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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. Population clocks: motor timing with neural dynamics

    PubMed Central

    Buonomano, Dean V.; Laje, Rodrigo

    2010-01-01

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

  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. PMID:22510393

  8. 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

  9. 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

  10. Dual Roles for Spike Signaling in Cortical Neural Populations

    PubMed Central

    Ballard, Dana H.; Jehee, Janneke F. M.

    2011-01-01

    A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding. PMID:21687798

  11. 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

  12. Neural networks within multi-core optic fibers.

    PubMed

    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

  13. 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.

  14. 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

  15. Automatic reconstruction of neural morphologies with multi-scale tracking.

    PubMed

    Choromanska, Anna; Chang, Shih-Fu; Yuste, Rafael

    2012-01-01

    Neurons have complex axonal and dendritic morphologies that are the structural building blocks of neural circuits. The traditional method to capture these morphological structures using manual reconstructions is time-consuming and partly subjective, so it appears important to develop automatic or semi-automatic methods to reconstruct neurons. Here we introduce a fast algorithm for tracking neural morphologies in 3D with simultaneous detection of branching processes. The method is based on existing tracking procedures, adding the machine vision technique of multi-scaling. Starting from a seed point, our algorithm tracks axonal or dendritic arbors within a sphere of a variable radius, then moves the sphere center to the point on its surface with the shortest Dijkstra path, detects branching points on the surface of the sphere, scales it until branches are well separated and then continues tracking each branch. We evaluate the performance of our algorithm on preprocessed data stacks obtained by manual reconstructions of neural cells, corrupted with different levels of artificial noise, and unprocessed data sets, achieving 90% precision and 81% recall in branch detection. We also discuss limitations of our method, such as reconstructing highly overlapping neural processes, and suggest possible improvements. Multi-scaling techniques, well suited to detect branching structures, appear a promising strategy for automatic neuronal reconstructions. PMID:22754498

  16. Neural probes with multi-drug delivery capability.

    PubMed

    Shin, Hyogeun; Lee, Hyunjoo J; Chae, Uikyu; Kim, Huiyoung; Kim, Jeongyeon; Choi, Nakwon; Woo, Jiwan; Cho, Yakdol; Lee, C Justin; Yoon, Eui-Sung; Cho, Il-Joo

    2015-01-01

    Multi-functional neural probes are promising platforms to conduct efficient and effective in-depth studies of brain by recording neural signals as well as modulating the signals with various stimuli. Here we present a neural probe with an embedded microfluidic channel (chemtrode) with multi-drug delivery capability suitable for small animal experiments. We integrated a staggered herringbone mixer (SHM) in a 3-inlet microfluidic chip directly into our chemtrode. This chip, which also serves as a compact interface for the chemtrode, allows for efficient delivery of small volumes of multiple or concentration-modulated drugs via chaotic mixing. We demonstrated the successful infusion of combinatorial inputs of three chemicals with a low flow rate (170 nl min(-1)). By sequentially delivering red, green, and blue inks from each inlet and conducting visual inspections at the tip of the chemtrode, we measured a short residence time of 14 s which corresponds to a small swept volume of 66 nl. Finally, we demonstrated the potential of our proposed chemtrode as an enabling tool through extensive in vivo mice experiments. Through simultaneous infusions of a chemical (pilocarpine or tetrodotoxin (TTX) at inlet 1), a buffer solution (saline at inlet 2), and 4',6-diamidino-2-phenylindole (DAPI at inlet 3) locally into a mouse brain, we not only modulated the neural activities by varying the concentration of the chemical but also locally stained the cells at our target region (CA1 in hippocampus). More specifically, infusion of pilocarpine with a higher concentration resulted in an increase in neural activities while infusion of TTX with a higher concentration resulted in a distinctive reduction. For each chemical, we acquired multiple sets of data using only one mouse through a single implantation of the chemtrode. Our proposed chemtrode offers 1) multiplexed delivery of three drugs through a compact packaging with a small swept volume and 2) simultaneous recording to monitor near

  17. 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

  18. 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

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

    PubMed Central

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

    2015-01-01

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

  20. 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. PMID:26316190

  1. Measuring Fisher Information Accurately in Correlated Neural Populations

    PubMed Central

    Kohn, Adam; Pouget, Alexandre

    2015-01-01

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

  2. Critical behavior of large maximally informative neural populations

    NASA Astrophysics Data System (ADS)

    Berkowitz, John; Sharpee, Tatyana

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

  3. 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.

  4. Fuzzy Neural Classifiers for Multi-Wavelength Interdigital Sensors

    NASA Astrophysics Data System (ADS)

    Xenides, D.; Vlachos, D. S.; Simos, T. E.

    2007-12-01

    The use of multi-wavelength interdigital sensors for non-destructive testing is based on the capability of the measuring system to classify the measured impendence according to some physical properties of the material under test. By varying the measuring frequency and the wavelength of the sensor (and thus the penetration depth of the electric field inside the material under test) we can produce images that correspond to various configurations of dielectric materials under different geometries. The implementation of a fuzzy neural network witch inputs these images for both quantitative and qualitative sensing is demonstrated. The architecture of the system is presented with some references to the general theory of fuzzy sets and fuzzy calculus. Experimental results are presented in the case of a set of 8 well characterized dielectric layers. Finally the effect of network parameters to the functionality of the system is discussed, especially in the case of functions evaluating the fuzzy AND and OR operations.

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

    PubMed

    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

  6. 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

  7. 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.

  8. Independent Components of Neural Activity Carry Information on Individual Populations

    PubMed Central

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

    2014-01-01

    Local field potential (LFP), the low-frequency part of the potential recorded extracellularly in the brain, reflects neural activity at the population level. The interpretation of LFP is complicated because it can mix activity from remote cells, on the order of millimeters from the electrode. To understand better the relation between the recordings and the local activity of cells we used a large-scale network thalamocortical model to compute simultaneous LFP, transmembrane currents, and spiking activity. We used this model to study the information contained in independent components obtained from the reconstructed Current Source Density (CSD), which smooths transmembrane currents, decomposed further with Independent Component Analysis (ICA). We found that the three most robust components matched well the activity of two dominating cell populations: superior pyramidal cells in layer 2/3 (rhythmic spiking) and tufted pyramids from layer 5 (intrinsically bursting). The pyramidal population from layer 2/3 could not be well described as a product of spatial profile and temporal activation, but by a sum of two such products which we recovered in two of the ICA components in our analysis, which correspond to the two first principal components of PCA decomposition of layer 2/3 population activity. At low noise one more cell population could be discerned but it is unlikely that it could be recovered in experiment given typical noise ranges. PMID:25153730

  9. Independent component analysis of neural populations from multielectrode field potential measurements.

    PubMed

    Tanskanen, Jarno M A; Mikkonen, Jarno E; Penttonen, Markku

    2005-06-30

    Independent component analysis (ICA) is proposed for analysis of neural population activity from multichannel electrophysiological field potential measurements. The proposed analysis method provides information on spatial extents of active neural populations, locations of the populations with respect to each other, population evolution, including merging and splitting of populations in time, and on time lag differences between the populations. In some cases, results of the proposed analysis may also be interpreted as independent information flows carried by neurons and neural populations. In this paper, a detailed description of the analysis method is given. The proposed analysis is demonstrated with an illustrative simulation, and with an exemplary analysis of an in vivo multichannel recording from rat hippocampus. The proposed method can be applied in analysis of any recordings of neural networks in which contributions from a number of neural populations or information flows are simultaneously recorded via a number of measurement points, as well in vivo as in vitro. PMID:15922038

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

    PubMed Central

    Simonyan, Kristina

    2014-01-01

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

  11. Multi-area neural mass modeling of EEG and MEG signals.

    PubMed

    Babajani-Feremi, Abbas; Soltanian-Zadeh, Hamid

    2010-09-01

    We previously proposed an integrated electroencephalography (EEG), magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI) model based on an extended neural mass model (ENMM) within a single cortical area. In the ENMM, a cortical area contains several minicolumns where strengths of their connections diminish exponentially with their distances. The ENMM was derived based on the physiological principles of the cortical minicolumns and their connections within a single cortical area to generate EEG, MEG, and fMRI signals. The purpose of this paper is to further extend the ENMM model from a single-area to a multi-area model to develop a neural mass model of the entire brain that generates EEG and MEG signals. For multi-area modeling, two connection types are considered: short-range connections (SRCs) and long-range connections (LRCs). The intra-area SRCs among the minicolumns within the areas were previously modeled in the ENMM. To define inter-area LRCs among the cortical areas, we consider that the cell populations of all minicolumns in the destination area are affected by the excitatory afferent of the pyramidal cells of all minicolumns in the source area. The state-space representation of the multi-area model is derived considering the intra-minicolumn, SRCs', and LRCs' parameters. Using simulations, we evaluate effects of parameters of the model on its dynamics and, based on stability analysis, find valid ranges for parameters of the model. In addition, we evaluate reducing redundancy of the model parameters using simulation results and conclude that the parameters of the model can be limited to the LRCs and SRCs while the intra-minicolumn parameters stay at their physiological mean values. The proposed multi-area integrated E/MEG model provides an efficient neuroimaging technique for effective connectivity analysis in healthy subjects as well as neurological and psychiatric patients. PMID:20080193

  12. 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

  13. 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.

  14. Molecular Diversity Subdivides the Adult Forebrain Neural Stem Cell Population

    PubMed Central

    Giachino, Claudio; Basak, Onur; Lugert, Sebastian; Knuckles, Philip; Obernier, Kirsten; Fiorelli, Roberto; Frank, Stephan; Raineteau, Olivier; Alvarez–Buylla, Arturo; Taylor, Verdon

    2014-01-01

    Neural stem cells (NSCs) in the ventricular domain of the subventricular zone (V-SVZ) of rodents produce neurons throughout life while those in humans become largely inactive or may be lost during infancy. Most adult NSCs are quiescent, express glial markers, and depend on Notch signaling for their self-renewal and the generation of neurons. Using genetic markers and lineage tracing, we identified subpopulations of adult V-SVZ NSCs (type 1, 2, and 3) indicating a striking heterogeneity including activated, brain lipid binding protein (BLBP, FABP7) expressing stem cells. BLBP+ NSCs are mitotically active components of pinwheel structures in the lateral ventricle walls and persistently generate neurons in adulthood. BLBP+ NSCs express epidermal growth factor (EGF) receptor, proliferate in response to EGF, and are a major clonogenic population in the SVZ. We also find BLBP expressed by proliferative ventricular and sub-ventricular progenitors in the fetal and postnatal human brain. Loss of BLBP+ stem/progenitor cells correlates with reduced neurogenesis in aging rodents and postnatal humans. These findings of molecular heterogeneity and proliferative differences subdivide the NSC population and have implications for neurogenesis in the forebrain of mammals during aging. PMID:23964022

  15. 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.

  16. 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

  17. 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.

  18. When are two multi-layer cellular neural networks the same?

    PubMed

    Ban, Jung-Chao; Chang, Chih-Hung

    2016-07-01

    This paper aims to characterize whether a multi-layer cellular neural network is of deep architecture; namely, when can an n-layer cellular neural network be replaced by an m-layer cellular neural network for m

  19. Is there a familial link between Down's syndrome and neural tube defects? Population and familial survey

    PubMed Central

    Amorim, Márcia R; Castilla, Eduardo E; Orioli, Iêda M

    2004-01-01

    Objective To verify whether Down's syndrome and neural tube defects arise more often in the same family than expected by chance. Design Population and familial survey. Setting Network of maternity hospitals in the Latin American collaborative study of congenital malformations (ECLAMC) in Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, and Venezuela between 1982 and 2000. Probands 2421 cases of neural tube defects, 952 of hydrocephalus, and 3095 of Down's syndrome registered from a total of 1 583 838 live births and stillbirths. Main outcome measures Observed number of cases of Down's syndrome among siblings of probands with a neural tube defect or hydrocephalus and number expected on the basis of maternal age; observed number of cases of neural tube defects or hydrocephalus among siblings of probands with Down's syndrome and number expected according to the prevalence in the same population. Results Five cases of Down's syndrome occurred among 5404 pregnancies previous to a case of neural tube defect or hydrocephalus, compared with 5.13 expected after adjustment by maternal age. Twelve cases of neural tube defect or hydrocephalus occurred among 8066 pregnancies previous to a case of Down's syndrome, compared with 17.18 expected on the basis of the birth prevalence for neural tube defects plus hydrocephalus in the same population. Conclusion No association occurred between families at risk of neural tube defects and those at risk of Down's syndrome. PMID:14662523

  20. 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

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

    PubMed

    Ursino, Mauro; Zavaglia, Melissa

    2007-12-01

    In our research, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. First, we showed that a single neural population, stimulated with white noise, can oscillate with its intrinsic rhythm, and that the position of this rhythm can be finely tuned (in the alpha, beta or gamma frequency ranges), acting on the population synaptic kinetics. Subsequently, we analyzed more complex circuits, composed of two or three interconnected populations, each with a different synaptic kinetics between neural groups within a population (hence, with a different intrinsic rhythm). The results demonstrates apex that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). The analysis of coherence, and of the position of the peaks in power spectral density, reveals important information on the possible connections among populations, and is especially useful to follow temporal changes in connectivity. In perspective, the results may be of value for a deeper comprehension of the mechanisms causing EEGs rhythms, for the study of connectivity among different neural populations and for the test of neurophysiological hypotheses. PMID:18181270

  2. 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.

  3. Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Baskaran, Subbiah; Ramachandran, Narayanan; Noever, David

    1998-01-01

    The use of probabilistic (PNN) and multilayer feed forward (MLFNN) neural networks are investigated for calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of networks are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this article.

  4. 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.

  5. Multi-layer multi-class dasymetric mapping to estimate population distribution.

    PubMed

    Su, Ming-Dawa; Lin, Mei-Chun; Hsieh, Hsin-I; Tsai, Bor-Wen; Lin, Chun-Hung

    2010-09-15

    The spatial patterns of population distribution are very important information for most regional planning and management decisions. But the socioeconomic data are usually published in areal aggregated format due to privacy concerns. Although choropleth maps are used extensively to display spatial distributions of these areal aggregated data, patterns may be distorted due to assumptions of homogeneous distributions and the modifiable areal unit problem. Most human activity, including population distribution, is spatially heterogeneous due to variations in topography and regional development. A multi-layer multi-class dasymetric (MLMCD) framework was proposed in this study to better redistribute the regionally aggregated population statistics into smaller areal units and reveal more realistic spatial population distribution pattern. The Taipei metropolitan area in Taiwan was used as a case study area to demonstrate the disaggregation ability of the proposed framework and the improvements to the traditional binary or multi-class dasymetric method. Assorted data, including remote sensing images, land use zoning, topography, transportation and accessibility to facilities were introduced in different layers to improve the redistribution of aggregated regional population data. The concept of multi-layer multi-class dasymetric modeling is both useful and flexible. Different levels of accuracy in this population redistribution process can be achieved depending on data and budget availabilities and the needs for different data usage purposes. PMID:20621331

  6. Regulation of neural stem cells by choroid plexus cells population.

    PubMed

    Roballo, Kelly C S; Gonçalves, Natalia J N; Pieri, Naira C G; Souza, Aline F; Andrade, André F C; Ambrósio, Carlos E

    2016-07-28

    The choroid plexus is a tissue on the central nervous system responsible for producing cerebrospinal fluid, maintaining homeostasis and neural stem cells support; though, all of its functions still unclear. This study aimed to demonstrate the niches of choroid plexus cells for a better understanding of the cell types and functions, using the porcine as the animal model. The collected material was analyzed by histology, immunohistochemistry, and cell culture. The cell culture was characterizated by immunocytochemistry and flow cytometry. Our results showed OCT-4, TUBIII, Nestin, CD45, CD73, CD90 positive expression and GFAP, CD105 negative expression, also methylene blue histological staining confirmed the presence of telocytes cells. We realized that the choroid plexus is a unique and incomparable tissue with different niches of cells as pluripotent, hematopoietic, neuronal progenitors and telocyte cells, which provide its complexity, differentiated functionality and responsibility on brain balance and neural stem cells regulation. PMID:27181512

  7. Patient barriers to insulin use in multi-ethnic populations.

    PubMed

    Visram, Hasina

    2013-06-01

    Insulin administration is often required in the management of type 2 diabetes mellitus for optimal glycemic control. Despite this, however, many patients are reluctant to initiate insulin treatment. In the general population, there are multiple factors leading to this reluctance including fear of hypoglycemia, needle phobia and weight gain. These barriers are also present in multi-ethnic populations. However, there are several patient barriers that are more prevalent in various ethnic backgrounds that need to be addressed. These barriers include language barriers, poor health literacy, social factors and religious implications. The awareness of these factors as well as potential strategies to help overcome them can lead to the improved management of patients with diabetes from multi-ethnic populations. PMID:24070844

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

    PubMed

    Herzfeld, D J; Beardsley, S A

    2010-08-01

    Current efforts to decode control signals from multi-unit (MU) recordings rely on the use of spike sorting to differentiate neurons and the use of firing rates estimated over tens of milliseconds to reconstruct sensorimotor signals. The computational bottleneck associated with the need to identify and sort individual neuron responses poses challenges for the development of portable, real-time, neural decoding systems that can be incorporated into assistive and prosthetic devices for the disabled. Here, we investigate the ability of spike-based linear filtering to reduce computational overhead and improve the accuracy of decoding neuronal signals for populations of spiking neurons. Using a population temporal (PT) decoding framework, the speed and accuracy of spike-based MU decoding were compared with firing rate-based approaches using simulated populations of motor neurons tuned for the velocity of intended movement. For the two linear filtering approaches, the accuracy of decoded movements was examined as a function of the number of recorded neurons, amount of noise, with and without spike sorting, and for training and test motions whose statistics were either similar or dissimilar. Our results suggest that the use of a PT decoding framework can offset the loss in accuracy associated with decoding unsorted MU neural signals. Coupled with up to a 20-fold reduction in the number of decoding weights and the ability to implement the filtering in hardware, this approach could reduce the computational requirements and thus increase the portability of next generation brain-machine interfaces. PMID:20644245

  9. 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

  10. 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.

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

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

  12. 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.

  13. Sparse low-order interaction network underlies a highly correlated and learnable neural population code

    PubMed Central

    Ganmor, Elad; Segev, Ronen; Schneidman, Elad

    2011-01-01

    Information is carried in the brain by the joint activity patterns of large groups of neurons. Understanding the structure and function of population neural codes is challenging because of the exponential number of possible activity patterns and dependencies among neurons. We report here that for groups of ~100 retinal neurons responding to natural stimuli, pairwise-based models, which were highly accurate for small networks, are no longer sufficient. We show that because of the sparse nature of the neural code, the higher-order interactions can be easily learned using a novel model and that a very sparse low-order interaction network underlies the code of large populations of neurons. Additionally, we show that the interaction network is organized in a hierarchical and modular manner, which hints at scalability. Our results suggest that learnability may be a key feature of the neural code. PMID:21602497

  14. Multi-agent systems and neural networks for automatic target recognition on air images

    NASA Astrophysics Data System (ADS)

    Cozien, Roger F.; Rosenberger, Christophe; Eyherabide, Partrick; Rossettini, Joaquim; Ceyrolle, Arnaud

    2000-08-01

    Our purpose is, in medium term, to detect in air images, characteristic shapes and objects such as airports, industrial plants, planes, tanks, trucks, ... with great accuracy and low rate of mistakes. However, we also want to value whether the link between neural networks and multi-agents systems is relevant and effective. If it appears to be really effective, we hope to use this kind of technology in other fields. That would be an easy and convenient way to depict and to use the agents' knowledge which is distributed and fragmented. After a first phase of preliminary tests to know if agents are able to give relevant information to a neural network, we verify that only a few agents running on an image are enough to inform the network and let it generalize the agents' distributed and fragmented knowledge. In a second phase, we developed a distributed architecture allowing several multi- agents systems running at the same time on different computers with different images. All those agents send information to a 'multi neural networks system' whose job is to identify the shapes detected by the agents. The name we gave to our project is Jarod.

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

    PubMed Central

    Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

    2011-01-01

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

  16. 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.

  17. 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.

  18. Deducing the multi-trader population driving a financial market

    NASA Astrophysics Data System (ADS)

    Gupta, Nachi; Hauser, Raphael; Johnson, Neil

    2005-12-01

    We have previously laid out a basic framework for predicting financial movements and pockets of predictability by tracking the distribution of a multi-trader population playing on an artificial financial market model. This work explores extensions to this basic framework. We allow for more intelligent agents with a richer strategy set, and we no longer constrain the distribution over these agents to a probability space. We then introduce a fusion scheme which accounts for multiple runs of randomly chosen sets of possible agent types. We also discuss a mechanism for bias removal on the estimates.

  19. 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.

  20. 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.

  1. 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.

  2. 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. PMID:26764727

  3. 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

  4. Stochastic Nonlinear Evolutional Model of the Large-Scaled Neuronal Population and Dynamic Neural Coding Subject to Stimulation

    SciTech Connect

    Wang Rubin; Yu Wei

    2005-08-25

    In this paper, we investigate how the population of neuronal oscillators deals with information and the dynamic evolution of neural coding when the external stimulation acts on it. Numerically computing method is used to describe the evolution process of neural coding in three-dimensioned space. The numerical result proves that only the suitable stimulation can change the coupling structure and plasticity of neurons.

  5. 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

  6. Reinforced recurrent neural networks for multi-step-ahead flood forecasts

    NASA Astrophysics Data System (ADS)

    Chen, Pin-An; Chang, Li-Chiu; Chang, Fi-John

    2013-08-01

    Considering true values cannot be available at every time step in an online learning algorithm for multi-step-ahead (MSA) forecasts, a MSA reinforced real-time recurrent learning algorithm for recurrent neural networks (R-RTRL NN) is proposed. The main merit of the proposed method is to repeatedly adjust model parameters with the current information including the latest observed values and model's outputs to enhance the reliability and the forecast accuracy of the proposed method. The sequential formulation of the R-RTRL NN is derived. To demonstrate its reliability and effectiveness, the proposed R-RTRL NN is implemented to make 2-, 4- and 6-step-ahead forecasts in a famous benchmark chaotic time series and a reservoir flood inflow series in North Taiwan. For comparison purpose, three comparative neural networks (two dynamic and one static neural networks) were performed. Numerical and experimental results indicate that the R-RTRL NN not only achieves superior performance to comparative networks but significantly improves the precision of MSA forecasts for both chaotic time series and reservoir inflow case during typhoon events with effective mitigation in the time-lag problem.

  7. From artificial neural networks to spiking neuron populations and back again.

    PubMed

    de Kamps, M; van der Velde, F

    2001-01-01

    In this paper, we investigate the relation between Artificial Neural Networks (ANNs) and networks of populations of spiking neurons. The activity of an artificial neuron is usually interpreted as the firing rate of a neuron or neuron population. Using a model of the visual cortex, we will show that this interpretation runs into serious difficulties. We propose to interpret the activity of an artificial neuron as the steady state of a cross-inhibitory circuit, in which one population codes for 'positive' artificial neuron activity and another for 'negative' activity. We will show that with this interpretation it is possible, under certain circumstances, to transform conventional ANNs (e.g. trained with 'back-propagation') into biologically plausible networks of spiking populations. However, in general, the use of biologically motivated spike response functions introduces artificial neurons that behave differently from the ones used in the classical ANN paradigm. PMID:11665784

  8. An improved multi-objective evolutionary memetic algorithm based on multi-population and its application

    NASA Astrophysics Data System (ADS)

    Xiao, Zhongliang

    2012-04-01

    In this paper, we set up a mathematical model to solve the problem of airport ground services. In this model, we set objective function of cost and time, and the purpose is making it minimized. Base on the analysis of scheduling characteristic, we use the multi-population co-evolutionary Memetic algorithm (MAMC) which is with the elitist strategy to realize the model. From the result we can see that our algorithm is better than the genetic algorithm in this problem and we can see that our algorithm is convergence. So we can summarize that it can be a better optimization to airport ground services problem.

  9. 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

  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. Asynchrony adaptation reveals neural population code for audio-visual timing

    PubMed Central

    Roach, Neil W.; Heron, James; Whitaker, David; McGraw, Paul V.

    2011-01-01

    The relative timing of auditory and visual stimuli is a critical cue for determining whether sensory signals relate to a common source and for making inferences about causality. However, the way in which the brain represents temporal relationships remains poorly understood. Recent studies indicate that our perception of multisensory timing is flexible—adaptation to a regular inter-modal delay alters the point at which subsequent stimuli are judged to be simultaneous. Here, we measure the effect of audio-visual asynchrony adaptation on the perception of a wide range of sub-second temporal relationships. We find distinctive patterns of induced biases that are inconsistent with the previous explanations based on changes in perceptual latency. Instead, our results can be well accounted for by a neural population coding model in which: (i) relative audio-visual timing is represented by the distributed activity across a relatively small number of neurons tuned to different delays; (ii) the algorithm for reading out this population code is efficient, but subject to biases owing to under-sampling; and (iii) the effect of adaptation is to modify neuronal response gain. These results suggest that multisensory timing information is represented by a dedicated population code and that shifts in perceived simultaneity following asynchrony adaptation arise from analogous neural processes to well-known perceptual after-effects. PMID:20961905

  12. An in vitro method to manipulate the direction and functional strength between neural populations.

    PubMed

    Pan, Liangbin; Alagapan, Sankaraleengam; Franca, Eric; Leondopulos, Stathis S; DeMarse, Thomas B; Brewer, Gregory J; Wheeler, Bruce C

    2015-01-01

    We report the design and application of a Micro Electro Mechanical Systems (MEMs) device that permits investigators to create arbitrary network topologies. With this device investigators can manipulate the degree of functional connectivity among distinct neural populations by systematically altering their geometric connectivity in vitro. Each polydimethylsilxane (PDMS) device was cast from molds and consisted of two wells each containing a small neural population of dissociated rat cortical neurons. Wells were separated by a series of parallel micrometer scale tunnels that permitted passage of axonal processes but not somata; with the device placed over an 8 × 8 microelectrode array, action potentials from somata in wells and axons in microtunnels can be recorded and stimulated. In our earlier report we showed that a one week delay in plating of neurons from one well to the other led to a filling and blocking of the microtunnels by axons from the older well resulting in strong directionality (older to younger) of both axon action potentials in tunnels and longer duration and more slowly propagating bursts of action potentials between wells. Here we show that changing the number of tunnels, and hence the number of axons, connecting the two wells leads to changes in connectivity and propagation of bursting activity. More specifically, the greater the number of tunnels the stronger the connectivity, the greater the probability of bursting propagating between wells, and shorter peak-to-peak delays between bursts and time to first spike measured in the opposing well. We estimate that a minimum of 100 axons are needed to reliably initiate a burst in the opposing well. This device provides a tool for researchers interested in understanding network dynamics who will profit from having the ability to design both the degree and directionality connectivity among multiple small neural populations. PMID:26236198

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

    PubMed

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

    2010-12-01

    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. PMID:21147999

  14. An in vitro method to manipulate the direction and functional strength between neural populations

    PubMed Central

    Pan, Liangbin; Alagapan, Sankaraleengam; Franca, Eric; Leondopulos, Stathis S.; DeMarse, Thomas B.; Brewer, Gregory J.; Wheeler, Bruce C.

    2015-01-01

    We report the design and application of a Micro Electro Mechanical Systems (MEMs) device that permits investigators to create arbitrary network topologies. With this device investigators can manipulate the degree of functional connectivity among distinct neural populations by systematically altering their geometric connectivity in vitro. Each polydimethylsilxane (PDMS) device was cast from molds and consisted of two wells each containing a small neural population of dissociated rat cortical neurons. Wells were separated by a series of parallel micrometer scale tunnels that permitted passage of axonal processes but not somata; with the device placed over an 8 × 8 microelectrode array, action potentials from somata in wells and axons in microtunnels can be recorded and stimulated. In our earlier report we showed that a one week delay in plating of neurons from one well to the other led to a filling and blocking of the microtunnels by axons from the older well resulting in strong directionality (older to younger) of both axon action potentials in tunnels and longer duration and more slowly propagating bursts of action potentials between wells. Here we show that changing the number of tunnels, and hence the number of axons, connecting the two wells leads to changes in connectivity and propagation of bursting activity. More specifically, the greater the number of tunnels the stronger the connectivity, the greater the probability of bursting propagating between wells, and shorter peak-to-peak delays between bursts and time to first spike measured in the opposing well. We estimate that a minimum of 100 axons are needed to reliably initiate a burst in the opposing well. This device provides a tool for researchers interested in understanding network dynamics who will profit from having the ability to design both the degree and directionality connectivity among multiple small neural populations. PMID:26236198

  15. 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.

  16. Near-infrared spectroscopic measurements of blood analytes using multi-layer perceptron neural networks.

    PubMed

    Kalamatianos, Dimitrios; Liatsis, Panos; Wellstead, Peter E

    2006-01-01

    Near-infrared (NIR) spectroscopy is being applied to the solution of problems in many areas of biomedical and pharmaceutical research. In this paper we investigate the use of NIR spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose and oxyhemoglobin (HbO2). Measurements have been made in vitro with a portable spectrometer developed in our labs that consists of a two beam interferometer operating in the range of 800-2300 nm. For the data analysis a pattern recognition philosophy was used with a preprocessing stage and a multi-layer perceptron (MLP) neural network for the measurement stage. Results show that the interferogram signatures of the above compounds are sufficiently strong in that spectral range. Measurements of three different concentrations were possible with mean squared error (MSE) of the order of 10(-6). PMID:17947035

  17. 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.

  18. 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.

  19. 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.

  20. Chronic multi-electrode neural recording in free-roaming monkeys

    PubMed Central

    Eliades, Steven J.; Wang, Xiaoqin

    2008-01-01

    Many behaviors of interest to neurophysiologists are difficult to study under laboratory conditions because such behaviors are often inhibited when an animal is restrained and socially isolated. Even under the best conditions, such behaviors may be sparse enough as to require long duration neural recordings or simultaneous recording of multiple neurons to gather a sufficient amount of data for analysis. We have developed a preparation for chronic, multi-electrode recordings in the auditory cortex of marmoset monkeys, small primates, as well as techniques for neurophysiological recordings when the animals are free-roaming while singly caged in the environment of the monkey colony. In this report, we describe our solutions to overcome the problems associated with chronic recordings in free-roaming animals, where three-dimensional movements present particular challenges. PMID:18572250

  1. 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.

