea-ice contraction, and early-to-mid 20th century exploitation of fish and whale populations. PMID:26488299

  6. Spatially Extensive Standardized Surveys Reveal Widespread, Multi-Decadal Increase in East Antarctic Adélie Penguin Populations.

    PubMed

    Southwell, Colin; Emmerson, Louise; McKinlay, John; Newbery, Kym; Takahashi, Akinori; Kato, Akiko; Barbraud, Christophe; DeLord, Karine; Weimerskirch, Henri

    2015-01-01

    Seabirds are considered to be useful and practical indicators of the state of marine ecosystems because they integrate across changes in the lower trophic levels and the physical environment. Signals from this key group of species can indicate broad scale impacts or response to environmental change. Recent studies of penguin populations, the most commonly abundant Antarctic seabirds in the west Antarctic Peninsula and western Ross Sea, have demonstrated that physical changes in Antarctic marine environments have profound effects on biota at high trophic levels. Large populations of the circumpolar-breeding Adélie penguin occur in East Antarctica, but direct, standardized population data across much of this vast coastline have been more limited than in other Antarctic regions. We combine extensive new population survey data, new population estimation methods, and re-interpreted historical survey data to assess decadal-scale change in East Antarctic Adélie penguin breeding populations. We show that, in contrast to the west Antarctic Peninsula and western Ross Sea where breeding populations have decreased or shown variable trends over the last 30 years, East Antarctic regional populations have almost doubled in abundance since the 1980's and have been increasing since the earliest counts in the 1960's. The population changes are associated with five-year lagged changes in the physical environment, suggesting that the changing environment impacts primarily on the pre-breeding age classes. East Antarctic marine ecosystems have been subject to a number of changes over the last 50 years which may have influenced Adélie penguin population growth, including decadal-scale climate variation, an inferred mid-20th century sea-ice contraction, and early-to-mid 20th century exploitation of fish and whale populations.

  7. Cell mass and cell cycle dynamics of an asynchronous budding yeast population: experimental observations, flow cytometry data analysis, and multi-scale modeling.

    PubMed

    Lencastre Fernandes, Rita; Carlquist, Magnus; Lundin, Luisa; Heins, Anna-Lena; Dutta, Abhishek; Sørensen, Søren J; Jensen, Anker D; Nopens, Ingmar; Lantz, Anna Eliasson; Gernaey, Krist V

    2013-03-01

    Despite traditionally regarded as identical, cells in a microbial cultivation present a distribution of phenotypic traits, forming a heterogeneous cell population. Moreover, the degree of heterogeneity is notably enhanced by changes in micro-environmental conditions. A major development in experimental single-cell studies has taken place in the last decades. It has however not been fully accompanied by similar contributions within data analysis and mathematical modeling. Indeed, literature reporting, for example, quantitative analyses of experimental single-cell observations and validation of model predictions for cell property distributions against experimental data is scarce. This study focuses on the experimental and mathematical description of the dynamics of cell size and cell cycle position distributions, of a population of Saccharomyces cerevisiae, in response to the substrate consumption observed during batch cultivation. The good agreement between the proposed multi-scale model (a population balance model [PBM] coupled to an unstructured model) and experimental data (both the overall physiology and cell size and cell cycle distributions) indicates that a mechanistic model is a suitable tool for describing the microbial population dynamics in a bioreactor. This study therefore contributes towards the understanding of the development of heterogeneous populations during microbial cultivations. More generally, it consists of a step towards a paradigm change in the study and description of cell cultivations, where average cell behaviors observed experimentally now are interpreted as a potential joint result of various co-existing single-cell behaviors, rather than a unique response common to all cells in the cultivation.

  8. Prevention of neural tube defects by the fortification of flour with folic acid: a population-based retrospective study in Brazil

    PubMed Central

    Lecca, Roberto Carlos Reyes; Cortez-Escalante, Juan Jose; Sanchez, Mauro Niskier; Rodrigues, Humberto Gabriel

    2016-01-01

    Abstract Objective To determine if the fortification of wheat and maize flours with iron and folic acid – which became mandatory in Brazil from June 2004 – is effective in the prevention of neural tube defects. Methods Using data from national information systems on births in central, south-eastern and southern Brazil, we determined the prevalence of neural tube defects among live births and stillbirths in a pre-fortification period – i.e. 2001–2004 – and in a post-fortification period – i.e. 2005–2014. We distinguished between anencephaly, encephalocele, meningocele, myelomeningocele and other forms of spina bifida. Findings There were 8554 neural tube defects for 17 925 729 live births notified between 2001 and 2014. For the same period, 2673 neural tube defects were reported for 194 858 stillbirths. The overall prevalence of neural tube defects fell from 0.79 per 1000 pre-fortification to 0.55 per 1000 post-fortification (prevalence ratio, PR: 1.43; 95% confidence interval, CI: 1.38–1.50). For stillbirths, prevalence fell from 17.74 per 1000 stillbirths pre-fortification to 11.70 per 1000 stillbirths post-fortification. The corresponding values among live births were 0.57 and 0.44, respectively. Conclusion The introduction of the mandatory fortification of flour with iron and folic acid in Brazil was followed by a significant reduction in the prevalence of neural tube defects in our study area. PMID:26769993

  9. Fumonisins disrupt sphingolipid metabolism, folate transport, and neural tube development in embryo culture and in vivo: a potential risk factor for human neural tube defects among populations consuming fumonisin-contaminated maize.

    PubMed

    Marasas, Walter F O; Riley, Ronald T; Hendricks, Katherine A; Stevens, Victoria L; Sadler, Thomas W; Gelineau-van Waes, Janee; Missmer, Stacey A; Cabrera, Julio; Torres, Olga; Gelderblom, Wentzel C A; Allegood, Jeremy; Martínez, Carolina; Maddox, Joyce; Miller, J David; Starr, Lois; Sullards, M Cameron; Roman, Ana Victoria; Voss, Kenneth A; Wang, Elaine; Merrill, Alfred H

    2004-04-01

    Fumonisins are a family of toxic and carcinogenic mycotoxins produced by Fusarium verticillioides (formerly Fusarium moniliforme), a common fungal contaminant of maize. Fumonisins inhibit ceramide synthase, causing accumulation of bioactive intermediates of sphingolipid metabolism (sphinganine and other sphingoid bases and derivatives) as well as depletion of complex sphingolipids, which interferes with the function of some membrane proteins, including the folate-binding protein (human folate receptor alpha). Fumonisin causes neural tube and craniofacial defects in mouse embryos in culture. Many of these effects are prevented by supplemental folic acid. Recent studies in LMBc mice found that fumonisin exposure in utero increases the frequency of developmental defects and administration of folate or a complex sphingolipid is preventive. High incidences of neural tube defects (NTD) occur in some regions of the world where substantial consumption of fumonisins has been documented or plausibly suggested (Guatemala, South Africa, and China); furthermore, a recent study of NTD in border counties of Texas found a significant association between NTD and consumption of tortillas during the first trimester. Hence, we propose that fumonisins are potential risk factors for NTD, craniofacial anomalies, and other birth defects arising from neural crest cells because of their apparent interference with folate utilization.

  10. Neural Networks

    DTIC Science & Technology

    1990-01-01

    FUNDING NUMBERS PROGRAM PROJECT TASK WORK UNIT ELEMENT NO. NO. NO. ACCESSION NO 11 TITLE (Include Security Classification) NEURAL NETWORKS 12. PERSONAL...SUB-GROUP Neural Networks Optical Architectures Nonlinear Optics Adaptation 19. ABSTRACT (Continue on reverse if necessary and identify by block number...341i Y C-odes , lo iii/(iv blank) 1. INTRODUCTION Neural networks are a type of distributed processing system [1

  11. Gradient liquid chromatographic retention time prediction for suspect screening applications: A critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods.

    PubMed

    Barron, Leon P; McEneff, Gillian L

    2016-01-15

    For the first time, the performance of a generalised artificial neural network (ANN) approach for the prediction of 2492 chromatographic retention times (tR) is presented for a total of 1117 chemically diverse compounds present in a range of complex matrices and across 10 gradient reversed-phase liquid chromatography-(high resolution) mass spectrometry methods. Probabilistic, generalised regression, radial basis function as well as 2- and 3-layer multilayer perceptron-type neural networks were investigated to determine the most robust and accurate model for this purpose. Multi-layer perceptrons most frequently yielded the best correlations in 8 out of 10 methods. Averaged correlations of predicted versus measured tR across all methods were R(2)=0.918, 0.924 and 0.898 for the training, verification and test sets respectively. Predictions of blind test compounds (n=8-84 cases) resulted in an average absolute accuracy of 1.02±0.54min for all methods. Within this variation, absolute accuracy was observed to marginally improve for shorter runtimes, but was found to be relatively consistent with respect to analyte retention ranges (~5%). Finally, optimised and replicated network dependency on molecular descriptor data is presented and critically discussed across all methods. Overall, ANNs were considered especially suitable for suspects screening applications and could potentially be utilised in bracketed-type analyses in combination with high resolution mass spectrometry.

  12. Modelling the role of multi-transmission routes in the epidemiology of bovine tuberculosis in cattle and buffalo populations.

    PubMed

    Phepa, Patrick B; Chirove, Faraimunashe; Govinder, Keshlan S

    2016-07-01

    A mathematical model that describes the transmission dynamics of bovine tuberculosis (BTB) in both buffalo and cattle populations is proposed. The model incorporates cross-infection and contaminated environment transmission routes. A full analysis of the model is undertaken. The reproduction number of the entire model is comprised of cross-infection and contaminated parameters. This underscores the importance of including both cross-infection and contaminated environment transmission routes. Crucially our simulations suggest that the disease has a more devastating effect on cattle populations than on buffalo populations when all transmission routes are involved. This has important implications for agriculture and tourism.

  13. A Complexity Theory of Neural Networks

    DTIC Science & Technology

    1991-08-09

    Significant progress has been made in laying the foundations of a complexity theory of neural networks . The fundamental complexity classes have been...identified and studied. The class of problems solvable by small, shallow neural networks has been found to be the same class even if (1) probabilistic...behaviour (2)Multi-valued logic, and (3)analog behaviour, are allowed (subject to certain resonable technical assumptions). Neural networks can be

  14. Bidirectional Neural Interfaces

    PubMed Central

    Masters, Matthew R.; Thakor, Nitish V.

    2016-01-01

    A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very-large-scale integration (VLSI) has advanced the design of complex integrated circuits. System-on-chip (SoC) devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems. PMID:26753776

  15. Multi-scale temporal and spatial variation in genotypic composition of Cladophora-borne Escherichia coli populations in Lake Michigan

    USGS Publications Warehouse

    Badgley, B.D.; Ferguson, J.; Heuvel, A.V.; Kleinheinz, G.T.; McDermott, C.M.; Sandrin, T.R.; Kinzelman, J.; Junion, E.A.; Byappanahalli, M.N.; Whitman, R.L.; Sadowsky, M.J.

    2011-01-01

    High concentrations of Escherichia coli in mats of Cladophora in the Great Lakes have raised concern over the continued use of this bacterium as an indicator of microbial water quality. Determining the impacts of these environmentally abundant E. coli, however, necessitates a better understanding of their ecology. In this study, the population structure of 4285 Cladophora-borne E. coli isolates, obtained over multiple three day periods from Lake Michigan Cladophora mats in 2007-2009, was examined by using DNA fingerprint analyses. In contrast to previous studies that have been done using isolates from attached Cladophora obtained over large time scales and distances, the extensive sampling done here on free-floating mats over successive days at multiple sites provided a large dataset that allowed for a detailed examination of changes in population structure over a wide range of spatial and temporal scales. While Cladophora-borne E. coli populations were highly diverse and consisted of many unique isolates, multiple clonal groups were also present and accounted for approximately 33% of all isolates examined. Patterns in population structure were also evident. At the broadest scales, E. coli populations showed some temporal clustering when examined by year, but did not show good spatial distinction among sites. E. coli population structure also showed significant patterns at much finer temporal scales. Populations were distinct on an individual mat basis at a given site, and on individual days within a single mat. Results of these studies indicate that Cladophora-borne E. coli populations consist of a mixture of stable, and possibly naturalized, strains that persist during the life of the mat, and more unique, transient strains that can change over rapid time scales. It is clear that further study of microbial processes at fine spatial and temporal scales is needed, and that caution must be taken when interpolating short term microbial dynamics from results obtained

  16. Return flight to the Canary Islands--the key role of peripheral populations of Afrocanarian blue tits (Aves: Cyanistes teneriffae) in multi-gene reconstructions of colonization pathways.

    PubMed

    Päckert, Martin; Martens, Jochen; Hering, Jens; Kvist, Laura; Illera, Juan Carlos

    2013-05-01

    Afrocanarian blue tits (Cyanistes teneriffae) have a scattered distribution on the Canary Islands and on the North African continent. To date, the Canary Islands have been considered the species' main Pleistocene evolutionary center, but their colonization pathways remain uncertain. We set out to reconstruct a dated multi-gene phylogeny and ancestral ranges for Cyanistes tit species including the currently unstudied, peripheral Libyan population of C. t. cyrenaicae. In all reconstructions the most easterly and westerly peripheral populations (in Libya and on La Palma) represented basal offshoots of C. teneriffae. These two peripheral populations shared all four major indels and differed in this respect from all other members of the Afrocanarian core group. The basal split of Afrocanarian blue tits from their European relatives was dated to the early Pliocene. The two ancestral area reconstructions were contradictory and suggested either a Canarian or a North African origin of C. teneriffae - but unambiguously ruled out a continental European ancestral range. We conclude that the peripheral populations of C. teneriffae represent relic lineages of a first faunal interchange, presumably downstream colonization from North Africa to the Canary Islands. Subsequent eastward stepping-stone colonization within the Canarian Archipelago culminated in a very recent late (possibly even post-) Pleistocene back-colonization from the Canary Islands to North Africa.

  17. Binary populations in Milky Way satellite galaxies: Constraints from multi-epoch data in the Carina, Fornax, Sculptor, and Sextans dwarf spheroidal galaxies

    SciTech Connect

    Minor, Quinn E.

    2013-12-20

    We introduce a likelihood analysis of multi-epoch stellar line-of-sight velocities to constrain the binary fractions and binary period distributions of dwarf spheroidal galaxies. This method is applied to multi-epoch data from the Magellan/MMFS survey of the Carina, Fornax, Sculptor, and Sextans dSph galaxies, after applying a model for the measurement errors that accounts for binary orbital motion. We find that the Fornax, Sculptor, and Sextans dSphs are consistent with having binary populations similar to that of Milky Way field binaries to within 68% confidence limits, whereas the Carina dSph is remarkably deficient in binaries with periods less than ∼10 yr. If Carina is assumed to have a period distribution identical to that of the Milky Way field, its best-fit binary fraction is 0.14{sub −0.05}{sup +0.28}, and is constrained to be less than 0.5 at the 90% confidence level; thus it is unlikely to host a binary population identical to that of the Milky Way field. By contrast, the best-fit binary fraction of the combined sample of all four galaxies is 0.46{sub −0.09}{sup +0.13}, consistent with that of Milky Way field binaries. More generally, we infer probability distributions in binary fraction, mean orbital period, and dispersion of periods for each galaxy in the sample. Looking ahead to future surveys, we show that the allowed parameter space of binary fraction and period distribution parameters in dSphs will be narrowed significantly by a large multi-epoch survey. However, there is a degeneracy between the parameters that is unlikely to be broken unless the measurement error is of order ∼0.1 km s{sup –1} or smaller, presently attainable only by a high-resolution spectrograph.

  18. Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands

    PubMed Central

    Arroyo Ohori, Ken; Ledoux, Hugo; Peters, Ravi; Stoter, Jantien

    2016-01-01

    The remote estimation of a region’s population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, we investigate to what extent they can be used for the same purpose. Based on the assumption that housing space is a proxy for the number of its residents, we use two methods to estimate the population with 3D city models in two directions: (1) disaggregation (areal interpolation) to estimate the population of small administrative entities (e.g. neighbourhoods) from that of larger ones (e.g. municipalities); and (2) a statistical modelling approach to estimate the population of large entities from a sample composed of their smaller ones (e.g. one acquired by a government register). Starting from a complete Dutch census dataset at the neighbourhood level and a 3D model of all 9.9 million buildings in the Netherlands, we compare the population estimates obtained by both methods with the actual population as reported in the census, and use it to evaluate the quality that can be achieved by estimations at different administrative levels. We also analyse how the volume-based estimation enabled by 3D city models fares in comparison to 2D methods using building footprints and floor areas, as well as how it is affected by different levels of semantic detail in a 3D city model. We conclude that 3D city models are useful for estimations of large areas (e.g. for a country), and that the 3D approach has clear advantages over the 2D approach. PMID:27254151

  19. A multi-scale study of Orthoptera species richness and human population size controlling for sampling effort

    NASA Astrophysics Data System (ADS)

    Cantarello, Elena; Steck, Claude E.; Fontana, Paolo; Fontaneto, Diego; Marini, Lorenzo; Pautasso, Marco

    2010-03-01

    Recent large-scale studies have shown that biodiversity-rich regions also tend to be densely populated areas. The most obvious explanation is that biodiversity and human beings tend to match the distribution of energy availability, environmental stability and/or habitat heterogeneity. However, the species-people correlation can also be an artefact, as more populated regions could show more species because of a more thorough sampling. Few studies have tested this sampling bias hypothesis. Using a newly collated dataset, we studied whether Orthoptera species richness is related to human population size in Italy’s regions (average area 15,000 km2) and provinces (2,900 km2). As expected, the observed number of species increases significantly with increasing human population size for both grain sizes, although the proportion of variance explained is minimal at the provincial level. However, variations in observed Orthoptera species richness are primarily associated with the available number of records, which is in turn well correlated with human population size (at least at the regional level). Estimated Orthoptera species richness (Chao2 and Jackknife) also increases with human population size both for regions and provinces. Both for regions and provinces, this increase is not significant when controlling for variation in area and number of records. Our study confirms the hypothesis that broad-scale human population-biodiversity correlations can in some cases be artefactual. More systematic sampling of less studied taxa such as invertebrates is necessary to ascertain whether biogeographical patterns persist when sampling effort is kept constant or included in models.