  2. 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. PMID:25343768

  3. Aggression- and sex-induced neural activity across vasotocin populations in the brown anole.

    PubMed

    Kabelik, David; Alix, Veronica C; Burford, Emily R; Singh, Leah J

    2013-03-01

    Activity within the social behavior neural network is modulated by the neuropeptide arginine vasotocin (AVT) and its mammalian homologue arginine vasopressin (AVP). However, central AVT/AVP release causes different behavioral effects across species and social environments. These differences may be due to the activation of different neuronal AVT/AVP populations or to similar activity patterns causing different behavioral outputs. We examined neural activity (assessed as Fos induction) within AVT neurons in male brown anole lizards (Anolis sagrei) participating in aggressive or sexual encounters. Lizards possess simple amniote nervous systems, and their examination provides a comparative framework to complement avian and mammalian studies. In accordance with findings in other species, AVT neurons in the anole paraventricular nucleus (PVN) were activated during aggressive encounters; but unlike in other species, a positive correlation was found between aggression levels and activation. Activation of AVT neurons within the supraoptic nucleus (SON) occurred nonspecifically with participation in either aggressive or sexual encounters. Activation of AVT neurons in the preoptic area (POA) and bed nucleus of the stria terminalis (BNST) was associated with engagement in sexual behaviors. The above findings are congruent with neural activation patterns observed in other species, even when the behavioral outputs (i.e., aggression level) differed. However, aggressive encounters also increased activation of AVT neurons in the BNST, which is incongruous with findings in other species. Thus, some species differences involve the encoding of social stimuli as different neural activation patterns within the AVT/AVP network, whereas other behavioral differences arise downstream of this system. PMID:23201179

  4. Neural Potential of a Stem Cell Population in the Hair Follicle

    PubMed Central

    Mignone, John L.; Roig-Lopez, Jose L.; Fedtsova, Natalia; Schones, Dustin E.; Manganas, Louis N.; Maletic-Savatic, Mirjana; Keyes, William M.; Mills, Alea A.; Gleiberman, Anatoli; Zhang, Michael Q.; Enikolopov, Grigori

    2013-01-01

    The bulge region of the hair follicle serves as a repository for epithelial stem cells that can regenerate the follicle in each hair growth cycle and contribute to epidermis regeneration upon injury. Here we describe a population of multipotential stem cells in the hair follicle bulge region; these cells can be identified by fluorescence in transgenic nestin-GFP mice. The morphological features of these cells suggest that they maintain close associations with each other and with the surrounding niche. Upon explantation, these cells can give rise to neurosphere-like structures in vitro. When these cells are permitted to differentiate, they produce several cell types, including cells with neuronal, astrocytic, oligodendrocytic, smooth muscle, adipocytic, and other phenotypes. Furthermore, upon implantation into the developing nervous system of chick, these cells generate neuronal cells in vivo. We used transcriptional profiling to assess the relationship between these cells and embryonic and postnatal neural stem cells and to compare them with other stem cell populations of the bulge. Our results show that nestin-expressing cells in the bulge region of the hair follicle have stem cell-like properties, are multipotent, and can effectively generate cells of neural lineage in vitro and in vivo. PMID:17873521

  5. 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.

  6. 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.

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

    NASA Astrophysics Data System (ADS)

    Dan-guang, Pan; Yan-hua, Gao; Jun-lei, Song

    2010-05-01

    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.

  8. DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection.

    PubMed

    Li, Xi; Zhao, Liming; Wei, Lina; Yang, Ming-Hsuan; Wu, Fei; Zhuang, Yueting; Ling, Haibin; Wang, Jingdong

    2016-08-01

    A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner. In this paper, we propose a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (whole saliency maps). In principle, the proposed saliency model takes a data-driven strategy for encoding the underlying saliency prior information, and then sets up a multi-task learning scheme for exploring the intrinsic correlations between saliency detection and semantic image segmentation. Through collaborative feature learning from such two correlated tasks, the shared fully convolutional layers produce effective features for object perception. Moreover, it is capable of capturing the semantic information on salient objects across different levels using the fully convolutional layers, which investigate the feature-sharing properties of salient object detection with a great reduction of feature redundancy. Finally, we present a graph Laplacian regularized nonlinear regression model for saliency refinement. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches. PMID:27305676

  9. DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

    NASA Astrophysics Data System (ADS)

    Li, Xi; Zhao, Liming; Wei, Lina; Yang, Ming-Hsuan; Wu, Fei; Zhuang, Yueting; Ling, Haibin; Wang, Jingdong

    2016-08-01

    A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner. In this paper, we propose a multi-task deep saliency model based on a fully convolutional neural network (FCNN) with global input (whole raw images) and global output (whole saliency maps). In principle, the proposed saliency model takes a data-driven strategy for encoding the underlying saliency prior information, and then sets up a multi-task learning scheme for exploring the intrinsic correlations between saliency detection and semantic image segmentation. Through collaborative feature learning from such two correlated tasks, the shared fully convolutional layers produce effective features for object perception. Moreover, it is capable of capturing the semantic information on salient objects across different levels using the fully convolutional layers, which investigate the feature-sharing properties of salient object detection with great feature redundancy reduction. Finally, we present a graph Laplacian regularized nonlinear regression model for saliency refinement. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.

  10. Acute pancreatitis in a multi-ethnic population.

    PubMed

    Kandasami, P; Harunarashid, Hanafiah; Kaur, Harjit

    2002-06-01

    There is very little information in literature describing ethnic variations in etiologic and clinical outcome of acute pancreatitis in the Asian population. This study describes the demographic, etiologic and clinical course of acute pancreatitis among the three main races in Malaysia namely, the Malays, Chinese and Indians. One hundred and thirty-three consecutive patients were admitted for acute pancreatitis for the period January 1994 to July 1999 and they consisted of 77 males and 56 females with a mean age of 43.5 years (SD+/- 14.7). The racial breakdown of acute pancreatitis was: Malays 38 (28.6%), Chinese 19 (14.3%), Indians 75 (56.4%) and 1 (0.8%) patient was an orang asli. The incidence of alcohol association with acute pancreatitis was significantly increased in the males, while gallstone pancreatitis was principally a disease of the female. Alcohol was identified as the predominant factor associated with acute pancreatitis among the Indians (73.3%) and in contrast, gallstone was the commonest associated etiologic factor for the Malays and Chinese. No etiologic factor could be identified in a substantial proportion of the Malay patients (60.5%) when compared to the Chinese (36.8%) and Indians (35%). Severe disease developed in 25% of the cases reviewed but there was no difference in of the rate of severe pancreatitis in terms of ethnic groupings or etiologic factors. The overall mortality rate was 7.5% and the commonest cause of death was multi-organ failure. The study recognises that there are differences in the characteristics of acute pancreatitis among the three major races in the country and this divergence is primarily due to sociocultural habits. PMID:12380724

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

    NASA Astrophysics Data System (ADS)

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

    2006-12-01

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

  12. Multi-unit Recording Methods to Characterize Neural Activity in the Locust (Schistocerca Americana) Olfactory Circuits

    PubMed Central

    Saha, Debajit; Leong, Kevin; Katta, Nalin; Raman, Baranidharan

    2013-01-01

    Detection and interpretation of olfactory cues are critical for the survival of many organisms. Remarkably, species across phyla have strikingly similar olfactory systems suggesting that the biological approach to chemical sensing has been optimized over evolutionary time1. In the insect olfactory system, odorants are transduced by olfactory receptor neurons (ORN) in the antenna, which convert chemical stimuli into trains of action potentials. Sensory input from the ORNs is then relayed to the antennal lobe (AL; a structure analogous to the vertebrate olfactory bulb). In the AL, neural representations for odors take the form of spatiotemporal firing patterns distributed across ensembles of principal neurons (PNs; also referred to as projection neurons)2,3. The AL output is subsequently processed by Kenyon cells (KCs) in the downstream mushroom body (MB), a structure associated with olfactory memory and learning4,5. Here, we present electrophysiological recording techniques to monitor odor-evoked neural responses in these olfactory circuits. First, we present a single sensillum recording method to study odor-evoked responses at the level of populations of ORNs6,7. We discuss the use of saline filled sharpened glass pipettes as electrodes to extracellularly monitor ORN responses. Next, we present a method to extracellularly monitor PN responses using a commercial 16-channel electrode3. A similar approach using a custom-made 8-channel twisted wire tetrode is demonstrated for Kenyon cell recordings8. We provide details of our experimental setup and present representative recording traces for each of these techniques. PMID:23380828

  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. Critical and maximally informative encoding between neural populations in the retina

    PubMed Central

    Kastner, David B.; Baccus, Stephen A.; Sharpee, Tatyana O.

    2015-01-01

    Computation in the brain involves multiple types of neurons, yet the organizing principles for how these neurons work together remain unclear. Information theory has offered explanations for how different types of neurons can maximize the transmitted information by encoding different stimulus features. However, recent experiments indicate that separate neuronal types exist that encode the same filtered version of the stimulus, but then the different cell types signal the presence of that stimulus feature with different thresholds. Here we show that the emergence of these neuronal types can be quantitatively described by the theory of transitions between different phases of matter. The two key parameters that control the separation of neurons into subclasses are the mean and standard deviation (SD) of noise affecting neural responses. The average noise across the neural population plays the role of temperature in the classic theory of phase transitions, whereas the SD is equivalent to pressure or magnetic field, in the case of liquid–gas and magnetic transitions, respectively. Our results account for properties of two recently discovered types of salamander Off retinal ganglion cells, as well as the absence of multiple types of On cells. We further show that, across visual stimulus contrasts, retinal circuits continued to operate near the critical point whose quantitative characteristics matched those expected near a liquid–gas critical point and described by the nearest-neighbor Ising model in three dimensions. By operating near a critical point, neural circuits can maximize information transmission in a given environment while retaining the ability to quickly adapt to a new environment. PMID:25675497

  15. Neural crest and Schwann cell progenitor-derived melanocytes are two spatially segregated populations similarly regulated by Foxd3

    PubMed Central

    Nitzan, Erez; Pfaltzgraff, Elise R.; Labosky, Patricia A.; Kalcheim, Chaya

    2013-01-01

    Skin melanocytes arise from two sources: either directly from neural crest progenitors or indirectly from neural crest-derived Schwann cell precursors after colonization of peripheral nerves. The relationship between these two melanocyte populations and the factors controlling their specification remains poorly understood. Direct lineage tracing reveals that neural crest and Schwann cell progenitor-derived melanocytes are differentially restricted to the epaxial and hypaxial body domains, respectively. Furthermore, although both populations are initially part of the Foxd3 lineage, hypaxial melanocytes lose Foxd3 at late stages upon separation from the nerve, whereas we recently found that epaxial melanocytes segregate earlier from Foxd3-positive neural progenitors while still residing in the dorsal neural tube. Gain- and loss-of-function experiments in avians and mice, respectively, reveal that Foxd3 is both sufficient and necessary for regulating the balance between melanocyte and Schwann cell development. In addition, Foxd3 is also sufficient to regulate the switch between neuronal and glial fates in sensory ganglia. Together, we propose that differential fate acquisition of neural crest-derived cells depends on their progressive segregation from the Foxd3-positive lineage. PMID:23858437

  16. Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model

    PubMed Central

    Becker, Robert; Knock, Stuart; Ritter, Petra; Jirsa, Viktor

    2015-01-01

    Oscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) – functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations, and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals. PMID:26335064

  17. 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.

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

    PubMed

    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. PMID:26520086

  19. Multi-analyte assay for triazines using cross-reactive antibodies and neural networks.

    PubMed

    Reder, Sabine; Dieterle, Frank; Jansen, Hendrikus; Alcock, Susan; Gauglitz, Günter

    2003-12-30

    A biosensor system based on total internal reflectance fluorescence (TIRF) was used to discriminate a mixture of the triazines atrazine and simazine. Only cross-reactive antibodies were available for these two analytes. The biosensor is fully automated and can be regenerated allowing several hundreds of measurements without any user input. Even a remote control for online monitoring in the field is possible. The multivariate calibration of the sensor signal was performed using artificial neural networks, as the relationship between the sensor signals and the concentration of the analytes is highly non-linear. For the development of a multi-analyte immunoassay consisting of two polyclonal antibodies with cross-reactivity to atrazine and simazine and different derivatives immobilised on the transducer surface, the binding characteristics between these substances like binding capacity and cross-reactivity were characterised. The examination of three different measurement procedures showed that a two-step measurement using only one antibody per step allows a quantification of both analytes in a mixture with limits of detection of 0.2 microg/l for atrazine and 0.3 microg/l for simazine. The biosensor is suitable for online monitoring in the field and remote control is possible. PMID:14623469

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

    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

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

    PubMed

    Chapman, Sandra B; Mudar, Raksha A

    2014-01-01

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

  3. 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

  4. 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

  5. Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity

    PubMed Central

    Yu, Byron M.; Cunningham, John P.; Santhanam, Gopal; Ryu, Stephen I.; Shenoy, Krishna V.; Sahani, Maneesh

    2009-01-01

    We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed over time, then a static dimensionality-reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way and that account for spiking variability, which may vary both across neurons and across time. We then present a novel method for extracting neural trajectories—Gaussian-process factor analysis (GPFA)—which unifies the smoothing and dimensionality-reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that the proposed extensions improved the predictive ability of the two-stage methods. The predictive ability was further improved by going to GPFA. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject's behavior on a single-trial basis. Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent

  6. Inversion of Three Layers Multi-Scale SPM Model Based on Neural Network Technique for the Retrieval of Soil Multi-Scale Roughness and Moisture Parameters

    NASA Astrophysics Data System (ADS)

    Hosni, I.; JaafriGhamki, M.; Bennaceur Farah, L.; Naceur, M. S.

    2015-04-01

    In this paper, a multi-layered multi-scale backscattering model for a lossy medium and a neural network inversion procedure has been presented. We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat algorithm to describe natural surface roughness. An adapted three layers 2D MLS small perturbations (SPM) model has been used to describe radar backscattering response of semiarid sub-surfaces. The total reflection coefficients of the natural soil are computed using the multilayer model, and volumetric scattering is approximated by the internal reflections between layers. The original multi-scale SPM model includes only the surface scattering of the natural bare soil, while the multilayer soil modified 2D MLS SPM model includes both the surface scattering and the volumetric scattering within the soil. This multi-layered model has been used to calculate the total surface reflection coefficients of a natural soil surface for both horizontal and vertical co-polarizations. A parametric analysis presents the dependence of the backscattering coefficient on multi scale roughness and soil. The overall objective of this work is to retrieve soil surfaces parameters namely roughness and soil moisture related to the dielectric constant by inverting the radar backscattered signal from natural soil surfaces. To perform the inversion of the modified three layers 2D MLS SPM model, we used a multilayer neural network (NN) architecture trained by a back-propagation learning rule.

  7. 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...

  8. 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.

  9. Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy.

    PubMed

    Selver, M Alper

    2014-03-01

    Precise measurements on abdominal organs are vital prior to the important clinical procedures. Such measurements require accurate segmentation of these organs, which is a very challenging task due to countless anatomical variations and technical difficulties. Although, several features with various classifiers have been designed to overcome these challenges, abdominal organ segmentation via classification is still an emerging field in order to reach desired precision. Recent studies on multiple feature-classifier combinations show that hierarchical systems outperform composite feature-single classifier models. In this study, how hierarchical formations can translate to improved accuracy, when large size feature spaces are involved, is explored for the problem of abdominal organ segmentation. As a result, a semi-automatic, slice-by-slice segmentation method is developed using a novel multi-level and hierarchical neural network (MHNN). MHNN is designed to collect complementary information about organs at each level of the hierarchy via different feature-classifier combinations. Moreover, each level of MHNN receives residual data from the previous level. The residual data is constructed to preserve zero false positive error until the last level of the hierarchy, where only most challenging samples remain. The algorithm mimics analysis behaviour of a radiologist by using the slice-by-slice iteration, which is supported with adjacent slice similarity features. This enables adaptive determination of system parameters and turns into the advantage of online training, which is done in parallel to the segmentation process. Proposed design can perform robust and accurate segmentation of abdominal organs as validated by using diverse data sets with various challenges. PMID:24480371

  10. 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.

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

    PubMed Central

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

    2015-01-01

    Summary: Current strategies for SNP and INDEL discovery incorporate sequence alignments from multiple individuals to maximize sensitivity and specificity. It is widely accepted that this approach also improves structural variant (SV) detection. However, multisample SV analysis has been stymied by the fundamental difficulties of SV calling, e.g. library insert size variability, SV alignment signal integration and detecting long-range genomic rearrangements involving disjoint loci. Extant tools suffer from poor scalability, which limits the number of genomes that can be co-analyzed and complicates analysis workflows. We have developed an approach that enables multisample SV analysis in hundreds to thousands of human genomes using commodity hardware. Here, we describe Hydra-Multi and measure its accuracy, speed and scalability using publicly available datasets provided by The 1000 Genomes Project and by The Cancer Genome Atlas (TCGA). Availability and implementation: Hydra-Multi is written in C++ and is freely available at https://github.com/arq5x/Hydra. Contact: aaronquinlan@gmail.com or ihall@genome.wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25527832

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

    PubMed Central

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

    2016-01-01

    HLDA10 collated a panel of monoclonal antibodies (mAbs) that primarily recognised molecules on human myeloid cell and dendritic cell (DC) populations. As part of the studies, we validated a backbone of mAbs to delineate monocyte and DC populations from peripheral blood. The mAb backbone allowed identification of monocyte and DC subsets using fluorochromes that were compatible with most ‘off the shelf' or routine flow cytometers. Three laboratories used this mAb backbone to assess the HLDA10 panel on blood monocytes and DCs. Each laboratory was provided with enough mAbs to perform five repeat experiments. The data were collated and analysed using Spanning-tree Progression Analysis of Density-normalised Events (SPADE). The data were interrogated for inter- and intra-laboratory variability. The results highlight the definition of DC populations using current readily available reagents. This collaborative process provides the broader scientific community with an invaluable data set that validates mAbs to leucocyte surface molecules. PMID:27195111

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

    PubMed Central

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

    2013-01-01

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

  14. 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

  15. Maternal ethnicity and risk of neural tube defects: a population-based study

    PubMed Central

    Ray, Joel G.; Vermeulen, Marian J.; Meier, Chris; Cole, David E.C.; Wyatt, Philip R.

    2004-01-01

    Background Maternal body mass and the presence of diabetes mellitus are probable risk factors for neural tube defects (NTDs). The association between maternal ethnicity and the risk of NTDs remains poorly understood, however. Methods We performed a retrospective population-based study and included all women in Ontario who underwent antenatal maternal screening (MSS) at 15 to 20 weeks' gestation between 1994 and late 2000. Self-declared maternal date of birth, ethnicity and weight and the presence of pregestational diabetes mellitus were recorded in a standardized fashion on the MSS requisition sheet. NTDs were detected antenatally by ultrasonography or fetal autopsy and postnatally by considering all live and stillborn affected infants beyond 20 weeks' gestation. The risk of open NTD was evaluated across the 5 broad ethnic groups used for MSS, with white ethnicity as the referent. Results Compared with white women (n = 290 799), women of First Nations origin (n = 1551) were at increased associated risk of an NTD-affected pregnancy (adjusted odds ratio [OR] 5.2, 95% confidence interval [CI] 2.1–12.9). Women of other ethnic origins were not at increased associated risk compared with white women (women of Asian origin [n = 75 590]: adjusted OR 0.9, 95% CI 0.6–1.3; black women [n = 25 966]: adjusted OR 0.6, 95% CI 0.3–1.1; women of “other” ethnic origin [n = 10 009]: adjusted OR 0.1, 95% CI 0.02–0.9). Interpretation The associated risk of NTD-affected pregnancies was higher among women of First Nations origin than among women of other ethnic origins. The mechanisms for this discrepancy should be explored. PMID:15313993

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

    PubMed Central

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

    2012-01-01

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

  17. A new multi-electrode array design for chronic neural recording, with independent and automatic hydraulic positioning.

    PubMed

    Sato, T; Suzuki, T; Mabuchi, K

    2007-02-15

    We report on a new microdrive design, which enables the construction of multi-electrode arrays capable of chronically recording the multi-unit neural activity of waking animals. Our principal motivation for inventing this device was to simplify the task of positioning electrodes, which consumes a considerable amount of time and requires a high level of skill. With the new microdrives, each electrode is independently and automatically driven into place. A hydraulic drive system is adopted to reduce the size, weight, and cost of the structure. The hydraulic fluid is also used as a part of the electrical circuit, and facilitates the wiring of the electrodes. A routing system has been attached to reduce the number of tube connections. The microdrive is cylindrical, has a diameter of 23.5 mm, a height of 37 mm, and a weight of 15 g. It allows for up to 22 electrodes, which are arranged on a 0.35 mm grid. Each electrode can be positioned at any depth up to approximately 4mm. The microdrive was evaluated under acute and chronic recording experiments, and is shown to be capable of automatically positioning each electrode and successfully recording the neural signals of waking rats. PMID:16996616

  18. 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.

  19. 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. PMID:26627568

  20. 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.

  1. 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.

  2. 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

  3. Add HOC?: dendritic nonlinearities shape higher-than-pairwise correlations and improve coding in noisy (spiking) neural populations

    NASA Astrophysics Data System (ADS)

    Zylberberg, Joel; Shea-Brown, Eric

    2013-03-01

    Recent experiments with relatively large neural populations show significant higher-order correlations (HOC): the data are poorly fit by pair-wise maximum entropy models, but well-fit by higher-order models. We seek to understand how HOC are shaped by the properties of networks and of the neurons therein, and how these HOC affect population coding. In our presentation, we will demonstrate that dendritic non-linearities similar to those observed in physiology experiments are equivalent to beyond-pairwise interactions in a spin-glass-type statistical model: they can either increase, or decrease, the magnitude of the HOC relative to the pair-wise correlations. We will then discuss a population coding model with parameterized pairwise- and higher-order interactions, revealing the conditions under which the beyond-pairwise interactions (dendritic nonlinearities) can increase the mutual information between a given set of stimuli, and the population responses. For jointly Gaussian stimuli, coding performance can be slightly improved by shaping the output HOC via dendritic nonlinearities, if the neural firing rates are low. For skewed stimulus distributions, like the distribution of luminance values in natural images, the performance gains are much larger. This work was supported by NSF grant DMS-1056125 and a Career Award at the Scientific Interface from the Burroughs-Wellcome Fund.

  4. Long term trends in prevalence of neural tube defects in Europe: population based study

    PubMed Central

    Loane, Maria; de Walle, Hermien; Arriola, Larraitz; Addor, Marie-Claude; Barisic, Ingeborg; Beres, Judit; Bianchi, Fabrizio; Dias, Carlos; Draper, Elizabeth; Garne, Ester; Gatt, Miriam; Haeusler, Martin; Klungsoyr, Kari; Latos-Bielenska, Anna; Lynch, Catherine; McDonnell, Bob; Nelen, Vera; Neville, Amanda J; O’Mahony, Mary T; Queisser-Luft, Annette; Rankin, Judith; Rissmann, Anke; Ritvanen, Annukka; Rounding, Catherine; Sipek, Antonin; Tucker, David; Verellen-Dumoulin, Christine; Wellesley, Diana; Dolk, Helen

    2015-01-01

    Study question What are the long term trends in the total (live births, fetal deaths, and terminations of pregnancy for fetal anomaly) and live birth prevalence of neural tube defects (NTD) in Europe, where many countries have issued recommendations for folic acid supplementation but a policy for mandatory folic acid fortification of food does not exist? Methods This was a population based, observational study using data on 11 353 cases of NTD not associated with chromosomal anomalies, including 4162 cases of anencephaly and 5776 cases of spina bifida from 28 EUROCAT (European Surveillance of Congenital Anomalies) registries covering approximately 12.5 million births in 19 countries between 1991 and 2011. The main outcome measures were total and live birth prevalence of NTD, as well as anencephaly and spina bifida, with time trends analysed using random effects Poisson regression models to account for heterogeneities across registries and splines to model non-linear time trends. Summary answer and limitations Overall, the pooled total prevalence of NTD during the study period was 9.1 per 10 000 births. Prevalence of NTD fluctuated slightly but without an obvious downward trend, with the final estimate of the pooled total prevalence of NTD in 2011 similar to that in 1991. Estimates from Poisson models that took registry heterogeneities into account showed an annual increase of 4% (prevalence ratio 1.04, 95% confidence interval 1.01 to 1.07) in 1995-99 and a decrease of 3% per year in 1999-2003 (0.97, 0.95 to 0.99), with stable rates thereafter. The trend patterns for anencephaly and spina bifida were similar, but neither anomaly decreased substantially over time. The live birth prevalence of NTD generally decreased, especially for anencephaly. Registration problems or other data artefacts cannot be excluded as a partial explanation of the observed trends (or lack thereof) in the prevalence of NTD. What this study adds In the absence of mandatory fortification

  5. 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…

  6. 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

  7. Modeling survival at multi-population scales using mark-recapture data.

    PubMed

    Grosbois, V; Harris, M P; Anker-Nilssen, T; McCleery, R H; Shaw, D N; Morgan, B J T; Gimenez, O

    2009-10-01

    The demography of vertebrate populations is governed in part by processes operating at large spatial scales that have synchronizing effects on demographic parameters over large geographic areas, and in part, by local processes that generate fluctuations that are independent across populations. We describe a statistical model for the analysis of individual monitoring data at the multi-population scale that allows us to (1) split up temporal variation in survival into two components that account for these two types of processes and (2) evaluate the role of environmental factors in generating these two components. We derive from this model an index of synchrony among populations in the pattern of temporal variation in survival, and we evaluate the extent to which environmental factors contribute to synchronize or desynchronize survival variation among populations. When applied to individual monitoring data from four colonies of the Atlantic Puffin (Fratercula arctica), 67% of between-year variance in adult survival was accounted for by a global spatial-scale component, indicating substantial synchrony among colonies. Local sea surface temperature (SST) accounted for 40% of the global spatial-scale component but also for an equally large fraction of the local-scale component. SST thus acted at the same time as both a synchronizing and a desynchronizing agent. Between-year variation in adult survival not explained by the effect of local SST was as synchronized as total between-year variation, suggesting that other unknown environmental factors acted as synchronizing agents. Our approach, which focuses on demographic mechanisms at the multi-population scale, ideally should be combined with investigations of population size time series in order to characterize thoroughly the processes that underlie patterns of multi-population dynamics and, ultimately, range dynamics. PMID:19886500

  8. 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.

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

    PubMed

    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

  12. 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. PMID:20852896

  13. 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’.

  14. 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).

  15. 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

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

    PubMed

    Kuo, R J.; Cohen, P H.

    1999-03-01

    On-line tool wear estimation plays a very critical role in industry automation for higher productivity and product quality. In addition, appropriate and timely decision for tool change is significantly required in the machining systems. Thus, this paper is dedicated to develop an estimation system through integration of two promising technologies, artificial neural networks (ANN) and fuzzy logic. An on-line estimation system consisting of five components: (1) data collection; (2) feature extraction; (3) pattern recognition; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis function (RBF) network is employed to recognize the extracted features. Thereafter, the decisions from multiple sensors are integrated through a proposed fuzzy neural network (FNN) model. Such a model is self-organizing and self-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed system. The results show that the proposed system can significantly increase the accuracy of the product profile. PMID:12662710

  17. 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

  18. 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

    2015-01-01

    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. PMID:26099302

  19. Shifting Spike Times or Adding and Deleting Spikes-How Different Types of Noise Shape Signal Transmission in Neural Populations.

    PubMed

    Voronenko, Sergej O; Stannat, Wilhelm; Lindner, Benjamin

    2015-12-01

    We study a population of spiking neurons which are subject to independent noise processes and a strong common time-dependent input. We show that the response of output spikes to independent noise shapes information transmission of such populations even when information transmission properties of single neurons are left unchanged. In particular, we consider two Poisson models in which independent noise either (i) adds and deletes spikes (AD model) or (ii) shifts spike times (STS model). We show that in both models suprathreshold stochastic resonance (SSR) can be observed, where the information transmitted by a neural population is increased with addition of independent noise. In the AD model, the presence of the SSR effect is robust and independent of the population size or the noise spectral statistics. In the STS model, the information transmission properties of the population are determined by the spectral statistics of the noise, leading to a strongly increased effect of SSR in some regimes, or an absence of SSR in others. Furthermore, we observe a high-pass filtering of information in the STS model that is absent in the AD model. We quantify information transmission by means of the lower bound on the mutual information rate and the spectral coherence function. To this end, we derive the signal-output cross-spectrum, the output power spectrum, and the cross-spectrum of two spike trains for both models analytically. PMID:26458900

  20. 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

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. Satellite rainfall monitoring over Africa using multi-spectral MSG data in an artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Chadwick, Robin; Grimes, David

    2010-05-01

    Rainfall monitoring over Africa is crucial for a variety of humanitarian and agricultural purposes, and satellites have been used for some time to provide real-time rainfall estimates over the region. Several recent applications of satellite rainfall estimates, such as flash-flood warning systems and crop-yield models, require accurate rainfall totals at daily timescales or below. Multi-spectral Meteosat Second Generation (MSG) data provide information on cloud properties such as optical depth and cloud particle size and phase. These parameters are all relevant to the probability of rainfall occurring from a cloud and the likely intensity of that rainfall, so the use of MSG data should lead to improved satellite rainfall estimates. An artificial neural network (ANN) using multi-spectral inputs from MSG has been trained to provide daily rainfall estimates over Ethiopia, using daily rain-gauge data for calibration. Although ANN methods have previously been applied to the problem of producing rainfall estimates from multi-spectral satellite data, in general precipitation radar data have been used for calibration. The advantage of using rain-gauge data is that gauges are far more widespread over Africa than radar networks, so this method can be easily transferred and if necessary re-calibrated in different climatological regions of the continent. The ANN estimates have been validated against independent Ethiopian gauge data at a variety of time and space scales. The ANN shows an improvement in accuracy at daily timescale when compared to rainfall estimates from the TAMSAT algorithm, which uses only single channel MSG data.

  6. 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

  7. Population structuring of multi-copy, antigen-encoding genes in Plasmodium falciparum

    PubMed Central

    Artzy-Randrup, Yael; Rorick, Mary M; Day, Karen; Chen, Donald; Dobson, Andrew P; Pascual, Mercedes

    2012-01-01

    The coexistence of multiple independently circulating strains in pathogen populations that undergo sexual recombination is a central question of epidemiology with profound implications for control. An agent-based model is developed that extends earlier ‘strain theory’ by addressing the var gene family of Plasmodium falciparum. The model explicitly considers the extensive diversity of multi-copy genes that undergo antigenic variation via sequential, mutually exclusive expression. It tracks the dynamics of all unique var repertoires in a population of hosts, and shows that even under high levels of sexual recombination, strain competition mediated through cross-immunity structures the parasite population into a subset of coexisting dominant repertoires of var genes whose degree of antigenic overlap depends on transmission intensity. Empirical comparison of patterns of genetic variation at antigenic and neutral sites supports this role for immune selection in structuring parasite diversity. DOI: http://dx.doi.org/10.7554/eLife.00093.001 PMID:23251784

  8. 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.