  20. A multi-model framework for simulating wildlife population response to land-use and climate change

    USGS Publications Warehouse

    McRae, B.H.; Schumaker, N.H.; McKane, R.B.; Busing, R.T.; Solomon, A.M.; Burdick, C.A.

    2008-01-01

    Reliable assessments of how human activities will affect wildlife populations are essential for making scientifically defensible resource management decisions. A principle challenge of predicting effects of proposed management, development, or conservation actions is the need to incorporate multiple biotic and abiotic factors, including land-use and climate change, that interact to affect wildlife habitat and populations through time. Here we demonstrate how models of land-use, climate change, and other dynamic factors can be integrated into a coherent framework for predicting wildlife population trends. Our framework starts with land-use and climate change models developed for a region of interest. Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model. These predictions are combined with spatially explicit animal habitat models to map changes in the distribution and quality of wildlife habitat expected under the various scenarios. Animal population responses to habitat changes and other factors are then projected using a flexible, individual-based animal population model. As an example application, we simulated animal population trends under three future land-use scenarios and four climate change scenarios in the Cascade Range of western Oregon. We chose two birds with contrasting habitat preferences for our simulations: winter wrens (Troglodytes troglodytes), which are most abundant in mature conifer forests, and song sparrows (Melospiza melodia), which prefer more open, shrubby habitats. We used climate and land-use predictions from previously published studies, as well as previously published predictions of vegetation responses using FORCLIM, an individual-based forest dynamics simulator. Vegetation predictions were integrated with other factors in PATCH, a spatially explicit, individual-based animal population simulator. Through incorporating effects of landscape history and limited

  1. Neural Network Design on the SRC-6 Reconfigurable Computer

    DTIC Science & Technology

    2006-12-01

    speeds of FPGA systems. This thesis explores the use of a Feed-forward, Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) architecture... Implementation of a Fast Artificial Neural Network Library (FANN), Graduate Project Report, Department of Computer Science, University of Copenhagen (DIKU...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited NEURAL NETWORK

  2. Field Efficacy of New Larvicide Products for Control of Multi-Resistant Aedes aegypti Populations in Martinique (French West Indies)

    PubMed Central

    Marcombe, Sébastien; Darriet, Frédéric; Agnew, Philip; Etienne, Manuel; Yp-Tcha, Marie-Michelle; Yébakima, André; Corbel, Vincent

    2011-01-01

    World-wide dengue vector control is hampered by the spread of insecticide resistance in Aedes aegypti. We report the resistance status of a wild Ae. aegypti population from Martinique (Vauclin) to conventional larvicides (Bacillus thuringiensis var israeliensis [Bti] and temephos) and potential alternatives (spinosad, diflubenzuron, and pyriproxyfen). The efficacy and residual activity of these insecticides were evaluated under simulated and field conditions. The Vauclin strain exhibited a high level of resistance to temephos, a tolerance to insect growth regulators, and full susceptibility to spinosad and Bti. In simulated trials, pyriproxyfen and Bti showed long residual activities in permanent breeding containers (28 and 37 weeks), whereas under field conditions they failed to curtail Ae. aegypti populations after four weeks. Conversely, diflubenzuron and spinosad showed a residual efficacy of 16 weeks, suggesting that these chemicals may be promising alternatives to Bti and temephos for controlling insecticide-resistant Ae. aegypti populations. PMID:21212213

  3. Field efficacy of new larvicide products for control of multi-resistant Aedes aegypti populations in Martinique (French West Indies).

    PubMed

    Marcombe, Sébastien; Darriet, Frédéric; Agnew, Philip; Etienne, Manuel; Yp-Tcha, Marie-Michelle; Yébakima, André; Corbel, Vincent

    2011-01-01

    World-wide dengue vector control is hampered by the spread of insecticide resistance in Aedes aegypti. We report the resistance status of a wild Ae. aegypti population from Martinique (Vauclin) to conventional larvicides (Bacillus thuringiensis var israeliensis [Bti] and temephos) and potential alternatives (spinosad, diflubenzuron, and pyriproxyfen). The efficacy and residual activity of these insecticides were evaluated under simulated and field conditions. The Vauclin strain exhibited a high level of resistance to temephos, a tolerance to insect growth regulators, and full susceptibility to spinosad and Bti. In simulated trials, pyriproxyfen and Bti showed long residual activities in permanent breeding containers (28 and 37 weeks), whereas under field conditions they failed to curtail Ae. aegypti populations after four weeks. Conversely, diflubenzuron and spinosad showed a residual efficacy of 16 weeks, suggesting that these chemicals may be promising alternatives to Bti and temephos for controlling insecticide-resistant Ae. aegypti populations.

  4. Human Deciduous Teeth Stem Cells (SHED) Display Neural Crest Signature Characters

    PubMed Central

    Ramírez-García, Luis R.

    2017-01-01

    Human dental tissues are sources of neural crest origin multipotent stem cells whose regenerative potential is a focus of extensive studies. Rational programming of clinical applications requires a more detailed knowledge of the characters inherited from neural crest. Investigation of neural crest cells generated from human pluripotent stem cells provided opportunity for their comparison with the postnatal dental cells. The purpose of this study was to investigate the role of the culture conditions in the expression by dental cells of neural crest characters. The results of the study demonstrate that specific neural crest cells requirements, serum-free, active WNT signaling and inactive SMAD 2/3, are needed for the activity of the neural crest characters in dental cells. Specifically, the decreasing concentration of fetal bovine serum (FBS) from regularly used for dental cells 10% to 2% and below, or using serum-free medium, led to emergence of a subset of epithelial-like cells expressing the two key neural crest markers, p75 and HNK-1. Further, the serum-free medium supplemented with neural crest signaling requirements (WNT inducer BIO and TGF-β inhibitor REPSOX), induced epithelial-like phenotype, upregulated the p75, Sox10 and E-Cadherin and downregulated the mesenchymal genes (SNAIL1, ZEB1, TWIST). An expansion medium containing 2% FBS allowed to obtain an epithelial/mesenchymal SHED population showing high proliferation, clonogenic, multi-lineage differentiation capacities. Future experiments will be required to determine the effects of these features on regenerative potential of this novel SHED population. PMID:28125654

  5. Major transcriptome re-organisation and abrupt changes in signalling, cell cycle and chromatin regulation at neural differentiation in vivo.

    PubMed

    Olivera-Martinez, Isabel; Schurch, Nick; Li, Roman A; Song, Junfang; Halley, Pamela A; Das, Raman M; Burt, Dave W; Barton, Geoffrey J; Storey, Kate G

    2014-08-01

    Here, we exploit the spatial separation of temporal events of neural differentiation in the elongating chick body axis to provide the first analysis of transcriptome change in progressively more differentiated neural cell populations in vivo. Microarray data, validated against direct RNA sequencing, identified: (1) a gene cohort characteristic of the multi-potent stem zone epiblast, which contains neuro-mesodermal progenitors that progressively generate the spinal cord; (2) a major transcriptome re-organisation as cells then adopt a neural fate; and (3) increasing diversity as neural patterning and neuron production begin. Focussing on the transition from multi-potent to neural state cells, we capture changes in major signalling pathways, uncover novel Wnt and Notch signalling dynamics, and implicate new pathways (mevalonate pathway/steroid biogenesis and TGFβ). This analysis further predicts changes in cellular processes, cell cycle, RNA-processing and protein turnover as cells acquire neural fate. We show that these changes are conserved across species and provide biological evidence for reduced proteasome efficiency and a novel lengthening of S phase. This latter step may provide time for epigenetic events to mediate large-scale transcriptome re-organisation; consistent with this, we uncover simultaneous downregulation of major chromatin modifiers as the neural programme is established. We further demonstrate that transcription of one such gene, HDAC1, is dependent on FGF signalling, making a novel link between signals that control neural differentiation and transcription of a core regulator of chromatin organisation. Our work implicates new signalling pathways and dynamics, cellular processes and epigenetic modifiers in neural differentiation in vivo, identifying multiple new potential cellular and molecular mechanisms that direct differentiation.

  6. Neural organization of the locomotive oscillator.

    PubMed

    Willner, B E; Miranker, W L; Lu, C P

    1993-01-01

    We study the relation of neural development, organization, and activity to behavior. We provide a model of the locomotive oscillator, a neural system supplying alternating stimulation to extensor and flexor muscles creating an oscillatory motion. We propose a protocol by which this neural system starting from unstructured, unconnected neural populations develops structure and function. The protocol is studied by both computer simulation and mathematical analysis. Our main results are 1. The locomotive oscillator self-organizes and maintains its organization, assuming certain properties of the neural populations. 2. Imperfections disturbing the functional adequacy of the neural populations may lead to the deterioration and disappearance of the oscillatory behavior. 3. The locomotive oscillator may fail to organize if the development is not staged in time.

  7. Inferences about population dynamics from count data using multi-state models: A comparison to capture-recapture approaches

    USGS Publications Warehouse

    Grant, Evan H. Campbell; Zipkin, Elise; Scott, Sillett T.; Chandler, Richard; Royle, J. Andrew

    2014-01-01

    Wildlife populations consist of individuals that contribute disproportionately to growth and viability. Understanding a population's spatial and temporal dynamics requires estimates of abundance and demographic rates that account for this heterogeneity. Estimating these quantities can be difficult, requiring years of intensive data collection. Often, this is accomplished through the capture and recapture of individual animals, which is generally only feasible at a limited number of locations. In contrast, N-mixture models allow for the estimation of abundance, and spatial variation in abundance, from count data alone. We extend recently developed multistate, open population N-mixture models, which can additionally estimate demographic rates based on an organism's life history characteristics. In our extension, we develop an approach to account for the case where not all individuals can be assigned to a state during sampling. Using only state-specific count data, we show how our model can be used to estimate local population abundance, as well as density-dependent recruitment rates and state-specific survival. We apply our model to a population of black-throated blue warblers (Setophaga caerulescens) that have been surveyed for 25 years on their breeding grounds at the Hubbard Brook Experimental Forest in New Hampshire, USA. The intensive data collection efforts allow us to compare our estimates to estimates derived from capture–recapture data. Our model performed well in estimating population abundance and density-dependent rates of annual recruitment/immigration. Estimates of local carrying capacity and per capita recruitment of yearlings were consistent with those published in other studies. However, our model moderately underestimated annual survival probability of yearling and adult females and severely underestimates survival probabilities for both of these male stages. The most accurate and precise estimates will necessarily require some amount of intensive

  8. Large-scale mitochondrial DNA analysis in Southeast Asia reveals evolutionary effects of cultural isolation in the multi-ethnic population of Myanmar

    PubMed Central

    2014-01-01

    Background Myanmar is the largest country in mainland Southeast Asia with a population of 55 million people subdivided into more than 100 ethnic groups. Ruled by changing kingdoms and dynasties and lying on the trade route between India and China, Myanmar was influenced by numerous cultures. Since its independence from British occupation, tensions between the ruling Bamar and ethnic minorities increased. Results Our aim was to search for genetic footprints of Myanmar’s geographic, historic and sociocultural characteristics and to contribute to the picture of human colonization by describing and dating of new mitochondrial DNA (mtDNA) haplogroups. Therefore, we sequenced the mtDNA control region of 327 unrelated donors and the complete mitochondrial genome of 44 selected individuals according to highest quality standards. Conclusion Phylogenetic analyses of the entire mtDNA genomes uncovered eight new haplogroups and three unclassified basal M-lineages. The multi-ethnic population and the complex history of Myanmar were reflected in its mtDNA heterogeneity. Population genetic analyses of Burmese control region sequences combined with population data from neighboring countries revealed that the Myanmar haplogroup distribution showed a typical Southeast Asian pattern, but also Northeast Asian and Indian influences. The population structure of the extraordinarily diverse Bamar differed from that of the Karen people who displayed signs of genetic isolation. Migration analyses indicated a considerable genetic exchange with an overall positive migration balance from Myanmar to neighboring countries. Age estimates of the newly described haplogroups point to the existence of evolutionary windows where climatic and cultural changes gave rise to mitochondrial haplogroup diversification in Asia. PMID:24467713

  9. Monitoring population dynamics of the thermophilic Bacillus licheniformis CCMI 1034 in batch and continuous cultures using multi-parameter flow cytometry.

    PubMed

    Reis, Alberto; da Silva, Teresa Lopes; Kent, Christopher A; Kosseva, Maria; Roseiro, J Carlos; Hewitt, Christopher J

    2005-01-26

    Multi-parameter flow cytometry was used to monitor the population dynamics of Bacillus licheniformis continuous cultivations and the physiological responses to a starvation period and a glucose pulse. Using a mixture of two specific fluorescent stains, DiOC6(3) (3,3'-dihexylocarbocyanine iodide), and PI (propidium iodide), flow cytometric analysis revealed cell physiological heterogeneity. Four sub-populations of cells could be easily identified based on their differential fluorescent staining, these correspond to healthy cells (A) stained with DiOC6(3); cells or spores with a depolarised cytoplasmic membrane (B), no staining; cells with a permeabilised depolarised cytoplasmic membrane (C), stained with PI; and permeablised cells with a disrupted cytoplasmic membrane 'ghost cells' (D), stained with both DiOC6(3) and PI. Transmission electron micrographs of cells starved of energy showed different cell lysis process stages, highlighting 'ghost cells' which were associated with the double stained sub-population. It was shown, at the individual cell level, that there was a progressive inherent fluctuation in physiological heterogeneity in response to changing environmental conditions. All four sub-populations were shown to be present during glucose-limited continuous cultures, revealing a higher physiological stress level when compared with a glucose pulsed batch. A starvation period (batch without additional nutrients) increased the number of cells in certain sub-populations (cells with depolarised cytoplasmic membranes and cells with permeabilised depolarised cytoplasmic membranes), indicating that such stress may be caused by glucose limitation. Such information could be used to enhance process efficiency.

  10. Association mapping for frost tolerance using multi-parent advanced generation inter-cross (MAGIC) population in faba bean (Vicia faba L.).

    PubMed

    Sallam, Ahmed; Martsch, Regina

    2015-08-01

    A multi-parent advanced generation inter-cross (MAGIC) derived from 11 founder lines in faba bean was used in this study to identify quantitative trait loci (QTL) for frost tolerance traits using the association mapping method with 156 SNP markers. This MAGIC population consists of a set of 189 genotypes from the Göttingen Winter Bean Population. The association panel was tested in two different experiments, i.e. a frost and a hardening experiment. Six morphological traits, leaf fatty acid composition, relative water content in shoots were scored in this study. The genotypes presented a large genetic variation for all traits that were highly heritable after frost and after hardening. High phenotypic significant correlations were established between traits. The principal coordinates analysis resulted in no clear structure in the current population. Association mapping was performed using a general linear model and mixed linear model with kinship. A False discovery rate of 0.20 (and 0.05) was used to test the significance of marker-trait association. As a result, many putative QTLs for 13 morphological and physiological traits were detected using both models. The results reveal that QTL mapping by association analysis is a powerful method of detecting the alleles associated with frost tolerance in the winter faba bean which can be used in accelerating breeding programs.

  11. Ghosts of Yellowstone: Multi-Decadal Histories of Wildlife Populations Captured by Bones on a Modern Landscape

    PubMed Central

    Miller, Joshua H.

    2011-01-01

    Natural accumulations of skeletal material (death assemblages) have the potential to provide historical data on species diversity and population structure for regions lacking decades of wildlife monitoring, thereby contributing valuable baseline data for conservation and management strategies. Previous studies of the ecological and temporal resolutions of death assemblages from terrestrial large-mammal communities, however, have largely focused on broad patterns of community composition in tropical settings. Here, I expand the environmental sampling of large-mammal death assemblages into a temperate biome and explore more demanding assessments of ecological fidelity by testing their capacity to record past population fluctuations of individual species in the well-studied ungulate community of Yellowstone National Park (Yellowstone). Despite dramatic ecological changes following the 1988 wildfires and 1995 wolf re-introduction, the Yellowstone death assemblage is highly faithful to the living community in species richness and community structure. These results agree with studies of tropical death assemblages and establish the broad capability of vertebrate remains to provide high-quality ecological data from disparate ecosystems and biomes. Importantly, the Yellowstone death assemblage also correctly identifies species that changed significantly in abundance over the last 20 to ∼80 years and the directions of those shifts (including local invasions and extinctions). The relative frequency of fresh versus weathered bones for individual species is also consistent with documented trends in living population sizes. Radiocarbon dating verifies the historical source of bones from Equus caballus (horse): a functionally extinct species. Bone surveys are a broadly valuable tool for obtaining population trends and baseline shifts over decadal-to-centennial timescales. PMID:21464921

  12. Multi-Locus Phylogeographic and Population Genetic Analysis of Anolis carolinensis: Historical Demography of a Genomic Model Species

    PubMed Central

    Tollis, Marc; Ausubel, Gavriel; Ghimire, Dhruba; Boissinot, Stéphane

    2012-01-01

    The green anole (Anolis carolinensis) has been widely used as an animal model in physiology and neurobiology but has recently emerged as an important genomic model. The recent sequencing of its genome has shed new light on the evolution of vertebrate genomes and on the process that govern species diversification. Surprisingly, the patterns of genetic diversity within natural populations of this widespread and abundant North American lizard remain relatively unknown. In the present study, we use 10 novel nuclear DNA sequence loci (N = 62 to 152) and one mitochondrial locus (N = 226) to delimit green anole populations and infer their historical demography. We uncovered four evolutionarily distinct and geographically restricted lineages of green anoles using phylogenetics, Bayesian clustering, and genetic distance methods. Molecular dating indicates that these lineages last shared a common ancestor ∼2 million years ago. Summary statistics and analysis of the frequency distributions of DNA polymorphisms strongly suggest range-wide expansions in population size. Using Bayesian Skyline Plots, we inferred the timing of population size expansions, which differ across lineages, and found evidence for a relatively recent and rapid westward expansion of green anoles across the Gulf Coastal Plain during the mid-Pleistocene. One surprising result is that the distribution of genetic diversity is not consistent with a latitudinal shift caused by climatic oscillations as is observed for many co-distributed taxa. This suggests that the most recent Pleistocene glacial cycles had a limited impact on the geographic distribution of the green anole at the northern limits of its range. PMID:22685573

  13. Ghosts of yellowstone: multi-decadal histories of wildlife populations captured by bones on a modern landscape.

    PubMed

    Miller, Joshua H

    2011-03-28

    Natural accumulations of skeletal material (death assemblages) have the potential to provide historical data on species diversity and population structure for regions lacking decades of wildlife monitoring, thereby contributing valuable baseline data for conservation and management strategies. Previous studies of the ecological and temporal resolutions of death assemblages from terrestrial large-mammal communities, however, have largely focused on broad patterns of community composition in tropical settings. Here, I expand the environmental sampling of large-mammal death assemblages into a temperate biome and explore more demanding assessments of ecological fidelity by testing their capacity to record past population fluctuations of individual species in the well-studied ungulate community of Yellowstone National Park (Yellowstone). Despite dramatic ecological changes following the 1988 wildfires and 1995 wolf re-introduction, the Yellowstone death assemblage is highly faithful to the living community in species richness and community structure. These results agree with studies of tropical death assemblages and establish the broad capability of vertebrate remains to provide high-quality ecological data from disparate ecosystems and biomes. Importantly, the Yellowstone death assemblage also correctly identifies species that changed significantly in abundance over the last 20 to ∼80 years and the directions of those shifts (including local invasions and extinctions). The relative frequency of fresh versus weathered bones for individual species is also consistent with documented trends in living population sizes. Radiocarbon dating verifies the historical source of bones from Equus caballus (horse): a functionally extinct species. Bone surveys are a broadly valuable tool for obtaining population trends and baseline shifts over decadal-to-centennial timescales.

  14. Analyses of a multi-parent population derived from two diverse alfalfa germplasms: testcross evaluations and phenotype-DNA associations.

    PubMed

    Maureira-Butler, I J; Udall, J A; Osborn, T C

    2007-10-01

    In a previous study, we showed that the genetic variation present in the Medicago sativa subsp. sativa Peruvian and M. sativa subsp. falcata WISFAL germplasms could be used to improve forage yields when favorable alleles were recombined and used in hybrid combination with cultivated alfalfa. In this paper, we present testcross forage yield and fall growth data for two seasons of a C0 population generated after intermating the Peruvian x WISFAL population for several generations. In addition, we conducted marker-trait association analysis as an attempt to identify Peruvian and WISFAL genomics regions affecting the targeted traits. Five and seven genomic regions were found significantly associated with forage yield and fall growth, respectively. In the case of fall growth, alleles from both accessions were positively associated with plant height. However, more alleles from WISFAL were positively associated with forage yield than from Peruvian. WISFAL is known for its winter hardiness and genomic regions with large effects on winter survival may have masked the effect of forage yield from Peruvian. The fact that most of the genomic regions discovered in this study have been previously associated with traits involved in winter hardiness validates our findings and suggests that associations between DNA fragments and agronomic traits can be detected without the necessity of developing bi-parental mapping populations.

  15. Alternative Isoform Analysis of Ttc8 Expression in the Rat Pineal Gland Using a Multi-Platform Sequencing Approach Reveals Neural Regulation.

    PubMed

    Hartley, Stephen W; Mullikin, James C; Klein, David C; Park, Morgan; Coon, Steven L

    Alternative isoform regulation (AIR) vastly increases transcriptome diversity and plays an important role in numerous biological processes and pathologies. However, the detection and analysis of isoform-level differential regulation is difficult, particularly in the face of complex and incompletely-annotated transcriptomes. Here we have used Illumina short-read/high-throughput RNA-Seq to identify 55 genes that exhibit neurally-regulated AIR in the pineal gland, and then used two other complementary experimental platforms to further study and characterize the Ttc8 gene, which is involved in Bardet-Biedl syndrome and non-syndromic retinitis pigmentosa. Use of the JunctionSeq analysis tool led to the detection of several novel exons and splice junctions in this gene, including two novel alternative transcription start sites which were found to display disproportionately strong neurally-regulated differential expression in several independent experiments. These high-throughput sequencing results were validated and augmented via targeted qPCR and long-read Pacific Biosciences SMRT sequencing. We confirmed the existence of numerous novel splice junctions and the selective upregulation of the two novel start sites. In addition, we identified more than 20 novel isoforms of the Ttc8 gene that are co-expressed in this tissue. By using information from multiple independent platforms we not only greatly reduce the risk of errors, biases, and artifacts influencing our results, we also are able to characterize the regulation and splicing of the Ttc8 gene more deeply and more precisely than would be possible via any single platform. The hybrid method outlined here represents a powerful strategy in the study of the transcriptome.

  16. Alternative Isoform Analysis of Ttc8 Expression in the Rat Pineal Gland Using a Multi-Platform Sequencing Approach Reveals Neural Regulation

    PubMed Central

    Mullikin, James C.; Klein, David C.; Park, Morgan; Coon, Steven L.

    2016-01-01

    Alternative isoform regulation (AIR) vastly increases transcriptome diversity and plays an important role in numerous biological processes and pathologies. However, the detection and analysis of isoform-level differential regulation is difficult, particularly in the face of complex and incompletely-annotated transcriptomes. Here we have used Illumina short-read/high-throughput RNA-Seq to identify 55 genes that exhibit neurally-regulated AIR in the pineal gland, and then used two other complementary experimental platforms to further study and characterize the Ttc8 gene, which is involved in Bardet-Biedl syndrome and non-syndromic retinitis pigmentosa. Use of the JunctionSeq analysis tool led to the detection of several novel exons and splice junctions in this gene, including two novel alternative transcription start sites which were found to display disproportionately strong neurally-regulated differential expression in several independent experiments. These high-throughput sequencing results were validated and augmented via targeted qPCR and long-read Pacific Biosciences SMRT sequencing. We confirmed the existence of numerous novel splice junctions and the selective upregulation of the two novel start sites. In addition, we identified more than 20 novel isoforms of the Ttc8 gene that are co-expressed in this tissue. By using information from multiple independent platforms we not only greatly reduce the risk of errors, biases, and artifacts influencing our results, we also are able to characterize the regulation and splicing of the Ttc8 gene more deeply and more precisely than would be possible via any single platform. The hybrid method outlined here represents a powerful strategy in the study of the transcriptome. PMID:27684375

  17. Monitoring activity in neural circuits with genetically encoded indicators

    PubMed Central

    Broussard, Gerard J.; Liang, Ruqiang; Tian, Lin

    2014-01-01

    Recent developments in genetically encoded indicators of neural activity (GINAs) have greatly advanced the field of systems neuroscience. As they are encoded by DNA, GINAs can be targeted to genetically defined cellular populations. Combined with fluorescence microscopy, most notably multi-photon imaging, GINAs allow chronic simultaneous optical recordings from large populations of neurons or glial cells in awake, behaving mammals, particularly rodents. This large-scale recording of neural activity at multiple temporal and spatial scales has greatly advanced our understanding of the dynamics of neural circuitry underlying behavior—a critical first step toward understanding the complexities of brain function, such as sensorimotor integration and learning. Here, we summarize the recent development and applications of the major classes of GINAs. In particular, we take an in-depth look at the design of available GINA families with a particular focus on genetically encoded calcium indicators (GCaMPs), sensors probing synaptic activity, and genetically encoded voltage indicators. Using the family of the GCaMP as an example, we review established sensor optimization pipelines. We also discuss practical considerations for end users of GINAs about experimental methods including approaches for gene delivery, imaging system requirements, and data analysis techniques. With the growing toolbox of GINAs and with new microscopy techniques pushing beyond their current limits, the age of light can finally achieve the goal of broad and dense sampling of neuronal activity across time and brain structures to obtain a dynamic picture of brain function. PMID:25538558

  18. Stellar Populations in Compact Galaxy Groups: a Multi-wavelength Study of HCGs 16, 22, and 42, Their Star Clusters, and Dwarf Galaxies

    NASA Technical Reports Server (NTRS)

    Konstantopoulos, I. S.; Maybhate, A.; Charlton, J. C.; Fedotov, K.; Durrell, P. R.; Mulchaey, J. S.; English, J.; Desjardins, T. D.; Gallagher, S. C.; Walker, L. M.; Johnson, K. E.; Tzanavaris, Panayiotis; Gronwall, C.

    2013-01-01

    We present a multi-wavelength analysis of three compact galaxy groups, Hickson compact groups (HCGs) 16, 22, and 42, which describe a sequence in terms of gas richness, from space- (Swift, Hubble Space Telescope (HST), and Spitzer) and ground-based (Las Campanas Observatory and Cerro Tololo Inter-American Observatory) imaging and spectroscopy.We study various signs of past interactions including a faint, dusty tidal feature about HCG 16A, which we tentatively age-date at <1 Gyr. This represents the possible detection of a tidal feature at the end of its phase of optical observability. Our HST images also resolve what were thought to be double nuclei in HCG 16C and D into multiple, distinct sources, likely to be star clusters. Beyond our phenomenological treatment, we focus primarily on contrasting the stellar populations across these three groups. The star clusters show a remarkable intermediate-age population in HCG 22, and identify the time at which star formation was quenched in HCG 42. We also search for dwarf galaxies at accordant redshifts. The inclusion of 33 members and 27 "associates" (possible members) radically changes group dynamical masses, which in turn may affect previous evolutionary classifications. The extended membership paints a picture of relative isolation in HCGs 16 and 22, but shows HCG 42 to be part of a larger structure, following a dichotomy expected from recent studies. We conclude that (1) star cluster populations provide an excellent metric of evolutionary state, as they can age-date the past epochs of star formation; and (2) the extended dwarf galaxy population must be considered in assessing the dynamical state of a compact group.

  19. Multi-Population Selective Genotyping to Identify Soybean [Glycine max (L.) Merr.] Seed Protein and Oil QTLs

    PubMed Central

    Phansak, Piyaporn; Soonsuwon, Watcharin; Hyten, David L.; Song, Qijian; Cregan, Perry B.; Graef, George L.; Specht, James E.

    2016-01-01

    Plant breeders continually generate ever-higher yielding cultivars, but also want to improve seed constituent value, which is mainly protein and oil, in soybean [Glycine max (L.) Merr.]. Identification of genetic loci governing those two traits would facilitate that effort. Though genome-wide association offers one such approach, selective genotyping of multiple biparental populations offers a complementary alternative, and was evaluated here, using 48 F2:3 populations (n = ∼224 plants) created by mating 48 high protein germplasm accessions to cultivars of similar maturity, but with normal seed protein content. All F2:3 progeny were phenotyped for seed protein and oil, but only 22 high and 22 low extreme progeny in each F2:3 phenotypic distribution were genotyped with a 1536-SNP chip (ca. 450 bimorphic SNPs detected per mating). A significant quantitative trait locus (QTL) on one or more chromosomes was detected for protein in 35 (73%), and for oil in 25 (52%), of the 48 matings, and these QTL exhibited additive effects of ≥ 4 g kg–1 and R2 values of 0.07 or more. These results demonstrated that a multiple-population selective genotyping strategy, when focused on matings between parental phenotype extremes, can be used successfully to identify germplasm accessions possessing large-effect QTL alleles. Such accessions would be of interest to breeders to serve as parental donors of those alleles in cultivar development programs, though 17 of the 48 accessions were not unique in terms of SNP genotype, indicating that diversity among high protein accessions in the germplasm collection is less than what might ordinarily be assumed. PMID:27172185

  20. Tobacco smoking in Poland in 2003-2014. Multi-centre National Population Health Examination Survey (WOBASZ).

    PubMed

    Polakowska, Maria; Kaleta, Dorota; Piotrowski, Walerian; Topór-Mądry, Roman; Puch-Walczak, Aleksandra; Niklas, Arkadiusz; Bielecki, Wojciech; Kozakiewicz, Krystyna; Pająk, Andrzej; Tykarski, Andrzej; Zdrojewski, Tomasz; Drygas, Wojciech

    2017-01-17

    INTRODUCTION    The reduction of tobacco smoking is a challenging problem of public health. OBJECTIVES    The main purpose of this work was to evaluate the prevalence and tobacco use patterns in the adult population of Poles and its changes in a period between year 2003 and 2014. Furthermore, changes in the smoking addiction, the declared reasons for smoking as well as readiness and motivation to stop smoking has been assessed.  PATIENTS AND METHODS    Based on data from the Polish studies - WOBASZ and WOBASZ II, the analysis covered a population of 14576 persons from the 1st study (6906 men and 7670 women) and 5696 persons from the 2nd study (2578 men and 3118 women), aged 20 - 74. RESULTS    According to the WOBASZ II study, in Poland 30% of men and 21% of women smoked,  the shares being 9 and 4 % lower for men and women respectively in comparison with the WOBASZ (p<0.001). The average number of cigarettes smoked daily per smoker significantly decreased in the period of observation among men (from 17.9 to 15.8 cigarettes/day) and women (from 13.7 to 12.1). The percentage of never smoking men rose from 29.8% to 36.1% (p<0.0001). The proportion of never smoking women no changed. However, the percentage of those expressing unwillingness to quit tobacco smoking nearly doubled in WOBASZ II vs WOBASZ. CONCLUSIONS    Although we found smoking rates in Poland have declined over the past decade, smoking remains prevalent among men and women. Therefore it is necessary to optimize the tobacco control in Poland including fiscal policy, counseling and tobacco addiction treatment, promotional and educational activities, with a special emphasis on the female population.

  1. A rapid method for simultaneous screening of multi-gene mutations associated with hearing loss in the Korean population.

    PubMed

    Sagong, Borum; Baek, Jeong-In; Oh, Se-Kyung; Na, Kyung Jin; Bae, Jae Woong; Choi, Soo Young; Jeong, Ji Yun; Choi, Jae Young; Lee, Sang-Heun; Lee, Kyu-Yup; Kim, Un-Kyung

    2013-01-01

    Hearing loss (HL) is a congenital disease with a high prevalence, and patients with hearing loss need early diagnosis for treatment and prevention. The GJB2, MT-RNR1, and SLC26A4 genes have been reported as common causative genes of hearing loss in the Korean population and some mutations of these genes are the most common mutations associated with hearing loss. Accordingly, we developed a method for the simultaneous detection of seven mutations (c.235delC of GJB2, c.439A>G, c.919-2A>G, c.1149+3A>G, c.1229C>T, c.2168A>G of SLC26A4, and m.1555A>G of the MT-RNR1 gene) using multiplex SNaPshot minisequencing to enable rapid diagnosis of hereditary hearing loss. This method was confirmed in patients with hearing loss and used for genetic diagnosis of controls with normal hearing and neonates. We found that 4.06% of individuals with normal hearing and 4.32% of neonates were heterozygous carriers. In addition, we detected that an individual is heterozygous for two different mutations of GJB2 and SLC26A4 gene, respectively and one normal hearing showing the heteroplasmy of m.1555A>G. These genotypes corresponded to those determined by direct sequencing. Overall, we successfully developed a robust and cost-effective diagnosis method that detects common causative mutations of hearing loss in the Korean population. This method will be possible to detect up to 40% causative mutations associated with prelingual HL in the Korean population and serve as a useful genetic technique for diagnosis of hearing loss for patients, carriers, neonates, and fetuses.

  2. Evolvable synthetic neural system

    NASA Technical Reports Server (NTRS)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  3. Neural Networks

    SciTech Connect

    Smith, Patrick I.