  9. 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. PMID:25959336

  10. A microsystem integration platform dedicated to build multi-chip-neural interfaces.

    PubMed

    Ayoub, Amer E; Gosselin, Benoit; Sawan, Mohamad

    2007-01-01

    In this paper, we present an electrical discharge machining (EDM) technique associated with electrochemical steps to construct an appropriate biological interface to neural tissues. The presented microprobe design permits to short the time of production compared to available techniques, while improving the integrity of the electrodes. In addition, we are using a 3D approach to create compact and independent microsystem integration platefrom incorporating array of electrodes and signal processing chips. System-in-package and die-stacking are used to connect the integrated circuits and the array of electrodes on the platform. This approach enables to build a device that will fit in a volume smaller than 1.7 x 1.7 x 3.0 mm(3). This demonstrates the possibility of creating small devices that are suitable to fit in restricted areas for interfacing the brain. PMID:18003539

  11. Epigenetic Marks Define the Lineage and Differentiation Potential of Two Distinct Neural Crest-Derived Intermediate Odontogenic Progenitor Populations

    PubMed Central

    Gopinathan, Gokul; Kolokythas, Antonia

    2013-01-01

    Epigenetic mechanisms, such as histone modifications, play an active role in the differentiation and lineage commitment of mesenchymal stem cells. In the present study, epigenetic states and differentiation profiles of two odontogenic neural crest-derived intermediate progenitor populations were compared: dental pulp (DP) and dental follicle (DF). ChIP on chip assays revealed substantial H3K27me3-mediated repression of odontoblast lineage genes DSPP and dentin matrix protein 1 (DMP1) in DF cells, but not in DP cells. Mineralization inductive conditions caused steep increases of mineralization and patterning gene expression levels in DP cells when compared to DF cells. In contrast, mineralization induction resulted in a highly dynamic histone modification response in DF cells, while there was only a subdued effect in DP cells. Both DF and DP progenitors featured H3K4me3-active marks on the promoters of early mineralization genes RUNX2, MSX2, and DLX5, while OSX, IBSP, and BGLAP promoters were enriched for H3K9me3 or H3K27me3. Compared to DF cells, DP cells expressed higher levels of three pluripotency-associated genes, OCT4, NANOG, and SOX2. Finally, gene ontology comparison of bivalent marks unique for DP and DF cells highlighted cell–cell attachment genes in DP cells and neurogenesis genes in DF cells. In conclusion, the present study indicates that the DF intermediate odontogenic neural crest lineage is distinguished from its DP counterpart by epigenetic repression of DSPP and DMP1 genes and through dynamic histone enrichment responses to mineralization induction. Findings presented here highlight the crucial role of epigenetic regulatory mechanisms in the terminal differentiation of odontogenic neural crest lineages. PMID:23379639

  12. 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. PMID:22346714

  13. 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

  14. 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.

  15. Weak pairwise correlations imply strongly correlated network states in a neural population

    PubMed Central

    Schneidman, Elad; Berry, Michael J.; Segev, Ronen; Bialek, William

    2006-01-01

    Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher-order interactions among large groups of elements have an important role. Here we show, in the vertebrate retina, that weak correlations between pairs of neurons coexist with strongly collective behaviour in the responses of ten or more neurons. We find that this collective behaviour is described quantitatively by models that capture the observed pairwise correlations but assume no higher-order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behaviour. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons. PMID:16625187

  16. 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.

  17. 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

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

    NASA Astrophysics Data System (ADS)

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

    2007-06-01

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

  19. 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. PMID:21399748

  20. 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

  1. 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.

  2. 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

  3. Nondestructive evaluation of loose assemblies using multi-frequency eddy currents and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Vourc'h, Eric; Joubert, Pierre-Yves; Le Gac, Guillaume; larzabal, Pascal

    2013-12-01

    This paper considers the problem of the evaluation of metallic assemblies in an aeronautical context, by means of a non-invasive method. The problems lies in the estimation of the distance separating two aluminum plates representative of a loose assembly (up to 300 µm), the top plate being possibly of unknown thickness ranging from 1 to 8 mm. To do so, the eddy current (EC) method is chosen, because it allows non-contact evaluation of conducting media to be carried out, which is sensitive to electrical conductivity changes in the part under evaluation, and hence to the presence of an air gap between parts. The problem falls into the category of evaluation of a multilayered conductive structure starting from EC data, which is an ill-posed problem. In order to bypass these difficulties, as well as to deal with the uncertainties that may be introduced by the experimental set-up, a ‘non-model’ approach is implemented by means of an artificial neural network (ANN). The latter is elaborated in a statistical learning approach starting from the experimental EC data provided by a ferrite cored coil EC probe used to investigate an assembly mockup of adjustable configuration. Moreover, in order to build a learning database allowing a robust and accurate ANN to be elaborated, as well as to deal with assemblies of unknown thicknesses, we consider EC data obtained at different frequencies chosen in an adjusted frequency bandwidth, experimentally determined so as to optimize the sensitivity toward the presence of an air gap between parts. The implementation of the proposed approach for distances between parts ranging from 60 to 300 µm provided estimated root mean square errors ranging from 7 μm up to 50 µm for the estimation of the distance between parts, and ranging from 20 µm up to 1.4 mm for the estimation of the top plates, ranging from 1 to 8 mm, respectively.

  4. 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.

  5. 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

  6. Multi-objective dynamic population shuffled frog-leaping biclustering of microarray data

    PubMed Central

    2012-01-01

    Background Multi-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot of large-scale datasets. Biclustering technique can simultaneously cluster rows and columns of a dataset, and hlep to extract more accurate information from those datasets. Biclustering need optimize several conflicting objectives, and can be solved with MOO methods. As a heuristics-based optimization approach, the particle swarm optimization (PSO) simulate the movements of a bird flock finding food. The shuffled frog-leaping algorithm (SFL) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. SFL is used to solve the optimization problems of the large-scale datasets. Results This paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data. Experimental results show that the proposed MODPSFLB algorithm can effectively find significant biological structures in terms of related biological processes, components and molecular functions. Conclusions The proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research. PMID:22759615

  7. 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. PMID:24879967

  8. 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

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

    ERIC Educational Resources Information Center

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

    2015-01-01

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

  10. Vitamin D Deficiency in Healthy Male Population: Results of the Iranian Multi- Center Osteoporosis Study

    PubMed Central

    Rahnavard, Z; Eybpoosh, S; Homami, M Rezaei; Meybodi, HR Aghaei; Azemati, B; Heshmat, R; Larijani, B

    2010-01-01

    Background: The prevalence of vitamin D deficiency and its causative factors has been estimated more frequently in elder population, women, and patients with osteoporosis in different countries, but this issue is less defined in male population within different age groups especially in Asian countries. Therefore, we studied the role of effective factors in vitamin D deficiency and its prevalence in Iranian healthy men. Methods: This study was a multi center and carried out in five metropolitans in Iran. Serum 25 Hydroxy vitamin D and other biochemical variables were determined in 2396 healthy men in late winter of 2001. Results: 68.8% of participants suffered from vitamin D deficiency. Vitamin D levels were the highest in Bushehr (n= 111, 40.3%) (P< 0.05) and between Shiraz and Tabriz, Shiraz had the better values (P< 0.05). Tehran had the highest prevalence of vitamin D deficiency (n= 380, n= 85.7%). Geographical zone independently predicted vitamin D status (P< 0.05). There was not any association among age (r= 0.035, P> 0.05), physical activity (r= 0.023, P> 0.05), and exposure of face & hands to sunlight (r= 0.022, P> 0.05) with vitamin D levels. Conclusion: Prevalence of vitamin D deficiency in Iranian male population is high, considering Iranian cultural and geographical zones, food fortification and life style modification is recommended. PMID:23113022

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

    PubMed Central

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

    2012-01-01

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

  12. 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.

  13. 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. PMID

  14. MODELING IN VITRO INHIBITION OF BUTYRYLCHOLINESTERASE USING MOLECULAR DOCKING, MULTI-LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK APPROACHES

    PubMed Central

    Zheng, Fang; Zhan, Max; Huang, Xiaoqin; AbdulHameed, Mohamed Diwan M.; Zhan, Chang-Guo

    2013-01-01

    Butyrylcholinesterase (BChE) has been an important protein used for development of anti-cocaine medication. Through computational design, BChE mutants with ~2000-fold improved catalytic efficiency against cocaine have been discovered in our lab. To study drug-enzyme interaction it is important to build mathematical model to predict molecular inhibitory activity against BChE. This report presents a neural network (NN) QSAR study, compared with multi-linear regression (MLR) and molecular docking, on a set of 93 small molecules that act as inhibitors of BChE by use of the inhibitory activities (pIC50 values) of the molecules as target values. The statistical results for the linear model built from docking generated energy descriptors were: r2 = 0.67, rmsd = 0.87, q2 = 0.65 and loormsd = 0.90; The statistical results for the ligand-based MLR model were: r2 = 0.89, rmsd = 0.51, q2 = 0.85 and loormsd = 0.58; the statistical results for the ligand-based NN model were the best: r2 = 0.95, rmsd = 0.33, q2 = 0.90 and loormsd = 0.48, demonstrating that the NN is powerful in analysis of a set of complicated data. As BChE is also an established drug target to develop new treatment for Alzheimer’s disease (AD). The developped QSAR models provide tools for rationalizing identification of potential BChE inhibitors or selection of compounds for synthesis in the discovery of novel effective inhibitors of BChE in the future. PMID:24290065

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

    PubMed

    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. 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

  17. Differential Classical Conditioning Selectively Heightens Response Gain of Neural Population Activity in Human Visual Cortex

    PubMed Central

    Song, Inkyung; Keil, Andreas

    2015-01-01

    Neutral cues, after being reliably paired with noxious events, prompt defensive engagement and amplified sensory responses. To examine the neurophysiology underlying these adaptive changes, we quantified the contrast-response function of visual cortical population activity during differential aversive conditioning. Steady-state visual evoked potentials (ssVEPs) were recorded while participants discriminated the orientation of rapidly flickering grating stimuli. During each trial, luminance contrast of the gratings was slowly increased and then decreased. Right-tilted gratings (CS+) were paired with loud white noise but left-tilted gratings (CS−) were not. The contrast-following waveform envelope of ssVEPs showed selective amplification of the CS+ only during the high-contrast stage of the viewing epoch. Findings support the notion that motivational relevance, learned in a time frame of minutes, affects vision through a response gain mechanism. PMID:24981277

  18. Design of aspherical surfaces for panoramic imagers using multi-populations genetic algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Li-Ping; Liang, Zhong-zhu; Jin, Chun-Shui

    2009-05-01

    A design method of aspherical surface for panoramic imaging system with two mirrors using multi-populations genetic algorithms is proposed. Astigmatism induced by mirrors may significantly compromise image resolution. To solve this problem, we induced algebraic expression of astigmatism in panoramic imager based on generalized Coddington equation and theory of geometric optics. Then, we propose an optimization process for mirror profile design to eliminate astigmatism and provide purposely-designed projection formula with aid of MPGA. A series of polynomial expressions of aspherical surfaces are obtained and procedures of the design are presented. In order to facilitate ray tracing and aberration calculation, even asphere surface model is obtained by using of hybrid schemes combining MPGA and damped least squares. Finally, a prototype of the catadioptric panoramic imager has been developed and panoramic ring image is obtained.

  19. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

  1. Comparison of Gender Differences in Intracerebral Hemorrhage in a Multi-Ethnic Asian Population

    PubMed Central

    Hsieh, Justin T.; Ang, Beng Ti; Ng, Yew Poh; Allen, John C.; King, Nicolas K. K.

    2016-01-01

    Background Intracerebral hemorrhage (ICH) accounts for 10–15% of all first time strokes and with incidence twice as high in the Asian compared to Western population. This study aims to investigate gender differences in ICH patient outcomes in a multi-ethnic Asian population. Method Data for 1,192 patients admitted for ICH were collected over a four-year period. Multivariate logistic regression was used to identify independent predictors and odds ratios were computed for 30-day mortality and Glasgow Outcome Scale (GOS) comparing males and females. Result Males suffered ICH at a younger age than females (62.2 ± 13.2 years vs. 66.3 ± 15.3 years; P<0.001). The occurrence of ICH was higher among males than females at all ages until 80 years old, beyond which the trend was reversed. Females exhibited increased severity on admission as measured by Glasgow Coma Scale compared to males (10.9 ± 4.03 vs. 11.4 ± 4.04; P = 0.030). No difference was found in 30-day mortality between females and males (F: 30.5% [155/508] vs. M: 27.0% [186/688]), with unadjusted and adjusted odds ratio (F/M) of 1.19 (P = 0.188) and 1.21 (P = 0.300). At discharge, there was a non-statistically significant but potentially clinically relevant morbidity difference between the genders as measured by GOS (dichotomized GOS of 4–5: F: 23.7% [119/503] vs. M: 28.7% [194/677]), with unadjusted and adjusted odds ratio (F/M) of 0.77 (P = 0.055) and 0.87 (P = 0.434). Conclusion In our multi-ethnic Asian population, males developed ICH at a younger age and were more susceptible to ICH than women at all ages other than the beyond 80-year old age group. In contrast to the Western population, neurological status of female ICH patients at admission was poorer and their 30-day mortality was not reduced. Although the study was not powered to detect significance, female showed a trend toward worse 30-day morbidity at discharge. PMID:27050549

  2. Decoding stimulus identity from multi-unit activity and local field potentials along the ventral auditory stream in the awake primate: implications for cortical neural prostheses

    NASA Astrophysics Data System (ADS)

    Smith, Elliot; Kellis, Spencer; House, Paul; Greger, Bradley

    2013-02-01

    Objective. Hierarchical processing of auditory sensory information is believed to occur in two streams: a ventral stream responsible for stimulus identity and a dorsal stream responsible for processing spatial elements of a stimulus. The objective of the current study is to examine neural coding in this processing stream in the context of understanding the possibility for an auditory cortical neural prosthesis. Approach. We examined the selectivity for species-specific primate vocalizations in the ventral auditory processing stream by applying a statistical classifier to neural data recorded from microelectrode arrays. Multi-unit activity (MUA) and local field potential (LFP) data recorded simultaneously from primary auditory complex (AI) and rostral parabelt (PBr) were decoded on a trial-by-trial basis. Main results. While decode performance in AI was well above chance, mean performance in PBr did not deviate >15% from chance level. Mean performance levels were similar for MUA and LFP decodes. Increasing the spectral and temporal resolution improved decode performance; while inter-electrode spacing could be as large as 1.14 mm without degrading decode performance. Significance. These results serve as preliminary guidance for a human auditory cortical neural prosthesis; instructing interface implementation, microstimulation patterns and anatomical placement.

  3. 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

  4. Multiplanar and three-dimensional multi-detector row CT of thoracic vessels and airways in the pediatric population.

    PubMed

    Siegel, Marilyn J

    2003-12-01

    Multi-detector row computed tomography (CT) has changed the approach to imaging of thoracic anatomy and disease in the pediatric population. At the author's institution, multi-detector row CT with multiplanar and three-dimensional reconstruction has become an important examination in the evaluation of systemic and pulmonary vasculature and the tracheobronchial tree. In some clinical situations, multi-detector row CT with reformatted images is obviating conventional angiography, which is associated with higher radiation doses and longer sedation times. Although multi-detector row CT with multiplanar and three-dimensional reconstruction is expanding the applications of CT of the thorax, its role as a diagnostic tool still needs to be better defined. The purposes of this article are to describe how to perform multi-detector row CT with multiplanar and three-dimensional reconstruction in young patients, to discuss various reconstruction techniques available, and to discuss applications in the evaluation of vascular and airways diseases. PMID:14563904

  5. Reward System Dysfunction as a Neural Substrate of Symptom Expression Across the General Population and Patients With Schizophrenia.

    PubMed

    Simon, Joe J; Cordeiro, Sheila A; Weber, Marc-André; Friederich, Hans-Christoph; Wolf, Robert C; Weisbrod, Matthias; Kaiser, Stefan

    2015-11-01

    Dysfunctional patterns of activation in brain reward networks have been suggested as a core element in the pathophysiology of schizophrenia. However, it remains unclear whether this dysfunction is specific to schizophrenia or can be continuously observed across persons with different levels of nonclinical and clinical symptom expression. Therefore, we sought to investigate whether the pattern of reward system dysfunction is consistent with a dimensional or categorical model of psychosis-like symptom expression. 23 patients with schizophrenia and 37 healthy control participants with varying levels of psychosis-like symptoms, separated into 3 groups of low, medium, and high symptom expression underwent event-related functional magnetic resonance imaging while performing a Cued Reinforcement Reaction Time task. We observed lower activation in the ventral striatum during the expectation of high vs no reward to be associated with higher symptom expression across all participants. No significant difference between patients with schizophrenia and healthy participants with high symptom expression was found. However, connectivity between the ventral striatum and the medial orbitofrontal cortex was specifically reduced in patients with schizophrenia. Dysfunctional local activation of the ventral striatum depends less on diagnostic category than on the degree of symptom expression, therefore showing a pattern consistent with a psychosis continuum. In contrast, aberrant connectivity in the reward system is specific to patients with schizophrenia, thereby supporting a categorical view. Thus, the results of the present study provide evidence for both continuous and discontinuous neural substrates of symptom expression across patients with schizophrenia and the general population. PMID:26006262

  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. PMID:26993122

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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. PMID:25750063

  12. 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

  13. 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

  14. 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. PMID:21096380

  15. Assessment of a multi-assay biological diagnostic test for mood disorders in a Japanese population.

    PubMed

    Yamamori, Hidenaga; Ishima, Tamaki; Yasuda, Yuka; Fujimoto, Michiko; Kudo, Noriko; Ohi, Kazutaka; Hashimoto, Kenji; Takeda, Masatoshi; Hashimoto, Ryota

    2016-01-26

    The current diagnostic tests for mood disorders, including major depressive disorder (MDD) and bipolar disorder (BD), have limitations. Inflammatory markers, growth factors, and oxidative stress markers are involved in the pathophysiology of mood disorders. A multi-assay biological diagnostic test combining these biomarkers might improve diagnostic efficiency. The plasma levels of soluble tumor necrosis factor receptor 2 (sTNFR2), epidermal growth factor (EGF), and myeloperoxidase were measured in 40 MDD patients, 40 BD patients and 40 controls in a Japanese population. We also investigated the plasma levels of these markers in 40 patients with schizophrenia to determine the utility of these markers in differential diagnosis. The plasma levels of sTNFR2 were significantly higher in BD and schizophrenia patients than in controls. The plasma levels of EGF and myeloperoxidase were significantly higher in patients with BD than in controls. The correct classification rate obtained from discriminant analysis with sTNFR2 and EGF between controls and mood disorders was 69.2%, with a sensitivity and specificity of 62.5% and 82.5%, respectively. The correct classification rate obtained from discriminant analysis with sTNFR2 and EGF between controls and BD was 85.0%, with a sensitivity and specificity of 77.6% and 92.5%, respectively. Our results suggest that sTNFR2 and EGF could be biological markers of BD. Further studies are needed to determine the utility of these markers in diagnostic tests for mood disorders. PMID:26687272

  16. Effect of population heterogenization on the reproducibility of mouse behavior: a multi-laboratory study.

    PubMed

    Richter, S Helene; Garner, Joseph P; Zipser, Benjamin; Lewejohann, Lars; Sachser, Norbert; Touma, Chadi; Schindler, Britta; Chourbaji, Sabine; Brandwein, Christiane; Gass, Peter; van Stipdonk, Niek; van der Harst, Johanneke; Spruijt, Berry; Võikar, Vootele; Wolfer, David P; Würbel, Hanno

    2011-01-01

    In animal experiments, animals, husbandry and test procedures are traditionally standardized to maximize test sensitivity and minimize animal use, assuming that this will also guarantee reproducibility. However, by reducing within-experiment variation, standardization may limit inference to the specific experimental conditions. Indeed, we have recently shown in mice that standardization may generate spurious results in behavioral tests, accounting for poor reproducibility, and that this can be avoided by population heterogenization through systematic variation of experimental conditions. Here, we examined whether a simple form of heterogenization effectively improves reproducibility of test results in a multi-laboratory situation. Each of six laboratories independently ordered 64 female mice of two inbred strains (C57BL/6NCrl, DBA/2NCrl) and examined them for strain differences in five commonly used behavioral tests under two different experimental designs. In the standardized design, experimental conditions were standardized as much as possible in each laboratory, while they were systematically varied with respect to the animals' test age and cage enrichment in the heterogenized design. Although heterogenization tended to improve reproducibility by increasing within-experiment variation relative to between-experiment variation, the effect was too weak to account for the large variation between laboratories. However, our findings confirm the potential of systematic heterogenization for improving reproducibility of animal experiments and highlight the need for effective and practicable heterogenization strategies. PMID:21305027

  17. 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. PMID:24324814

  18. 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

  19. Effect of Population Heterogenization on the Reproducibility of Mouse Behavior: A Multi-Laboratory Study

    PubMed Central

    Richter, S. Helene; Garner, Joseph P.; Zipser, Benjamin; Lewejohann, Lars; Sachser, Norbert; Touma, Chadi; Schindler, Britta; Chourbaji, Sabine; Brandwein, Christiane; Gass, Peter; van Stipdonk, Niek; van der Harst, Johanneke; Spruijt, Berry; Võikar, Vootele; Wolfer, David P.; Würbel, Hanno

    2011-01-01

    In animal experiments, animals, husbandry and test procedures are traditionally standardized to maximize test sensitivity and minimize animal use, assuming that this will also guarantee reproducibility. However, by reducing within-experiment variation, standardization may limit inference to the specific experimental conditions. Indeed, we have recently shown in mice that standardization may generate spurious results in behavioral tests, accounting for poor reproducibility, and that this can be avoided by population heterogenization through systematic variation of experimental conditions. Here, we examined whether a simple form of heterogenization effectively improves reproducibility of test results in a multi-laboratory situation. Each of six laboratories independently ordered 64 female mice of two inbred strains (C57BL/6NCrl, DBA/2NCrl) and examined them for strain differences in five commonly used behavioral tests under two different experimental designs. In the standardized design, experimental conditions were standardized as much as possible in each laboratory, while they were systematically varied with respect to the animals' test age and cage enrichment in the heterogenized design. Although heterogenization tended to improve reproducibility by increasing within-experiment variation relative to between-experiment variation, the effect was too weak to account for the large variation between laboratories. However, our findings confirm the potential of systematic heterogenization for improving reproducibility of animal experiments and highlight the need for effective and practicable heterogenization strategies. PMID:21305027

  20. Survival and prognostic factors of motor neuron disease in a multi-ethnic Asian population.

    PubMed

    Goh, Khean-Jin; Tian, Sharen; Shahrizaila, Nortina; Ng, Chiu-Wan; Tan, Chong-Tin

    2011-03-01

    Our objective was to determine the survival and prognostic factors of motor neuron disease (MND) in a multi-ethnic cohort of Malaysian patients. All patients seen at a university medical centre between January 2000 and December 2009 had their case records reviewed for demographic, clinical and follow-up data. Mortality data, if unavailable from records, were obtained by telephone interview of relatives or from the national mortality registry. Of the 73 patients, 64.4% were Chinese, 19.2% Malays and 16.4% Indians. Male: female ratio was 1.43: 1. Mean age at onset was 51.5 + 11.3 years. Onset was spinal in 75.3% and bulbar in 24.7% of the patients; 94.5% were ALS and 5.5% were progressive muscular atrophy (PMA). Overall median survival was 44.9 + 5.8 months. Ethnic Indians had shorter interval from symptom onset to diagnosis and shorter median survival compared to non-Indians. On Cox proportional hazards analysis, poor prognostic factors were bulbar onset, shorter interval from symptom onset to diagnosis and worse functional score at presentation. In conclusion, age of onset and median survival duration are similar to previous reports in Asians. Clinical features and prognostic factors are similar to other populations. In our cohort, ethnic Indians had more rapid disease course accounting for their shorter survival. PMID:21039118

  1. Studying populations of eclipsing binaries using large scale multi-epoch photometric surveys

    NASA Astrophysics Data System (ADS)

    Mowlavi, Nami; Barblan, Fabio; Holl, Berry; Rimoldini, Lorenzo; Lecoeur-Taïbi, Isabelle; Süveges, Maria; Eyer, Laurent; Guy, Leanne; Nienartowicz, Krzysztof; Ordonez, Diego; Charnas, Jonathan; Jévardat de Fombelle, Grégory

    2015-08-01

    Large scale multi-epoch photometric surveys provide unique opportunities to study populations of binary stars through the study of eclipsing binaries, provided the basic properties of binary systems can be derived from their light curves without the need to fully model the binary system. Those systems can then be classified into various types from, for example, close to wide systems, from circular to highly elliptical systems, or from systems with similar components to highly asymmetric systems. The challenge is to extract physically relevant information from the light curve geometry.In this contribution, we present the study of eclipsing binaries in the Large Magellanic Clouds (LMC) from the OGLE-III survey. The study is based on the analysis of the geometry of their light curves parameterized using a two-Gaussian model. We show what physical parameters could be extracted from such an analysis, and the results for the LMC eclipsing binaries. The method is very well adapted to process large-scale surveys containing millions of eclipsing binaries, such as is expected from the current Gaia mission or the future LSST survey.

  2. 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. PMID:24569275

  3. "Proprioceptive signature" of cursive writing in humans: a multi-population coding.

    PubMed

    Roll, Jean-Pierre; Albert, Frédéric; Ribot-Ciscar, Edith; Bergenheim, Mikael

    2004-08-01

    The goal of the present study was to investigate the firing behavior of populations of muscle spindle afferents in all the muscles acting on the ankle while this joint was being subjected to "writing-like" movements. First it was proposed to determine whether the ensemble of muscle spindles give rise to a unique, specific, and reproducible feedback information characterizing each letter, number or short word. Secondly, we analyzed how the proprioceptive feedback on the whole encodes the spatial and temporal characteristics of writing movements using the "vector population model". The unitary activity of 51 primary and secondary muscle spindle afferents was recorded in the tibial and common peroneal nerves at the level of the popliteal fossea, using the microneurographic method. The units recorded from belonged to the tibialis anterior, the extensor digitorum longus, the extensor hallucis longus, the peroneus lateralis, the gastrocnemius-soleus and the tibialis posterior muscles. The "writing-like" movements were randomly imposed at a "natural" velocity via a computer-controlled machine in a two-dimensional space. In general, muscle spindle afferents from any of the six muscles responded according to the tuning properties of the parent muscle, i.e. increasing their discharge rate during the phases where the direction of movement was within the preferred sensory sector of the parent muscle. The whole trajectory of the writing movements was coded in turn by the activity of Ia afferents arising from all the muscles acting on the joint. Both single afferent responses and population responses were found to be highly specific and reproducible with each graphic sign. The complex multi-muscle afferent pattern involved, with its timing and distribution in the muscle space, seems to constitute a true "proprioceptive signature" for each graphic symbol. The ensemble of muscle spindle afferents were therefore found to encode the instantaneous direction and velocity of writing

  4. 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. PMID:26054839

  5. 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.

  6. 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

  7. 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.

  8. Multi-wavelength population studies of Active Galactic Nuclei and Galaxies using PRIMUS and AEGIS

    NASA Astrophysics Data System (ADS)

    Mendez, Alexander John

    This dissertation uses large galaxy redshift surveys and multi-wavelength imaging to place observational constraints on the evolution of galaxies and the supermassive black holes that they host since the Universe was roughly half its current age. In the first chapter, we use data from the AEGIS survey to present quantitative morphological measurements of green valley galaxies, to constrain the mechanism(s) responsible for quenching star formation in this transition population and creating elliptical galaxies. We show that green galaxies are generally massive (M*~1010.5M sun) disk galaxies with high concentrations of light. We find that major mergers are not the dominant mechanism responsible for quenching star formation, and we find that either more mild external processes or internal secular processes play a crucial role in halting star formation. In the second chapter, we use data from the PRIMUS survey to investigate Spitzer/IRAC and X-ray AGN selection techniques in order to quantify the overlap, uniqueness, contamination, and completeness of each AGN selection. For roughly similar depth IR and X-ray data, we find that ~75% of IR-selected AGN are also identified as X-ray AGN. For the deepest X-ray data, this fraction increases to ~90%, indicating that at most ~10% of IR-selected AGN may be heavily obscured. While similar overall, the IR-AGN samples preferentially contain more luminous AGN, while the X-ray AGN samples identify AGN with a wider range of accretion rates, where the host galaxy light dominates at IR wavelengths. A more complete AGN sample is created by combining both IR and X-ray selected AGN. Finally, we present a clustering study of X-ray AGN, radio AGN and IR AGN selected AGN using spectroscopic redshifts from the PRIMUS and DEEP2 redshift surveys. Using the cross-correlation of AGN with dense galaxy samples, we find differences in the clustering of AGN selected at different wavelengths. However, we find no significant differences in the

  9. 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

  10. Gender Disparities among Intracerebral Hemorrhage Patients from a Multi-ethnic Population

    PubMed Central

    Galati, Alexandra; King, Sage L

    2015-01-01

    Background: Intracerebral hemorrhage (ICH) is a hemorrhagic stroke with high morbidity and mortality. Recent studies have shown that minorities such as Native Hawaiians and other Pacific Islanders (NHOPI) with ICH are significantly younger compared to whites. However, the interaction of race and gender, and its impact on observed disparities among a multi-ethnic population in Hawai‘i, have not been studied. Methods: Consecutive ICH patients (whites, Asians or NHOPI), who were hospitalized at a single tertiary center on O‘ahu between 2006 and 2013 were retrospectively studied. Clinical characteristics were compared between men and women among the entire cohort, and within the major racial groups. Results: A total of 791 patients (NHOPI 19%, Asians 65%, whites 16%) were studied. Overall, men were younger than women (62±16 years vs 67±18 years respectively, P < .0001). Among whites, ages of men and women were similar (men: 67±14 years vs women: 67±17 years, P = .86). However, among Asians, men were significantly younger than women (men: 63±16 years vs women: 70±17 years, P < .0001). Among NHOPI, ages of men and women were similar (men: 53±15 years vs women: 56±17 years, P = .34), although NHOPI group overall had significantly younger age compared to whites and Asians (NHOPI: 54±16 years vs whites: 67±15 years, P < .0001; vs Asians: 66±17, P < .0001). Conclusions: Overall, men have younger age of ICH presentation than women. However, this observed gender difference was most significant among Asians, but not among whites or NHOPI. PMID:26793409

  11. 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

  12. 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.

  13. 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 ...

  14. 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.

  15. 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. PMID:26343866

  16. A Multi-Scale Distribution Model for Non-Equilibrium Populations Suggests Resource Limitation in an Endangered Rodent

    PubMed Central

    Bean, William T.; Stafford, Robert; Butterfield, H. Scott; Brashares, Justin S.