    2003-09-23

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  4. Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

    PubMed Central

    Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

    2015-01-01

    PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas. PMID:26426030

  5. Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

    PubMed

    Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

    2015-09-29

    PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

  6. Statin Therapy and Levels of Hemostatic Factors in a Healthy Population: the Multi-Ethnic Study of Atherosclerosis

    PubMed Central

    Adams, Nathan B.; Lutsey, Pamela L; Folsom, Aaron R; Herrington, David H; Sibley, Christopher T; Zakai, Neil A; Ades, Steven; Burke, Gregory L; Cushman, Mary

    2013-01-01

    Background HMG CoA reductase inhibitors (statins) reduce risk of venous thromboembolism (VTE) in healthy people. Statins reduce levels of inflammation biomarkers, however the mechanism for reduction in VTE risk is unknown. In a large cohort of healthy people, we studied associations of statin use with plasma hemostatic factors related to VTE risk. Methods Cross-sectional analyses were performed in the Multi-Ethnic Study of Atherosclerosis (MESA), a cohort study of 6814 healthy men and women age 45–84, free of clinical cardiovascular disease at baseline; 1001 were using statins at baseline. Twenty-three warfarin users were excluded. Age, race, and sex-adjusted mean hemostatic factor levels were compared between statin users and nonusers, and multivariable linear regression models were used to assess associations of statin use with hemostasis factors, adjusted for age, race/ethnicity, education, income, hormone replacement therapy (in women), and major cardiovascular risk factors. Results Participants using statins had lower adjusted levels of D-dimer (−9%), C-reactive protein (−21%) and factor VIII (−3%) than non-users (p<0.05). Homocysteine and von Willebrand factor were non-significantly lower with statin use. Higher fibrinogen (2%) and PAI-1 (22%) levels were observed among statin users than nonusers (p<0.05). Further adjustment for LDL and triglyceride levels did not attenuate the observed differences in these factors by statin use. Conclusions Findings of lower D-dimer, factor VIII and C-reactive protein levels with statin use suggest hypotheses for mechanisms whereby statins might lower VTE risk. A prospective study or clinical trial linking these biochemical differences to VTE outcomes in statin users and nonusers is warranted. PMID:23565981

  7. A multi-stage approach to maximizing geocoding success in a large population-based cohort study through automated and interactive processes.

    PubMed

    Sonderman, Jennifer S; Mumma, Michael T; Cohen, Sarah S; Cope, Elizabeth L; Blot, William J; Signorello, Lisa B

    2012-05-01

    To enable spatial analyses within a large, prospective cohort study of nearly 86,000 adults enrolled in a 12-state area in the southeastern United States of America from 2002-2009, a multi-stage geocoding protocol was developed to efficiently maximize the proportion of participants assigned an address level geographic coordinate. Addresses were parsed, cleaned and standardized before applying a combination of automated and interactive geocoding tools. Our full protocol increased the non-Post Office (PO) Box match rate from 74.5% to 97.6%. Overall, we geocoded 99.96% of participant addresses, with only 5.2% at the ZIP code centroid level (2.8% PO Box and 2.3% non-PO Box addresses). One key to reducing the need for interactive geocoding was the use of multiple base maps. Still, addresses in areas with population density <44 persons/km2 were much more likely to require resource-intensive interactive geocoding than those in areas with >920 persons/km2 (odds ratio (OR) = 5.24; 95% confidence interval (CI) = 4.23, 6.49), as were addresses collected from participants during in-person interviews compared with mailed questionnaires (OR = 1.83; 95% CI = 1.59, 2.11). This study demonstrates that population density and address ascertainment method can influence automated geocoding results and that high success in address level geocoding is achievable for large-scale studies covering wide geographical areas.

  8. ParallelStructure: a R package to distribute parallel runs of the population genetics program STRUCTURE on multi-core computers.

    PubMed

    Besnier, Francois; Glover, Kevin A

    2013-01-01

    This software package provides an R-based framework to make use of multi-core computers when running analyses in the population genetics program STRUCTURE. It is especially addressed to those users of STRUCTURE dealing with numerous and repeated data analyses, and who could take advantage of an efficient script to automatically distribute STRUCTURE jobs among multiple processors. It also consists of additional functions to divide analyses among combinations of populations within a single data set without the need to manually produce multiple projects, as it is currently the case in STRUCTURE. The package consists of two main functions: MPI_structure() and parallel_structure() as well as an example data file. We compared the performance in computing time for this example data on two computer architectures and showed that the use of the present functions can result in several-fold improvements in terms of computation time. ParallelStructure is freely available at https://r-forge.r-project.org/projects/parallstructure/.

  9. Metastable dynamics in heterogeneous neural fields

    PubMed Central

    Schwappach, Cordula; Hutt, Axel; beim Graben, Peter

    2015-01-01

    We present numerical simulations of metastable states in heterogeneous neural fields that are connected along heteroclinic orbits. Such trajectories are possible representations of transient neural activity as observed, for example, in the electroencephalogram. Based on previous theoretical findings on learning algorithms for neural fields, we directly construct synaptic weight kernels from Lotka-Volterra neural population dynamics without supervised training approaches. We deliver a MATLAB neural field toolbox validated by two examples of one- and two-dimensional neural fields. We demonstrate trial-to-trial variability and distributed representations in our simulations which might therefore be regarded as a proof-of-concept for more advanced neural field models of metastable dynamics in neurophysiological data. PMID:26175671

  10. Patients with knee osteoarthritis demonstrate improved gait pattern and reduced pain following a non-invasive biomechanical therapy: a prospective multi-centre study on Singaporean population

    PubMed Central

    2014-01-01

    Background Previous studies have shown the effect of a unique therapy with a non-invasive biomechanical foot-worn device (AposTherapy) on Caucasian western population suffering from knee osteoarthritis. The purpose of the current study was to evaluate the effect of this therapy on the level of symptoms and gait patterns in a multi-ethnic Singaporean population suffering from knee osteoarthritis. Methods Fifty-eight patients with bilateral medial compartment knee osteoarthritis participated in the study. All patients underwent a computerized gait test and completed two self-assessment questionnaires (WOMAC and SF-36). The biomechanical device was calibrated to each patient, and therapy commenced. Changes in gait patterns and self-assessment questionnaires were reassessed after 3 and 6 months of therapy. Results A significant improvement was seen in all of the gait parameters following 6 months of therapy. Specifically, gait velocity increased by 15.9%, step length increased by 10.3%, stance phase decreased by 5.9% and single limb support phase increased by 2.7%. In addition, pain, stiffness and functional limitation significantly decreased by 68.3%, 66.7% and 75.6%, respectively. SF-36 physical score and mental score also increased significantly following 6 months of therapy (46.1% and 22.4%, respectively) (P < 0.05 for all parameters). Conclusions Singaporean population with medial compartment knee osteoarthritis demonstrated improved gait patterns, reported alleviation in symptoms and improved function and quality of life following 6 months of therapy with a unique biomechanical device. Trial registration Registration number NCT01562652. PMID:24383821

  11. Historical gene flow and profound spatial genetic structure among golden pheasant populations suggested by multi-locus analysis.

    PubMed

    He, Ke; Liu, Hong-Yi; Ge, Yun-Fa; Wu, Shao-Ying; Wan, Qiu-Hong

    2017-05-01

    Major histocompatibility complex (MHC) is a good marker system for geographical genetics since they are functional genes in the immune system that are likely to affect the fitness of the individual, and the survival and evolutionary potential of a population in a changing environment. Golden pheasant (Chrysolophus pictus) is a wild Phasianidae distributed in central and north China. In this study, we used a locus-specific genotyping technique for MHC IIB genes of golden pheasant. Combining with microsatellites (simple sequence repeat, SSR) and mitochondrial DNA (mtDNA) D-loop region, we investigated the demographic history and illuminate genetic structure of this bird in detail. SYR (south of Yangtze river) - NYR (north of Yangtze river) lineages, separated by Yangtze River, were defined in genetic structure of MHC IIB. NYR was supposed as refuge during glacial period, suggested by diversity parameters and more ancient alleles in this region. Based on this hypothesis, there was gene flow from NYR to SYR, which was proved by three pieces of evidence: (1) distinct demographic histories of SYR (kept stable) and NYR (experienced expansion); (2) specific affiliation of LC in genetic structure of SSR and MHC genes; (3) significant gene flow from NYR to SYR. Moreover, we also found balancing selection by combination of three Grouping A2's regions (SC, QL and North) into one in Grouping B4 (NYR) and no pattern of isolation by distance (IBD) found in MHC IIB, whereas for SSR we found a relatively strong and significant IBD. Several mechanisms in the evolution of MHC IIB genes, including recombination, historically positive selection, trans-species evolution and concerted evolution, were shown by molecular and phylogenetic analysis. Overall these results suggest the Yangtze River was inferred to be a geological barrier for this avian and NYR might experience population expansion, which invaded into a neighboring region. This study contributes to the understanding of the

  12. Socioeconomic Inequalities and Multi-Disability among the Population Aged 15–64 Years from 1987 to 2006 in China

    PubMed Central

    Wang, Zhenjie; Chen, Gong; Guo, Chao; Pang, Lihua; Zheng, Xiaoying

    2016-01-01

    Socioeconomic inequalities associated with multiple disabilities have not been explored in China. This is the first study to explore changes in multiple disabilities among persons aged 15–64 years in China. Data were derived from the 1987 and 2006 China National Sample Surveys on Disability, which are nationally representative population-based surveys. Both surveys used multistage, stratified, cluster random sampling with probability proportional to size to derive nationally representative samples. We used standard weighting procedures to construct sample weights considering the multistage stratified cluster sampling survey scheme. The impact of socioeconomic inequalities on multiple disabilities was examined by using logistic regression. Higher prevalence rates among rural residents than urban residents were observed. Male was more vulnerable than female in the present study. Minority ethnicity did increase the risk of multiple disabilities, but this association inversed in the logistic regression model. The widening discrepancy between urban and rural areas indicates that the most important priorities of disability prevention in China are to reinforce health promotion and to improve health services in rural communities. PMID:27775678

  13. Collocated cokriging and neural-network multi-attribute transform in the prediction of effective porosity: A comparative case study for the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA

    NASA Astrophysics Data System (ADS)

    Moon, Seonghoon; Lee, Gwang H.; Kim, Hyeonju; Choi, Yosoon; Kim, Han-Joon

    2016-08-01

    Collocated cokriging (CCK) and neural-network multi-attribute transform (NN-MAT) are widely used in the prediction of reservoir properties because they can integrate sparsely-distributed, high-resolution well-log data and densely-sampled, low-resolution seismic data. CCK is a linear-weighted averaging method based on spatial covariance model. NN-MAT, based on a nonlinear relationship between seismic attributes and log values, treats data as spatially independent observations. In this study, we analyzed 3-D seismic and well-log data from the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA to investigate: (1) how CCK and NN-MAT perform in the prediction of porosity and (2) how the number of wells affects the results. Among a total of 64 wells, 25 wells were selected for CCK and NN-MAT and 39 wells were withheld for validation. We examined four cases: 25, 20, 15, and 10 wells. CCK overpredicted the porosity in the validation wells for all cases likely due to the strong influence of high values, but failed to predict very large porosities. Overprediction of CCK porosity becomes more pronounced with decreasing number of wells. NN-MAT largely underpredicted the porosity for all cases probably due to the band-limited nature of seismic data. The performance of CCK appears to be not affected significantly by the number of wells. Overall, NN-MAT performed better than CCK although its performance decreases continuously with decreasing number of wells.

  14. The Multi-factor Predictive Seis &Gis Model of Ecological, Genetical, Population Health Risk and Bio-geodynamic Processes In Geopathogenic Zones

    NASA Astrophysics Data System (ADS)

    Bondarenko, Y.

    I. Goal and Scope. Human birth rate decrease, death-rate growth and increase of mu- tagenic deviations risk take place in geopathogenic and anthropogenic hazard zones. Such zones create unfavourable conditions for reproductive process of future genera- tions. These negative trends should be considered as a protective answer of the com- plex biosocial system to the appearance of natural and anthropogenic risk factors that are unfavourable for human health. The major goals of scientific evaluation and de- crease of risk of appearance of hazardous processes on the territory of Dnipropetrovsk, along with creation of the multi-factor predictive Spirit-Energy-Information Space "SEIS" & GIS Model of ecological, genetical and population health risk in connection with dangerous bio-geodynamic processes, were: multi-factor modeling and correla- tion of natural and anthropogenic environmental changes and those of human health; determination of indicators that show the risk of destruction structures appearance on different levels of organization and functioning of the city ecosystem (geophys- ical and geochemical fields, soil, hydrosphere, atmosphere, biosphere); analysis of regularities of natural, anthropogenic, and biological rhythms' interactions. II. Meth- ods. The long spatio-temporal researches (Y. Bondarenko, 1996, 2000) have proved that the ecological, genetic and epidemiological processes are in connection with de- velopment of dangerous bio-geophysical and bio-geodynamic processes. Mathemat- ical processing of space photos, lithogeochemical and geophysical maps with use of JEIS o and ERDAS o computer systems was executed at the first stage of forma- tion of multi-layer geoinformation model "Dnipropetrovsk ARC View GIS o. The multi-factor nonlinear correlation between solar activity and cosmic ray variations, geophysical, geodynamic, geochemical, atmospheric, technological, biological, socio- economical processes and oncologic case rate frequency, general and primary

  15. Polymorphisms in methylenetetrahydrofolate reductase gene and risk of non-Hodgkin lymphoma in a multi-ethnic population.

    PubMed

    Suthandiram, Sujatha; Gan, Gin Gin; Zain, Shamsul Mohd; Haerian, Batoul Sadat; Bee, Ping Chong; Lian, Lay Hoong; Chang, Kian Meng; Ong, Tee Chuan; Mohamed, Zahurin

    2014-05-01

    An imbalance in folate metabolism can adversely affect DNA synthesis and methylation systems which can lead to susceptibility to non-Hodgkin lymphoma (NHL). Whether single nucleotide polymorphisms (SNPs) and their haplotypes in the methylenetetrahydrofolate reductase (MTHFR) are associated with NHL, remain inconclusive. We investigated the association between MTHFR C677T and A1298C SNPs and NHL risk in a population which is made up of Malay, Chinese and Indian ethnic subgroups. A total of 372 NHL patients and 722 controls were genotyped using the Sequenom MassARRAY platform. Our results of the pooled subjects failed to demonstrate significant association between the MTHFR C677T and A1298C SNPs with NHL and its subtypes. The results were in agreement with the previous meta-analyses. In the Indian ethnic subgroup however, single locus analysis of MTHFR A1298C appears to confer risk to NHL (Odds ratio (OR) 1.91, 95% confidence interval (95% CI) 1.22-3.00, P=0.006). The risk is almost doubled in homozygous carrier of MTHFR 1298CC (OR 4.03, 95% CI 1.56-10.43, P=0.004). Haplotype analysis revealed higher frequency of CC in the Indian NHL patients compared with controls (OR 1.86, 95% CI 1.18-2.93, P=0.007). There is lack of evidence to suggest an association between MTHFR C677T and A1298C with the risk of NHL in the Malays and Chinese. In the Indians however, the MTHFR A1298C confers risk to NHL. This study suggests ethnicity modifies the relationship between polymorphisms in the folate-metabolizing gene and NHL.

  16. Cardiovascular Toxicity of Multi-Tyrosine Kinase Inhibitors in Advanced Solid Tumors: A Population-Based Observational Study

    PubMed Central

    Srikanthan, Amirrtha; Ethier, Josee-Lyne; Ocana, Alberto; Seruga, Bostjan; Krzyzanowska, Monika K.; Amir, Eitan

    2015-01-01

    Background Treatment with small molecule tyrosine kinase inhibitors (TKIs) has improved survival in many cancers, yet has been associated with an increased risk of adverse events. Warnings of cardiovascular events are common in drug labels of many TKIs. Despite these warnings, cardiovascular toxicity of patients treated with TKIs remains unclear. Here, we evaluate the cardiovascular outcomes of advanced cancer patients treated with small molecule tyrosine kinase inhibitors. Methods A population based cohort study was undertaken involving adults aged >18 years in Ontario, Canada, diagnosed with any advanced malignancy between 2006 and 2012. Data were extracted from linked administrative governmental databases. Adults with advanced cancer receiving TKIs were identified and followed throughout the time period. The main outcomes of interest were rates of hospitalization for ischemic heart disease (acute myocardial infarction and angina) or cerebrovascular accidents and death. Results 1642 patients with a mean age of 62.5 years were studied; 1046 were treated with erlotinib, 166 with sorafenib and 430 with sunitinib. Over the 380 day median follow-up period (range 6-1970 days), 1.1% of all patients had ischemic heart events, 0.7% had cerebrovascular accidents and 72.1% died. Rates of cardiovascular events were similar to age and gender-matched individuals without cancer. In a subgroup analysis of treatment patients with a prior history of ischemic heart disease, 3.3% had ischemic heart events while 1.2% had cerebrovascular accidents. Conclusions TKIs do not appear to increase the cause-specific hazard of ischemic heart disease and cerebrovascular accidents compared to age and gender-matched individuals without advanced cancer. PMID:25815472

  17. What stresses men? predictors of perceived stress in a population-based multi-ethnic cross sectional cohort

    PubMed Central

    2013-01-01

    Background Perceived stress (PS) is a risk factor for a variety of diseases. However, relatively little is known about age- or ethnicity-specific differences in the effect of potential predictors of PS in men. Methods We used a population-based survey of 6,773 White, 1,681 Black, and 617 Hispanic men in Southeastern Pennsylvania to evaluate the relationship of self-reported PS and financial security, health status, social factors, and health behaviors. Interactions across levels of age and ethnicity were tested using logistic regression models adjusted for overall health status, education, and household poverty. Results High PS decreased significantly with age (p < 0.0001) and varied by ethnicity (p = 0.0001). Exposure to health-related and economic factors were more consistently associated with elevated PS in all ethnicities and ages, while social factors and health behaviors were less strongly or not at all associated with PS in most groups. Significant differences in the relationship of high PS by age and ethnicity were observed among men who are medically uninsured (p = 0.0002), reported missing a meal due to cost (p < 0.0001), or had spent a night in the hospital (p = 0.020). In contrast, not filling a prescription due to cost and diagnosed with a mental health condition were associated with high PS but did not differ by age and ethnicity subgroup. Conclusions These data suggest that some, but not all, factors associated with high PS differ by age and/or ethnicity. Research, clinical, or public health initiatives that involve social stressors should consider differences by age and ethnicity. PMID:23388399

  18. Concentrated affluence, concentrated disadvantage, and children's readiness for school: a population-based, multi-level investigation.