    2014-01-01

    Species distributions are known to be limited by biotic and abiotic factors at multiple temporal and spatial scales. Species distribution models, however, frequently assume a population at equilibrium in both time and space. Studies of habitat selection have repeatedly shown the difficulty of estimating resource selection if the scale or extent of analysis is incorrect. Here, we present a multi-step approach to estimate the realized and potential distribution of the endangered giant kangaroo rat. First, we estimate the potential distribution by modeling suitability at a range-wide scale using static bioclimatic variables. We then examine annual changes in extent at a population-level. We define “available” habitat based on the total suitable potential distribution at the range-wide scale. Then, within the available habitat, model changes in population extent driven by multiple measures of resource availability. By modeling distributions for a population with robust estimates of population extent through time, and ecologically relevant predictor variables, we improved the predictive ability of SDMs, as well as revealed an unanticipated relationship between population extent and precipitation at multiple scales. At a range-wide scale, the best model indicated the giant kangaroo rat was limited to areas that received little to no precipitation in the summer months. In contrast, the best model for shorter time scales showed a positive relation with resource abundance, driven by precipitation, in the current and previous year. These results suggest that the distribution of the giant kangaroo rat was limited to the wettest parts of the drier areas within the study region. This multi-step approach reinforces the differing relationship species may have with environmental variables at different scales, provides a novel method for defining “available” habitat in habitat selection studies, and suggests a way to create distribution models at spatial and temporal scales

  17. A multi-scale distribution model for non-equilibrium populations suggests resource limitation in an endangered rodent.

    PubMed

    Bean, William T; Stafford, Robert; Butterfield, H Scott; Brashares, Justin S

    2014-01-01

    Species distributions are known to be limited by biotic and abiotic factors at multiple temporal and spatial scales. Species distribution models, however, frequently assume a population at equilibrium in both time and space. Studies of habitat selection have repeatedly shown the difficulty of estimating resource selection if the scale or extent of analysis is incorrect. Here, we present a multi-step approach to estimate the realized and potential distribution of the endangered giant kangaroo rat. First, we estimate the potential distribution by modeling suitability at a range-wide scale using static bioclimatic variables. We then examine annual changes in extent at a population-level. We define "available" habitat based on the total suitable potential distribution at the range-wide scale. Then, within the available habitat, model changes in population extent driven by multiple measures of resource availability. By modeling distributions for a population with robust estimates of population extent through time, and ecologically relevant predictor variables, we improved the predictive ability of SDMs, as well as revealed an unanticipated relationship between population extent and precipitation at multiple scales. At a range-wide scale, the best model indicated the giant kangaroo rat was limited to areas that received little to no precipitation in the summer months. In contrast, the best model for shorter time scales showed a positive relation with resource abundance, driven by precipitation, in the current and previous year. These results suggest that the distribution of the giant kangaroo rat was limited to the wettest parts of the drier areas within the study region. This multi-step approach reinforces the differing relationship species may have with environmental variables at different scales, provides a novel method for defining "available" habitat in habitat selection studies, and suggests a way to create distribution models at spatial and temporal scales

  18. Planar cell polarity gene mutations contribute to the etiology of human neural tube defects in our population.

    PubMed

    De Marco, Patrizia; Merello, Elisa; Piatelli, Gianluca; Cama, Armando; Kibar, Zoha; Capra, Valeria

    2014-08-01

    Neural Tube Defects (NTDs) are congenital malformations that involve failure of the neural tube closure during the early phases of development at any level of the rostro-caudal axis. The planar cell polarity (PCP) pathway is a highly conserved, noncanonical Wnt-Frizzled-Dishevelled signaling cascade, that was first identified in the fruit fly Drosophila. We are here reviewing the role of the PCP pathway genes in the etiology of human NTDs, updating the list of the rare and deleterious mutations identified so far. We report 50 rare nonsynonymous mutations of PCP genes in 54 patients having a pathogenic effect on the protein function. Thirteen mutations that have previously been reported as novel are now reported in public databases, although at very low frequencies. The mutations were private, mostly missense, and transmitted by a healthy parent. To date, no clear genotype-phenotype correlation has been possible to create. Even if PCP pathway genes are involved in the pathogenesis of neural tube defects, future studies will be necessary to better dissect the genetic causes underlying these complex malformations. PMID:24838524

  19. Dealing with incomplete and variable detectability in multi-year, multi-site monitoring of ecological populations

    USGS Publications Warehouse

    Converse, Sarah J.; Royle, J. Andrew

    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

  20. 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…

  1. 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

    2016-01-01

    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

  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. Moroccan Leishmania infantum: Genetic Diversity and Population Structure as Revealed by Multi-Locus Microsatellite Typing

    PubMed Central

    Lemrani, Meryem; Mouna, Idrissi; Mohammed, Hida; Mostafa, Sabri; Rhajaoui, Mohamed; Hamarsheh, Omar; Schönian, Gabriele

    2013-01-01

    Leishmania infantum causes Visceral and cutaneous leishmaniasis in northern Morocco. It predominantly affects children under 5 years with incidence of 150 cases/year. Genetic variability and population structure have been investigated for 33 strains isolated from infected dogs and humans in Morocco. A multilocus microsatellite typing (MLMT) approach was used in which a MLMtype based on size variation in 14 independent microsatellite markers was compiled for each strain. MLMT profiles of 10 Tunisian, 10 Algerian and 21 European strains which belonged to zymodeme MON-1 and non-MON-1 according to multilocus enzyme electrophoresis (MLEE) were included for comparison. A Bayesian model-based approach and phylogenetic analysis inferred two L.infantum sub-populations; Sub-population A consists of 13 Moroccan strains grouped with all European strains of MON-1 type; and sub-population B consists of 15 Moroccan strains grouped with the Tunisian and Algerian MON-1 strains. Theses sub-populations were significantly different from each other and from the Tunisian, Algerian and European non MON-1 strains which constructed one separate population. The presence of these two sub-populations co-existing in Moroccan endemics suggests multiple introduction of L. infantum from/to Morocco; (1) Introduction from/to the neighboring North African countries, (2) Introduction from/to the Europe. These scenarios are supported by the presence of sub-population B and sub-population A respectively. Gene flow was noticed between sub-populations A and B. Five strains showed mixed A/B genotypes indicating possible recombination between the two populations. MLMT has proven to be a powerful tool for eco-epidemiological and population genetic investigations of Leishmania. PMID:24147078

  4. Assessing the health of fish populations in the Clinch River system: Application of multi-response bioindicators

    SciTech Connect

    Adams, M.; Greeley, M.; LeHew, R.; Ham, K.; Bevelhimer, M.

    1995-12-31

    As a component of the Clinch River Remedial Investigation Project, multi-response bioindicators have been used as integrative and holistic measures of fish population and community health. The integrated bioindicator approach involves measuring a suite of selected indicators at several levels of biological organization from the biomolecular to the community levels. Multi-response indicators of stress at several levels of biological organization provides insights into causal mechanisms between contaminant exposure and population-level effects and provides a basis for which the effectiveness of future remedial actions on fish population health can be evaluated. Bioindicator responses were grouped into six functional categories representing indicators of (1) contaminant exposure (detoxification enzymes), (2) organ dysfunction, (3) histopathology, (4) overall fish health (condition indices), (5) feeding and nutritional status, and (6) fish community integrity. Detoxification enzyme induction, histopathological effects, reproductive dysfunction, bioenergetic impairment, and reduced fish community diversity was observed at several sample sites in the Clinch River System. When all the bioindicators were evaluated together in a canonical variate analysis procedure, the integrated site responses segregated clearly into contaminant affected sites and reference areas. Most of these effects appear to be related to the downstream gradient in contaminant loading from the Oak Ridge Reservation and to the pattern of specific PCB congeners occurring at these sites.

  5. Using a multi-port architecture of neural-net associative memory based on the equivalency paradigm for parallel cluster image analysis and self-learning

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Lazarev, Alexander A.; Grabovlyak, Sveta K.; Nikitovich, Diana V.

    2013-01-01

    We consider equivalency models, including matrix-matrix and matrix-tensor and with the dual adaptive-weighted correlation, multi-port neural-net auto-associative and hetero-associative memory (MP NN AAM and HAP), which are equivalency paradigm and the theoretical basis of our work. We make a brief overview of the possible implementations of the MP NN AAM and of their architectures proposed and investigated earlier by us. The main base unit of such architectures is a matrix-matrix or matrix-tensor equivalentor. We show that the MP NN AAM based on the equivalency paradigm and optoelectronic architectures with space-time integration and parallel-serial 2D images processing have advantages such as increased memory capacity (more than ten times of the number of neurons!), high performance in different modes (1010 - 1012 connections per second!) And the ability to process, store and associatively recognize highly correlated images. Next, we show that with minor modifications, such MP NN AAM can be successfully used for highperformance parallel clustering processing of images. We show simulation results of using these modifications for clustering and learning models and algorithms for cluster analysis of specific images and divide them into categories of the array. Show example of a cluster division of 32 images (40x32 pixels) letters and graphics for 12 clusters with simultaneous formation of the output-weighted space allocated images for each cluster. We discuss algorithms for learning and self-learning in such structures and their comparative evaluations based on Mathcad simulations are made. It is shown that, unlike the traditional Kohonen self-organizing maps, time of learning in the proposed structures of multi-port neuronet classifier/clusterizer (MP NN C) on the basis of equivalency paradigm, due to their multi-port, decreases by orders and can be, in some cases, just a few epochs. Estimates show that in the test clustering of 32 1280- element images into 12

  6. 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...

  7. Integration of silicon-based neural probes and micro-drive arrays for chronic recording of large populations of neurons in behaving animals

    NASA Astrophysics Data System (ADS)

    Michon, Frédéric; Aarts, Arno; Holzhammer, Tobias; Ruther, Patrick; Borghs, Gustaaf; McNaughton, Bruce; Kloosterman, Fabian

    2016-08-01

    Objective. Understanding how neuronal assemblies underlie cognitive function is a fundamental question in system neuroscience. It poses the technical challenge to monitor the activity of populations of neurons, potentially widely separated, in relation to behaviour. In this paper, we present a new system which aims at simultaneously recording from a large population of neurons from multiple separated brain regions in freely behaving animals. Approach. The concept of the new device is to combine the benefits of two existing electrophysiological techniques, i.e. the flexibility and modularity of micro-drive arrays and the high sampling ability of electrode-dense silicon probes. Main results. Newly engineered long bendable silicon probes were integrated into a micro-drive array. The resulting device can carry up to 16 independently movable silicon probes, each carrying 16 recording sites. Populations of neurons were recorded simultaneously in multiple cortical and/or hippocampal sites in two freely behaving implanted rats. Significance. Current approaches to monitor neuronal activity either allow to flexibly record from multiple widely separated brain regions (micro-drive arrays) but with a limited sampling density or to provide denser sampling at the expense of a flexible placement in multiple brain regions (neural probes). By combining these two approaches and their benefits, we present an alternative solution for flexible and simultaneous recordings from widely distributed populations of neurons in freely behaving rats.

  8. Epigenetic Profiles in Children with a Neural Tube Defect; A Case-Control Study in Two Populations

    PubMed Central

    Stolk, Lisette; Bouwland-Both, Marieke I.; 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. PMID

  9. 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...

  10. 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. PMID:26838675

  11. Multi-step-ahead predictor design for effective long-term forecast of hydrological signals using a novel wavelet neural network hybrid model

    NASA Astrophysics Data System (ADS)

    Yang, J.-S.; Yu, S.-P.; Liu, G.-M.

    2013-12-01

    In order to increase the accuracy of serial-propagated long-range multi-step-ahead (MSA) prediction, which has high practical value but also great implementary difficulty because of huge error accumulation, a novel wavelet neural network hybrid model - CDW-NN - combining continuous and discrete wavelet transforms (CWT and DWT) and neural networks (NNs), is designed as the MSA predictor for the effective long-term forecast of hydrological signals. By the application of 12 types of hybrid and pure models in estuarine 1096-day river stages forecasting, the different forecast performances and the superiorities of CDW-NN model with corresponding driving mechanisms are discussed. One type of CDW-NN model, CDW-NF, which uses neuro-fuzzy as the forecast submodel, has been proven to be the most effective MSA predictor for the prominent accuracy enhancement during the overall 1096-day long-term forecasts. The special superiority of CDW-NF model lies in the CWT-based methodology, which determines the 15-day and 28-day prior data series as model inputs by revealing the significant short-time periodicities involved in estuarine river stage signals. Comparing the conventional single-step-ahead-based long-term forecast models, the CWT-based hybrid models broaden the prediction range in each forecast step from 1 day to 15 days, and thus reduce the overall forecasting iteration steps from 1096 steps to 74 steps and finally create significant decrease of error accumulations. In addition, combination of the advantages of DWT method and neuro-fuzzy system also benefits filtering the noisy dynamics in model inputs and enhancing the simulation and forecast ability for the complex hydro-system.

  12. Cognitive and neural correlates of the 5-repeat allele of the dopamine D4 receptor gene in a population lacking the 7-repeat allele.

    PubMed

    Takeuchi, Hikaru; Tomita, Hiroaki; Taki, Yasuyuki; Kikuchi, Yoshie; Ono, Chiaki; Yu, Zhiqian; Sekiguchi, Atsushi; Nouchi, Rui; Kotozaki, Yuka; Nakagawa, Seishu; Miyauchi, Carlos Makoto; Iizuka, Kunio; Yokoyama, Ryoichi; Shinada, Takamitsu; Yamamoto, Yuki; Hanawa, Sugiko; Araki, Tsuyoshi; Hashizume, Hiroshi; Kunitoki, Keiko; Sassa, Yuko; Kawashima, Ryuta

    2015-04-15

    The 5-repeat allele of a common length polymorphism in the gene that encodes the dopamine D4 receptor (DRD4) is robustly associated with the risk of attention deficit hyperactivity disorder (ADHD) and substantially exists in Asian populations, which have a lower ADHD prevalence. In this study, we investigated the effect of this allele on microstructural properties of the brain and on its functional activity during externally directed attention-demanding tasks and creative performance in the 765 Asian subjects. For this purpose, we employed diffusion tensor imaging, N-back functional magnetic resonance imaging paradigms, and a test to measure creativity by divergent thinking. The 5-repeat allele was significantly associated with increased originality in the creative performance, increased mean diffusivity (the measure of how the tissue includes water molecules instead of neural and vessel components) in the widespread gray and white matter areas of extensive areas, particularly those where DRD4 is expressed, and reduced task-induced deactivation in the areas that are deactivated during the tasks in the course of both the attention-demanding working memory task and simple sensorimotor task. The observed neural characteristics of 5-repeat allele carriers may lead to an increased risk of ADHD and behavioral deficits. Furthermore, the increased originality of creative thinking observed in the 5-repeat allele carriers may support the notion of the side of adaptivity of the widespread risk allele of psychiatric diseases. PMID:25659462

  13. An obligatory bacterial mutualism in a multi-drug environment exhibits strong oscillatory population dynamics

    NASA Astrophysics Data System (ADS)

    Conwill, Arolyn; Yurtsev, Eugene; Gore, Jeff

    2014-03-01

    A common mechanism of antibiotic resistance in bacteria involves the production of an enzyme that inactivates the antibiotic. By inactivating the antibiotic, resistant cells can protect other cells in the population that would otherwise be sensitive to the drug. In a multidrug environment, an obligatory mutualism arises because populations of different strains rely on each other to breakdown antibiotics in the environment. Here, we experimentally track the population dynamics of two E. coli strains in the presence of two different antibiotics: ampicillin and chloramphenicol. Together the strains are able to grow in antibiotic concentrations that inhibit growth of either one of the strains alone. Although mutualisms are often thought to stabilize population dynamics, we observe strong oscillatory dynamics even when there is long-term coexistence between the two strains. We expect that our results will provide insight into the evolution of antibiotic resistance and, more generally, the evolutionary origin of phenotypic diversity, cooperation, and ecological stability.

  14. CONSTRUCTING, PERTURBATION ANALYSIIS AND TESTING OF A MULTI-HABITAT PERIODIC MATRIX POPULATION MODEL

    EPA Science Inventory

    We present a matrix model that explicitly incorporates spatial habitat structure and seasonality and discuss preliminary results from a landscape level experimental test. Ecological risk to populations is often modeled without explicit treatment of spatially or temporally distri...

  15. 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…

  16. Combination of Wearable Multi-Biosensor Platform and Resonance Frequency Training for Stress Management of the Unemployed Population

    PubMed Central

    Wu, Wanqing; Gil, Yeongjoon; Lee, Jungtae

    2012-01-01

    Currently considerable research is being directed toward developing methodologies for controlling emotion or releasing stress. An applied branch of the basic field of psychophysiology, known as biofeedback, has been developed to fulfill clinical and non-clinical needs related to such control. Wearable medical devices have permitted unobtrusive monitoring of vital signs and emerging biofeedback services in a pervasive manner. With the global recession, unemployment has become one of the most serious social problems; therefore, the combination of biofeedback techniques with wearable technology for stress management of unemployed population is undoubtedly meaningful. This article describes a wearable biofeedback system based on combining integrated multi-biosensor platform with resonance frequency training (RFT) biofeedback strategy for stress management of unemployed population. Compared to commercial system, in situ experiments with multiple subjects indicated that our biofeedback system was discreet, easy to wear, and capable of offering ambulatory RFT biofeedback.Moreover, the comparative studies on the altered autonomic nervous system (ANS) modulation before and after three week RFT biofeedback training was performed in unemployed population with the aid of our wearable biofeedback system. The achieved results suggested that RFT biofeedback in combination with wearable technology was capable of significantly increasingoverall HRV, which indicated by decreasing sympathetic activities, increasing parasympathetic activities, and increasing ANS synchronization. After 3-week RFT-based respiration training, the ANS's regulating function and coping ability of unemployed population have doubled, and tended toward a dynamic balance. PMID:23201994

  17. Combination of wearable multi-biosensor platform and resonance frequency training for stress management of the unemployed population.

    PubMed

    Wu, Wanqing; Gil, Yeongjoon; Lee, Jungtae

    2012-01-01

    Currently considerable research is being directed toward developing methodologies for controlling emotion or releasing stress. An applied branch of the basic field of psychophysiology, known as biofeedback, has been developed to fulfill clinical and non-clinical needs related to such control. Wearable medical devices have permitted unobtrusive monitoring of vital signs and emerging biofeedback services in a pervasive manner. With the global recession, unemployment has become one of the most serious social problems; therefore, the combination of biofeedback techniques with wearable technology for stress management of unemployed population is undoubtedly meaningful. This article describes a wearable biofeedback system based on combining integrated multi-biosensor platform with resonance frequency training (RFT) biofeedback strategy for stress management of unemployed population. Compared to commercial system, in situ experiments with multiple subjects indicated that our biofeedback system was discreet, easy to wear, and capable of offering ambulatory RFT biofeedback.Moreover, the comparative studies on the altered autonomic nervous system (ANS) modulation before and after three week RFT biofeedback training was performed in unemployed population with the aid of our wearable biofeedback system. The achieved results suggested that RFT biofeedback in combination with wearable technology was capable of significantly increasingoverall HRV, which indicated by decreasing sympathetic activities, increasing parasympathetic activities, and increasing ANS synchronization. After 3-week RFT-based respiration training, the ANS's regulating function and coping ability of unemployed population have doubled, and tended toward a dynamic balance. PMID:23201994

  18. Salmonella enterica bacteraemia: a multi-national population-based cohort study

    PubMed Central

    2010-01-01

    Background Salmonella enterica is an important emerging cause of invasive infections worldwide. However, population-based data are limited. The objective of this study was to define the occurrence of S. enterica bacteremia in a large international population and to evaluate temporal and regional differences. Methods We conducted population-based laboratory surveillance for all salmonella bacteremias in six regions (annual population at risk 7.7 million residents) in Finland, Australia, Denmark, and Canada during 2000-2007. Results A total of 622 cases were identified for an annual incidence of 1.02 per 100,000 population. The incidence of typhoidal (serotypes Typhi and Paratyphi) and non-typhoidal (other serotypes) disease was 0.21 and 0.81 per 100,000/year. There was major regional and moderate seasonal and year to year variability with an increased incidence observed in the latter years of the study related principally to increasing rates of non-typhoidal salmonella bacteremias. Advancing age and male gender were significant risk factors for acquiring non-typhoidal salmonella bacteremia. In contrast, typhoidal salmonella bacteremia showed a decreasing incidence with advancing age and no gender-related excess risk. Conclusions Salmonella enterica is an important emerging pathogen and regional determinants of risk merits further investigation. PMID:20398281

  19. [Multi-layer perceptron neural network based algorithm for simultaneous retrieving temperature and emissivity from hyperspectral FTIR data].

    PubMed

    Cheng, Jie; Xiao, Qing; Li, Xiao-Wen; Liu, Qin-Huo; Du, Yong-Ming

    2008-04-01

    The present paper firstly points out the defect of typical temperature and emissivity separation algorithms when dealing with hyperspectral FTIR data: the conventional temperature and emissivity algorithms can not reproduce correct emissivity value when the difference between the ground-leaving radiance and object's blackbody radiation at its true temperature and the instrument random noise are on the same order, and this phenomenon is very prone to occur rence near 714 and 1 250 cm(-1) in the field measurements. In order to settle this defect, a three-layer perceptron neural network has been introduced into the simultaneous inversion of temperature and emissivity from hyperspectral FTIR data. The soil emissivity spectra from the ASTER spectral library were used to produce the training data, the soil emissivity spectra from the MODIS spectral library were used to produce the test data, and the result of network test shows the MLP is robust. Meanwhile, the ISSTES algorithm was used to retrieve the temperature and emissivity form the test data. By comparing the results of MLP and ISSTES, we found the MLP can overcome the disadvantage of typical temperature and emisivity separation, although the rmse of derived emissivity using MLP is lower than the ISSTES as a whole. Hence, the MLP can be regarded as a beneficial complementarity of the typical temperature and emissivity separation. PMID:18619297

  20. Control of Neural Daughter Cell Proliferation by Multi-level Notch/Su(H)/E(spl)-HLH Signaling.

    PubMed

    Bivik, Caroline; MacDonald, Ryan B; Gunnar, Erika; Mazouni, Khalil; Schweisguth, Francois; Thor, Stefan

    2016-04-01

    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

  1. 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

  2. Optimization of simulation models with GADELO: a multi-population genetic algorithm.

    PubMed

    Elketroussi, M; Fan, D P

    1994-02-01

    In this paper, a new Genetic Algorithm based on the Dynamic Exploration of Local Optima (GADELO) was used to estimate the parameters of the MRD (Micro-population model of Risk-group Dynamics) micro-population model for smoking cessation by minimizing a deviation function between the model's predictions and the smoking cessation data of the Multiple Risk Factor Intervention Trial (MRFIT). The efficiency and accuracy of the GADELO estimations were consistently superior to those obtained using the standard genetic algorithm or the simplex algorithm of Nelder-Mead. PMID:8175209

  3. Framing air pollution epidemiology in terms of population interventions, with applications to multi-pollutant modeling

    PubMed Central

    Snowden, Jonathan M.; Reid, Colleen E.; Tager, Ira B.

    2015-01-01

    Air pollution epidemiology continues moving toward the study of mixtures and multi-pollutant modeling. Simultaneously, there is a movement in epidemiology to estimate policy-relevant health effects that can be understood in reference to specific interventions. Scaling regression coefficients from a regression model by an interquartile range (IQR) is one common approach to presenting multi-pollutant health effect estimates. We are unaware of guidance on how to interpret these effect estimates as an intervention. To illustrate the issues of interpretability of IQR-scaled air pollution health effects, we analyzed how daily concentration changes in two air pollutants (NO2 and PM2.5; nitrogen dioxide and particulate matter with aerodynamic diameter ≤ 2.5μm) related to one another within two seasons (summer and winter), within three cities with distinct air pollution profiles (Burbank, California; Houston, Texas; and Pittsburgh, Pennsylvania). In each city-season, we examined how realistically IQR-scaling in multipollutant lag-1 time-series studies reflects a hypothetical intervention that is possible given the observed data. We proposed 2 causal conditions to explicitly link IQR-scaled effects to a clearly defined hypothetical intervention. Condition 1 specified that the index pollutant had to experience a daily concentration change of greater than one IQR, reflecting the notion that the IQR is an appropriate measure of variability between consecutive days. Condition 2 specified that the co-pollutant had to remain relatively constant. We found that in some city-seasons, there were very few instances in which these conditions were satisfied (e.g., 1 day in Pittsburgh during summer). We discuss the practical implications of IQR scaling and suggest alternative approaches to presenting multi-pollutant effects that are supported by empirical data. PMID:25643106

  4. 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.

  5. 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.

  6. The changing epidemiology of group B streptococcus bloodstream infection: a multi-national population-based assessment.

    PubMed

    Ballard, Mark S; Schønheyder, Henrik C; Knudsen, Jenny Dahl; Lyytikäinen, Outi; Dryden, Matthew; Kennedy, Karina J; Valiquette, Louis; Pinholt, Mette; Jacobsson, Gunnar; Laupland, Kevin B

    2016-05-01

    Background Population-based studies conducted in single regions or countries have identified significant changes in the epidemiology of invasive group B streptococcus (GBS) infection. However, no studies have concurrently compared the epidemiology of GBS infections among multiple different regions and countries over time. The study objectives were to define the contemporary incidence and determinants of GBS bloodstream infection (BSI) and assess temporal changes in a multi-national population. Methods Population-based surveillance for GBS BSI was conducted in nine regions in Australia, Canada, Denmark, Sweden, Finland and the UK during 2000-2010. Incidence rates were age- and gender-standardised to the EU population. Results During 114 million patient-years of observation, 3464 cases of GBS BSI were identified for an overall annual incidence of 3.4 patients per 100 000 persons. There were marked differences in the overall (range = 1.8-4.1 per 100 000 person-year) and neonatal (range = 0.19-0.83 per 1000 live births) incidences of GBS BSI observed among the study regions. The overall incidence significantly (p = 0.05) increased. Rates of neonatal disease were stable, while the incidence in individuals older than 60 years doubled (p = 0.003). In patients with detailed data (n = 1018), the most common co-morbidity was diabetes (25%). During the study period, the proportion of cases associated with diabetes increased. Conclusions While marked variability in the incidence of GBS BSI was observed among these regions, it was consistently found that rates increased among older adults, especially in association with diabetes. The burden of this infection may be expected to continue to increase in ageing populations worldwide. PMID:26759190

  7. Variation in Population Synchrony in a Multi-Species Seabird Community: Response to Changes in Predator Abundance.

    PubMed

    Robertson, Gail S; Bolton, Mark; Morrison, Paul; Monaghan, Pat

    2015-01-01

    Ecologically similar sympatric species, subject to typical environmental conditions, may be expected to exhibit synchronous temporal fluctuations in demographic parameters, while populations of dissimilar species might be expected to show less synchrony. Previous studies have tested for synchrony in different populations of single species, and those including data from more than one species have compared fluctuations in only one demographic parameter. We tested for synchrony in inter-annual changes in breeding population abundance and productivity among four tern species on Coquet Island, northeast England. We also examined how manipulation of one independent environmental variable (predator abundance) influenced temporal changes in ecologically similar and dissimilar tern species. Changes in breeding abundance and productivity of ecologically similar species (Arctic Sterna paradisaea, Common S. hirundo and Roseate Terns S. dougallii) were synchronous with one another over time, but not with a species with different foraging and breeding behaviour (Sandwich Terns Thalasseus sandvicensis). With respect to changes in predator abundance, there was no clear pattern. Roseate Tern abundance was negatively correlated with that of large gulls breeding on the island from 1975 to 2013, while Common Tern abundance was positively correlated with number of large gulls, and no significant correlations were found between large gull and Arctic and Sandwich Tern populations. Large gull abundance was negatively correlated with productivity of Arctic and Common Terns two years later, possibly due to predation risk after fledging, while no correlation with Roseate Tern productivity was found. The varying effect of predator abundance is most likely due to specific differences in the behaviour and ecology of even these closely-related species. Examining synchrony in multi-species assemblages improves our understanding of how whole communities react to long-term changes in the

  8. Variation in Population Synchrony in a Multi-Species Seabird Community: Response to Changes in Predator Abundance

    PubMed Central

    Robertson, Gail S.; Bolton, Mark; Morrison, Paul; Monaghan, Pat

    2015-01-01

    Ecologically similar sympatric species, subject to typical environmental conditions, may be expected to exhibit synchronous temporal fluctuations in demographic parameters, while populations of dissimilar species might be expected to show less synchrony. Previous studies have tested for synchrony in different populations of single species, and those including data from more than one species have compared fluctuations in only one demographic parameter. We tested for synchrony in inter-annual changes in breeding population abundance and productivity among four tern species on Coquet Island, northeast England. We also examined how manipulation of one independent environmental variable (predator abundance) influenced temporal changes in ecologically similar and dissimilar tern species. Changes in breeding abundance and productivity of ecologically similar species (Arctic Sterna paradisaea, Common S. hirundo and Roseate Terns S. dougallii) were synchronous with one another over time, but not with a species with different foraging and breeding behaviour (Sandwich Terns Thalasseus sandvicensis). With respect to changes in predator abundance, there was no clear pattern. Roseate Tern abundance was negatively correlated with that of large gulls breeding on the island from 1975 to 2013, while Common Tern abundance was positively correlated with number of large gulls, and no significant correlations were found between large gull and Arctic and Sandwich Tern populations. Large gull abundance was negatively correlated with productivity of Arctic and Common Terns two years later, possibly due to predation risk after fledging, while no correlation with Roseate Tern productivity was found. The varying effect of predator abundance is most likely due to specific differences in the behaviour and ecology of even these closely-related species. Examining synchrony in multi-species assemblages improves our understanding of how whole communities react to long-term changes in the

  9. 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

    2015-01-01

    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. PMID:26408175

  10. Genetic Diversity and Population Structure of Leishmania infantum from Southeastern France: Evaluation Using Multi-Locus Microsatellite Typing.