    PubMed

    Carpiano, Richard M; Lloyd, Jennifer E V; Hertzman, Clyde

    2009-08-01

    A number of studies demonstrates a relationship between neighbourhood concentration of affluence and disadvantage and the health and development of its residents. We contribute to this literature by testing hypotheses about the relationship between neighbourhood-level concentrated affluence/disadvantage and child-level developmental outcomes in a study population of 37,798 Kindergarten children residing in 433 neighbourhoods throughout the province of British Columbia, Canada. We utilise a previously-validated measure of neighbourhood socioeconomic composition--the Index of Concentration at the Extremes (ICE)--which not only allows for more precise estimation of the competing influences of concentrated affluence and disadvantage, but also facilitates examination of the potential impact of neighbourhood-level income inequality. Our findings show that increases in neighbourhood affluence are associated with increases in children's scores on the Early Development Instrument (EDI), a holistic measure of Kindergarteners' readiness for school. Particularly noteworthy is that, for four of the five EDI scales (physical, social, emotional, and communication) and the total score, results indicate a significant curvilinear relationship--whereby the highest average child-level outcomes are not found in locations with the highest concentrations of affluence, but rather in locations with relatively equal proportions of affluent and disadvantaged families. This finding suggests, first, that concentrated affluence may have diminishing rates of return on contributing to enhanced child development, and, second, that children residing in mixed-income neighbourhoods may benefit both from the presence of affluent residents and from the presence of services and institutions aimed at assisting lower-income residents. Implications and future directions are discussed.

  19. Temporal and basin-specific population trends of quagga mussels on soft sediment of a multi-basin reservoir

    USGS Publications Warehouse

    Caldwell, Timothy J; Rosen, Michael R.; Chandra, Sudeep; Acharya, Kumud; Caires, Andrea M; Davis, Clinton J.; Thaw, Melissa; Webster, Daniel M.

    2015-01-01

    Invasive quagga (Dreissena bugnesis) and zebra (Dreissena ploymorpha) mussels have rapidly spread throughout North America. Understanding the relationships between environmental variables and quagga mussels during the early stages of invasion will help management strategies and allow researchers to predict patterns of future invasions. Quagga mussels were detected in Lake Mead, NV/AZ in 2007, we monitored early invasion dynamics in 3 basins (Boulder Basin, Las Vegas Bay, Overton Arm) bi-annually from 2008-2011. Mean quagga density increased over time during the first year of monitoring and stabilized for the subsequent two years at the whole-lake scale (8 to 132 individuals·m-2, geometric mean), in Boulder Basin (73 to 875 individuals·m-2), and in Overton Arm(2 to 126 individuals·m-2). In Las Vegas Bay, quagga mussel density was low (9 to 44 individuals·m-2), which was correlated with high sediment metal concentrations and warmer (> 30°C) water temperatures associated with that basin. Carbon content in the sediment increased with depth in Lake Mead and during some sampling periods quagga density was also positively correlated with depth, but more research is required to determine the significance of this interaction. Laboratory growth experiments suggested that food quantity may limit quagga growth in Boulder Basin, indicating an opportunity for population expansion in this basin if primary productivity were to increase, but was not the case in Overton Arm. Overall quagga mussel density in Lake Mead is highly variable and patchy, suggesting that temperature, sediment size, and sediment metal concentrations, and sediment carbon content all contribute to mussel distribution patterns. Quagga mussel density in the soft sediment of Lake Mead expanded during initial colonization, and began to stabilize approximately 3 years after the initial invasion.

  20. Patterns and expenditures of multi-morbidity in an insured working population in the United States: insights for a sustainable health care system and building healthier lives.

    PubMed

    Greene, Robert; Dasso, Edwin; Ho, Sam; Frank, Jerry; Scandrett, Graeme; Genaidy, Ash

    2013-12-01

    The U.S. health care system is currently heading toward unsustainable health care expenditures and increased dissatisfaction with health outcomes. The objective of this population-based study is to uncover practical insights regarding patients with 1 or more chronic illnesses. A cross-sectional investigation was designed to gather data from health records drawn from diverse US geographic markets. A database of 9.74 million fully-insured, working individuals was used, together with members in the same households. Among nearly 3.43 million patients with claims, 2.22 million had chronic conditions. About 24.3% had 1 chronic condition and 40.4% had multi-morbidity. Health care expenditures for chronic conditions accounted for 92% of all costs (52% for chronic costs and 40% for nonchronic costs). Psychiatry, orthopedics-rheumatology, endocrinology, and cardiology areas accounted for two thirds of these chronic condition costs; nonchronic condition costs were dominated by otolaryngology, gastroenterology, dermatology, orthopedics-rheumatology conditions, and preventive services. About 50.1% of all households had 2 or more members with chronic conditions. In summary, multi-morbidity is prevalent not only among those older than age 65 years but also in younger and working individuals, and commonly occurs among several members of a household. The authors suggest that the disease-focused model of medicine should change to a more holistic illness-wellness model, emphasizing not only the physical but also the mental and social elements that can influence individual health. In that way the chronic care model could be broadened in context and content to improve the health of patients and households.

  1. Knowledge-driven analysis identifies a gene-gene interaction affecting high-density lipoprotein cholesterol levels in multi-ethnic populations.

    PubMed

    Ma, Li; Brautbar, Ariel; Boerwinkle, Eric; Sing, Charles F; Clark, Andrew G; Keinan, Alon

    2012-01-01

    Total cholesterol, low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels are among the most important risk factors for coronary artery disease. We tested for gene-gene interactions affecting the level of these four lipids based on prior knowledge of established genome-wide association study (GWAS) hits, protein-protein interactions, and pathway information. Using genotype data from 9,713 European Americans from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and a locus near LIPC in their effect on HDL-C levels (Bonferroni corrected P(c) = 0.002). Using an adaptive locus-based validation procedure, we successfully validated this gene-gene interaction in the European American cohorts from the Framingham Heart Study (P(c) = 0.002) and the Multi-Ethnic Study of Atherosclerosis (MESA; P(c) = 0.006). The interaction between these two loci is also significant in the African American sample from ARIC (P(c) = 0.004) and in the Hispanic American sample from MESA (P(c) = 0.04). Both HMGCR and LIPC are involved in the metabolism of lipids, and genome-wide association studies have previously identified LIPC as associated with levels of HDL-C. However, the effect on HDL-C of the novel gene-gene interaction reported here is twice as pronounced as that predicted by the sum of the marginal effects of the two loci. In conclusion, based on a knowledge-driven analysis of epistasis, together with a new locus-based validation method, we successfully identified and validated an interaction affecting a complex trait in multi-ethnic populations.

  2. Multi-Generational Kinship, Multiple Mating, and Flexible Modes of Parental Care in a Breeding Population of the Veery (Catharus fuscescens), a Trans-Hemispheric Migratory Songbird.

    PubMed

    Halley, Matthew R; Heckscher, Christopher M; Kalavacharla, Venugopal

    2016-01-01

    We discovered variable modes of parental care in a breeding population of color-banded Veeries (Catharus fuscescens), a Nearctic-Neotropical migratory songbird, long thought to be socially monogamous, and performed a multi-locus DNA microsatellite analysis to estimate parentage and kinship in a sample of 37 adults and 21 offspring. We detected multiple mating in both sexes, and four modes of parental care that varied in frequency within and between years including multiple male feeders at some nests, and males attending multiple nests in the same season, each with a different female. Unlike other polygynandrous systems, genetic evidence indicates that multi-generational patterns of kinship occur among adult Veeries at our study site, and this was corroborated by the capture of an adult male in 2013 that had been banded as a nestling in 2011 at a nest attended by multiple male feeders. All genotyped adults (n = 37) were related to at least one other bird in the sample at the cousin level or greater (r ≥ 0.125), and 81% were related to at least one other bird at the half-sibling level or greater (r ≥ 0.25, range 0.25-0.60). Although our sample size is small, it appears that the kin structure is maintained by natal philopatry in both sexes, and that Veeries avoid mating with close genetic kin. At nests where all adult feeders were genotyped (n = 9), the male(s) were unrelated to the female (mean r = -0.11 ± 0.15), whereas genetic data suggest close kinship (r = 0.254) between two male co-feeders at the nests of two females in 2011, and among three of four females that were mated to the same polygynous male in 2012. To our knowledge, this is the first evidence of polygynandry occurring among multiple generations of close genetic kin on the breeding ground of a Nearctic-Neotropical migratory songbird.

  3. The Pharmaco –, Population and Evolutionary Dynamics of Multi-drug Therapy: Experiments with S. aureus and E. coli and Computer Simulations

    PubMed Central

    Ankomah, Peter; Johnson, Paul J. T.; Levin, Bruce R.

    2013-01-01

    There are both pharmacodynamic and evolutionary reasons to use multiple rather than single antibiotics to treat bacterial infections; in combination antibiotics can be more effective in killing target bacteria as well as in preventing the emergence of resistance. Nevertheless, with few exceptions like tuberculosis, combination therapy is rarely used for bacterial infections. One reason for this is a relative dearth of the pharmaco-, population- and evolutionary dynamic information needed for the rational design of multi-drug treatment protocols. Here, we use in vitro pharmacodynamic experiments, mathematical models and computer simulations to explore the relative efficacies of different two-drug regimens in clearing bacterial infections and the conditions under which multi-drug therapy will prevent the ascent of resistance. We estimate the parameters and explore the fit of Hill functions to compare the pharmacodynamics of antibiotics of four different classes individually and in pairs during cidal experiments with pathogenic strains of Staphylococcus aureus and Escherichia coli. We also consider the relative efficacy of these antibiotics and antibiotic pairs in reducing the level of phenotypically resistant but genetically susceptible, persister, subpopulations. Our results provide compelling support for the proposition that the nature and form of the interactions between drugs of different classes, synergy, antagonism, suppression and additivity, has to be determined empirically and cannot be inferred from what is known about the pharmacodynamics or mode of action of these drugs individually. Monte Carlo simulations of within-host treatment incorporating these pharmacodynamic results and clinically relevant refuge subpopulations of bacteria indicate that: (i) the form of drug-drug interactions can profoundly affect the rate at which infections are cleared, (ii) two-drug therapy can prevent treatment failure even when bacteria resistant to single drugs are present

  4. Multi-Generational Kinship, Multiple Mating, and Flexible Modes of Parental Care in a Breeding Population of the Veery (Catharus fuscescens), a Trans-Hemispheric Migratory Songbird

    PubMed Central

    Kalavacharla, Venugopal

    2016-01-01

    We discovered variable modes of parental care in a breeding population of color-banded Veeries (Catharus fuscescens), a Nearctic-Neotropical migratory songbird, long thought to be socially monogamous, and performed a multi-locus DNA microsatellite analysis to estimate parentage and kinship in a sample of 37 adults and 21 offspring. We detected multiple mating in both sexes, and four modes of parental care that varied in frequency within and between years including multiple male feeders at some nests, and males attending multiple nests in the same season, each with a different female. Unlike other polygynandrous systems, genetic evidence indicates that multi-generational patterns of kinship occur among adult Veeries at our study site, and this was corroborated by the capture of an adult male in 2013 that had been banded as a nestling in 2011 at a nest attended by multiple male feeders. All genotyped adults (n = 37) were related to at least one other bird in the sample at the cousin level or greater (r ≥ 0.125), and 81% were related to at least one other bird at the half-sibling level or greater (r ≥ 0.25, range 0.25–0.60). Although our sample size is small, it appears that the kin structure is maintained by natal philopatry in both sexes, and that Veeries avoid mating with close genetic kin. At nests where all adult feeders were genotyped (n = 9), the male(s) were unrelated to the female (mean r = -0.11 ± 0.15), whereas genetic data suggest close kinship (r = 0.254) between two male co-feeders at the nests of two females in 2011, and among three of four females that were mated to the same polygynous male in 2012. To our knowledge, this is the first evidence of polygynandry occurring among multiple generations of close genetic kin on the breeding ground of a Nearctic-Neotropical migratory songbird. PMID:27331399

  5. Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep

    PubMed Central

    2014-01-01

    Background Discerning the traits evolving under neutral conditions from those traits evolving rapidly because of various selection pressures is a great challenge. We propose a new method, composite selection signals (CSS), which unifies the multiple pieces of selection evidence from the rank distribution of its diverse constituent tests. The extreme CSS scores capture highly differentiated loci and underlying common variants hauling excess haplotype homozygosity in the samples of a target population. Results The data on high-density genotypes were analyzed for evidence of an association with either polledness or double muscling in various cohorts of cattle and sheep. In cattle, extreme CSS scores were found in the candidate regions on autosome BTA-1 and BTA-2, flanking the POLL locus and MSTN gene, for polledness and double muscling, respectively. In sheep, the regions with extreme scores were localized on autosome OAR-2 harbouring the MSTN gene for double muscling and on OAR-10 harbouring the RXFP2 gene for polledness. In comparison to the constituent tests, there was a partial agreement between the signals at the four candidate loci; however, they consistently identified additional genomic regions harbouring no known genes. Persuasively, our list of all the additional significant CSS regions contains genes that have been successfully implicated to secondary phenotypic diversity among several subpopulations in our data. For example, the method identified a strong selection signature for stature in cattle capturing selective sweeps harbouring UQCC-GDF5 and PLAG1-CHCHD7 gene regions on BTA-13 and BTA-14, respectively. Both gene pairs have been previously associated with height in humans, while PLAG1-CHCHD7 has also been reported for stature in cattle. In the additional analysis, CSS identified significant regions harbouring multiple genes for various traits under selection in European cattle including polledness, adaptation, metabolism, growth rate, stature

  6. Neural correlates of consciousness reconsidered.

    PubMed

    Neisser, Joseph

    2012-06-01

    It is widely accepted among philosophers that neuroscientists are conducting a search for the neural correlates of consciousness, or NCC. Chalmers (2000) conceptualized this research program as the attempt to correlate the contents of conscious experience with the contents of representations in specific neural populations. A notable claim on behalf of this interpretation is that the neutral language of "correlates" frees us from philosophical disputes over the mind/body relation, allowing the science to move independently. But the experimental paradigms and explanatory canons of neuroscience are not neutral about the mechanical relation between consciousness and the brain. I argue that NCC research is best characterized as an attempt to locate a causally relevant neural mechanism and not as an effort to identify a discrete neural representation, the content of which correlates with some actual experience. It might be said that the first C in "NCC" should stand for "causes" rather than "correlates."

  7. 30-Year Trends in Stroke Rates and Outcome in Auckland, New Zealand (1981-2012): A Multi-Ethnic Population-Based Series of Studies

    PubMed Central

    Feigin, Valery L.; Krishnamurthi, Rita V.; Barker-Collo, Suzanne; McPherson, Kathryn M.; Barber, P. Alan; Parag, Varsha; Arroll, Bruce; Bennett, Derrick A.; Tobias, Martin; Jones, Amy; Witt, Emma; Brown, Paul; Abbott, Max; Bhattacharjee, Rohit; Rush, Elaine; Suh, Flora Minsun; Theadom, Alice; Rathnasabapathy, Yogini; Te Ao, Braden; Parmar, Priya G.; Anderson, Craig; Bonita, Ruth

    2015-01-01

    Background Insufficient data exist on population-based trends in morbidity and mortality to determine the success of prevention strategies and improvements in health care delivery in stroke. The aim of this study was to determine trends in incidence and outcome (1-year mortality, 28-day case-fatality) in relation to management and risk factors for stroke in the multi-ethnic population of Auckland, New Zealand (NZ) over 30-years. Methods Four stroke incidence population-based register studies were undertaken in adult residents (aged ≥15 years) of Auckland NZ in 1981–1982, 1991–1992, 2002–2003 and 2011–2012. All used standard World Health Organization (WHO) diagnostic criteria and multiple overlapping sources of case-ascertainment for hospitalised and non-hospitalised, fatal and non-fatal, new stroke events. Ethnicity was consistently self-identified into four major groups. Crude and age-adjusted (WHO world population standard) annual incidence and mortality with corresponding 95% confidence intervals (CI) were calculated per 100,000 people, assuming a Poisson distribution. Results 5400 new stroke patients were registered in four 12 month recruitment phases over the 30-year study period; 79% were NZ/European, 6% Māori, 8% Pacific people, and 7% were of Asian or other origin. Overall stroke incidence and 1-year mortality decreased by 23% (95% CI 5%-31%) and 62% (95% CI 36%-86%), respectively, from 1981 to 2012. Whilst stroke incidence and mortality declined across all groups in NZ from 1991, Māori and Pacific groups had the slowest rate of decline and continue to experience stroke at a significantly younger age (mean ages 60 and 62 years, respectively) compared with NZ/Europeans (mean age 75 years). There was also a decline in 28-day stroke case fatality (overall by 14%, 95% CI 11%-17%) across all ethnic groups from 1981 to 2012. However, there were significant increases in the frequencies of pre-morbid hypertension, myocardial infarction, and diabetes

  8. A population of serumdeprivation-induced bone marrow stem cells (SD-BMSC) expresses marker typical for embryonic and neural stem cells

    SciTech Connect

    Sauerzweig, Steven Munsch, Thomas; Lessmann, Volkmar; Reymann, Klaus G.; Braun, Holger

    2009-01-01

    The bone marrow represents an easy accessible source of adult stem cells suitable for various cell based therapies. Several studies in recent years suggested the existence of pluripotent stem cells within bone marrow stem cells (BMSC) expressing marker proteins of both embryonic and tissue committed stem cells. These subpopulations were referred to as MAPC, MIAMI and VSEL-cells. Here we describe SD-BMSC (serumdeprivation-induced BMSC) which are induced as a distinct subpopulation after complete serumdeprivation. SD-BMSC are generated from small-sized nestin-positive BMSC (S-BMSC) organized as round-shaped cells in the top layer of BMSC-cultures. The generation of SD-BMSC is caused by a selective proliferation of S-BMSC and accompanied by changes in both morphology and gene expression. SD-BMSC up-regulate not only markers typical for neural stem cells like nestin and GFAP, but also proteins characteristic for embryonic cells like Oct4 and SOX2. We hypothesize, that SD-BMSC like MAPC, MIAMI and VSEL-cells represent derivatives from a single pluripotent stem cell fraction within BMSC exhibiting characteristics of embryonic and tissue committed stem cells. The complete removal of serum might offer a simple way to specifically enrich this fraction of pluripotent embryonic like stem cells in BMSC cultures.