    PubMed

    Pomares, Christelle; Marty, Pierre; Bañuls, Anne Laure; Lemichez, Emmanuel; Pratlong, Francine; Faucher, Benoît; Jeddi, Fakhri; Moore, Sandy; Michel, Grégory; Aluru, Srikanth; Piarroux, Renaud; Hide, Mallorie

    2016-01-01

    In the south of France, Leishmania infantum is responsible for numerous cases of canine leishmaniasis (CanL), sporadic cases of human visceral leishmaniasis (VL) and rare cases of cutaneous and muco-cutaneous leishmaniasis (CL and MCL, respectively). Several endemic areas have been clearly identified in the south of France including the Pyrénées-Orientales, Cévennes (CE), Provence (P), Alpes-Maritimes (AM) and Corsica (CO). Within these endemic areas, the two cities of Nice (AM) and Marseille (P), which are located 150 km apart, and their surroundings, concentrate the greatest number of French autochthonous leishmaniasis cases. In this study, 270 L. infantum isolates from an extended time period (1978-2011) from four endemic areas, AM, P, CE and CO, were assessed using Multi-Locus Microsatellite Typing (MLMT). MLMT revealed a total of 121 different genotypes with 91 unique genotypes and 30 repeated genotypes. Substantial genetic diversity was found with a strong genetic differentiation between the Leishmania populations from AM and P. However, exchanges were observed between these two endemic areas in which it seems that strains spread from AM to P. The genetic differentiations in these areas suggest strong epidemiological structuring. A model-based analysis using STRUCTURE revealed two main populations: population A (consisting of samples primarily from the P and AM endemic areas with MON-1 and non-MON-1 strains) and population B consisting of only MON-1 strains essentially from the AM endemic area. For four patients, we observed several isolates from different biological samples which provided insight into disease relapse and re-infection. These findings shed light on the transmission dynamics of parasites in humans. However, further data are required to confirm this hypothesis based on a limited sample set. This study represents the most extensive population analysis of L. infantum strains using MLMT conducted in France. PMID:26808522

  11. Genetic Diversity and Population Structure of Leishmania infantum from Southeastern France: Evaluation Using Multi-Locus Microsatellite Typing

    PubMed Central

    Pomares, Christelle; Marty, Pierre; Bañuls, Anne Laure; Lemichez, Emmanuel; Pratlong, Francine; Faucher, Benoît; Jeddi, Fakhri; Moore, Sandy; Michel, Grégory; Aluru, Srikanth; Piarroux, Renaud; Hide, Mallorie

    2016-01-01

    In the south of France, Leishmania infantum is responsible for numerous cases of canine leishmaniasis (CanL), sporadic cases of human visceral leishmaniasis (VL) and rare cases of cutaneous and muco-cutaneous leishmaniasis (CL and MCL, respectively). Several endemic areas have been clearly identified in the south of France including the Pyrénées-Orientales, Cévennes (CE), Provence (P), Alpes-Maritimes (AM) and Corsica (CO). Within these endemic areas, the two cities of Nice (AM) and Marseille (P), which are located 150 km apart, and their surroundings, concentrate the greatest number of French autochthonous leishmaniasis cases. In this study, 270 L. infantum isolates from an extended time period (1978–2011) from four endemic areas, AM, P, CE and CO, were assessed using Multi-Locus Microsatellite Typing (MLMT). MLMT revealed a total of 121 different genotypes with 91 unique genotypes and 30 repeated genotypes. Substantial genetic diversity was found with a strong genetic differentiation between the Leishmania populations from AM and P. However, exchanges were observed between these two endemic areas in which it seems that strains spread from AM to P. The genetic differentiations in these areas suggest strong epidemiological structuring. A model-based analysis using STRUCTURE revealed two main populations: population A (consisting of samples primarily from the P and AM endemic areas with MON-1 and non-MON-1 strains) and population B consisting of only MON-1 strains essentially from the AM endemic area. For four patients, we observed several isolates from different biological samples which provided insight into disease relapse and re-infection. These findings shed light on the transmission dynamics of parasites in humans. However, further data are required to confirm this hypothesis based on a limited sample set. This study represents the most extensive population analysis of L. infantum strains using MLMT conducted in France. PMID:26808522

  12. 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.

  13. 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

  14. Joint Interpretation of Multi-parameter Tomographic Models (e.g., Seismic P and S Velocity, Anisotropy, Attenuation): A Neural Network Approach

    NASA Astrophysics Data System (ADS)

    Bauer, K.

    2008-12-01

    Seismic tomography can provide a set of models which represent different properties of the same target region. A typical example is the development of coincident P and S velocity cross sections from travel time tomography. Other applications may include additional determination of attenuation and anisotropy. Self-organizing maps (SOM) are powerful neural network techniques to classify and interpret multi-attribute data sets. The coincident tomographic images are translated to a set of data vectors in order to train a Kohonen layer. The total gradient of the model vectors is determined for the trained SOM and a watershed segmentation algorithm is used to visualize and map the lithological clusters with well-defined seismic signatures. The principal working flow is demonstrated for a synthetic data set. Further examples include P and S velocity tomography across a sub-volcanic ring complex in Namibia, and combination of velocity, anisotropy, and attenuation tomography to characterize gas hydrate bearing sediments in the Mackenzie Delta, NW Canada.

  15. 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. PMID:26573316

  16. 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

    2015-01-01

    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

  17. A Single-Chip Full-Duplex High Speed Transceiver for Multi-Site Stimulating and Recording Neural Implants.

    PubMed

    Mirbozorgi, S Abdollah; Bahrami, Hadi; Sawan, Mohamad; Rusch, Leslie A; Gosselin, Benoit

    2016-06-01

    We present a novel, fully-integrated, low-power full-duplex transceiver (FDT) to support high-density and bidirectional neural interfacing applications (high-channel count stimulating and recording) with asymmetric data rates: higher rates are required for recording (uplink signals) than stimulation (downlink signals). The transmitter (TX) and receiver (RX) share a single antenna to reduce implant size and complexity. The TX uses impulse radio ultra-wide band (IR-UWB) based on an edge combining approach, and the RX uses a novel 2.4-GHz on-off keying (OOK) receiver. Proper isolation (>20 dB) between the TX and RX path is implemented 1) by shaping the transmitted pulses to fall within the unregulated UWB spectrum (3.1-7 GHz), and 2) by space-efficient filtering (avoiding a circulator or diplexer) of the downlink OOK spectrum in the RX low-noise amplifier. The UWB 3.1-7 GHz transmitter can use either OOK or binary phase shift keying (BPSK) modulation schemes. The proposed FDT provides dual band 500-Mbps TX uplink data rate and 100 Mbps RX downlink data rate, and it is fully integrated into standard TSMC 0.18- μm CMOS within a total size of 0.8 mm(2). The total measured power consumption is 10.4 mW in full duplex mode (5 mW at 100 Mbps for RX, and 5.4 mW at 500 Mbps or 10.8 pJ/bit for TX). Additionally, a 3-coil inductive link along with on-chip power management circuits allows to powering up the implantable transceiver wirelessly by delivering 25 mW extracted from a 13.56-MHz carrier signal, at a total efficiency of 41.6%. PMID:26469635

  18. 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

  19. High genetic differentiation of Aegla longirostri (Crustacea, Decapoda, Anomura) populations in southern Brazil revealed by multi-loci microsatellite analysis.

    PubMed

    Bartholomei-Santos, M L; Roratto, P A; Santos, S

    2011-01-01

    Species with a broad distribution rarely have the same genetic make-up throughout their entire range. In some cases, they may constitute a cryptic complex consisting of a few species, each with a narrow distribution, instead of a single-, widely distributed species. These differences can have profound impacts for biodiversity conservation planning. The genetic differentiation of four populations of Aegla longirostri, a freshwater crab found in two geographically isolated basins in Rio Grande do Sul State, Brazil, was investigated by analyzing pentanucleotide multi-loci microsatellites in a heteroduplex assay. Although no morphological differences were evident, we found significant genetic differentiation among the four populations, based on F(ST) values and clustering analysis. This high level of differentiation may be indicative of cryptic species in these populations. If this hypothesis is correct, then the species occurring in the Ibicuí-Mirim River, at the southern limit of the Atlantic Rain Forest, would be under threat, considering its very restricted distribution. PMID:22179994

  20. 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

  1. Exposure of methyl mercury in utero and the risk of neural tube defects in a Chinese population.

    PubMed

    Jin, Lei; Liu, Ming; Zhang, Le; Li, Zhiwen; Yu, Jingru; Liu, Jianmeng; Ye, Rrongwei; Chen, Laiguo; Ren, Aiguo

    2016-06-01

    To determine if exposure to methyl mercury (MeHg) in utero is associated with an elevated risk of neural tube defects (NTDs), we measured its concentration in the placentas of 36 anencephalic and 44 spina bifida cases, as well as in 50 healthy controls. The median MeHg concentration in NTD cases (0.49ng/g) was higher than that in controls (0.33ng/g). The crude and adjusted odds ratios (ORs) for a MeHg concentration above the median were 3.54 (95% confidence interval (CI), 1.68-7.49) and 3.64 (95% CI, 1.66-7.99), respectively. Both anencephaly and spina bifida subtypes had higher levels of MeHg than the controls. NTD risk increased for subjects in the second and third highest tertile of MeHg concentrations, with an OR of 2.24 (95% CI, 0.93-5.40) and 2.85 (95% CI, 1.17-6.94), respectively. In summary, higher placental levels of MeHg are associated with an elevated risk of NTDs. PMID:27049578

  2. 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.

  3. Genetics of spinosad resistance in a multi-resistant field-selected population of Plutella xylostella.

    PubMed

    Sayyed, Ali H; Omar, Dzolkhifli; Wright, Denis J

    2004-08-01

    Resistance to the bacteria-derived insecticides spinosad (Conserve), abamectin (Vertimec), Bacillus thuringiensis var kurstaki (Btk) (Dipel), B thuringiensis var aizawai (Bta) (Xentari), B thuringiensis crystal endotoxins Cry1Ac and Cry1Ca, and to the synthetic insecticide fipronil was estimated in a freshly-collected field population (CH1 strain) of Plutella xylostella (L) from the Cameron Highlands, Malaysia. Laboratory bioassays at G1 indicated significant levels of resistance to spinosad, abamectin, Cry1Ac, Btk, Cry1Ca, fipronil and Bta when compared with a laboratory insecticide-susceptible population. Logit regression analysis of F1 reciprocal crosses indicated that resistance to spinosad in the CH1 population was inherited as a co-dominant trait. At the highest dose of spinosad tested, resistance was close to completely recessive, while at the lowest dose it was incompletely dominant. A direct test of monogenic inheritance based on a back-cross of F1 progeny with CH1 suggested that resistance to spinosad was controlled by a single locus. PMID:15307676

  4. 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.

  5. 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

  6. 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).

  7. Evaluation of 64 Candidate Single Nucleotide Polymorphisms as Risk Factors for Neural Tube Defects in a Large Irish Study Population

    PubMed Central

    Carter, Tonia C.; Pangilinan, Faith; Troendle, James F.; Molloy, Anne M.; VanderMeer, Julia; Mitchell, Adam; Kirke, Peadar N.; Conley, Mary R.; Shane, Barry; Scott, John M.; Brody, Lawrence C.; Mills, James L.

    2012-01-01

    Individual studies of the genetics of neural tube defects (NTDs) contain results on a small number of genes in each report. To identify genetic risk factors for NTDs, we evaluated potentially functional single nucleotide polymorphisms (SNPs) that are biologically plausible risk factors for NTDs but that have never been investigated for an association with NTDs, examined SNPs that previously showed no association with NTDs in published studies, and tried to confirm previously reported associations in folate-related and non-folate-related genes. We investigated 64 SNPs in 34 genes for association with spina bifida in up to 558 case-families (520 cases, 507 mothers, 457 fathers) and 994 controls in Ireland. Case-control and mother-control comparisons of genotype frequencies, tests of transmission disequilibrium, and log-linear regression models were used to calculate effect estimates. Spina bifida was associated with over-transmission of the LEPR (leptin receptor) rs1805134 minor C allele (genotype relative risk (GRR): 1.5; 95% confidence interval (CI): 1.0, 2.1; P = 0.0264) and the COMT (catechol-O-methyltransferase) rs737865 major T allele (GRR: 1.4; 95% CI: 1.1, 2.0; P = 0.0206). After correcting for multiple comparisons, these individual test P-values exceeded 0.05. Consistent with previous reports, spina bifida was associated with MTHFR 677C>T, T (Brachyury) rs3127334, LEPR K109R, and PDGFRA promoter haplotype combinations. The associations between LEPR SNPs and spina bifida suggest a possible mechanism for the finding that obesity is a NTD risk factor. The association between a variant in COMT and spina bifida implicates methylation and epigenetics in NTDs. PMID:21204206

  8. 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

  9. 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

  10. 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

  11. Aqueous precipitation: Population balance modeling and control in multi-cation systems

    SciTech Connect

    Voigt, J.A.

    1996-03-01

    Efficient separation of metal species from aqueous streams by precipitation techniques requires a fundamental understanding of the processes that occur during precipitation. These processes include particle nucleation, particle growth by solute deposition, agglomerate formation, and agglomerate breakup. Population balance method has been used to develop a kinetic model that accounts for these competing kinetic processes. The usefulness of the model is illustrated through its application to precipitation of yttrium hydroxynitrate, YHN. Kinetic parameters calculated from the model equations and system-specific solution chemistry are used to describe several aspects of the effect of pH on YHN precipitation. Implications for simultaneous precipitation of more than one cation type are discussed with examples. Effects of solution chemistry, precipitator design, and solvent choice are considered.

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

    PubMed

    Li, Yong-Xiang; Li, Chunhui; Bradbury, Peter J; Liu, Xiaolei; Lu, Fei; Romay, Cinta M; Glaubitz, Jeffrey C; Wu, Xun; Peng, Bo; Shi, Yunsu; Song, Yanchun; Zhang, Dengfeng; Buckler, Edward S; Zhang, Zhiwu; Li, Yu; Wang, Tianyu

    2016-06-01

    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 lines under multiple Sino-United States environments. The population included two nested association mapping (NAM) panels and a natural association panel. Nearly 1 million single-nucleotide polymorphisms (SNPs) were used in the analyses. Through the parallel linkage analysis of the two NAM panels, both common and unique flowering time regions were detected. Genome wide, a total of 90 flowering time regions were identified. One-third of these regions were connected to traits associated with the environmental sensitivity of maize flowering time. The genome-wide association study of the three panels identified nearly 1000 flowering time-associated SNPs, mainly distributed around 220 candidate genes (within a distance of 1 Mb). Interestingly, two types of regions were significantly enriched for these associated SNPs - one was the candidate gene regions and the other was the approximately 5 kb regions away from the candidate genes. Moreover, the associated SNPs exhibited high accuracy for predicting flowering time. PMID:27012534

  13. Synergy from Silence in a Combinatorial Neural Code

    PubMed Central

    Schneidman, Elad; Puchalla, Jason L.; Segev, Ronen; Harris, Robert A.; Bialek, William; Berry, Michael J.

    2011-01-01

    The manner in which groups of neurons represent events in the external world is a central question in neuroscience. Estimation of the information encoded by small groups of neurons has shown that in many neural systems, cells carry mildly redundant information. These measures average over all the activity patterns of a neural population. Here, we analyze the population code of the salamander and guinea pig retinas by quantifying the information conveyed by specific multi-cell activity patterns. Synchronous spikes, even though they are relatively rare and highly informative, convey less information than the sum of either spike alone, making them redundant coding symbols. Instead, patterns of spiking in one cell and silence in others, which are relatively common and often overlooked as special coding symbols, were found to be mostly synergistic. Our results reflect that the mild average redundancy between ganglion cells that was previously reported is actually the result of redundant and synergistic multi-cell patterns, whose contributions partially cancel each other when taking the average over all patterns. We further show that similar coding properties emerge in a generic model of neural responses, suggesting that this form of combinatorial coding, in which specific compound patterns carry synergistic or redundant information, may exist in other neural circuits. PMID:22049416

  14. 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. PMID:9090158

  15. Multi-Detector Coronary CT Imaging for the Identification of Coronary Artery Stenoses in a “Real-World” Population

    PubMed Central

    Makaryus, Amgad N; Henry, Sonia; Loewinger, Lee; Makaryus, John N; Boxt, Lawrence

    2014-01-01

    BACKGROUND Multi-detector computed tomography (CT) has emerged as a modality for the non-invasive assessment of coronary artery disease (CAD). Prior studies have selected patients for evaluation and have excluded many of the “real-world” patients commonly encountered in daily practice. We compared 64-detector-CT (64-CT) to conventional coronary angiography (CA) to investigate the accuracy of 64-CT in determining significant coronary stenoses in a “real-world” clinical population. METHODS A total of 1,818 consecutive patients referred for 64-CT were evaluated. CT angiography was performed using the GE LightSpeed VCT (GE® Healthcare). Forty-one patients in whom 64-CT results prompted CA investigation were further evaluated, and results of the two diagnostic modalities were compared. RESULTS A total of 164 coronary arteries and 410 coronary segments were evaluated in 41 patients (30 men, 11 women, age 39–85 years) who were identified by 64-CT to have significant coronary stenoses and who thereafter underwent CA. The overall per-vessel sensitivity, specificity, positive predictive value, negative predictive value, and accuracy at the 50% stenosis level were 86%, 84%, 65%, 95%, and 85%, respectively, and 77%, 93%, 61%, 97%, and 91%, respectively, in the per-segment analysis at the 50% stenosis level. CONCLUSION 64-CT is an accurate imaging tool that allows a non-invasive assessment of significant CAD with a high diagnostic accuracy in a “real-world” population of patients. The sensitivity and specificity that we noted are not as high as those in prior reports, but we evaluated a population of patients that is typically encountered in clinical practice and therefore see more “real-world” results. PMID:25628513

  16. Comparing Two Questionnaires for Eliciting CAM Use in a Multi-Ethnic US Population of Older Adults

    PubMed Central

    Quandt, Sara A.; Ip, Edward H.; Saldana, Santiago; Arcury, Thomas A.

    2011-01-01

    Introduction The NAFKAM International CAM Questionnaire (I-CAM-Q) was designed to facilitate cross-study comparisons of CAM usage. This research presents the first empirical study of the I-CAM-Q’s performance. Materials and Methods Data were collected in two studies in a multi-ethnic (African American, American Indian, and white) population of older adults in the US. In 2010, 564 adults 60+ years were recruited. The I-CAM-Q was interviewer-administered. Data were compared to those collected in 2002 from a random sample of 701 Medicare recipients 65+ years. The 2002 survey included an extensive inventory of specific CAM therapies derived from local ethnographic research. Comparisons of the responses for 14 CAM modalities common to the two studies used logistic regression adjusted for demographics. Results There were no significant differences between the 2002 and 2010 surveys in the proportions reporting 10 modalities, including use of chiropractors, homeopaths, acupuncturists, herbalists, spiritual healers, vitamins, minerals, homeopathic remedies, Qigong, visualization, and prayer for health. Significantly less use of physicians and more use of relaxation techniques were reported in 2010. Herb use and garlic, as a specific herb, were reported significantly less in 2010. Conclusions Overall, the I-CAM-Q obtained results similar to those produced by a population-specific questionnaire. Those differences observed appear to reflect differences in the studies’ inclusion criteria or secular trends in CAM. This study supports the intention of the I-CAM-Q to substitute for local and regional surveys in order to allow cross-study comparisons of CAM use. Further tests, preferably through contemporaneous data collection are needed in other populations. PMID:22792131

  17. 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

  18. Low Child Survival Index in a Multi-Dimensionally Poor Amerindian Population in Venezuela

    PubMed Central

    Villalba, Julian A.; Liu, Yushi; Alvarez, Mauyuri K.; Calderon, Luisana; Canache, Merari; Cardenas, Gaudymar; Del Nogal, Berenice; Takiff, Howard E.; De Waard, Jacobus H.

    2013-01-01

    Background Warao Amerindians, who inhabit the Orinoco Delta, are the second largest indigenous group in Venezuela.  High Warao general mortality rates were mentioned in a limited study 21 years ago. However, there have been no comprehensive studies addressing child survival across the entire population. Objectives To determine the Child Survival-Index (CSI) (ratio: still-living children/total-live births) in the Warao population, the principal causes of childhood death and the socio-demographic factors associated with childhood deaths. Methods We conducted a cross-sectional epidemiological survey of 688 women from 97 communities in 7 different subregions of the Orinoco Delta. Data collected included socio-demographic characteristics and the reproductive history of each woman surveyed. The multidimensional poverty index (MPI) was used to classify the households as deprived across the three dimensions of the Human Development Index. Multivariable linear regression and Generalized Linear Model Procedures were used to identify socioeconomic and environmental characteristics statistically associated with the CSI. Findings The average CSI was 73.8% ±26. The two most common causes of death were gastroenteritis/diarrhea (63%) and acute respiratory tract Infection/pneumonia (18%).  Deaths in children under five years accounted for 97.3% of childhood deaths, with 54% occurring in the neonatal period or first year of life.  Most of the women (95.5%) were classified as multidimensionally poor.  The general MPI in the sample was 0.56.   CSI was negatively correlated with MPI, maternal age, residence in a traditional dwelling and profession of the head of household other than nurse or teacher. Conclusions The Warao have a low CSI which is correlated with MPI and maternal age.  Infectious diseases are responsible for 85% of childhood deaths.  The low socioeconomic development, lack of infrastructure and geographic and cultural isolation suggest that an

  19. CFD of mixing of multi-phase flow in a bioreactor using population balance model.

    PubMed

    Sarkar, Jayati; Shekhawat, Lalita Kanwar; Loomba, Varun; Rathore, Anurag S

    2016-05-01

    Mixing in bioreactors is known to be crucial for achieving efficient mass and heat transfer, both of which thereby impact not only growth of cells but also product quality. In a typical bioreactor, the rate of transport of oxygen from air is the limiting factor. While higher impeller speeds can enhance mixing, they can also cause severe cell damage. Hence, it is crucial to understand the hydrodynamics in a bioreactor to achieve optimal performance. This article presents a novel approach involving use of computational fluid dynamics (CFD) to model the hydrodynamics of an aerated stirred bioreactor for production of a monoclonal antibody therapeutic via mammalian cell culture. This is achieved by estimating the volume averaged mass transfer coefficient (kL a) under varying conditions of the process parameters. The process parameters that have been examined include the impeller rotational speed and the flow rate of the incoming gas through the sparger inlet. To undermine the two-phase flow and turbulence, an Eulerian-Eulerian multiphase model and k-ε turbulence model have been used, respectively. These have further been coupled with population balance model to incorporate the various interphase interactions that lead to coalescence and breakage of bubbles. We have successfully demonstrated the utility of CFD as a tool to predict size distribution of bubbles as a function of process parameters and an efficient approach for obtaining optimized mixing conditions in the reactor. The proposed approach is significantly time and resource efficient when compared to the hit and trial, all experimental approach that is presently used. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:613-628, 2016. PMID:26850863

  20. 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. PMID:27618626

  1. Initial and repeat mammography screening in a low income multi-ethnic population in Los Angeles.

    PubMed

    Bastani, R; Kaplan, C P; Maxwell, A E; Nisenbaum, R; Pearce, J; Marcus, A C

    1995-03-01

    Low income, older, minority women are at high risk for underutilization of screening mammography. One strategy for increasing utilization is to conduct interventions targeting local and state health departments where a majority of these women seek health care. A prerequisite for conducting effective screening programs is to obtain current and accurate information on baseline screening rates to understand the nature and scope of the problem and to plan appropriate intervention strategies. The sample consisted of 3240 women who were 50+ years of age from 2 hospitals and 2 comprehensive health centers operated by the Los Angeles County Department of Health Services. Reviews of medical records indicated that only 21% of the sample had received a mammogram in the 12 months prior to the clinic visit on which they were sampled and 23% of the sample received a mammogram in the following 9 months. Approximately 5% of the total sample received a repeat mammogram in the 21-month period over which they were tracked. Prospective independent predictors of screening were age, number of visits to primary care clinics, number of visits to specialty care clinics, and history of breast abnormalities. The results underscore the importance of implementing programs to increase mammography implementing programs to increase mammography screening within public facilities serving low income multiethnic women. An important finding is that a large number of older women are seen in specialty clinics, which represents an untapped resource for increasing screening in this population. Innovative interventions targeting such specialty clinics could substantially contribute to increasing screening rates. A comprehensive approach targeting system, physician, and patient barriers is recommended. PMID:7742724

  2. Assay-specific artificial neural networks for five different PSA assays and populations with PSA 2-10 ng/ml in 4,480 men.

    PubMed

    Stephan, Carsten; Xu, Chuanliang; Cammann, Henning; Graefen, Markus; Haese, Alexander; Huland, Hartwig; Semjonow, Axel; Diamandis, Eleftherios P; Remzi, Mesut; Djavan, Bob; Wildhagen, Mark F; Blijenberg, Bert G; Finne, Patrik; Stenman, Ulf-Hakan; Jung, Klaus; Meyer, Hellmuth-Alexander

    2007-03-01

    Use of percent free PSA (%fPSA) and artificial neural networks (ANNs) can eliminate unnecessary prostate biopsies. In a total of 4,480 patients from five centers with PSA concentrations in the range of 2-10 ng/ml an IMMULITE PSA-based ANN (iANN) was compared with other PSA assay-adapted ANNs (nANNs) to investigate the impact of different PSA assays. ANN data were generated with PSA, fPSA (assays from Abbott, Beckman, DPC, Roche or Wallac), age, prostate volume, and DRE status. In 15 different ROC analyses, the area under the curve (AUC) in the PSA ranges 2-4, 2-10, and 4-10 ng/ml for the nANN was always significantly larger than the AUC for %fPSA or PSA. The nANN and logistic regression models mostly also performed better than the iANN. Therefore, for each patient population, PSA assay-specific ANNs should be used to optimize the ANN outcome in order to reduce the number of unnecessary biopsies. PMID:17333205

  3. 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

  4. '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. PMID:26780172

  5. Discriminating Multi-Species Populations in Biofilms with Peptide Nucleic Acid Fluorescence In Situ Hybridization (PNA FISH)

    PubMed Central

    Almeida, Carina; Azevedo, Nuno F.; Santos, Sílvio; Keevil, Charles W.; Vieira, Maria J.

    2011-01-01

    Background Our current understanding of biofilms indicates that these structures are typically composed of many different microbial species. However, the lack of reliable techniques for the discrimination of each population has meant that studies focusing on multi-species biofilms are scarce and typically generate qualitative rather than quantitative data. Methodology/Principal Findings We employ peptide nucleic acid fluorescence in situ hybridization (PNA FISH) methods to quantify and visualize mixed biofilm populations. As a case study, we present the characterization of Salmonella enterica/Listeria monocytogenes/Escherichia coli single, dual and tri-species biofilms in seven different support materials. Ex-situ, we were able to monitor quantitatively the populations of ∼56 mixed species biofilms up to 48 h, regardless of the support material. In situ, a correct quantification remained more elusive, but a qualitative understanding of biofilm structure and composition is clearly possible by confocal laser scanning microscopy (CLSM) at least up to 192 h. Combining the data obtained from PNA FISH/CLSM with data from other established techniques and from calculated microbial parameters, we were able to develop a model for this tri-species biofilm. The higher growth rate and exopolymer production ability of E. coli probably led this microorganism to outcompete the other two [average cell numbers (cells/cm2) for 48 h biofilm: E. coli 2,1×108 (±2,4×107); L. monocytogenes 6,8×107 (±9,4×106); and S. enterica 1,4×106 (±4,1×105)]. This overgrowth was confirmed by CSLM, with two well-defined layers being easily identified: the top one with E. coli, and the bottom one with mixed regions of L. monocytogenes and S. enterica. Significance While PNA FISH has been described previously for the qualitative study of biofilm populations, the present investigation demonstrates that it can also be used for the accurate quantification and spatial distribution of species in

  6. The Maternal ITPK1 Gene Polymorphism Is Associated with Neural Tube Defects in a High-Risk Chinese Population

    PubMed Central

    Guo, Jin; Wang, Fang; Wang, Xiuwei; Li, Guannan; Xie, Qiu; Han, Xu; Niu, Bo; Zhang, Ting

    2014-01-01

    Background Epidemiological surveys and animal studies have revealed that inositol metabolism is associated with NTDs, but the mechanisms are not clear. Inositol 1,3,4-trisphosphate 5/6-kinase (ITPK1) is a pivotal regulatory enzyme in inositol metabolic pathway. The objective was to assess the potential impact of the maternal ITPK1 genotypes on the inositol parameter and on the NTD risk in a NTD high-risk area in China. Methodology/Results A case-control study of pregnant women affected with NTDs (n = 200) and controls (n = 320) was carried out. 13 tag SNPs of ITPK1 were selected and genotyped by the Sequenom MassArray system. We found that 4 tag SNPs were statistically significant in spina bifida group (P<0.05). MACH was used to impute the un-genotyped SNPs in ITPK1 locus and showed that 3 meaningful SNPs in the non-coding regions were significant. We also predicted the binding capacity of transcription factors in the positive SNPs using the bioinformatics method and found that only rs3783903 was located in the conserved sequence of activator protein-1 (AP-1). To further study the association between biochemical values and genotypes, maternal plasma inositol hexakisphosphate (IP6) levels were also assessed using LC-MS. The maternal plasma IP6 concentrations in the spina bifida subgroup were 7.1% lower than control (136.67 vs. 147.05 ng mL−1, P<0.05), and significantly lower in rs3783903 GG genotype than others (P<0.05). EMSA showed a different allelic binding capacity of AP-1 in rs3783903, which was affected by an A→G exchange. The RT-PCR suggested the ITPK1 expression was decreased significantly in mutant-type of rs3783903 compared with wild-type in the 60 healthy pregnancies (P<0.05). Conclusions/Significance These results suggested that the maternal rs3783903 of ITPK1 might be associated with spina bifida, and the allele G of rs3783903 might affect the binding of AP-1 and the decrease of maternal plasma IP6 concentration in this Chinese population

  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. Constraining Sub-parsec Binary Supermassive Black Holes in Quasars with Multi-epoch Spectroscopy. I. The General Quasar Population

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

    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 BLR, where R 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-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 binaries is much weakened

  9. 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

  10. Combining radial basis function neural network with genetic algorithm to QSPR modeling of adsorption on multi-walled carbon nanotubes surface

    NASA Astrophysics Data System (ADS)

    Hassanzadeh, Zeinabe; Kompany-Zareh, Mohsen; Ghavami, Raouf; Gholami, Somayeh; Malek-Khatabi, Atefe

    2015-10-01

    The configuring of a radial basis function neural network (RBFN) consists of optimizing the architecture and the network parameters (centers, widths, and weights). Methods such as genetic algorithm (GA), K-means and cluster analysis (CA) are among center selection methods. In the most of reports on RBFN modeling optimum centers are selected among rows of descriptors matrix. A combination of RBFN and GA is introduced for better description of quantitative structure-property relationships (QSPR) models. In this method, centers are not exactly rows of the independent matrix and can be located in any point of the samples space. In the proposed approach, initial centers are randomly selected from the calibration set. Then GA changes the locations of the initially selected centers to find the optimum positions of centers from the whole space of scores matrix, in order to obtain highest prediction ability. This approach is called whole space GA-RBFN (wsGA-RBFN) and applied to predict the adsorption coefficients (logk), of 40 small molecules on the surface of multi-walled carbon nanotubes (MWCNTs). The data consists of five solute descriptors [R, π, α, β, V] of the molecules and known as data set1. Prediction ability of wsGA-RBFN is compared to GA-RBFN and MLR models. The obtained Q2 values for wsGA-RBFN, GA-RBFN and MLR are 0.95, 0.85, and 0.78, respectively, which shows the merit of wsGA-RBFN. The method is also applied on the logarithm of surface area normalized adsorption coefficients (logKSA), of organic compounds (OCs) on MWCNTs surface. The data set2 includes 69 aromatic molecules with 13 physicochemical properties of the OCs. Thirty-nine of these molecules were similar to those of data set1 and the others were aromatic compounds included of small and big molecules. Prediction ability of wsGA-RBFN for second data set was compared to GA-RBF. The Q2 values for wsGA-RBFN and GA-RBF are obtained as 0.89 and 0.80, respectively.