  9. Levels of PAH-DNA adducts in placental tissue and the risk of fetal neural tube defects in a Chinese population.

    PubMed

    Yuan, Yue; Jin, Lei; Wang, Linlin; Li, Zhiwen; Zhang, Le; Zhu, Huiping; Finnell, Richard H; Zhou, Guodong; Ren, Aiguo

    2013-06-01

    We examined the relationship between PAH-DNA adduct levels in the placental tissue, measured by a highly sensitive (32)P-postlabeling assay, and the risk of fetal neural tube defects (NTDs). We further explored the interaction between PAH-DNA adducts and placental PAHs with respect to NTD risk. Placental tissues from 80 NTD-affected pregnancies and 50 uncomplicated normal pregnancies were included in this case-control study. Levels of PAH-DNA adducts were lower in the NTD group (8.12 per 10(8) nucleotides) compared to controls (9.92 per 10(8) nucleotides). PAH-DNA adduct concentrations below the median was associated with a 3-fold increased NTD risk. Women with a low PAH-DNA adduct level in concert with a high placental PAH level resulted in a 10-fold elevated risk of having an NTD-complicated pregnancy. A low level of placental PAH-DNA adducts was associated with an increased risk of NTDs; this risk increased dramatically when a low adduct level was coupled with a high placental PAH concentration.

  10. Physical inactivity is a strong risk factor for stroke in the oldest old: Findings from a multi-ethnic population (the Northern Manhattan Study).

    PubMed

    Willey, Joshua Z; Moon, Yeseon P; Sacco, Ralph L; Greenlee, Heather; Diaz, Keith M; Wright, Clinton B; Elkind, Mitchell Sv; Cheung, Yuen K

    2017-02-01

    Background The fastest growing segment of the population is those age ≥80 who have the highest stroke incidence. Risk factor management is complicated by polypharmacy-related adverse events. Aims To characterize the impact of physical inactivity for stroke by age in a multi-ethnic prospective cohort study (NOMAS, n = 3298). Methods Leisure time physical activity was assessed by a validated questionnaire and our primary exposure was physical inactivity (PI). Participants were followed annually for incident stroke. We fit Cox-proportional hazard models to calculate hazard ratios and 95% confidence intervals (HR 95% CI) for the association of PI and other risk factors with risk of stroke including two-way interaction terms between the primary exposures and age (<80 vs. ≥80). Results The mean age was 69 ± 10.3 years and 562 (17%) were ≥80 at enrolment. PI was common in the cohort (40.8%). Over a median of 14 years, we found 391 strokes. We found a significant interaction of age ≥80 on the risk of stroke with PI ( p = 0.03). In stratified models, PI versus any activity (adjusted HR 1.60, 95%CI 1.05-2.42) was associated with an increased risk of stroke among those ≥80. Conclusion Physical inactivity is a treatable risk factor for stroke among those older than age 80. Improving activity may reduce the risk of stroke in this segment of the population.

  11. Multi-level assessment of chronic toxicity of estuarine sediments with the amphipod Gammarus locusta: II. Organism and population-level endpoints.

    PubMed

    Costa, Filipe O; Neuparth, Teresa; Correia, Ana D; Costa, Maria Helena

    2005-07-01

    This study aimed to test the performance of the amphipod Gammarus locusta (L.) in chronic sediment toxicity tests. It constitutes part of a multi-level assessment of chronic toxicity of estuarine sediments, integrating organism and population-level endpoints with biochemical markers responses. Here we account for organism and population-level effects, while biomarker responses were reported in a companion article. Five moderately contaminated sediments from Sado and Tagus estuaries were tested, comprising 3 muddy and 2 sandy sediments. These sediments either did not show acute toxicity or were diluted with control sediment as much as required to remove acute toxicity. Subsequent chronic tests consisted of 28-day exposures with survival, individual growth and reproductive traits as endpoints. Two of the muddy sediments induced higher growth rates in the amphipods, and improved reproductive traits. This was understood to be a consequence of the amount of organic matter in the sediment, which was nutritionally beneficial to the amphipods, while concurrently decreasing contaminant bioavailability. Biomarker responses did not reveal toxicant-induced stress in amphipods exposed to these sediments. One of the sandy sediments was acutely toxic at 50% dilution, but in contrast stimulated amphipod growth when diluted 75%. This was presumed to be an indication of a hormetic response. Finally the two remaining contaminated sediments showed pronounced chronic toxicity, affecting survival and reproduction. The sex ratio of survivors was highly biased towards females, and offspring production was severely impaired. The particulars of the responses of this amphipod were examined, as well as strengths versus limitations of the sediment test. This study illustrates the utility of this chronic test for toxicity assessment of contaminated estuarine sediments, with potential application all along Atlantic Europe.

  12. Surgery or no surgery: What works best for the kidneys in primary hyperparathyroidism? A study in a multi-ethnic Asian population

    PubMed Central

    Tay, Yu Kwang Donovan; Khoo, Joan; Chandran, Manju

    2016-01-01

    Introduction: Whether parathyroidectomy is more beneficial to renal function when compared to medical therapy or observation in primary hyperparathyroidism (PHPT) is unclear. Neither has this premise been explored in non-Caucasian populations. The estimated glomerular filtration rate (eGFR) threshold below which parathyroid hormone (PTH) levels rise if at all in PHPT has also not been established. We determined if surgery was superior to medical therapy and observation in a multi-ethnic Asian patient population with PHPT and whether there was an eGFR threshold below which PTH levels further increased in them. Methods: Retrospective evaluation of patients with PHPT. Results: There were 68.6% Chinese, 17.4% Malays, 10.7% Indians, and 3.3% Eurasians. The median (interquartile range) follow-up was 18.0 months (4.5–46.8). At last follow-up, eGFR in the surgical (80 ± 30 ml/min) was higher than the medical (52 ± 32 ml/min) or observation groups (48 ± 33 ml/min); P < 0.01. This difference persisted after adjusting for age, gender, ethnicity, pre-intervention eGFR levels, nephrolithiasis, serum calcium, phosphate, urinary calcium, and duration of follow-up; P = 0.035. There was no definite eGFR level below which PTH values rose. Conclusion: Our study provides compelling evidence that in PHPT, surgery may be associated with a better renal outcome compared to medical management or observation. This has to be confirmed through prospective randomized controlled trials and the reasons for this finding have to be elucidated through functional and histological measures. The finding in our study of a lack of a specific eGFR threshold below which PTH levels further rose challenges the concept of a fixed renal threshold for secondary elevations of PTH in PHPT. PMID:26904469

  13. Chaotic time series prediction using artificial neural networks

    SciTech Connect

    Bartlett, E.B.

    1991-12-31

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  14. Chaotic time series prediction using artificial neural networks

    SciTech Connect

    Bartlett, E.B.

    1991-01-01

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  15. Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing.

    PubMed

    Lombardo, Michael V; Auyeung, Bonnie; Holt, Rosemary J; Waldman, Jack; Ruigrok, Amber N V; Mooney, Natasha; Bullmore, Edward T; Baron-Cohen, Simon; Kundu, Prantik

    2016-11-15

    Functional magnetic resonance imaging (fMRI) research is routinely criticized for being statistically underpowered due to characteristically small sample sizes and much larger sample sizes are being increasingly recommended. Additionally, various sources of artifact inherent in fMRI data can have detrimental impact on effect size estimates and statistical power. Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing/theory of mind. Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA). Without smoothing, group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more substantial boosts (40-149%) were observed in non-canonical cerebellar areas. Effect size boosting occurs via reduction of non-BOLD noise at the subject-level and consequent reductions in between-subject variance at the group-level. Smoothing can attenuate ME-ICA-related effect size improvements in certain circumstances. Power simulations demonstrate that ME-ICA-related effect size enhancements enable much higher-powered studies at traditional sample sizes. Cerebellar effects observed after applying ME-ICA may be unobservable with conventional imaging at traditional sample sizes. Thus, ME-ICA allows for principled design-agnostic non-BOLD artifact removal that can substantially improve effect size estimates and statistical power in task-fMRI contexts. ME-ICA could mitigate some issues regarding statistical power in fMRI studies and enable novel discovery of aspects of brain organization that are currently under-appreciated and not well understood.

  16. Chronic neural probe for simultaneous recording of single-unit, multi-unit, and local field potential activity from multiple brain sites

    NASA Astrophysics Data System (ADS)

    Pothof, F.; Bonini, L.; Lanzilotto, M.; Livi, A.; Fogassi, L.; Orban, G. A.; Paul, O.; Ruther, P.

    2016-08-01

    Objective. Drug resistant focal epilepsy can be treated by resecting the epileptic focus requiring a precise focus localisation using stereoelectroencephalography (SEEG) probes. As commercial SEEG probes offer only a limited spatial resolution, probes of higher channel count and design freedom enabling the incorporation of macro and microelectrodes would help increasing spatial resolution and thus open new perspectives for investigating mechanisms underlying focal epilepsy and its treatment. This work describes a new fabrication process for SEEG probes with materials and dimensions similar to clinical probes enabling recording single neuron activity at high spatial resolution. Approach. Polyimide is used as a biocompatible flexible substrate into which platinum electrodes and leads are integrated with a minimal feature size of 5 μm. The polyimide foils are rolled into the cylindrical probe shape at a diameter of 0.8 mm. The resulting probe features match those of clinically approved devices. Tests in saline solution confirmed the probe stability and functionality. Probes were implanted into the brain of one monkey (Macaca mulatta), trained to perform different motor tasks. Suitable configurations including up to 128 electrode sites allow the recording of task-related neuronal signals. Main results. Probes with 32 and 64 electrode sites were implanted in the posterior parietal cortex. Local field potentials and multi-unit activity were recorded as early as one hour after implantation. Stable single-unit activity was achieved for up to 26 days after implantation of a 64-channel probe. All recorded signals showed modulation during task execution. Significance. With the novel probes it is possible to record stable biologically relevant data over a time span exceeding the usual time needed for epileptic focus localisation in human patients. This is the first time that single units are recorded along cylindrical polyimide probes chronically implanted 22 mm deep into the

  17. Global rhythmic activities in hippocampal neural fields and neural coding.

    PubMed

    Ventriglia, Francesco

    2006-01-01

    Global oscillations of the neural field represent some of the most interesting expressions of the hippocampal activity, being related also to learning and memory. To study oscillatory activities of the CA3 field in theta range, a model of this sub-field of Hippocampus has been formulated. The model describes the firing activity of CA3 neuronal populations within the frame of a kinetic theory of neural systems and it has been used for computer simulations. The results show that the propagation of activities induced in the neural field by hippocampal afferents occurs only in narrow time windows confined by inhibitory barrages, whose time-course follows the theta rhythm. Moreover, during each period of a theta wave, the entire CA3 field bears a firing activity with peculiar space-time patterns, a sort of specific imprint, which can induce effects with similar patterns on brain regions driven by the hippocampal formation. The simulation has also demonstrated the ability of medial septum to influence the global activity of the CA3 pyramidal population through the control of the population of inhibitory interneurons. At last, the possible involvement of global population oscillations in neural coding has been discussed.

  18. Mapping small-effect and linked quantitative trait loci for complex traits in backcross or DH populations via a multi-locus GWAS methodology

    PubMed Central

    Wang, Shi-Bo; Wen, Yang-Jun; Ren, Wen-Long; Ni, Yuan-Li; Zhang, Jin; Feng, Jian-Ying; Zhang, Yuan-Ming

    2016-01-01

    Composite interval mapping (CIM) is the most widely-used method in linkage analysis. Its main feature is the ability to control genomic background effects via inclusion of co-factors in its genetic model. However, the result often depends on how the co-factors are selected, especially for small-effect and linked quantitative trait loci (QTL). To address this issue, here we proposed a new method under the framework of genome-wide association studies (GWAS). First, a single-locus random-SNP-effect mixed linear model method for GWAS was used to scan each putative QTL on the genome in backcross or doubled haploid populations. Here, controlling background via selecting markers in the CIM was replaced by estimating polygenic variance. Then, all the peaks in the negative logarithm P-value curve were selected as the positions of multiple putative QTL to be included in a multi-locus genetic model, and true QTL were automatically identified by empirical Bayes. This called genome-wide CIM (GCIM). A series of simulated and real datasets was used to validate the new method. As a result, the new method had higher power in QTL detection, greater accuracy in QTL effect estimation, and stronger robustness under various backgrounds as compared with the CIM and empirical Bayes methods. PMID:27435756

  19. Problem Specific applications for Neural Networks

    DTIC Science & Technology

    1988-12-01

    97 iv List Of Figures Figure Page 1. Neural Network Models ...... ............. 2 2. A Single - Layer Perceptron ..... ........... 4...the network is in use. Three of the most well-known neural networks are the single - layer perceptron , the multi-layer perceptron, and the Kohonen self...three of these networks can accept discrete (binary) or continuous inputs (5:6). 3 Single-Laver Perceptron. The single - layer perceptron (shown in Figure 2

  20. Distinct effects of Hoxa2 overexpression in cranial neural crest populations reveal that the mammalian hyomandibular-ceratohyal boundary maps within the styloid process.

    PubMed

    Kitazawa, Taro; Fujisawa, Kou; Narboux-Nême, Nicolas; Arima, Yuichiro; Kawamura, Yumiko; Inoue, Tsuyoshi; Wada, Youichiro; Kohro, Takahide; Aburatani, Hiroyuki; Kodama, Tatsuhiko; Kim, Ki-Sung; Sato, Takahiro; Uchijima, Yasunobu; Maeda, Kazuhiro; Miyagawa-Tomita, Sachiko; Minoux, Maryline; Rijli, Filippo M; Levi, Giovanni; Kurihara, Yukiko; Kurihara, Hiroki

    2015-06-15

    Most gnathostomata craniofacial structures derive from pharyngeal arches (PAs), which are colonized by cranial neural crest cells (CNCCs). The anteroposterior and dorsoventral identities of CNCCs are defined by the combinatorial expression of Hox and Dlx genes. The mechanisms associating characteristic Hox/Dlx expression patterns with the topology and morphology of PAs derivatives are only partially known; a better knowledge of these processes might lead to new concepts on the origin of taxon-specific craniofacial morphologies and of certain craniofacial malformations. Here we show that ectopic expression of Hoxa2 in Hox-negative CNCCs results in distinct phenotypes in different CNCC subpopulations. Namely, while ectopic Hoxa2 expression is sufficient for the morphological and molecular transformation of the first PA (PA1) CNCC derivatives into the second PA (PA2)-like structures, this same genetic alteration does not provoke the transformation of derivatives of other CNCC subpopulations, but severely impairs their development. Ectopic Hoxa2 expression results in the transformation of the proximal Meckel's cartilage and of the malleus, two ventral PA1 CNCCs derivatives, into a supernumerary styloid process (SP), a PA2-derived mammalian-specific skeletal structure. These results, together with experiments to inactivate and ectopically activate the Edn1-Dlx5/6 pathway, indicate a dorsoventral PA2 (hyomandibular/ceratohyal) boundary passing through the middle of the SP. The present findings suggest context-dependent function of Hoxa2 in CNCC regional specification and morphogenesis, and provide novel insights into the evolution of taxa-specific patterning of PA-derived structures.

  1. Deep multi-frequency radio imaging in the Lockman Hole using the GMRT and VLA - I. The nature of the sub-mJy radio population

    NASA Astrophysics Data System (ADS)

    Ibar, Edo; Ivison, R. J.; Biggs, A. D.; Lal, D. V.; Best, P. N.; Green, D. A.