  11. The positive mental health instrument: development and validation of a culturally relevant scale in a multi-ethnic asian population

    PubMed Central

    2011-01-01

    Background Instruments to measure mental health and well-being are largely developed and often used within Western populations and this compromises their validity in other cultures. A previous qualitative study in Singapore demonstrated the relevance of spiritual and religious practices to mental health, a dimension currently not included in exiting multi-dimensional measures. The objective of this study was to develop a self-administered measure that covers all key and culturally appropriate domains of mental health, which can be applied to compare levels of mental health across different age, gender and ethnic groups. We present the item reduction and validation of the Positive Mental Health (PMH) instrument in a community-based adult sample in Singapore. Methods Surveys were conducted among adult (21-65 years) residents belonging to Chinese, Malay and Indian ethnicities. Exploratory and confirmatory factor analysis (EFA, CFA) were conducted and items were reduced using item response theory tests (IRT). The final version of the PMH instrument was tested for internal consistency and criterion validity. Items were tested for differential item functioning (DIF) to check if items functioned in the same way across all subgroups. Results: EFA and CFA identified six first-order factor structure (General coping, Personal growth and autonomy, Spirituality, Interpersonal skills, Emotional support, and Global affect) under one higher-order dimension of Positive Mental Health (RMSEA = 0.05, CFI = 0.96, TLI = 0.96). A 47-item self-administered multi-dimensional instrument with a six-point Likert response scale was constructed. The slope estimates and strength of the relation to the theta for all items in each six PMH subscales were high (range:1.39 to 5.69), suggesting good discrimination properties. The threshold estimates for the instrument ranged from -3.45 to 1.61 indicating that the instrument covers entire spectrums for the six dimensions. The instrument demonstrated

  12. 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. PMID:26663415

  13. Cognitive Neural Prosthetics

    PubMed Central

    Andersen, Richard A.; Hwang, Eun Jung; Mulliken, Grant H.

    2010-01-01

    The cognitive neural prosthetic (CNP) is a very versatile method for assisting paralyzed patients and patients with amputations. The CNP records the cognitive state of the subject, rather than signals strictly related to motor execution or sensation. We review a number of high-level cortical signals and their application for CNPs, including intention, motor imagery, decision making, forward estimation, executive function, attention, learning, and multi-effector movement planning. CNPs are defined by the cognitive function they extract, not the cortical region from which the signals are recorded. However, some cortical areas may be better than others for particular applications. Signals can also be extracted in parallel from multiple cortical areas using multiple implants, which in many circumstances can increase the range of applications of CNPs. The CNP approach relies on scientific understanding of the neural processes involved in cognition, and many of the decoding algorithms it uses also have parallels to underlying neural circuit functions. PMID:19575625

  14. 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

  15. Neural synchrony in ventral cochlear nucleus neuron populations is not mediated by intrinsic processes but is stimulus induced: implications for auditory brainstem implants

    NASA Astrophysics Data System (ADS)

    Shivdasani, Mohit N.; Mauger, Stefan J.; Rathbone, Graeme D.; Paolini, Antonio G.

    2009-12-01

    The aim of this investigation was to elucidate if neural synchrony forms part of the spike time-based theory for coding of sound information in the ventral cochlear nucleus (VCN) of the auditory brainstem. Previous research attempts to quantify the degree of neural synchrony at higher levels of the central auditory system have indicated that synchronized firing of neurons during presentation of an acoustic stimulus could play an important role in coding complex sound features. However, it is unknown whether this synchrony could in fact arise from the VCN as it is the first station in the central auditory pathway. Cross-correlation analysis was conducted on 499 pairs of multiunit clusters recorded in the urethane-anesthetized rat VCN in response to pure tones and combinations of two tones to determine the presence of neural synchrony. The shift predictor correlogram was used as a measure for determining the synchrony owing to the effects of the stimulus. Without subtraction of the shift predictor, over 65% of the pairs of multiunit clusters exhibited significant correlation in neural firing when the frequencies of the tones presented matched their characteristic frequencies (CFs). In addition, this stimulus-evoked neural synchrony was dependent on the physical distance between electrode sites, and the CF difference between multiunit clusters as the number of correlated pairs dropped significantly for electrode sites greater than 800 µm apart and for multiunit cluster pairs with a CF difference greater than 0.5 octaves. However, subtraction of the shift predictor correlograms from the raw correlograms resulted in no remaining correlation between all VCN pairs. These results suggest that while neural synchrony may be a feature of sound coding in the VCN, it is stimulus induced and not due to intrinsic neural interactions within the nucleus. These data provide important implications for stimulation strategies for the auditory brainstem implant, which is used to

  16. 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

  17. Levels of PAH-DNA Adducts in Cord Blood and Cord Tissue and the Risk of Fetal Neural Tube Defects in a Chinese Population

    PubMed Central

    Yi, Deqing; Yuan, Yue; Jin, Lei; Zhou, Guodong; Zhu, Huiping; Finnell, Richard H.; Ren, Aiguo

    2014-01-01

    Introduction Maternal exposure to polycyclic aromatic hydrocarbons (PAHs) has been shown to be associated with an elevated risk for neural tube defects (NTDs). In the human body, PAHs are bioactivated and the resultant reactive epoxides can covalently bind to DNA to form PAH-DNA adducts, which may, in turn, cause transcription errors, changes in gene expression or altered patterns of apoptosis. During critical developmental phases, these changes can result in abnormal morphogenesis. Objectives We aimed to examine the relationship between the levels of PAH-DNA adducts in cord blood and cord tissue and the risk of NTDs. Methods From 2010 to 2012, sixty NTD cases and 60 healthy controls were recruited from a population-based birth defects surveillance system in five counties of Shanxi Province in Northern China, where the emission of PAHs remains one of the highest in the country and PAHs exposure is highly prevalent. PAH-DNA adducts in cord blood of 15 NTD cases and 15 control infants, and in cord tissue of 60 NTD cases and 60 control infants were measured using the 32P-postlabeling method. Results PAH-DNA adduct levels in cord blood tend to be higher in the NTD group (28.5 per 108 nucleotides) compared with controls (19.7 per 108 nucleotides), although the difference was not statistically significant (P=0.377). PAH-DNA adducts in cord tissue were significantly higher in the NTD group (24.6 per 106 nucleotides) than in the control group (15.3 per 106 nucleotides), P=0.010. A positive dose-response relationship was found between levels of PAH-DNA adducts in cord tissue and the risk of NTDs (P=0.009). When the lowest tertile was used as the referent and potential confounding factors were adjusted for, a 1.03-fold (95% CI, 0.37–2.89) and 2.96-fold (95% CI, 1.16–7.58) increase in the risk of NTDs was observed for fetuses whose cord tissue PAH-DNA adduct levels were in the second and highest tertile, respectively. Conclusions High levels of PAH-DNA adducts in fetal

  18. Neural constraints on learning

    PubMed Central

    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.

    2014-01-01

    Motor, sensory, and cognitive learning require networks of neurons to generate new activity patterns. Because some behaviors are easier to learn than others1,2, we wondered if some neural activity patterns are easier to generate than others. We asked whether the existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define the constraint. We employed a closed-loop intracortical brain-computer interface (BCI) learning paradigm in which Rhesus monkeys controlled a computer cursor by modulating neural activity patterns in primary motor cortex. Using the BCI 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. These patterns comprise a low-dimensional space (termed the intrinsic manifold, or IM) within the high-dimensional neural firing rate space. They presumably reflect constraints imposed by the underlying neural circuitry. We found that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the IM. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the IM. This result suggests that the existing structure of a network can shape learning. On the timescale of hours, it appears 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 possess3,4. PMID:25164754

  19. Multi-Scale Modeling of Riverine Ecosystems and Responses of Fish Populations in the Context of Global Climate Change and Predictive Uncertainty

    NASA Astrophysics Data System (ADS)

    Wildhaber, M. L.; Wikle, C. K.; Anderson, C. J.; Franz, K. J.; Moran, E. H.

    2010-12-01

    Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by course-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and hierarchical Bayesian modeling frameworks to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global change.

  20. 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

  1. 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

    Santoro, Simone; Pacios, Isa; Moreno, Sacramento; Bertó-Moran, Alejandro; Rouco, Carlos

    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

  2. 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

  3. 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

  4. 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. PMID:26592605

  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. PMID:26488299

  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. PMID:23055296

  8. Hierarchical Analysis and Diversity Studies of Xylella fastidiosa Populations in California by Multi-locus Simple Sequence Repeat Markers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Xylella fastidiosa is the causative agent of Pierce’s Disease (PD) in grapevine. Using 18 simple sequence repeat (SSR) markers, we assessed variation within and between populations of X. fastidiosa isolated from grapevine in California. Eighty-three X. fastidiosa isolates from 15 populations presen...

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

    PubMed Central

    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.

    2014-01-01

    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. PMID:25063452

  10. Age and diet effects on fecal populations of a multi-drug-resistant Escherichia coli in dairy calves

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The use of antimicrobial drugs is reported to increase the prevalence of resistant bacteria, including commensals. Dairy calves are colonized at a very young age by a multi-drug-resistant E. coli (MDR EC), and research indicates that the prevalence is not related to recent use of antimicrobials but...

  11. Population genetics of multi-drug resistant (MDR) IncA/C plasmid in Salmonella enterica isolated from animals

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Food animals harboring Multi-Drug Resistant (MDR) Salmonella enterica are a potential source for acquisition of zoonotic pathogens. Plasmids (small, self-replicating, extra-chromosomal DNA) are often associated with antimicrobial resistance and plasmids carrying MDR genes have been found to be a maj...

  12. Multi-locus genotyping reveals absence of genetic structure in field populations of the brown ear tick (Rhipicephalus appendiculatus) in Kenya.

    PubMed

    Kanduma, Esther G; Mwacharo, Joram M; Mwaura, Stephen; Njuguna, Joyce N; Nzuki, Inosters; Kinyanjui, Peter W; Githaka, Naftaly; Heyne, Heloise; Hanotte, Olivier; Skilton, Robert A; Bishop, Richard P

    2016-02-01

    Rhipicephalus appendiculatus is an important tick vector of several pathogens and parasitizes domestic and wild animals across eastern and southern Africa. However, its inherent genetic variation and population structure is poorly understood. To investigate whether mammalian host species, geographic separation and resulting reproductive isolation, or a combination of these, define the genetic structure of R. appendiculatus, we analyzed multi-locus genotype data from 392 individuals from 10 geographic locations in Kenya generated in an earlier study. These ticks were associated with three types of mammalian host situations; (1) cattle grazing systems, (2) cattle and wildlife co-grazing systems (3) wildlife grazing systems without livestock. We also analyzed data from 460 individuals from 10 populations maintained as closed laboratory stocks and 117 individuals from five other species in the genus Rhipicephalus. The pattern of genotypes observed indicated low levels of genetic differentiation between the ten field populations (FST=0.014±0.002) and a lack of genetic divergence corresponding to the degree of separation of the geographic sampling locations. There was also no clear association of particular tick genotypes with specific host species. This is consistent with tick dispersal over large geographic ranges and lack of host specificity. In contrast, the 10 laboratory populations (FST=0.248±0.015) and the five other species of Rhipicephalus (FST=0.368±0.032) were strongly differentiated into distinct genetic groups. Some laboratory bred populations diverged markedly from their field counterparts in spite of originally being sampled from the same geographic locations. Our results demonstrate a lack of defined population genetic differentiation in field populations of the generalist R. appendiculatus in Kenya, which may be a result of the frequent anthropogenic movement of livestock and mobility of its several wildlife hosts between different locations. PMID

  13. 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. PMID:27105864

  14. A low-power current-reuse dual-band analog front-end for multi-channel neural signal recording.

    PubMed

    Sepehrian, H; Gosselin, B

    2014-01-01

    Thoroughly studying the brain activity of freely moving subjects requires miniature data acquisition systems to measure and wirelessly transmit neural signals in real time. In this application, it is mandatory to simultaneously record the bioelectrical activity of a large number of neurons to gain a better knowledge of brain functions. However, due to limitations in transferring the entire raw data to a remote base station, employing dedicated data reduction techniques to extract the relevant part of neural signals is critical to decrease the amount of data to transfer. In this work, we present a new dual-band neural amplifier to separate the neuronal spike signals (SPK) and the local field potential (LFP) simultaneously in the analog domain, immediately after the pre-amplification stage. By separating these two bands right after the pre-amplification stage, it is possible to process LFP and SPK separately. As a result, the required dynamic range of the entire channel, which is determined by the signal-to-noise ratio of the SPK signal of larger bandwidth, can be relaxed. In this design, a new current-reuse low-power low-noise amplifier and a new dual-band filter that separates SPK and LFP while saving capacitors and pseudo resistors. A four-channel dual-band (SPK, LFP) analog front-end capable of simultaneously separating SPK and LFP is implemented in a TSMC 0.18 μm technology. Simulation results present a total power consumption per channel of 3.1 μw for an input referred noise of 3.28 μV and a NEF for 2.07. The cutoff frequency of the LFP band is fc=280 Hz, and fL=725 Hz and fL=11.2 KHz for SPK, with 36 dB gain for LFP band 46 dB gain for SPK band. PMID:25571186

  15. A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

    NASA Astrophysics Data System (ADS)

    Li, Xiaodong; Ling, Feng; Du, Yun; Feng, Qi; Zhang, Yihang

    2014-07-01

    The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial-temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the

  16. 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

  17. 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.

  18. 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

  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. 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

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

    PubMed

    Biljecki, Filip; 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

  2. 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.

  3. 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

  4. 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

  5. Nonlinear PLS modeling using neural networks

    SciTech Connect

    Qin, S.J.; McAvoy, T.J.

    1994-12-31

    This paper discusses the embedding of neural networks into the framework of the PLS (partial least squares) modeling method resulting in a neural net PLS modeling approach. By using the universal approximation property of neural networks, the PLS modeling method is genealized to a nonlinear framework. The resulting model uses neural networks to capture the nonlinearity and keeps the PLS projection to attain robust generalization property. In this paper, the standard PLS modeling method is briefly reviewed. Then a neural net PLS (NNPLS) modeling approach is proposed which incorporates feedforward networks into the PLS modeling. A multi-input-multi-output nonlinear modeling task is decomposed into linear outer relations and simple nonlinear inner relations which are performed by a number of single-input-single-output networks. Since only a small size network is trained at one time, the over-parametrized problem of the direct neural network approach is circumvented even when the training data are very sparse. A conjugate gradient learning method is employed to train the network. It is shown that, by analyzing the NNPLS algorithm, the global NNPLS model is equivalent to a multilayer feedforward network. Finally, applications of the proposed NNPLS method are presented with comparison to the standard linear PLS method and the direct neural network approach. The proposed neural net PLS method gives better prediction results than the PLS modeling method and the direct neural network approach.

  6. Testing for genetic associations in a spina bifida population: analysis of the HOX gene family and human candidate gene regions implicated by mouse models of neural tube defects.

    PubMed

    Volcik, K A; Blanton, S H; Kruzel, M C; Townsend, I T; Tyerman, G H; Mier, R J; Northrup, H

    2002-07-01

    Neural tube defects (NTDs) are among the most common severely disabling birth defects in the United States, affecting approximately 1-2 of every 1,000 live births. The etiology of NTDs is multifactorial, involving the combined action of both genetic and environmental factors. HOX genes play a central role in establishing the initial body plan by providing positional information along the anterior-posterior body and limb axis and have been implicated in neural tube closure. There are many mouse models that exhibit both naturally occurring NTDs in various mouse strains as well as NTDs that have been created by "knocking out" various genes. A nonparametric linkage method, the transmission disequilibrium test (TDT), was utilized to test the HOX gene family and human equivalents of genes (when known) or the syntenic region in humans to those in mouse models which could play a role in the formation of NTDs. DNA from 459 spina bifida (SB) affected individuals and their parents was tested for linkage and association utilizing polymorphic markers from within or very close to the HOXA, HOXB, HOXC, and HOXD genes as well as from within the genes/gene regions of eight mouse models that exhibit NTDs. No significant findings were obtained for the tested markers. PMID:12116226

  7. 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

  8. 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

    2015-10-01

    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

  9. Metastable dynamics in heterogeneous neural fields.

    PubMed

    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. 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

  11. The Impact of Multi-Dimensional Behavioral Interventions in Student Conduct Processes: Achieving Increased Learning Outcomes in Adult Student Populations

    ERIC Educational Resources Information Center

    Braddix, D'Andre Cortez

    2012-01-01

    As adult students constitute nearly half of all undergraduates in the United States, college practitioners need to identify effective disciplinary strategies for this population when violations of institutional rules and regulations occur. The purpose of this quasi-experimental, action research study was to modify the student conduct process for…

  12. 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

  13. 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

  14. 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. PMID:26041397

  15. Time-location patterns of a diverse population of older adults: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

    PubMed

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

    2016-06-01

    The primary aim of this analysis was to present and describe questionnaire data characterizing time-location patterns of an older, multiethnic population from six American cities. We evaluated the 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 h/week or 72%). More than 50% of the 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 with national cohorts demonstrated the 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 the time-location patterns in an older, multiethnic 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

  16. 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

  17. 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

  18. Multi-locus phylogeographic and population genetic analysis of Anolis carolinensis: historical demography of a genomic model species.

    PubMed

    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

  19. Predictive Markers for AD in a Multi-Modality Framework: An Analysis of MCI Progression in the ADNI Population

    PubMed Central

    Hinrichs, Chris; Singh, Vikas; Xu, Guofan; Johnson, Sterling C.

    2011-01-01

    Alzheimer’s Disease (AD) and other neurodegenerative diseases affect over 20 million people worldwide, and this number is projected to significantly increase in the coming decades. Proposed imaging-based markers have shown steadily improving levels of sensitivity/specificity in classifying individual subjects as AD or normal. Several of these efforts have utilized statistical machine learning techniques, using brain images as input, as means of deriving such AD-related markers. A common characteristic of this line of research is a focus on either (1) using a single imaging modality for classification, or (2) incorporating several modalities, but reporting separate results for each. One strategy to improve on the success of these methods is to leverage all available imaging modalities together in a single automated learning framework. The rationale is that some subjects may show signs of pathology in one modality but not in another – by combining all available images a clearer view of the progression of disease pathology will emerge. Our method is based on the Multi-Kernel Learning (MKL) framework, which allows the inclusion of an arbitrary number of views of the data in a maximum margin, kernel learning framework. The principal innovation behind MKL is that it learns an optimal combination of kernel (similarity) matrices while simultaneously training a classifier. In classification experiments MKL outperformed an SVM trained on all available features by 3% – 4%. We are especially interested in whether such markers are capable of identifying early signs of the disease. To address this question, we have examined whether our multi-modal disease marker (MMDM) can predict conversion from Mild Cognitive Impairment (MCI) to AD. Our experiments reveal that this measure shows significant group differences between MCI subjects who progressed to AD, and those who remained stable for 3 years. These differences were most significant in MMDMs based on imaging data. We also

  20. 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.

  1. STELLAR POPULATIONS IN COMPACT GALAXY GROUPS: A MULTI-WAVELENGTH STUDY OF HCGs 16, 22, AND 42, THEIR STAR CLUSTERS, AND DWARF GALAXIES

    SciTech Connect

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

    2013-06-20

    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.

  2. Stellar Populations in Compact Galaxy Groups: A Multi-wavelength Study of HCGs 16, 22, and 42, their Star Clusters, and Dwarf Galaxies

    NASA Astrophysics Data System (ADS)

    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, P.; Gronwall, C.

    2013-06-01

    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.

  3. 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

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

    PubMed

    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 R(2) 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

  5. No evidence of interaction between known lipid-associated genetic variants and smoking in the multi-ethnic PAGE population.

    PubMed

    Dumitrescu, Logan; Carty, Cara L; Franceschini, Nora; Hindorff, Lucia A; Cole, Shelley A; Bůžková, Petra; Schumacher, Fredrick R; Eaton, Charles B; Goodloe, Robert J; Duggan, David J; Haessler, Jeff; Cochran, Barbara; Henderson, Brian E; Cheng, Iona; Johnson, Karen C; Carlson, Chris S; Love, Shelly-Anne; Brown-Gentry, Kristin; Nato, Alejandro Q; Quibrera, Miguel; Shohet, Ralph V; Ambite, José Luis; Wilkens, Lynne R; Le Marchand, Loïc; Haiman, Christopher A; Buyske, Steven; Kooperberg, Charles; North, Kari E; Fornage, Myriam; Crawford, Dana C

    2013-12-01

    Genome-wide association studies (GWAS) have identified many variants that influence high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and/or triglycerides. However, environmental modifiers, such as smoking, of these known genotype-phenotype associations are just recently emerging in the literature. We have tested for interactions between smoking and 49 GWAS-identified variants in over 41,000 racially/ethnically diverse samples with lipid levels from the Population Architecture Using Genomics and Epidemiology (PAGE) study. Despite their biological plausibility, we were unable to detect significant SNP × smoking interactions. PMID:24100633

  6. Prevalence of PALB2 Mutations in Breast Cancer Patients in Multi-Ethnic Asian Population in Malaysia and Singapore

    PubMed Central

    Phuah, Sze Yee; Lee, Sheau Yee; Kang, Peter; Kang, In Nee; Yoon, Sook-Yee; Thong, Meow Keong; Hartman, Mikael; Sng, Jen-Hwei; Yip, Cheng Har; Taib, Nur Aishah Mohd; Teo, Soo-Hwang

    2013-01-01

    Background The partner and localizer of breast cancer 2 (PALB2) is responsible for facilitating BRCA2-mediated DNA repair by serving as a bridging molecule, acting as the physical and functional link between the breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) proteins. Truncating mutations in the PALB2 gene are rare but are thought to be associated with increased risks of developing breast cancer in various populations. Methods We evaluated the contribution of PALB2 germline mutations in 122 Asian women with breast cancer, all of whom had significant family history of breast and other cancers. Further screening for nine PALB2 mutations was conducted in 874 Malaysian and 532 Singaporean breast cancer patients, and in 1342 unaffected Malaysian and 541 unaffected Singaporean women. Results By analyzing the entire coding region of PALB2, we found two novel truncating mutations and ten missense mutations in families tested negative for BRCA1/2-mutations. One additional novel truncating PALB2 mutation was identified in one patient through genotyping analysis. Our results indicate a low prevalence of deleterious PALB2 mutations and a specific mutation profile within the Malaysian and Singaporean populations. PMID:23977390

  7. Enteric Neurospheres Are Not Specific to Neural Crest Cultures: Implications for Neural Stem Cell Therapies

    PubMed Central

    Cooper, Julie; Kronfli, Rania; Cananzi, Mara; Delalande, Jean-Marie; McCann, Conor; Burns, Alan J.; Thapar, Nikhil

    2015-01-01

    Objectives Enteric neural stem cells provide hope of curative treatment for enteric neuropathies. Current protocols for their harvesting from humans focus on the generation of ‘neurospheres’ from cultures of dissociated gut tissue. The study aims to better understand the derivation, generation and composition of enteric neurospheres. Design Gut tissue was obtained from Wnt1-Cre;Rosa26Yfp/Yfp transgenic mice (constitutively labeled neural crest cells) and paediatric patients. Gut cells were cultured either unsorted (mixed neural crest/non-neural crest), or following FACS selection into neural crest (murine-YFP+ve/human-p75+ve) or non-neural crest (YFP-ve/p75-ve) populations. Cultures and resultant neurospheres were characterized using immunolabelling in vitro and following transplantation in vivo. Results Cultures of (i) unsorted, (ii) neural crest, and (iii) non-neural crest cell populations generated neurospheres similar in numbers, size and morphology. Unsorted neurospheres were highly heterogeneous for neural crest content. Neural crest-derived (YFP+ve/p75+ve) neurospheres contained only neural derivatives (neurons and glia) and were devoid of non-neural cells (i.e. negative for SMA, c-Kit), with the converse true for non-neural crest-derived (YFP-ve/p75-ve) ‘neurospheres’. Under differentiation conditions only YFP+ve cells gave rise to neural derivatives. Both YFP+ve and YFP-ve cells displayed proliferation and spread upon transplantation in vivo, but YFP-ve cells did not locate or integrate within the host ENS. Conclusions Spherical accumulations of cells, so-called ‘neurospheres’ forming in cultures of dissociated gut contain variable proportions of neural crest-derived cells. If they are to be used for ENS cell replacement therapy then improved protocols for their generation, including cell selection, should be sought in order to avoid inadvertent transplantation of non-therapeutic, non-ENS cells. PMID:25799576

  8. Screening for Open Neural Tube Defects.

    PubMed

    Krantz, David A; Hallahan, Terrence W; Carmichael, Jonathan B

    2016-06-01

    Biochemical prenatal screening was initiated with the use of maternal serum alpha fetoprotein to screen for open neural tube defects. Screening now includes multiple marker and sequential screening protocols involving serum and ultrasound markers to screen for aneuploidy. Recently cell-free DNA screening for aneuploidy has been initiated, but does not screen for neural tube defects. Although ultrasound is highly effective in identifying neural tube defects in high-risk populations, in decentralized health systems maternal serum screening still plays a significant role. Abnormal maternal serum alpha fetoprotein alone or in combination with other markers may indicate adverse pregnancy outcome in the absence of open neural tube defects. PMID:27235920

  9. 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.

  10. Multi-sensor integration using neural networks for predicting quality characteristics of end-milled parts: part I--individual effects of training parameters

    NASA Astrophysics Data System (ADS)

    Okafor, Anthony C.; Adetona, O.

    1994-03-01

    This paper presents a systematic evaluation of the individual effects of training parameters: learning rate, momentum rate, number of hidden layer nodes, and processing element's transfer function, on the performance of back propagation networks in predicting quality characteristics of end milled parts. Multi-sensor signatures (acoustic emission, spindle vibration, and cutting force components) acquired during circular end-milling of 4140 steel and the corresponding measured quality characteristics (surface roughness and bore tolerance) were used to train the networks. The network is part of a proposed Intelligent Machining Monitoring and Diagnostic System for Quality Assurance of Machined Parts. The network performances were evaluated using four different criteria: maximum error, RMS error, mean error and number of training cycles. One of the results obtained shows that hyperbolic tangent transfer function gave a better performance than the sigmoid and sine functions respectively. Optimum combinations of training parameters have been observed. The effects of various combinations of training parameters are presented.

  11. Modeling of the electromagnetic field and level populations in a waveguide amplifier: a multi-scale time problem.

    PubMed

    Fafin, Alexandre; Cardin, Julien; Dufour, Christian; Gourbilleau, Fabrice

    2013-10-01

    A new algorithm based on auxiliary differential equation and finite difference time domain method (ADE-FDTD method) is presented to model a waveguide whose active layer is constituted of a silica matrix doped with rare-earth and silicon nanograins. The typical lifetime of rare-earth can be as large as some ms, whereas the electromagnetic field in a visible range and near-infrared is characterized by a period of the order of fs. Due to the large difference between these two characteristic times, the conventional ADE-FDTD method is not suited to treat such systems. A new algorithm is presented so that the steady state of rare earth and silicon nanograins electronic levels populations along with the electromagnetic field can be fully described. This algorithm is stable and applicable to a wide range of optical gain materials in which large differences of characteristic lifetimes are present. PMID:24104327

  12. Levels of PAH-DNA Adducts in Placental Tissue and the Risk of Fetal Neural Tube Defects in a Chinese Population

    PubMed Central

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

    2014-01-01

    We examined the relationship between PAH-DNA adduct levels in the placental tissue, measured by a highly sensitive 32P-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 108 nucleotides) compared to controls (9.92 per 108 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. PMID:23416326

  13. 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.

  14. The neural crest: a versatile organ system.

    PubMed

    Zhang, Dongcheng; Ighaniyan, Samiramis; Stathopoulos, Lefteris; Rollo, Benjamin; Landman, Kerry; Hutson, John; Newgreen, Donald

    2014-09-01

    The neural crest is the name given to the strip of cells at the junction between neural and epidermal ectoderm in neurula-stage vertebrate embryos, which is later brought to the dorsal neural tube as the neural folds elevate. The neural crest is a heterogeneous and multipotent progenitor cell population whose cells undergo EMT then extensively and accurately migrate throughout the embryo. Neural crest cells contribute to nearly every organ system in the body, with derivatives of neuronal, glial, neuroendocrine, pigment, and also mesodermal lineages. This breadth of developmental capacity has led to the neural crest being termed the fourth germ layer. The neural crest has occupied a prominent place in developmental biology, due to its exaggerated migratory morphogenesis and its remarkably wide developmental potential. As such, neural crest cells have become an attractive model for developmental biologists for studying these processes. Problems in neural crest development cause a number of human syndromes and birth defects known collectively as neurocristopathies; these include Treacher Collins syndrome, Hirschsprung disease, and 22q11.2 deletion syndromes. Tumors in the neural crest lineage are also of clinical importance, including the aggressive melanoma and neuroblastoma types. These clinical aspects have drawn attention to the selection or creation of neural crest progenitor cells, particularly of human origin, for studying pathologies of the neural crest at the cellular level, and also for possible cell therapeutics. The versatility of the neural crest lends itself to interlinked research, spanning basic developmental biology, birth defect research, oncology, and stem/progenitor cell biology and therapy. PMID:25227568

  15. 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

  16. Engineering articular cartilage with spatially-varying matrix composition and mechanical properties from a single stem cell population using a multi-layered hydrogel.

    PubMed

    Nguyen, Lonnissa H; Kudva, Abhijith K; Saxena, Neha S; Roy, Krishnendu

    2011-10-01

    Despite significant advances in stem cell differentiation and tissue engineering, directing progenitor cells into three-dimensionally (3D) organized, native-like complex structures with spatially-varying mechanical properties and extra-cellular matrix (ECM) composition has not yet been achieved. The key innovations needed to achieve this would involve methods for directing a single stem cell population into multiple, spatially distinct phenotypes or lineages within a 3D scaffold structure. We have previously shown that specific combinations of natural and synthetic biomaterials can direct marrow-derived stem cells (MSC) into varying phenotypes of chondrocytes that resemble cells from the superficial, transitional, and deep zones of articular cartilage. In this current study, we demonstrate that layer-by-layer organization of these specific biomaterial compositions creates 3D niches that allow a single MSC population to differentiate into zone-specific chondrocytes and organize into a complex tissue structure. Our results indicate that a three-layer polyethylene glycol (PEG)-based hydrogel with chondroitin sulfate (CS) and matrix metalloproteinase-sensitive peptides (MMP-pep) incorporated into the top layer (superficial zone, PEG:CS:MMP-pep), CS incorporated into the middle layer (transitional zone, PEG:CS) and hyaluronic acid incorporated in the bottom layer (deep zone, PEG:HA), creates native-like articular cartilage with spatially-varying mechanical and biochemical properties. Specifically, collagen II levels decreased gradually from the superficial to the deep zone, while collagen X and proteoglycan levels increased, leading to an increasing gradient of compressive modulus from the superficial to the deep zone. We conclude that spatially-varying biomaterial compositions within single 3D scaffolds can stimulate efficient regeneration of multi-layered complex tissues from a single stem cell population. PMID:21723599

  17. 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.