    2009-07-01

    In the run up to routine observations with the upcoming generation of radio facilities, the nature of sub-mJy radio population has been hotly debated. Here, we describe multi-frequency data designed to probe the emission mechanism that dominates in these faint radio sources. Our analysis is based on observations of the Lockman Hole using the Giant Metrewave Radio Telescope (GMRT) - the deepest 610-MHz imaging yet reported - together with 1.4-GHz imaging from the Very Large Array (VLA), well matched in resolution and sensitivity to the GMRT data: σ610MHz ~ 15μJybeam-1, σ1.4GHz ~ 6μJybeam-1, full width at half-maximum (FWHM) ~ 5arcsec. The GMRT and VLA data are cross-matched to obtain the radio spectral indices for the faint radio emitters. Statistical analyses show no clear evolution for the median spectral index, α610MHz1.4GHz (where Sν ~ να), as a function of flux density. α610MHz1.4GHz is found to be approximately -0.6 to -0.7, based on an almost unbiased 10σ criterion, down to a flux level of S1.4GHz >~100μJy. The fraction of inverted spectrum sources (α610MHz1.4GHz > 0) is less than 10 per cent. The results suggest that the most prevalent emission mechanism in the sub-mJy regime is optically thin synchrotron, ruling out a dominant flat spectrum or ultra-steep spectrum radio population. The spectral index distribution has a significant scatter, Δα ~ 0.4-0.5, which suggests a mixture of different populations at all flux levels. Spectroscopic classification of radio sources with X-ray emission has allowed us to estimate that the fraction of radio-quiet active galactic nuclei (AGN) at 30μJy <~ S1.4GHz <300μJy is roughly 25 +/- 10 per cent, suggesting that star-forming galaxies dominate the sub-mJy regime.

  2. NeuroMEMS: Neural Probe Microtechnologies

    PubMed Central

    HajjHassan, Mohamad; Chodavarapu, Vamsy; Musallam, Sam

    2008-01-01

    Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer's, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultra-long multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies. PMID:27873894

  3. Neural Network Function Classifier

    DTIC Science & Technology

    2003-02-07

    neural network sets. Each of the neural networks in a particular set is trained to recognize a particular data set type. The best function representation of the data set is determined from the neural network output. The system comprises sets of trained neural networks having neural networks trained to identify different types of data. The number of neural networks within each neural network set will depend on the number of function types that are represented. The system further comprises

  4. Neural Engineering

    NASA Astrophysics Data System (ADS)

    He, Bin

    About the Series: Bioelectric Engineering presents state-of-the-art discussions on modern biomedical engineering with respect to applications of electrical engineering and information technology in biomedicine. This focus affirms Springer's commitment to publishing important reviews of the broadest interest to biomedical engineers, bioengineers, and their colleagues in affiliated disciplines. Recent volumes have covered modeling and imaging of bioelectric activity, neural engineering, biosignal processing, bionanotechnology, among other topics.

  5. Description of interatomic interactions with neural networks

    NASA Astrophysics Data System (ADS)

    Hajinazar, Samad; Shao, Junping; Kolmogorov, Aleksey N.

    Neural networks are a promising alternative to traditional classical potentials for describing interatomic interactions. Recent research in the field has demonstrated how arbitrary atomic environments can be represented with sets of general functions which serve as an input for the machine learning tool. We have implemented a neural network formalism in the MAISE package and developed a protocol for automated generation of accurate models for multi-component systems. Our tests illustrate the performance of neural networks and known classical potentials for a range of chemical compositions and atomic configurations. Supported by NSF Grant DMR-1410514.

  6. Pricing financial derivatives with neural networks

    NASA Astrophysics Data System (ADS)

    Morelli, Marco J.; Montagna, Guido; Nicrosini, Oreste; Treccani, Michele; Farina, Marco; Amato, Paolo

    2004-07-01

    Neural network algorithms are applied to the problem of option pricing and adopted to simulate the nonlinear behavior of such financial derivatives. Two different kinds of neural networks, i.e. multi-layer perceptrons and radial basis functions, are used and their performances compared in detail. The analysis is carried out both for standard European options and American ones, including evaluation of the Greek letters, necessary for hedging purposes. Detailed numerical investigation show that, after a careful phase of training, neural networks are able to predict the value of options and Greek letters with high accuracy and competitive computational time.

  7. Genetic algorithm for neural networks optimization

    NASA Astrophysics Data System (ADS)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  8. Neural crest contributions to the lamprey head

    NASA Technical Reports Server (NTRS)

    McCauley, David W.; Bronner-Fraser, Marianne

    2003-01-01

    The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

  9. Distribution and factors associated with Salmonella enterica genotypes in a diverse population of humans and animals in Qatar using multi-locus sequence typing (MLST).

    PubMed

    Chang, Yu C; Scaria, Joy; Ibraham, Mariamma; Doiphode, Sanjay; Chang, Yung-Fu; Sultan, Ali; Mohammed, Hussni O

    2016-01-01

    Salmonella enterica is one of the most commonly reported causes of bacterial foodborne illness around the world. Understanding the sources of this pathogen and the associated factors that exacerbate its risk to humans will help in developing risk mitigation strategies. The genetic relatedness among Salmonella isolates recovered from human gastroenteritis cases and food animals in Qatar were investigated in the hope of shedding light on these sources, their possible transmission routes, and any associated factors. A repeat cross-sectional study was conducted in which the samples and associated data were collected from both populations (gastroenteritis cases and animals). Salmonella isolates were initially analyzed using multi-locus sequence typing (MLST) to investigate the genetic diversity and clonality. The relatedness among the isolates was assessed using the minimum spanning tree (MST). Twenty-seven different sequence types (STs) were identified in this study; among them, seven were novel, including ST1695, ST1696, ST1697, ST1698, ST1699, ST1702, and ST1703. The pattern of overall ST distribution was diverse; in particular, it was revealed that ST11 and ST19 were the most common sequence types, presenting 29.5% and 11.5% within the whole population. In addition, 20 eBurst Groups (eBGs) were identified in our data, which indicates that ST11 and ST19 belonged to eBG4 and eBG1, respectively. In addition, the potential association between the putative risk factors and eBGs were evaluated. There was no significant clustering of these eBGs by season; however, a significant association was identified in terms of nationality in that Qataris were six times more likely to present with eBG1 compared to non-Qataris. In the MST analysis, four major clusters were presented, namely, ST11, ST19, ST16, and ST31. The linkages between the clusters alluded to a possible transmission route. The results of the study have provided insight into the ST distributions of S. enterica and

  10. Multi-virulence-locus sequence typing of Staphylococcus lugdunensis generates results consistent with a clonal population structure and is reliable for epidemiological typing.

    PubMed

    Didi, Jennifer; Lemée, Ludovic; Gibert, Laure; Pons, Jean-Louis; Pestel-Caron, Martine

    2014-10-01

    Staphylococcus lugdunensis is an emergent virulent coagulase-negative staphylococcus responsible for severe infections similar to those caused by Staphylococcus aureus. To understand its potentially pathogenic capacity and have further detailed knowledge of the molecular traits of this organism, 93 isolates from various geographic origins were analyzed by multi-virulence-locus sequence typing (MVLST), targeting seven known or putative virulence-associated loci (atlLR2, atlLR3, hlb, isdJ, SLUG_09050, SLUG_16930, and vwbl). The polymorphisms of the putative virulence-associated loci were moderate and comparable to those of the housekeeping genes analyzed by multilocus sequence typing (MLST). However, the MVLST scheme generated 43 virulence types (VTs) compared to 20 sequence types (STs) based on MLST, indicating that MVLST was significantly more discriminating (Simpson's index [D], 0.943). No hypervirulent lineage or cluster specific to carriage strains was defined. The results of multilocus sequence analysis of known and putative virulence-associated loci are consistent with a clonal population structure for S. lugdunensis, suggesting a coevolution of these genes with housekeeping genes. Indeed, the nonsynonymous to synonymous evolutionary substitutions (dN/dS) ratio, the Tajima's D test, and Single-likelihood ancestor counting (SLAC) analysis suggest that all virulence-associated loci were under negative selection, even atlLR2 (AtlL protein) and SLUG_16930 (FbpA homologue), for which the dN/dS ratios were higher. In addition, this analysis of virulence-associated loci allowed us to propose a trilocus sequence typing scheme based on the intragenic regions of atlLR3, isdJ, and SLUG_16930, which is more discriminant than MLST for studying short-term epidemiology and further characterizing the lineages of the rare but highly pathogenic S. lugdunensis.

  11. Multi-Centre Evaluation of the Determine HIV Combo Assay when Used for Point of Care Testing in a High Risk Clinic-Based Population

    PubMed Central

    Conway, Damian P.; Holt, Martin; McNulty, Anna; Couldwell, Deborah L.; Smith, Don E.; Davies, Stephen C.; Cunningham, Philip; Keen, Phillip; Guy, Rebecca

    2014-01-01

    Background Determine HIV Combo (DHC) is the first point of care assay designed to increase sensitivity in early infection by detecting both HIV antibody and antigen. We conducted a large multi-centre evaluation of DHC performance in Sydney sexual health clinics. Methods We compared DHC performance (overall, by test component and in early infection) with conventional laboratory HIV serology (fourth generation screening immunoassay, supplementary HIV antibody, p24 antigen and Western blot tests) when testing gay and bisexual men attending four clinic sites. Early infection was defined as either acute or recent HIV infection acquired within the last six months. Results Of 3,190 evaluation specimens, 39 were confirmed as HIV-positive (12 with early infection) and 3,133 were HIV-negative by reference testing. DHC sensitivity was 87.2% overall and 94.4% and 0% for the antibody and antigen components, respectively. Sensitivity in early infection was 66.7% (all DHC antibody reactive) and the DHC antigen component detected none of nine HIV p24 antigen positive specimens. Median HIV RNA was higher in false negative than true positive cases (238,025 vs. 37,591 copies/ml; p = 0.022). Specificity overall was 99.4% with the antigen component contributing to 33% of false positives. Conclusions The DHC antibody component detected two thirds of those with early infection, while the DHC antigen component did not enhance performance during point of care HIV testing in a high risk clinic-based population. PMID:24714441

  12. Complexities of holistic community-based participatory research for a low income, multi-ethnic population exposed to multiple built-environment stressors in Worcester, Massachusetts.

    PubMed

    Downs, Timothy J; Ross, Laurie; Patton, Suzanne; Rulnick, Sarah; Sinha, Deb; Mucciarone, Danielle; Calvache, Maria; Parmenter, Sarah; Subedi, Rajendra; Wysokenski, Donna; Anderson, Erin; Dezan, Rebecca; Lowe, Kate; Bowen, Jennifer; Tejani, Amee; Piersanti, Kelly; Taylor, Octavia; Goble, Robert

    2009-11-01

    Low income, multi-ethnic communities in Main South/Piedmont neighborhoods of Worcester, Massachusetts are exposed to cumulative, chronic built-environment stressors, and have limited capacity to respond, magnifying their vulnerability to adverse health outcomes. "Neighborhood STRENGTH", our community-based participatory research (CBPR) project, comprised four partners: a youth center; an environmental non-profit; a community-based health center; and a university. Unlike most CBPR projects that are single topic-focused, our 'holistic', systems-based project targeted five priorities. The three research-focused/action-oriented components were: (1) participatory monitoring of indoor and outdoor pollution; (2) learning about health needs and concerns of residents through community-based listening sessions; (3) engaging in collaborative survey work, including a household vulnerability survey and an asthma prevalence survey for schoolchildren. The two action-focused/research-informed components were: (4) tackling persistent street trash and illegal dumping strategically; and (5) educating and empowering youth to promote environmental justice. We used a coupled CBPR-capacity building approach to design, vulnerability theory to frame, and mixed methods: quantitative environmental testing and qualitative surveys. Process and outcomes yielded important lessons: vulnerability theory helps frame issues holistically; having several topic-based projects yielded useful information, but was hard to manage and articulate to the public; access to, and engagement with, the target population was very difficult and would have benefited greatly from having representative residents who were paid at the partners' table. Engagement with residents and conflict burden varied highly across components. Notwithstanding, we built enabling capacity, strengthened our understanding of vulnerability, and are able to share valuable experiential knowledge.

  13. “Complexities of holistic community based participatory research for a low-income, multi-ethnic population exposed to multiple built-environment stressors in Worcester, Massachusetts”

    PubMed Central

    Downs, Timothy J.; Ross, Laurie; Patton, Suzanne; Rulnick, Sarah; Sinha, Deb; Mucciarone, Danielle; Calvache, Maria; Parmenter, Sarah; Subedi, Rajendra; Wysokenski, Donna; Anderson, Erin; Dezan, Rebecca; Lowe, Kate; Bowen, Jennifer; Tejani, Amee; Piersanti, Kelly; Taylor, Octavia; Goble, Robert

    2009-01-01

    Low income, multi-ethnic communities in Main South/Piedmont neighborhoods of Worcester, Massachusetts are exposed to cumulative, chronic built-environment stressors, and have limited capacity to respond, magnifying their vulnerability to adverse health outcomes. “Neighborhood STRENGTH”, our community based participatory research (CBPR) project, comprised four partners: a youth center; an environmental non-profit; a community based health center; and a university. Unlike most CBPR projects that are single topic-focused, our ‘holistic’, systems-based project targeted five priorities. The three research-focused/action-oriented components were: 1) participatory monitoring of indoor and outdoor pollution; 2) learning about health needs and concerns of residents through community based listening sessions; and 3) engaging in collaborative survey work, including a household vulnerability survey and an asthma prevalence survey for schoolchildren. The two action-focused/research-informed components were: 4) tackling persistent street trash and illegal dumping strategically; and 5) educating and empowering youth to promote environmental justice. We used a coupled CBPR-capacity building approach to design, vulnerability theory to frame, and mixed methods: quantitative environmental testing and qualitative surveys. Process and outcomes yielded important lessons: vulnerability theory helps frame issues holistically; having several topic-based projects yielded useful information, but was hard to manage and articulate to the public; access to, and engagement with, the target population was very difficult and would have benefited greatly from having representative residents who were paid at the partners' table. Engagement with residents and conflict burden varied highly across components. Notwithstanding, we built enabling capacity, strengthened our understanding of vulnerability, and are able to share valuable experiential knowledge. PMID:19762014

  14. Multi-Virulence-Locus Sequence Typing of Staphylococcus lugdunensis Generates Results Consistent with a Clonal Population Structure and Is Reliable for Epidemiological Typing

    PubMed Central

    Didi, Jennifer; Lemée, Ludovic; Gibert, Laure; Pons, Jean-Louis

    2014-01-01

    Staphylococcus lugdunensis is an emergent virulent coagulase-negative staphylococcus responsible for severe infections similar to those caused by Staphylococcus aureus. To understand its potentially pathogenic capacity and have further detailed knowledge of the molecular traits of this organism, 93 isolates from various geographic origins were analyzed by multi-virulence-locus sequence typing (MVLST), targeting seven known or putative virulence-associated loci (atlLR2, atlLR3, hlb, isdJ, SLUG_09050, SLUG_16930, and vwbl). The polymorphisms of the putative virulence-associated loci were moderate and comparable to those of the housekeeping genes analyzed by multilocus sequence typing (MLST). However, the MVLST scheme generated 43 virulence types (VTs) compared to 20 sequence types (STs) based on MLST, indicating that MVLST was significantly more discriminating (Simpson's index [D], 0.943). No hypervirulent lineage or cluster specific to carriage strains was defined. The results of multilocus sequence analysis of known and putative virulence-associated loci are consistent with a clonal population structure for S. lugdunensis, suggesting a coevolution of these genes with housekeeping genes. Indeed, the nonsynonymous to synonymous evolutionary substitutions (dN/dS) ratio, the Tajima's D test, and Single-likelihood ancestor counting (SLAC) analysis suggest that all virulence-associated loci were under negative selection, even atlLR2 (AtlL protein) and SLUG_16930 (FbpA homologue), for which the dN/dS ratios were higher. In addition, this analysis of virulence-associated loci allowed us to propose a trilocus sequence typing scheme based on the intragenic regions of atlLR3, isdJ, and SLUG_16930, which is more discriminant than MLST for studying short-term epidemiology and further characterizing the lineages of the rare but highly pathogenic S. lugdunensis. PMID:25078912

  15. Using Artificial Neural Networks to Predict Malignancy of Ovarian Tumors

    DTIC Science & Technology

    2007-11-02

    This paper discusses the application of artificial neural networks (ANNs) to preoperative discrimination between benign and malignant ovarian tumors...With the input variables selected by logistic regression analysis, two types of feed-forward neural networks were built: multi-layer perceptrons

  16. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, L.J.; Keller, P.E.

    1997-10-28

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

  17. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  18. Optimizing neural networks for river flow forecasting - Evolutionary Computation methods versus the Levenberg-Marquardt approach

    NASA Astrophysics Data System (ADS)

    Piotrowski, Adam P.; Napiorkowski, Jarosław J.

    2011-09-01

    SummaryAlthough neural networks have been widely applied to various hydrological problems, including river flow forecasting, for at least 15 years, they have usually been trained by means of gradient-based algorithms. Recently nature inspired Evolutionary Computation algorithms have rapidly developed as optimization methods able to cope not only with non-differentiable functions but also with a great number of local minima. Some of proposed Evolutionary Computation algorithms have been tested for neural networks training, but publications which compare their performance with gradient-based training methods are rare and present contradictory conclusions. The main goal of the present study is to verify the applicability of a number of recently developed Evolutionary Computation optimization methods, mostly from the Differential Evolution family, to multi-layer perceptron neural networks training for daily rainfall-runoff forecasting. In the present paper eight Evolutionary Computation methods, namely the first version of Differential Evolution (DE), Distributed DE with Explorative-Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization and Efficient Population Utilization Strategy Particle Swarm Optimization are tested against the Levenberg-Marquardt algorithm - probably the most efficient in terms of speed and success rate among gradient-based methods. The Annapolis River catchment was selected as the area of this study due to its specific climatic conditions, characterized by significant seasonal changes in runoff, rapid floods, dry summers, severe winters with snowfall, snow melting, frequent freeze and thaw, and presence of river ice - conditions which make flow forecasting more troublesome. The overall performance of the Levenberg-Marquardt algorithm and the DE with Global and Local Neighbors method for neural networks training turns out to be superior to other

  19. Signaling pathways and tissue interactions in neural plate border formation.

    PubMed

    Schille, Carolin; Schambony, Alexandra

    2017-01-01

    The neural crest is a transient cell population that gives rise to various cell types of multiple tissues and organs in the vertebrate embryo. Neural crest cells arise from the neural plate border, a region localized at the lateral borders of the prospective neural plate. Temporally and spatially coordinated interaction with the adjacent tissues, the non-neural ectoderm, the neural plate and the prospective dorsolateral mesoderm, is required for neural plate border specification. Signaling molecules, namely BMP, Wnt and FGF ligands and corresponding antagonists are derived from these tissues and interact to induce the expression of neural plate border specific genes. The present mini-review focuses on the current understanding of how the NPB territory is formed and accentuates the need for coordinated interaction of BMP and Wnt signaling pathways and precise tissue communication that are required for the definition of the prospective NC in the competent ectoderm.

  20. Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function

    SciTech Connect

    Ericson, Milton Nance; McKnight, Timothy E; Melechko, Anatoli Vasilievich; Simpson, Michael L; Morrison, Barclay; Yu, Zhe

    2012-01-01

    Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface can potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single cell level and even inside the cell.

  1. Constraining sub-parsec binary supermassive black holes in quasars with multi-epoch spectroscopy. II. The population with kinematically offset broad Balmer emission lines

    SciTech Connect

    Liu, Xin; Shen, Yue; Bian, Fuyan; Loeb, Abraham; Tremaine, Scott

    2014-07-10

    A small fraction of quasars have long been known to show bulk velocity offsets (of a few hundred to thousands of km s{sup –1}) in the broad Balmer lines with respect to the systemic redshift of the host galaxy. Models to explain these offsets usually invoke broad-line region gas kinematics/asymmetry around single black holes (BHs), orbital motion of massive (∼sub-parsec (sub-pc)) binary black holes (BBHs), or recoil BHs, but single-epoch spectra are unable to distinguish between these scenarios. The line-of-sight (LOS) radial velocity (RV) shifts from long-term spectroscopic monitoring can be used to test the BBH hypothesis. We have selected a sample of 399 quasars with kinematically offset broad Hβ lines from the Sloan Digital Sky Survey (SDSS) Seventh Data Release quasar catalog, and have conducted second-epoch optical spectroscopy for 50 of them. Combined with the existing SDSS spectra, the new observations enable us to constrain the LOS RV shifts of broad Hβ lines with a rest-frame baseline of a few years to nearly a decade. While previous work focused on objects with extreme velocity offset (>10{sup 3} km s{sup –1}), we explore the parameter space with smaller (a few hundred km s{sup –1}) yet significant offsets (99.7% confidence). Using cross-correlation analysis, we detect significant (99% confidence) radial accelerations in the broad Hβ lines in 24 of the 50 objects, of ∼10-200 km s{sup –1} yr{sup –1} with a median measurement uncertainty of ∼10 km s{sup –1} yr{sup –1}, implying a high fraction of variability of the broad-line velocity on multi-year timescales. We suggest that 9 of the 24 detections are sub-pc BBH candidates, which show consistent velocity shifts independently measured from a second broad line (either Hα or Mg II) without significant changes in the broad-line profiles. Combining the results on the general quasar population studied in Paper I, we find a tentative anti-correlation between the velocity offset in the

  2. Metal mixtures in urban and rural populations in the US: The Multi-Ethnic Study of Atherosclerosis and the Strong Heart Study☆

    PubMed Central

    Pang, Yuanjie; Peng, Roger D.; Jones, Miranda R.; Francesconi, Kevin A.; Goessler, Walter; Howard, Barbara V.; Umans, Jason G.; Best, Lyle G.; Guallar, Eliseo; Post, Wendy S.; Kaufman, Joel D.; Vaidya, Dhananjay; Navas-Acien, Ana

    2016-01-01

    Background Natural and anthropogenic sources of metal exposure differ for urban and rural residents. We searched to identify patterns of metal mixtures which could suggest common environmental sources and/or metabolic pathways of different urinary metals, and compared metal-mixtures in two population-based studies from urban/sub-urban and rural/town areas in the US: the Multi-Ethnic Study of Atherosclerosis (MESA) and the Strong Heart Study (SHS). Methods We studied a random sample of 308 White, Black, Chinese-American, and Hispanic participants in MESA (2000–2002) and 277 American Indian participants in SHS (1998–2003). We used principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) to evaluate nine urinary metals (antimony [Sb], arsenic [As], cadmium [Cd], lead [Pb], molybdenum [Mo], selenium [Se], tungsten [W], uranium [U] and zinc [Zn]). For arsenic, we used the sum of inorganic and methylated species (∑As). Results All nine urinary metals were higher in SHS compared to MESA participants. PCA and CA revealed the same patterns in SHS, suggesting 4 distinct principal components (PC) or clusters (∑As-U-W, Pb-Sb, Cd-Zn, Mo-Se). In MESA, CA showed 2 large clusters (∑As-Mo-Sb-U-W, Cd-Pb-Se-Zn), while PCA showed 4 PCs (Sb-U-W, Pb-Se-Zn, Cd-Mo, ∑As). LDA indicated that ∑As, U, W, and Zn were the most discriminant variables distinguishing MESA and SHS participants. Conclusions In SHS, the ∑As-U-W cluster and PC might reflect groundwater contamination in rural areas, and the Cd-Zn cluster and PC could reflect common sources from meat products or metabolic interactions. Among the metals assayed, ∑As, U, W and Zn differed the most between MESA and SHS, possibly reflecting disproportionate exposure from drinking water and perhaps food in rural Native communities compared to urban communities around the US. PMID:26945432

  3. Artificial Neural Networks: A Novel Approach to Analysing the Nutritional Ecology of a Blowfly Species, Chrysomya megacephala

    PubMed Central

    Bianconi, André; Zuben, Cláudio J. Von; Serapião, Adriane B. de S.; Govone, José S.

    2010-01-01

    Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies. PMID:20569135

  4. An optimization spiking neural p system for approximately solving combinatorial optimization problems.

    PubMed

    Zhang, Gexiang; Rong, Haina; Neri, Ferrante; Pérez-Jiménez, Mario J

    2014-08-01

    Membrane systems (also called P systems) refer to the computing models abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design a P system for directly obtaining the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to experimentally prove the viability and effectiveness of the proposed neural system.

  5. Signal dispersion within a hippocampal neural network

    NASA Technical Reports Server (NTRS)

    Horowitz, J. M.; Mates, J. W. B.

    1975-01-01

    A model network is described, representing two neural populations coupled so that one population is inhibited by activity it excites in the other. Parameters and operations within the model represent EPSPs, IPSPs, neural thresholds, conduction delays, background activity and spatial and temporal dispersion of signals passing from one population to the other. Simulations of single-shock and pulse-train driving of the network are presented for various parameter values. Neuronal events from 100 to 300 msec following stimulation are given special consideration in model calculations.

  6. Neural Network Studies

    DTIC Science & Technology

    1993-07-01

    basic useful theorems and general rules which apply to neural networks (in ’Overview of Neural Network Theory’), studies of training time as the...The Neural Network , Bayes- Gaussian, and k-Nearest Neighbor Classifiers’), an analysis of fuzzy logic and its relationship to neural network (in ’Fuzzy

  7. Electronic Neural Networks

    NASA Technical Reports Server (NTRS)

    Thakoor, Anil

    1990-01-01

    Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

  8. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

  9. Adaptive neural networks for mobile robotic control

    NASA Astrophysics Data System (ADS)

    Burnett, Jeff R.; Dagli, Cihan H.

    2001-03-01

    Movement of a differential drive robot has non-linear dependence on the current position and orientation. A controller must be able to deal with the non-linearity of the plant. The controller must either linearize the plant and deal with special cases, or be non-linear itself. Once the controller is designed, implementation on a real robotic platform presents challenges due to the varying parameters of the plant. Robots of the same model may have different motor frictions. The surface the robot maneuvers on may change e.g. carpet to tile. Batteries will drain, providing less power over time. A feed-forward neural network controller could overcome these challenges. The network could learn the non- linearities of the plant and monitor the error for parameter changes and adapt to them. In this manner, a single controller can be designed for an ideal robot, and then used to populate a multi-robot colony without manually fine tuning the controller for each robot. This paper shall demonstrate such a controller, outlining design in simulation and implementation on Khepera robotic platforms.

  10. Specific domains of FoxD4/5 activate and repress neural transcription factor genes to control the progression of immature neural ectoderm to differentiating neural plate.

    PubMed

    Neilson, Karen M; Klein, Steven L; Mhaske, Pallavi; Mood, Kathy; Daar, Ira O; Moody, Sally A

    2012-05-15

    FoxD4/5, a forkhead transcription factor, plays a critical role in establishing and maintaining the embryonic neural ectoderm. It both up-regulates genes that maintain a proliferative, immature neural ectoderm and down-regulates genes that promote the transition to a differentiating neural plate. We constructed deletion and mutant versions of FoxD4/5 to determine which domains are functionally responsible for these opposite activities, which regulate the critical developmental transition of neural precursors to neural progenitors to differentiating neural plate cells. Our results show that up-regulation of genes that maintain immature neural precursors (gem, zic2) requires the Acidic blob (AB) region in the N-terminal portion of the protein, indicating that the AB is the transactivating domain. Additionally, down-regulation of those genes that promote the transition to neural progenitors (sox) and those that lead to neural differentiation (zic, irx) involves: 1) an interaction with the Groucho co-repressor at the Eh-1 motif in the C-terminus; and 2) sequence downstream of this motif. Finally, the ability of FoxD4/5 to induce the ectopic expression of neural precursor genes in the ventral ectoderm also involves both the AB region and the Eh-1 motif; FoxD4/5 accomplishes ectopic neural induction by both activating neural precursor genes and repressing BMP signaling and epidermal genes. This study identifies the specific, conserved domains of the FoxD4/5 protein that allow this single transcription factor to regulate a network of genes that controls the transition of a proliferative neural ectodermal population to a committed neural plate population poised to begin differentiation.

  11. Neural Darwinism and consciousness.

    PubMed

    Seth, Anil K; Baars, Bernard J

    2005-03-01

    Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the 'dynamic core'). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.

  12. Complex Chebyshev-polynomial-based unified model (CCPBUM) neural networks

    NASA Astrophysics Data System (ADS)

    Jeng, Jin-Tsong; Lee, Tsu-Tian

    1998-03-01

    In this paper, we propose complex Chebyshev Polynomial Based unified model neural network for the approximation of complex- valued function. Based on this approximate transformable technique, we have derived the relationship between the single-layered neural network and multi-layered perceptron neural network. It is shown that the complex Chebyshev Polynomial Based unified model neural network can be represented as a functional link network that are based on Chebyshev polynomial. We also derived a new learning algorithm for the proposed network. It turns out that the complex Chebyshev Polynomial Based unified model neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional complex feedforward/recurrent neural network.

  13. A neural network approach to lung nodule segmentation

    NASA Astrophysics Data System (ADS)

    Hu, Yaoxiu; Menon, Prahlad G.

    2016-03-01

    Computed tomography (CT) imaging is a sensitive and specific lung cancer screening tool for the high-risk population and shown to be promising for detection of lung cancer. This study proposes an automatic methodology for detecting and segmenting lung nodules from CT images. The proposed methods begin with thorax segmentation, lung extraction and reconstruction of the original shape of the parenchyma using morphology operations. Next, a multi-scale hessian-based vesselness filter is applied to extract lung vasculature in lung. The lung vasculature mask is subtracted from the lung region segmentation mask to extract 3D regions representing candidate pulmonary nodules. Finally, the remaining structures are classified as nodules through shape and intensity features which are together used to train an artificial neural network. Up to 75% sensitivity and 98% specificity was achieved for detection of lung nodules in our testing dataset, with an overall accuracy of 97.62%+/-0.72% using 11 selected features as input to the neural network classifier, based on 4-fold cross-validation studies. Receiver operator characteristics for identifying nodules revealed an area under curve of 0.9476.

  14. Phenotypic chemical screening using a zebrafish neural crest EMT reporter identifies retinoic acid as an inhibitor of epithelial morphogenesis

    PubMed Central

    Jimenez, Laura; Wang, Jindong; Morrison, Monique A.; Whatcott, Clifford; Soh, Katherine K.; Warner, Steven; Bearss, David; Jette, Cicely A.; Stewart, Rodney A.

    2016-01-01

    ABSTRACT The epithelial-to-mesenchymal transition (EMT) is a highly conserved morphogenetic program essential for embryogenesis, regeneration and cancer metastasis. In cancer cells, EMT also triggers cellular reprogramming and chemoresistance, which underlie disease relapse and decreased survival. Hence, identifying compounds that block EMT is essential to prevent or eradicate disseminated tumor cells. Here, we establish a whole-animal-based EMT reporter in zebrafish for rapid drug screening, called Tg(snai1b:GFP), which labels epithelial cells undergoing EMT to produce sox10-positive neural crest (NC) cells. Time-lapse and lineage analysis of Tg(snai1b:GFP) embryos reveal that cranial NC cells delaminate from two regions: an early population delaminates adjacent to the neural plate, whereas a later population delaminates from within the dorsal neural tube. Treating Tg(snai1b:GFP) embryos with candidate small-molecule EMT-inhibiting compounds identified TP-0903, a multi-kinase inhibitor that blocked cranial NC cell delamination in both the lateral and medial populations. RNA sequencing (RNA-Seq) analysis and chemical rescue experiments show that TP-0903 acts through stimulating retinoic acid (RA) biosynthesis and RA-dependent transcription. These studies identify TP-0903 as a new therapeutic for activating RA in vivo and raise the possibility that RA-dependent inhibition of EMT contributes to its prior success in eliminating disseminated cancer cells. PMID:26794130

  15. Real-time prediction of neuronal population spiking activity using FPGA.

    PubMed

    Li, Will X Y; Cheung, Ray C C; Chan, Rosa H M; Song, Dong; Berger, Theodore W

    2013-08-01

    A field-programmable gate array (FPGA)-based hardware architecture is proposed and utilized for prediction of neuronal population firing activity. The hardware system adopts the multi-input multi-output (MIMO) generalized Laguerre-Volterra model (GLVM) structure to describe the nonlinear dynamic neural process of mammalian brain and can switch between the two important functions: estimation of GLVM coefficients and prediction of neuronal population spiking activity (model outputs). The model coefficients are first estimated using the in-sample training data; then the output is predicted using the out-of-sample testing data and the field estimated coefficients. Test results show that compared with previous software implementation of the generalized Laguerre-Volterra algorithm running on an Intel Core i7-2620M CPU, the FPGA-based hardware system can achieve up to 2.66×10(3) speedup in doing model parameters estimation and 698.84 speedup in doing model output prediction. The proposed hardware platform will facilitate research on the highly nonlinear neural process of the mammal brain, and the cognitive neural prosthesis design.

  16. Recent Advances in Neural Recording Microsystems

    PubMed Central

    Gosselin, Benoit

    2011-01-01

    The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field. PMID:22163863

  17. Neural Tube Defects

    MedlinePlus

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the first month ... she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In spina ...

  18. Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior.

    PubMed

    Panzeri, Stefano; Harvey, Christopher D; Piasini, Eugenio; Latham, Peter E; Fellin, Tommaso

    2017-02-08

    The two basic processes underlying perceptual decisions-how neural responses encode stimuli, and how they inform behavioral choices-have mainly been studied separately. Thus, although many spatiotemporal features of neural population activity, or "neural codes," have been shown to carry sensory information, it is often unknown whether the brain uses these features for perception. To address this issue, we propose a new framework centered on redefining the neural code as the neural features that carry sensory information used by the animal to drive appropriate behavior; that is, the features that have an intersection between sensory and choice information. We show how this framework leads to a new statistical analysis of neural activity recorded during behavior that can identify such neural codes, and we discuss how to combine intersection-based analysis of neural recordings with intervention on neural activity to determine definitively whether specific neural activity features are involved in a task.

  19. Wireless Microstimulators for Neural Prosthetics

    PubMed Central

    Sahin, Mesut; Pikov, Victor

    2016-01-01

    One of the roadblocks in the field of neural prosthetics is the lack of microelectronic devices for neural stimulation that can last a lifetime in the central nervous system. Wireless multi-electrode arrays are being developed to improve the longevity of implants by eliminating the wire interconnects as well as the chronic tissue reactions due to the tethering forces generated by these wires. An area of research that has not been sufficiently investigated is a simple single-channel passive microstimulator that can collect the stimulus energy that is transmitted wirelessly through the tissue and immediately convert it into the stimulus pulse. For example, many neural prosthetic approaches to intraspi