  18. Association study of inflammatory genes with rheumatic heart disease in North Indian population: A multi-analytical approach.

    PubMed

    Gupta, Usha; Mir, Snober S; Garg, Naveen; Agarwal, Surendra K; Pande, Shantanu; Mittal, Balraj

    2016-06-01

    Rheumatic heart disease (RHD) is an inflammatory, autoimmune disease; occurring as a consequence of group A streptococcal infection complicated by rheumatic fever (RF). An inappropriate immune response is the central signature tune to the complex pathogenesis of RHD. However, some of those infected develop RHD, and genetic host susceptibility factors are thought to play a key role in diseasedevelopment. Therefore, the present study was designed to explore the role of genetic variants in inflammatory genes in conferring risk of RHD. The study recruited total of 700 subjects, including 400 RHD patients and 300 healthy controls. We examined the associations of 8 selected polymorphisms in seven inflammatory genes: IL-6 [rs1800795G/C], IL-10 [rs1800896G/A], TNF-A [rs1800629G/A], IL-1β [rs2853550C/T], IL-1VNTR [rs2234663], TGF-β1 [rs1800469C/T]; [rs1982073T/C], and CTLA-4 [rs5742909C/T] with RHD risk. Genotyping for all the polymorphisms was done using PCR-ARMS/PCR/RFLP methods. Multifactor dimensionality reduction and classification and regression tree approaches were combined with logistic regression to discover high-order gene-gene interactions in studiedgenes involved in RHD susceptibility.In univariate logistic regression analysis, we found significant association of variant-containing genotypes (CT&TT) of TGF-β1 869T/C [rs1982073]; [p=0.0.004 & 0.001, OR (95% CI)=1.65 (1.2-2.3) & 2.25 (1.4-3.6) respectively], variant genotype (CC) of IL-1β -511C/T [rs2853550]; [p=0.001, OR (95% CI)=2.33 (1.4-3.8)] and IL-1 VNTR [rs2234663]; [p=0.03, OR (95% CI)=5.25 (1.2-23.4)] SNPs with RHD risk. CART analysis revealed that individuals with the combined genotypes of TGF-β1T/C_ rs1982073 (CT/TT) and IL-1 β_ rs2853550 (CC) had significantly higher susceptibility for RHD [p=0.0005, OR (95% CI)=5.91 (2.9-12.5)]. In MDR analysis, TGF-β1 869T>C yielded the highest testing accuracy of 0.562. In conclusion, using multi-analytical approaches, our study revealed important role of TGF

  19. 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.

  20. Characterization of a Self-renewing and Multi-potent Cell Population Isolated from Human Minor Salivary Glands.

    PubMed

    Lu, Lin; Li, Yan; Du, Ming-juan; Zhang, Chen; Zhang, Xiang-yu; Tong, Hai-zhou; Liu, Lei; Han, Ting-lu; Li, Wan-di; Yan, Li; Yin, Ning-bei; Li, Hai-dong; Zhao, Zhen-min

    2015-01-01

    Adult stem cells play an important role in maintaining tissue homeostasis. Although these cells are found in many tissues, the presence of stem cells in the human minor salivary glands is not well explored. Using the explant culture method, we isolated a population of cells with self-renewal and differentiation capacities harboring that reside in the human minor salivary glands, called human minor salivary gland mesenchymal stem cells (hMSGMSCs). These cells show embryonic stem cell and mesenchymal stem cell phenotypes. Our results demonstrate that hMSGMSCs have the potential to undergo mesodermal, ectodermal and endodermal differentiation in conditioned culture systems in vitro. Furthermore, in vivo transplantation of hMSGMSCs into SCID mice after partial hepatectomy shows that hMSGMSCs are able to survive and engraft, characterized by the survival of labeled cells and the expression of the hepatocyte markers AFP and KRT18. These data demonstrate the existence of hMSGMSCs and suggest their potential in cell therapy and regenerative medicine. PMID:26054627

  1. Characterization of a Self-renewing and Multi-potent Cell Population Isolated from Human Minor Salivary Glands

    PubMed Central

    Lu, Lin; Li, Yan; Du, Ming-juan; Zhang, Chen; Zhang, Xiang-yu; Tong, Hai-zhou; Liu, Lei; Han, Ting-lu; Li, Wan-di; Yan, Li; Yin, Ning-bei; Li, Hai-dong; Zhao, Zhen-min

    2015-01-01

    Adult stem cells play an important role in maintaining tissue homeostasis. Although these cells are found in many tissues, the presence of stem cells in the human minor salivary glands is not well explored. Using the explant culture method, we isolated a population of cells with self-renewal and differentiation capacities harboring that reside in the human minor salivary glands, called human minor salivary gland mesenchymal stem cells (hMSGMSCs). These cells show embryonic stem cell and mesenchymal stem cell phenotypes. Our results demonstrate that hMSGMSCs have the potential to undergo mesodermal, ectodermal and endodermal differentiation in conditioned culture systems in vitro. Furthermore, in vivo transplantation of hMSGMSCs into SCID mice after partial hepatectomy shows that hMSGMSCs are able to survive and engraft, characterized by the survival of labeled cells and the expression of the hepatocyte markers AFP and KRT18. These data demonstrate the existence of hMSGMSCs and suggest their potential in cell therapy and regenerative medicine. PMID:26054627

  2. Increased multi-drug resistance and reduced apoptosis in osteosarcoma side population cells are crucial factors for tumor recurrence

    PubMed Central

    WANG, YANG; TENG, JIA-SONG

    2016-01-01

    The present study investigated the characteristic features of cancer stem cells (CSCs) using an aggressive human osteosarcoma cell line OS-65. Hoechst 33342 dye exclusion was used to distinguish the cancer stem-like side population (SP) cells from OS-65 cells. Furthermore, the SP cells were characterized via chemoresistance and cell death assays, reverse transcription-quantitative polymerase chain reaction and immunofluorescence. The present study identified ~3.3% of cancer stem-like SP cells from OS-65 cells whose prevalence is reduced significantly (0.9%) following treatment with verapamil. It was demonstrated that osteosarcoma SP cells are highly efficient at generating additional sarcospheres as transcriptional regulation of stemness genes, including SOX2, OCT-4 and NANOG, is highly upregulated. Notably, these SP cells demonstrated high resistance against chemotherapeutic drugs and apoptosis via elevated transcriptional regulation of several ATPase binding cassette (ABC) transporter and anti-apoptotic proteins, including ABCG2, ABCB1/MDR1 ABCB5, B cell lymphoma-2 (Bcl-2) and Bcl-2 associated X protein, respectively. The results of the present study suggested that CSCs may be a novel therapeutic target for the prevention of tumor relapse. PMID:27347020

  3. 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

  4. Neurally augmented sexual function.

    PubMed

    Meloy, S

    2007-01-01

    Neurally Augmented Sexual Function (NASF) is a technique utilizing epidural electrodes to restore and improve sexual function. Orgasmic dysfunction is common in adult women, affecting roughly one quarter of populations studied. Many male patients suffering from erectile dysfunction are not candidates for phosphdiesterase therapy due to concomitant nitrate therapy. Positioning the electrodes at roughly the level of the cauda equina allows for stimulation of somatic efferents and afferents as well as modifying sympathetic and parasympathetic activity. Our series of women treated by NASF is described. Our experience shows that the evaluation of potential candidates for both correctable causes and psychological screening are important considerations. PMID:17691397

  5. 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.

  6. Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

    PubMed Central

    Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Friehs, Gerhard M.; Black, Michael J.

    2012-01-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain–computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (<3% in one participant). This study suggests that signals from a small ensemble of motor cortical neurons (~40) can be used for natural point-and-click 2-D cursor control of a personal computer. PMID:21278024

  7. Data compression using artificial neural networks

    SciTech Connect

    Watkins, B.E.

    1991-09-01

    This thesis investigates the application of artificial neural networks for the compression of image data. An algorithm is developed using the competitive learning paradigm which takes advantage of the parallel processing and classification capability of neural networks to produce an efficient implementation of vector quantization. Multi-Stage, tree searched, and classification vector quantization codebook design are adapted to the neural network design to reduce the computational cost and hardware requirements. The results show that the new algorithm provides a substantial reduction in computational costs and an improvement in performance.

  8. 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.

  9. 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

  10. 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. PMID:27331399

  11. A prospective, multi-centric, observational registry to evaluate performance of Excel™ DES in ‘Real World, All Comers’ patient population

    PubMed Central

    Hiremath, Shirish; Chandra, Praveen; Desai, Devang; Sivakumar, R.; Selvamani, S.; Srinivasan, Anand; Paulose, Madhu; Jose, Sajy; Kalmath, B.C.; Magarkar, Vilas.P.; Pathak, Abhijeet; Mhetre, Tushar

    2014-01-01

    Objectives This study aims to assess the safety and efficacy of a biodegradable polymer-coated Rapamycin-Eluting Stent (Excel) used in conjunction with six-month dual antiplatelet therapy in daily practice. Background The polymeric material of cardiac stents has been reported to adversely affect the safety profile of the drug-eluting stents and is also suspected to cause serious long-term complications. It has been proposed that the biodegradable polymer coatings may reduce such late-stage adverse effects. Methods This is a prospective, multi-center registry of 654 patients from across 9 cardiology centers in India, who were enrolled and exclusively treated with Excel stents between February 2008 and May 2010. The recommended antiplatelet regimen included clopidogrel and aspirin for 6 months period, followed by lifelong aspirin therapy. Results The study population included 46.94% diabetics, 24.31% smokers, 48.93% hypertensives and 14.98% hyperlipidemics. The cumulative rates of major adverse cardiac events were 0.153% at discharge and 1.38% at 12 months. The mean percentage of stenosis was 88.24 ± 9.17% No events occurred between 6 and 12 months. Conclusions This multi-center registry study on “real world, all comers” has, thus, showed that EXCEL™ stent which is PLA-coated biodegradable Rapamycin-Eluting Stent exhibited high efficacy and safety profile in treatment of patients undergoing PCI as evidenced by significantly lower rates of MACE and no case of stent thrombosis. There was no event even after DAPT was discontinued after 6 months. PMID:25634407

  12. 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

  13. Prevalence of and risk factors for Helicobacter pylori infection in a multi-racial dyspeptic Malaysian population undergoing endoscopy.

    PubMed

    Goh, K L

    1997-06-01

    The aim of the present study was to determine the risk factors for Helicobacter pylori in a dyspeptic Malaysian population. A cross-sectional survey of 1060 consecutive patients presenting with dyspepsia at the Endoscopic Unit, University Hospital, Kuala Lumpur, Malaysia from January 1994 to July 1995 was undertaken. All patients answered a detailed questionnaire and underwent endoscopy, with two antral biopsies taken for diagnosis of H. pylori using a rapid urease test. An overall H. pylori prevalence of 49.0% was recorded. Helicobacter pylori prevalence in relation to the major endoscopic diagnoses were as follows: non-ulcer dyspepsia (NUD) 31.2%; duodenal ulcer (DU) 91.4%; and gastric ulcer (GU) 74.1%. The prevalence among the races were as follows: Malay 16.4%; Chinese 48.5%; and Indians 61.8%. Multiple logistic regression analysis identified the following as independent risk factors: > 45 years old 1.5 (1.1,2.0); male gender 1.6 (1.2,2.1); ethnic group: Chinese 2.5 (1.7,3.7); Indians 4.9 (3.2,7.5); level of education: low 2.3 (1.5,3.5); middle 1.7 (1.1,2.6); and smoking 1.6 (1.2,2.3). Analysis was also performed on DU, GU and non-UD patients separately; in both DU and GU patients, H. pylori prevalence was high regardless of age, sex, race or level of education. However, in DU patients, Indian race had an independent risk factor (Odds ratio = 7.8 (1.2,48.4)). The findings in the NUD group reflected the findings in the ¿all patients' group; > 45 years old, male gender, Indian and Chinese race, and low level of education were also significant, independent risk factors. The overall differences in H. pylori prevalence between the different subgroups were mainly due to differences in the NUD group. The increased risk of H. pylori infection in Chinese and Indians points to either an inherent ethnic genetic predisposition or to socio-cultural practices peculiar to the particular race which may be responsible for transmission of the infection. PMID:9195409

  14. 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

  15. 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

  16. Multiscale Modeling of Cortical Neural Networks

    NASA Astrophysics Data System (ADS)

    Torben-Nielsen, Benjamin; Stiefel, Klaus M.

    2009-09-01

    In this study, we describe efforts at modeling the electrophysiological dynamics of cortical networks in a multi-scale manner. Specifically, we describe the implementation of a network model composed of simple single-compartmental neuron models, in which a single complex multi-compartmental model of a pyramidal neuron is embedded. The network is capable of generating Δ (2 Hz, observed during deep sleep states) and γ (40 Hz, observed during wakefulness) oscillations, which are then imposed onto the multi-compartmental model, thus providing realistic, dynamic boundary conditions. We furthermore discuss the challenges and chances involved in multi-scale modeling of neural function.

  17. 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. PMID:15649529

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

    PubMed

    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. 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

  20. Synchronization in neural nets

    NASA Technical Reports Server (NTRS)

    Vidal, Jacques J.; Haggerty, John

    1988-01-01

    The paper presents an artificial neural network concept (the Synchronizable Oscillator Networks) where the instants of individual firings in the form of point processes constitute the only form of information transmitted between joining neurons. In the model, neurons fire spontaneously and regularly in the absence of perturbation. When interaction is present, the scheduled firings are advanced or delayed by the firing of neighboring neurons. Networks of such neurons become global oscillators which exhibit multiple synchronizing attractors. From arbitrary initial states, energy minimization learning procedures can make the network converge to oscillatory modes that satisfy multi-dimensional constraints. Such networks can directly represent routing and scheduling problems that consist of ordering sequences of events.

  1. Artificial neural superposition eye.

    PubMed

    Brückner, Andreas; Duparré, Jacques; Dannberg, Peter; Bräuer, Andreas; Tünnermann, Andreas

    2007-09-17

    We propose an ultra-thin imaging system which is based on the neural superposition compound eye of insects. Multiple light sensitive pixels in the footprint of each lenslet of this multi-channel configuration enable the parallel imaging of the individual object points. Together with the digital superposition of related signals this multiple sampling enables advanced functionalities for artificial compound eyes. Using this technique, color imaging and a circumvention for the trade-off between resolution and sensitivity of ultra-compact camera devices have been demonstrated in this article. The optical design and layout of such a system is discussed in detail. Experimental results are shown which indicate the attractiveness of microoptical artificial compound eyes for applications in the field of machine vision, surveillance or automotive imaging. PMID:19547555

  2. Mapping neural circuits with activity-dependent nuclear import of a transcription factor.

    PubMed

    Masuyama, Kaoru; Zhang, Yi; Rao, Yi; Wang, Jing W

    2012-03-01

    Abstract: Nuclear factor of activated T cells (NFAT) is a calcium-responsive transcription factor. We describe here an NFAT-based neural tracing method-CaLexA (calcium-dependent nuclear import of LexA)-for labeling active neurons in behaving animals. In this system, sustained neural activity induces nuclear import of the chimeric transcription factor LexA-VP16-NFAT, which in turn drives green fluorescent protein (GFP) reporter expression only in active neurons. We tested this system in Drosophila and found that volatile sex pheromones excite specific neurons in the olfactory circuit. Furthermore, complex courtship behavior associated with multi-modal sensory inputs activated neurons in the ventral nerve cord. This method harnessing the mechanism of activity-dependent nuclear import of a transcription factor can be used to identify active neurons in specific neuronal population in behaving animals. PMID:22236090

  3. 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.

  4. 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.

  5. Neural induction, neural fate stabilization, and neural stem cells.

    PubMed

    Moody, Sally A; Je, Hyun-Soo

    2002-04-28

    The promise of stem cell therapy is expected to greatly benefit the treatment of neurodegenerative diseases. An underlying biological reason for the progressive functional losses associated with these diseases is the extremely low natural rate of self-repair in the nervous system. Although the mature CNS harbors a limited number of self-renewing stem cells, these make a significant contribution to only a few areas of brain. Therefore, it is particularly important to understand how to manipulate embryonic stem cells and adult neural stem cells so their descendants can repopulate and functionally repair damaged brain regions. A large knowledge base has been gathered about the normal processes of neural development. The time has come for this information to be applied to the problems of obtaining sufficient, neurally committed stem cells for clinical use. In this article we review the process of neural induction, by which the embryonic ectodermal cells are directed to form the neural plate, and the process of neural-fate stabilization, by which neural plate cells expand in number and consolidate their neural fate. We will present the current knowledge of the transcription factors and signaling molecules that are known to be involved in these processes. We will discuss how these factors may be relevant to manipulating embryonic stem cells to express a neural fate and to produce large numbers of neurally committed, yet undifferentiated, stem cells for transplantation therapies. PMID:12805974

  6. 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

  7. 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.

  8. Perceptual Spaces: Mathematical Structures to Neural Mechanisms

    PubMed Central

    Victor, Jonathan; McDermott, Josh; Geffen, Maria; Bensmaia, Sliman; Cleland, Thomas A.

    2013-01-01

    A central goal of neuroscience is to understand how populations of neurons build and manipulate representations of percepts that provide useful information about the environment. This symposium explores the fundamental properties of these representations and the perceptual spaces in which they are organized. Spanning the domains of color, visual texture, environmental sound, music, tactile quality, and odor, we show how the geometric structures of perceptual spaces can be determined experimentally and how these structures provide insights into the principles of neural coding and the neural mechanisms that generate the codes, and into the neural processing of complex sensory stimuli. The diversity of the neural architecture in these different sensory systems provides an opportunity to compare their different solutions to common problems: the need for dimensionality reduction, strategies for topographic or nontopographic mapping, the utility of the higher-order statistical structure inherent in natural sensory stimuli, and the constraints of neural hardware. PMID:24198350

  9. Perceptual spaces: mathematical structures to neural mechanisms.

    PubMed

    Zaidi, Qasim; Victor, Jonathan; McDermott, Josh; Geffen, Maria; Bensmaia, Sliman; Cleland, Thomas A

    2013-11-01

    A central goal of neuroscience is to understand how populations of neurons build and manipulate representations of percepts that provide useful information about the environment. This symposium explores the fundamental properties of these representations and the perceptual spaces in which they are organized. Spanning the domains of color, visual texture, environmental sound, music, tactile quality, and odor, we show how the geometric structures of perceptual spaces can be determined experimentally and how these structures provide insights into the principles of neural coding and the neural mechanisms that generate the codes, and into the neural processing of complex sensory stimuli. The diversity of the neural architecture in these different sensory systems provides an opportunity to compare their different solutions to common problems: the need for dimensionality reduction, strategies for topographic or nontopographic mapping, the utility of the higher-order statistical structure inherent in natural sensory stimuli, and the constraints of neural hardware. PMID:24198350

  10. 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.

  11. A carbon-fiber electrode array for long-term neural recording

    PubMed Central

    Guitchounts, Grigori; Markowitz, Jeffrey E.; Liberti, William A.; Gardner, Timothy J.

    2013-01-01

    Problem addressed Chronic neural recording in behaving animals is an essential method for studies of neural circuit function. However, stable recordings from small, densely packed neurons remains challenging, particularly over time-scales relevant for learning. Methodology We describe an assembly method for a 16 channel electrode array consisting of carbon fibers (<5 μm diameter) individually insulated with Parylene-C and fire-sharpened. The diameter of the array is approximately 26 microns, along the full extent of the implant. Results Carbon fiber arrays were tested in HVC (used as a proper name), a song motor nucleus, of singing zebra finches where individual neurons discharge with temporally precise patterns. Previous reports of activity in this population of neurons has required the use of high impedance electrodes on movable microdrives. Here, the carbon fiber electrodes provided stable multi-unit recordings over time-scales of months. Spike-sorting indicated that the multi-unit signals were dominated by one, or a small number of cells. Stable firing patterns during singing confirmed the stability of these clusters over time-scales of months. In addition, from a total of 10 surgeries, 16 projection neurons were found. This cell type is characterized by sparse - stereotyped firing firing patterns, providing unambiguous confirmation of single cell recordings. Significance Carbon fiber electrode bundles may provide a scalable solution for long-term neural recordings of densely packed neurons. PMID:23860226

  12. 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. PMID:25078912

  13. 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

  14. “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

  15. 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. PMID:19762014

  16. 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.

  17. Chimera States in Neural Oscillators

    NASA Astrophysics Data System (ADS)

    Bahar, Sonya; Glaze, Tera

    2014-03-01

    Chimera states have recently been explored both theoretically and experimentally, in various coupled nonlinear oscillators, ranging from phase-oscillator models to coupled chemical reactions. In a chimera state, both coherent and incoherent (or synchronized and desynchronized) states occur simultaneously in populations of identical oscillators. We investigate chimera behavior in a population of neural oscillators using the Huber-Braun model, a Hodgkin-Huxley-like model originally developed to characterize the temperature-dependent bursting behavior of mammalian cold receptors. One population of neurons is allowed to synchronize, with each neuron receiving input from all the others in its group (global within-group coupling). Subsequently, a second population of identical neurons is placed under an identical global within-group coupling, and the two populations are also coupled to each other (between-group coupling). For certain values of the coupling constants, the neurons in the two populations exhibit radically different synchronization behavior. We will discuss the range of chimera activity in the model, and discuss its implications for actual neural activity, such as unihemispheric sleep.

  18. Coronary Artery Diagnosis Aided by Neural Network

    NASA Astrophysics Data System (ADS)

    Stefko, Kamil

    2007-01-01

    Coronary artery disease is due to atheromatous narrowing and subsequent occlusion of the coronary vessel. Application of optimised feed forward multi-layer back propagation neural network (MLBP) for detection of narrowing in coronary artery vessels is presented in this paper. The research was performed using 580 data records from traditional ECG exercise test confirmed by coronary arteriography results. Each record of training database included description of the state of a patient providing input data for the neural network. Level and slope of ST segment of a 12 lead ECG signal recorded at rest and after effort (48 floating point values) was the main component of input data for neural network was. Coronary arteriography results (verified the existence or absence of more than 50% stenosis of the particular coronary vessels) were used as a correct neural network training output pattern. More than 96% of cases were correctly recognised by especially optimised and a thoroughly verified neural network. Leave one out method was used for neural network verification so 580 data records could be used for training as well as for verification of neural network.

  19. 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.

  20. Neural Synchrony in Schizophrenia: From Networks to New Treatments

    PubMed Central

    Ford, Judith M.; Krystal, John H.; Mathalon, Daniel H.

    2007-01-01

    Evidence is accumulating that brain regions communicate with each other in the temporal domain, relying on coincidence of neural activity to detect phasic relationships among neurons and neural assemblies. This coordination between neural populations has been described as “self-organizing,” an “emergent property” of neural networks arising from the temporal synchrony between synaptic transmission and firing of distinct neuronal populations. Evidence is also accumulating that communication and coordination failures between different brain regions may account for a wide range of problems in schizophrenia, from psychosis to cognitive dysfunction. We review the knowledge about the functional neuroanatomy and neurochemistry of neural oscillations and oscillation abnormalities in schizophrenia. Based on this, we argue that we can begin to use oscillations, across frequencies, to do translational studies to understand the neural basis of schizophrenia. PMID:17567628

  1. Genome-Wide Study of Percent Emphysema on Computed Tomography in the General Population. The Multi-Ethnic Study of Atherosclerosis Lung/SNP Health Association Resource Study

    PubMed Central

    Manichaikul, Ani; Hoffman, Eric A.; Smolonska, Joanna; Gao, Wei; Cho, Michael H.; Baumhauer, Heather; Budoff, Matthew; Austin, John H. M.; Washko, George R.; Carr, J. Jeffrey; Kaufman, Joel D.; Pottinger, Tess; Powell, Charles A.; Wijmenga, Cisca; Zanen, Pieter; Groen, Harry J. M.; Postma, Dirkje S.; Wanner, Adam; Rouhani, Farshid N.; Brantly, Mark L.; Powell, Rhea; Smith, Benjamin M.; Rabinowitz, Dan; Raffel, Leslie J.; Hinckley Stukovsky, Karen D.; Crapo, James D.; Beaty, Terri H.; Hokanson, John E.; Silverman, Edwin K.; Dupuis, Josée; O’Connor, George T.; Boezen, H. Marike; Rich, Stephen S.

    2014-01-01

    Rationale: Pulmonary emphysema overlaps partially with spirometrically defined chronic obstructive pulmonary disease and is heritable, with moderately high familial clustering. Objectives: To complete a genome-wide association study (GWAS) for the percentage of emphysema-like lung on computed tomography in the Multi-Ethnic Study of Atherosclerosis (MESA) Lung/SNP Health Association Resource (SHARe) Study, a large, population-based cohort in the United States. Methods: We determined percent emphysema and upper-lower lobe ratio in emphysema defined by lung regions less than −950 HU on cardiac scans. Genetic analyses were reported combined across four race/ethnic groups: non-Hispanic white (n = 2,587), African American (n = 2,510), Hispanic (n = 2,113), and Chinese (n = 704) and stratified by race and ethnicity. Measurements and Main Results: Among 7,914 participants, we identified regions at genome-wide significance for percent emphysema in or near SNRPF (rs7957346; P = 2.2 × 10−8) and PPT2 (rs10947233; P = 3.2 × 10−8), both of which replicated in an additional 6,023 individuals of European ancestry. Both single-nucleotide polymorphisms were previously implicated as genes influencing lung function, and analyses including lung function revealed independent associations for percent emphysema. Among Hispanics, we identified a genetic locus for upper-lower lobe ratio near the α-mannosidase–related gene MAN2B1 (rs10411619; P = 1.1 × 10−9; minor allele frequency [MAF], 4.4%). Among Chinese, we identified single-nucleotide polymorphisms associated with upper-lower lobe ratio near DHX15 (rs7698250; P = 1.8 × 10−10; MAF, 2.7%) and MGAT5B (rs7221059; P = 2.7 × 10−8; MAF, 2.6%), which acts on α-linked mannose. Among African Americans, a locus near a third α-mannosidase–related gene, MAN1C1 (rs12130495; P = 9.9 × 10−6; MAF, 13.3%) was associated with percent emphysema. Conclusions: Our results suggest that some genes previously identified as

  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. 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

  4. 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.

  5. FGF signaling transforms non-neural ectoderm into neural crest.

    PubMed

    Yardley, Nathan; García-Castro, Martín I

    2012-12-15

    The neural crest arises at the border between the neural plate and the adjacent non-neural ectoderm. It has been suggested that both neural and non-neural ectoderm can contribute to the neural crest. Several studies have examined the molecular mechanisms that regulate neural crest induction in neuralized tissues or the neural plate border. Here, using the chick as a model system, we address the molecular mechanisms by which non-neural ectoderm generates neural crest. We report that in response to FGF the non-neural ectoderm can ectopically express several early neural crest markers (Pax7, Msx1, Dlx5, Sox9, FoxD3, Snail2, and Sox10). Importantly this response to FGF signaling can occur without inducing ectopic mesodermal tissues. Furthermore, the non-neural ectoderm responds to FGF by expressing the prospective neural marker Sox3, but it does not express definitive markers of neural or anterior neural (Sox2 and Otx2) tissues. These results suggest that the non-neural ectoderm can launch the neural crest program in the absence of mesoderm, without acquiring definitive neural character. Finally, we report that prior to the upregulation of these neural crest markers, the non-neural ectoderm upregulates both BMP and Wnt molecules in response to FGF. Our results provide the first effort to understand the molecular events leading to neural crest development via the non-neural ectoderm in amniotes and present a distinct response to FGF signaling. PMID:23000357

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

    PubMed

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

    2016-04-01

    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, calledTg(snai1b:GFP), which labels epithelial cells undergoing EMT to producesox10-positive neural crest (NC) cells. Time-lapse and lineage analysis ofTg(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. TreatingTg(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 RAin vivoand raise the possibility that RA-dependent inhibition of EMT contributes to its prior success in eliminating disseminated cancer cells. PMID:26794130

  7. 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

  8. Neural Tube Defects

    MedlinePlus

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

  9. Morphological neural networks

    SciTech Connect

    Ritter, G.X.; Sussner, P.

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  10. Predicate calculus for an architecture of multiple neural networks

    NASA Astrophysics Data System (ADS)

    Consoli, Robert H.

    1990-08-01

    Future projects with neural networks will require multiple individual network components. Current efforts along these lines are ad hoc. This paper relates the neural network to a classical device and derives a multi-part architecture from that model. Further it provides a Predicate Calculus variant for describing the location and nature of the trainings and suggests Resolution Refutation as a method for determining the performance of the system as well as the location of needed trainings for specific proofs. 2. THE NEURAL NETWORK AND A CLASSICAL DEVICE Recently investigators have been making reports about architectures of multiple neural networksL234. These efforts are appearing at an early stage in neural network investigations they are characterized by architectures suggested directly by the problem space. Touretzky and Hinton suggest an architecture for processing logical statements1 the design of this architecture arises from the syntax of a restricted class of logical expressions and exhibits syntactic limitations. In similar fashion a multiple neural netword arises out of a control problem2 from the sequence learning problem3 and from the domain of machine learning. 4 But a general theory of multiple neural devices is missing. More general attempts to relate single or multiple neural networks to classical computing devices are not common although an attempt is made to relate single neural devices to a Turing machines and Sun et a!. develop a multiple neural architecture that performs pattern classification.

  11. Propagating waves can explain irregular neural dynamics.

    PubMed

    Keane, Adam; Gong, Pulin

    2015-01-28

    Cortical neurons in vivo fire quite irregularly. Previous studies about the origin of such irregular neural dynamics have given rise to two major models: a balanced excitation and inhibition model, and a model of highly synchronized synaptic inputs. To elucidate the network mechanisms underlying synchronized synaptic inputs and account for irregular neural dynamics, we investigate a spatially extended, conductance-based spiking neural network model. We show that propagating wave patterns with complex dynamics emerge from the network model. These waves sweep past neurons, to which they provide highly synchronized synaptic inputs. On the other hand, these patterns only emerge from the network with balanced excitation and inhibition; our model therefore reconciles the two major models of irregular neural dynamics. We further demonstrate that the collective dynamics of propagating wave patterns provides a mechanistic explanation for a range of irregular neural dynamics, including the variability of spike timing, slow firing rate fluctuations, and correlated membrane potential fluctuations. In addition, in our model, the distributions of synaptic conductance and membrane potential are non-Gaussian, consistent with recent experimental data obtained using whole-cell recordings. Our work therefore relates the propagating waves that have been widely observed in the brain to irregular neural dynamics. These results demonstrate that neural firing activity, although appearing highly disordered at the single-neuron level, can form dynamical coherent structures, such as propagating waves at the population level. PMID:25632135

  12. 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

  13. Patterns in neural processing

    NASA Astrophysics Data System (ADS)

    Engineer, Sunu

    2012-03-01

    In this paper we propose a model for neural processing that addresses both the evolutionary and functional aspects of neural systems that are observed in nature, from the simplest neural collections to dense large scale associations such as human brains. We propose both an architecture and a process in which these components interact to create the emergent behavior that we define as the 'mind'.

  14. Nested Neural Networks

    NASA Technical Reports Server (NTRS)

    Baram, Yoram

    1992-01-01

    Report presents analysis of nested neural networks, consisting of interconnected subnetworks. Analysis based on simplified mathematical models more appropriate for artificial electronic neural networks, partly applicable to biological neural networks. Nested structure allows for retrieval of individual subpatterns. Requires fewer wires and connection devices than fully connected networks, and allows for local reconstruction of damaged subnetworks without rewiring entire network.

  15. 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 intraspinal microstimulation require only a few channels of stimulation. Wired spinal cord implants are not practical for human subjects because of the extensive flexions and rotations that the spinal cord experiences. Thus, intraspinal microstimulation may be a pioneering application that can benefit from submillimetersize floating stimulators. Possible means of energizing such a floating microstimulator, such as optical, acoustic, and electromagnetic waves, are discussed. PMID:21488815

  16. Multiresolution training of Kohonen neural networks

    NASA Astrophysics Data System (ADS)

    Tamir, Dan E.

    2007-09-01

    This paper analyses a trade-off between convergence rate and distortion obtained through a multi-resolution training of a Kohonen Competitive Neural Network. Empirical results show that a multi-resolution approach can improve the training stage of several unsupervised pattern classification algorithms including K-means clustering, LBG vector quantization, and competitive neural networks. While, previous research concentrated on convergence rate of on-line unsupervised training. New results, reported in this paper, show that the multi-resolution approach can be used to improve training quality (measured as a derivative of the rate distortion function) on the account of convergence speed. The probability of achieving a desired point in the quality/convergence-rate space of Kohonen Competitive Neural Networks (KCNN) is evaluated using a detailed Monte Carlo set of experiments. It is shown that multi-resolution can reduce the distortion by a factor of 1.5 to 6 while maintaining the convergence rate of traditional KCNN. Alternatively, the convergence rate can be improved without loss of quality. The experiments include a controlled set of synthetic data, as well as, image data. Experimental results are reported and evaluated.

  17. Interval neural networks

    SciTech Connect

    Patil, R.B.

    1995-05-01

    Traditional neural networks like multi-layered perceptrons (MLP) use example patterns, i.e., pairs of real-valued observation vectors, ({rvec x},{rvec y}), to approximate function {cflx f}({rvec x}) = {rvec y}. To determine the parameters of the approximation, a special version of the gradient descent method called back-propagation is widely used. In many situations, observations of the input and output variables are not precise; instead, we usually have intervals of possible values. The imprecision could be due to the limited accuracy of the measuring instrument or could reflect genuine uncertainty in the observed variables. In such situation input and output data consist of mixed data types; intervals and precise numbers. Function approximation in interval domains is considered in this paper. We discuss a modification of the classical backpropagation learning algorithm to interval domains. Results are presented with simple examples demonstrating few properties of nonlinear interval mapping as noise resistance and finding set of solutions to the function approximation problem.

  18. 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. PMID:23893208

  19. Digital Neural Networks for New Media

    NASA Astrophysics Data System (ADS)

    Spaanenburg, Lambert; Malki, Suleyman

    Neural Networks perform computationally intensive tasks offering smart solutions for many new media applications. A number of analog and mixed digital/analog implementations have been proposed to smooth the algorithmic gap. But gradually, the digital implementation has become feasible, and the dedicated neural processor is on the horizon. A notable example is the Cellular Neural Network (CNN). The analog direction has matured for low-power, smart vision sensors; the digital direction is gradually being shaped into an IP-core for algorithm acceleration, especially for use in FPGA-based high-performance systems. The chapter discusses the next step towards a flexible and scalable multi-core engine using Application-Specific Integrated Processors (ASIP). This topographic engine can serve many new media tasks, as illustrated by novel applications in Homeland Security. We conclude with a view on the CNN kaleidoscope for the year 2020.

  20. Access to Educational Opportunity in Rural Communities: Alternative Patterns of Delivering Vocational Education in Sparsely Populated Areas. Volume 3: The Northwest Multi-District: A Mobile Facilities Center.

    ERIC Educational Resources Information Center

    Peterson, Roland L.; And Others

    Representing the mobile facilities pattern of inter-district cooperation, the Northwest Multi-District case is one of four studies addressing access of rural students to vocational education through inter-school district cooperation. The report identifies essential features of this form of cooperation, details factors facilitating/impeding the…

  1. Age and diet effects on fecal populations and antibiotic resistance of a multi-drug resistant Escherichia coli in dairy calves

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The use of antimicrobial drugs is reported to increase the prevalence of resistant bacteria, including commensals. Dairy calves are colonized at a very young age by a multi-drug-resistant E. coli (MDR EC), and research indicates that the prevalence is not related to recent use of antimicrobials but...

  2. Population policy.

    PubMed

    1987-03-01

    decrease fertility, control international migration, and modify the spatial distribution of the population. To reduce its population growth rate, Pakistan has adopted a multi-sectoral, multidimensional approach to family planning. The policy of the government of the Philippines is to bring the population growth rate in line with the availability of natural resources and employment opportunities. In its 5-year plan covering 1982-86, the government of the Republic of Korea emphasized social development, attempting to more fully integrate population and development policies and programs within relevant sectors. To reduce its population growth rate to 1.3% by 1992, the government of Thailand is expanding the reach of its family planning program. PMID:12341036

  3. Reprogramming of avian neural crest axial identity and cell fate.

    PubMed

    Simoes-Costa, Marcos; Bronner, Marianne E

    2016-06-24

    Neural crest populations along the embryonic body axis of vertebrates differ in developmental potential and fate, so that only the cranial neural crest can contribute to the craniofacial skeleton in vivo. We explored the regulatory program that imbues the cranial crest with its specialized features. Using axial-level specific enhancers to isolate and perform genome-wide profiling of the cranial versus trunk neural crest in chick embryos, we identified and characterized regulatory relationships between a set of cranial-specific transcription factors. Introducing components of this circuit into neural crest cells of the trunk alters their identity and endows these cells with the ability to give rise to chondroblasts in vivo. Our results demonstrate that gene regulatory circuits that support the formation of particular neural crest derivatives may be used to reprogram specific neural crest-derived cell types. PMID:27339986

  4. An Overview of Bayesian Methods for Neural Spike Train Analysis

    PubMed Central

    2013-01-01

    Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed. PMID:24348527

  5. Analog hardware for learning neural networks

    NASA Technical Reports Server (NTRS)

    Eberhardt, Silvio P. (Inventor)

    1991-01-01

    This is a recurrent or feedforward analog neural network processor having a multi-level neuron array and a synaptic matrix for storing weighted analog values of synaptic connection strengths which is characterized by temporarily changing one connection strength at a time to determine its effect on system output relative to the desired target. That connection strength is then adjusted based on the effect, whereby the processor is taught the correct response to training examples connection by connection.

  6. Program PSNN (Plasma Spectroscopy Neural Network)

    SciTech Connect

    Morgan, W.L.; Larsen, J.T.

    1993-08-01

    This program uses the standard ``delta rule`` back-propagation supervised training algorithm for multi-layer neural networks. The inputs are line intensities in arbitrary units, which are then normalized within the program. The outputs are T{sub e}(eV), N{sub e}(cm{sup {minus}3}), and a fractional ionization, which in our testing using H- and He-like spectra, was N(He)/[N(H) + N(He)].

  7. Chemotaxis during neural crest migration.

    PubMed

    Shellard, Adam; Mayor, Roberto

    2016-07-01

    Chemotaxis refers to the directional migration of cells towards external, soluble factors along their gradients. It is a process that is used by many different cell types during development for tissue organisation and the formation of embryonic structures, as well as disease like cancer metastasis. The neural crest (NC) is a multipotent, highly migratory cell population that contribute to a range of tissues. It has been hypothesised that NC migration, at least in part, is reliant on chemotactic signals. This review will explore the current evidence for proposed chemoattractants of NC cells, and outline mechanisms for the chemotactic response of the NC to them. PMID:26820523

  8. Neural Events in the Reinforcement Contingency

    PubMed Central

    Teresa Araujo Silva, Maria; Leyser Gonçalves, Fábio; Garcia-Mijares, Miriam

    2007-01-01

    When neural events are analyzed as stimuli and responses, functional relations among them and among overt stimuli and responses can be unveiled. The integration of neuroscience and the experimental analysis of behavior is beginning to provide empirical evidence of involvement of neural events in the three-term contingency relating discriminative stimuli, responses, and consequences. This paper is aimed at highlighting exemplar instances in the development of this issue. It has long been known that the electrical stimulation of certain cerebral areas can have a reinforcing function. Extraordinary technological advances in recent years show that neural activity can be selected by consequences. For example, the activity of in vitro isolated neurons that receive dopamine as a reinforcer functions as a cellular analogue of operant conditioning. The in vivo activity of populations of neurons of rats and monkeys can be recorded on an instant-to-instant basis and can then be used to move mechanical arms or track a target as a function of consequences. Neural stimulation acts as a discriminative stimulus for operant responses that are in turn maintained by neural consequences. Together with investigations on the molecular basis of classical conditioning, those studies are examples of possibilities that are being created for the study of behavior–environment interactions within the organism. More important, they show that, as an element in the three-term contingency, neural activity follows the same laws as other events. PMID:22478485

  9. A TLD dose algorithm using artificial neural networks

    SciTech Connect

    Moscovitch, M.; Rotunda, J.E.; Tawil, R.A.; Rathbone, B.A.

    1995-12-31

    An artificial neural network was designed and used to develop a dose algorithm for a multi-element thermoluminescence dosimeter (TLD). The neural network architecture is based on the concept of functional links network (FLN). Neural network is an information processing method inspired by the biological nervous system. A dose algorithm based on neural networks is fundamentally different as compared to conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with given responses of a multi-element dosimeter (input) many times. The algorithm, being trained that way, eventually is capable to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personal dosimetry, the output consists of the desired dose components: deep dose, shallow dose and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. The neural network approach was applied to the Harshaw Type 8825 TLD, and was shown to significantly improve the performance of this dosimeter, well within the U.S. accreditation requirements for personnel dosimeters.

  10. A self-organized neural comparator.

    PubMed

    Ludueña, Guillermo A; Gros, Claudius

    2013-04-01

    Learning algorithms need generally the ability to compare several streams of information. Neural learning architectures hence need a unit, a comparator, able to compare several inputs encoding either internal or external information, for instance, predictions and sensory readings. Without the possibility of comparing the values of predictions to actual sensory inputs, reward evaluation and supervised learning would not be possible. Comparators are usually not implemented explicitly. Necessary comparisons are commonly performed by directly comparing the respective activities one-to-one. This implies that the characteristics of the two input streams (like size and encoding) must be provided at the time of designing the system. It is, however, plausible that biological comparators emerge from self-organizing, genetically encoded principles, which allow the system to adapt to the changes in the input and the organism. We propose an unsupervised neural circuitry, where the function of input comparison emerges via self-organization only from the interaction of the system with the respective inputs, without external influence or supervision. The proposed neural comparator adapts in an unsupervised form according to the correlations present in the input streams. The system consists of a multilayer feedforward neural network, which follows a local output minimization (anti-Hebbian) rule for adaptation of the synaptic weights. The local output minimization allows the circuit to autonomously acquire the capability of comparing the neural activities received from different neural populations, which may differ in population size and the neural encoding used. The comparator is able to compare objects never encountered before in the sensory input streams and evaluate a measure of their similarity even when differently encoded. PMID:23339611

  11. Calcified coronary artery plaque measurement with cardiac CT in population-based studies: standardized protocol of Multi-Ethnic Study of Atherosclerosis (MESA) and Coronary Artery Risk Development in Young Adults (CARDIA) study.

    PubMed

    Carr, J Jeffrey; Nelson, Jennifer Clark; Wong, Nathan D; McNitt-Gray, Michael; Arad, Yadon; Jacobs, David R; Sidney, Stephan; Bild, Diane E; Williams, O Dale; Detrano, Robert C

    2005-01-01

    Calcified coronary artery plaque, measured at cardiac computed tomography (CT), is a predictor of cardiovascular disease and may play an increasing role in cardiovascular disease risk assessment. The Multi-Ethnic Study of Atherosclerosis (MESA) and the Coronary Artery Risk Development in Young Adults (CARDIA) study of the National Heart, Lung, and Blood Institute are population-based studies in which calcified coronary artery plaque was measured with electron-beam and multi-detector row CT and a standardized protocol in 6814 (MESA) and 3044 (CARDIA study) participants. The studies were approved by the appropriate institutional review board from the study site or agency, and written informed consent was obtained from each participant. Participation in the CT examination was high, image quality was good, and agreement for the presence of calcified plaque was high (kappa = 0.92, MESA; kappa = 0.77, CARDIA study). Extremely high agreement was observed between and within CT image analysts for the presence (kappa > 0.90, all) and amount (intraclass correlation coefficients, >0.99) of calcified plaque. Measurement of calcified coronary artery plaque with cardiac CT is well accepted by participants and can be implemented with consistently high-quality results with a standardized protocol and trained personnel. If predictive value of calcified coronary artery plaque for cardiovascular events proves sufficient to justify screening a segment of the population, then a standardized cardiac CT protocol is feasible and will provide reproducible results for health care providers and the public. PMID:15618373

  12. Neural network technologies for image classification

    NASA Astrophysics Data System (ADS)

    Korikov, A. M.; Tungusova, A. V.

    2015-11-01

    We analyze the classes of problems with an objective necessity to use neural network technologies, i.e. representation and resolution problems in the neural network logical basis. Among these problems, image recognition takes an important place, in particular the classification of multi-dimensional data based on information about textural characteristics. These problems occur in aerospace and seismic monitoring, materials science, medicine and other. We reviewed different approaches for the texture description: statistical, structural, and spectral. We developed a neural network technology for resolving a practical problem of cloud image classification for satellite snapshots from the spectroradiometer MODIS. The cloud texture is described by the statistical characteristics of the GLCM (Gray Level Co- Occurrence Matrix) method. From the range of neural network models that might be applied for image classification, we chose the probabilistic neural network model (PNN) and developed an implementation which performs the classification of the main types and subtypes of clouds. Also, we chose experimentally the optimal architecture and parameters for the PNN model which is used for image classification.

  13. Exploring neural network technology

    SciTech Connect

    Naser, J.; Maulbetsch, J.

    1992-12-01

    EPRI is funding several projects to explore neural network technology, a form of artificial intelligence that some believe may mimic the way the human brain processes information. This research seeks to provide a better understanding of fundamental neural network characteristics and to identify promising utility industry applications. Results to date indicate that the unique attributes of neural networks could lead to improved monitoring, diagnostic, and control capabilities for a variety of complex utility operations. 2 figs.

  14. Fuzzy and neural control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  15. Neural crest development in fetal alcohol syndrome.

    PubMed

    Smith, Susan M; Garic, Ana; Flentke, George R; Berres, Mark E

    2014-09-01

    Fetal alcohol spectrum disorder (FASD) is a leading cause of neurodevelopmental disability. Some affected individuals possess distinctive craniofacial deficits, but many more lack overt facial changes. An understanding of the mechanisms underlying these deficits would inform their diagnostic utility. Our understanding of these mechanisms is challenged because ethanol lacks a single receptor when redirecting cellular activity. This review summarizes our current understanding of how ethanol alters neural crest development. Ample evidence shows that ethanol causes the "classic" fetal alcohol syndrome (FAS) face (short palpebral fissures, elongated upper lip, deficient philtrum) because it suppresses prechordal plate outgrowth, thereby reducing neuroectoderm and neural crest induction and causing holoprosencephaly. Prenatal alcohol exposure (PAE) at premigratory stages elicits a different facial appearance, indicating FASD may represent a spectrum of facial outcomes. PAE at this premigratory period initiates a calcium transient that activates CaMKII and destabilizes transcriptionally active β-catenin, thereby initiating apoptosis within neural crest populations. Contributing to neural crest vulnerability are their low antioxidant responses. Ethanol-treated neural crest produce reactive oxygen species and free radical scavengers attenuate their production and prevent apoptosis. Ethanol also significantly impairs neural crest migration, causing cytoskeletal rearrangements that destabilize focal adhesion formation; their directional migratory capacity is also lost. Genetic factors further modify vulnerability to ethanol-induced craniofacial dysmorphology and include genes important for neural crest development, including shh signaling, PDFGA, vangl2, and ribosomal biogenesis. Because facial and brain development are mechanistically and functionally linked, research into ethanol's effects on neural crest also informs our understanding of ethanol's CNS pathologies. PMID

  16. Neural Crest Development in Fetal Alcohol Syndrome

    PubMed Central

    Smith, Susan M.; Garic, Ana; Flentke, George R.; Berres, Mark E.

    2016-01-01

    Fetal alcohol spectrum disorder (FASD) is a leading cause of neurodevelopmental disability. Some affected individuals possess distinctive craniofacial deficits, but many more lack overt facial changes. An understanding of the mechanisms underlying these deficits would inform their diagnostic utility. Our understanding of these mechanisms is challenged because ethanol lacks a single receptor when redirecting cellular activity. This review summarizes our current understanding of how ethanol alters neural crest development. Ample evidence shows that ethanol causes the “classic” fetal alcohol syndrome (FAS) face (short palpebral fissures, elongated upper lip, deficient philtrum) because it suppresses prechordal plate outgrowth, thereby reducing neuroectoderm and neural crest induction and causing holoprosencephaly. Prenatal alcohol exposure (PAE) at premigratory stages elicits a different facial appearance, indicating FASD may represent a spectrum of facial outcomes. PAE at this premigratory period initiates a calcium transient that activates CaMKII and destabilizes transcriptionally active β-catenin, thereby initiating apoptosis within neural crest populations. Contributing to neural crest vulnerability are their low antioxidant responses. Ethanol-treated neural crest produce reactive oxygen species, and free radical scavengers attenuate their production and prevent apoptosis. Ethanol also significantly impairs neural crest migration, causing cytoskeletal rearrangements that destabilize focal adhesion formation; their directional migratory capacity is also lost. Genetic factors further modify vulnerability to ethanol-induced craniofacial dysmorphology, and include genes important for neural crest development including shh signaling, PDFGA, vangl2, and ribosomal biogenesis. Because facial and brain development are mechanistically and functionally linked, research into ethanol’s effects on neural crest also informs our understanding of ethanol’s CNS pathologies

  17. Unsupervised classification of neural spikes with a hybrid multilayer artificial neural network.

    PubMed

    García, P; Suárez, C P; Rodríguez, J; Rodríguez, M

    1998-07-01

    The understanding of the brain structure and function and its computational style is one of the biggest challenges both in Neuroscience and Neural Computation. In order to reach this and to test the predictions of neural network modeling, it is necessary to observe the activity of neural populations. In this paper we propose a hybrid modular computational system for the spike classification of multiunits recordings. It works with no knowledge about the waveform, and it consists of two moduli: a Preprocessing (Segmentation) module, which performs the detection and centering of spike vectors using programmed computation; and a Processing (Classification) module, which implements the general approach of neural classification: feature extraction, clustering and discrimination, by means of a hybrid unsupervised multilayer artificial neural network (HUMANN). The operations of this artificial neural network on the spike vectors are: (i) compression with a Sanger Layer from 70 points vector to five principal component vector; (ii) their waveform is analyzed by a Kohonen layer; (iii) the electrical noise and overlapping spikes are rejected by a previously unreported artificial neural network named Tolerance layer; and (iv) finally the spikes are labeled into spike classes by a Labeling layer. Each layer of the system has a specific unsupervised learning rule that progressively modifies itself until the performance of the layer has been automatically optimized. The procedure showed a high sensitivity and specificity also when working with signals containing four spike types. PMID:10223516

  18. A consensual neural network

    NASA Technical Reports Server (NTRS)

    Benediktsson, J. A.; Ersoy, O. K.; Swain, P. H.

    1991-01-01

    A neural network architecture called a consensual neural network (CNN) is proposed for the classification of data from multiple sources. Its relation to hierarchical and ensemble neural networks is discussed. CNN is based on the statistical consensus theory and uses nonlinearly transformed input data. The input data are transformed several times, and the different transformed data are applied as if they were independent inputs. The independent inputs are classified using stage neural networks and outputs from the stage networks are then weighted and combined to make a decision. Experimental results based on remote-sensing data and geographic data are given.

  19. Multi-Genetic Marker Approach and Spatio-Temporal Analysis Suggest There Is a Single Panmictic Population of Swordfish Xiphias gladius in the Indian Ocean

    PubMed Central

    Muths, Delphine; Le Couls, Sarah; Evano, Hugues; Grewe, Peter; Bourjea, Jerome

    2013-01-01

    Genetic population structure of swordfish Xiphias gladius was examined based on 2231 individual samples, collected mainly between 2009 and 2010, among three major sampling areas within the Indian Ocean (IO; twelve distinct sites), Atlantic (two sites) and Pacific (one site) Oceans using analysis of nineteen microsatellite loci (n = 2146) and mitochondrial ND2 sequences (n = 2001) data. Sample collection was stratified in time and space in order to investigate the stability of the genetic structure observed with a special focus on the South West Indian Ocean. Significant AMOVA variance was observed for both markers indicating genetic population subdivision was present between oceans. Overall value of F-statistics for ND2 sequences confirmed that Atlantic and Indian Oceans swordfish represent two distinct genetic stocks. Indo-Pacific differentiation was also significant but lower than that observed between Atlantic and Indian Oceans. However, microsatellite F-statistics failed to reveal structure even at the inter-oceanic scale, indicating that resolving power of our microsatellite loci was insufficient for detecting population subdivision. At the scale of the Indian Ocean, results obtained from both markers are consistent with swordfish belonging to a single unique panmictic population. Analyses partitioned by sampling area, season, or sex also failed to identify any clear structure within this ocean. Such large spatial and temporal homogeneity of genetic structure, observed for such a large highly mobile pelagic species, suggests as satisfactory to consider swordfish as a single panmictic population in the Indian Ocean. PMID:23717447

  20. Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation

    PubMed Central

    Wu, Si; Wong, K Y Michael; Fung, C C Alan; Mi, Yuanyuan; Zhang, Wenhao

    2016-01-01

    Owing to its many computationally desirable properties, the model of continuous attractor neural networks (CANNs) has been successfully applied to describe the encoding of simple continuous features in neural systems, such as orientation, moving direction, head direction, and spatial location of objects. Recent experimental and computational studies revealed that complex features of external inputs may also be encoded by low-dimensional CANNs embedded in the high-dimensional space of neural population activity. The new experimental data also confirmed the existence of the M-shaped correlation between neuronal responses, which is a correlation structure associated with the unique dynamics of CANNs. This body of evidence, which is reviewed in this report, suggests that CANNs may serve as a canonical model for neural information representation. PMID:26937278

  1. What Are Neural Tube Defects?

    MedlinePlus

    ... NICHD Research Information Clinical Trials Resources and Publications Neural Tube Defects (NTDs): Condition Information Skip sharing on ... media links Share this: Page Content What are neural tube defects? Neural (pronounced NOOR-uhl ) tube defects ...

  2. Neighborhood-Level Socioeconomic Deprivation Predicts Weight Gain in a Multi-Ethnic Population: Longitudinal Data from the Dallas Heart Study

    PubMed Central

    Powell-Wiley, Tiffany M.; Ayers, Colby; Agyemang, Priscilla; Leonard, Tammy; Berrigan, David; Barbash, Rachel Ballard; Lian, Min; Das, Sandeep R.; Hoehner, Christine M.

    2014-01-01

    Objective To examine relationship between neighborhood-level socioeconomic deprivation and weight change in a multi-ethnic cohort from Dallas County, Texas and whether behavioral/psychosocial factors attenuate the relationship. Methods Non-movers (those in the same neighborhood throughout the study period) aged 18–65 (N=939) in Dallas Heart Study (DHS) underwent weight measurements between 2000–2009 (median 7-year follow-up). Geocoded home addresses defined block groups; a neighborhood deprivation index (NDI) was created (higher NDI=greater deprivation). Multi-level modeling determined weight change relative to NDI. Model fit improvement was examined with adding physical activity and neighborhood environment perceptions (higher score=more unfavorable perceptions) as covariates. A significant interaction between residence length and NDI was found (p-interaction=0.04); results were stratified by median residence length (11 years). Results Adjusting for age, sex, race/ethnicity, smoking, education/income, those who lived in neighborhood>11 years gained 1.0 kilograms (kg) per one-unit increment of NDI (p=0.03), or 6 kg for those in highest NDI tertile compared with those in the lowest tertile. Physical activity improved model fit; NDI remained associated with weight gain after adjustment for physical activity and neighborhood environment perceptions. There was no significant relationship between NDI and weight change for those in their neighborhood≤11 years. Conclusions Living in more socioeconomically deprived neighborhoods over a longer time period was associated with weight gain in DHS. PMID:24875231

  3. Multistage neural network model for dynamic scene analysis

    SciTech Connect

    Ajjimarangsee, P.

    1989-01-01

    This research is concerned with dynamic scene analysis. The goal of scene analysis is to recognize objects and have a meaningful interpretation of the scene from which images are obtained. The task of the dynamic scene analysis process generally consists of region identification, motion analysis and object recognition. The objective of this research is to develop clustering algorithms using neural network approach and to investigate a multi-stage neural network model for region identification and motion analysis. The research is separated into three parts. First, a clustering algorithm using Kohonens' self-organizing feature map network is developed to be capable of generating continuous membership valued outputs. A newly developed version of the updating algorithm of the network is introduced to achieve a high degree of parallelism. A neural network model for the fuzzy c-means algorithm is proposed. In the second part, the parallel algorithms of a neural network model for clustering using the self-organizing feature maps approach and a neural network that models the fuzzy c-means algorithm are modified for implementation on a distributed memory parallel architecture. In the third part, supervised and unsupervised neural network models for motion analysis are investigated. For a supervised neural network, a three layer perceptron network is trained by a series of images to recognize the movement of the objects. For the unsupervised neural network, a self-organizing feature mapping network will learn to recognize the movement of the objects without an explicit training phase.

  4. Critical Branching Neural Networks

    ERIC Educational Resources Information Center

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  5. Multi-strategy coevolving aging particle optimization.

    PubMed

    Iacca, Giovanni; Caraffini, Fabio; Neri, Ferrante

    2014-02-01

    We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer. PMID:24344695

  6. High-performance neural networks. [Neural computers

    SciTech Connect

    Dress, W.B.

    1987-06-01

    The new Forth hardware architectures offer an intermediate solution to high-performance neural networks while the theory and programming details of neural networks for synthetic intelligence are developed. This approach has been used successfully to determine the parameters and run the resulting network for a synthetic insect consisting of a 200-node ''brain'' with 1760 interconnections. Both the insect's environment and its sensor input have thus far been simulated. However, the frequency-coded nature of the Browning network allows easy replacement of the simulated sensors by real-world counterparts.

  7. Integrating physiology, population dynamics and climate to make multi-scale predictions for the spread of an invasive insect: the Argentine ant at Haleakala National Park, Hawaii

    USGS Publications Warehouse

    Hartley, Stephen; Krushelnycky, Paul D.; Lester, Philip J.

    2010-01-01

    Mechanistic models for predicting species’ distribution patterns present particular advantages and challenges relative to models developed from statistical correlations between distribution and climate. They can be especially useful for predicting the range of invasive species whose distribution has not yet reached equilibrium. Here, we illustrate how a physiological model of development for the invasive Argentine ant can be connected to differences in micro-site suitability, population dynamics and climatic gradients; processes operating at quite different spatial scales. Our study is located in the subalpine shrubland of Haleakala National Park, Hawaii, where the spread of Argentine ants Linepithema humile has been documented for the past twenty-five years. We report four main results. First, at a microsite level, the accumulation of degree-days recorded in potential ant nest sites under bare ground or rocks was significantly greater than under a groundcover of grassy vegetation. Second, annual degree-days measured where population boundaries have not expanded (456-521 degree-days), were just above the developmental requirements identified from earlier laboratory studies (445 degree-days above 15.98C). Third, rates of population expansion showed a strong linear relationship with annual degree-days. Finally, an empirical relationship between soil degree-days and climate variables mapped at a broader scale predicts the potential for future range expansion of Argentine ants at Haleakala, particularly to the west of the lower colony and the east of the upper colony. Variation in the availability of suitable microsites, driven by changes in vegetation cover and ultimately climate, provide a hierarchical understanding of the distribution of Argentine ants close to their cold-wet limit of climatic tolerances. We conclude that the integration of physiology, population dynamics and climate mapping holds much promise for making more robust predictions about

  8. Purification of Immune Cell Populations from Freshly Isolated Murine Tumors and Organs by Consecutive Magnetic Cell Sorting and Multi-parameter Flow Cytometry-Based Sorting.

    PubMed

    Salvagno, Camilla; de Visser, Karin E

    2016-01-01

    It is well established that tumors evolve together with nonmalignant cells, such as fibroblasts, endothelial cells, and immune cells. These cells constantly entangle and interact with each other creating the tumor microenvironment. Immune cells can exert both tumor-promoting and tumor-protective functions. Detailed phenotypic and functional characterization of intra-tumoral immune cell subsets has become increasingly important in the field of cancer biology and cancer immunology. In this chapter, we describe a method for isolation of viable and pure immune cell subsets from freshly isolated murine solid tumors and organs. First, we describe a protocol for the generation of single-cell suspensions from tumors and organs using mechanical and enzymatic strategies. In addition, we describe how immune cell subsets can be purified by consecutive magnetic cell sorting and multi-parameter flow cytometry-based cell sorting. PMID:27581019

  9. Comparative analysis of neural crest cell death, migration, and function during vertebrate embryogenesis.

    PubMed

    Kulesa, Paul; Ellies, Debra L; Trainor, Paul A

    2004-01-01

    Cranial neural crest cells are a multipotent, migratory population that generates most of the cartilage, bone, connective tissue and peripheral nervous system in the vertebrate head. Proper neural crest cell patterning is essential for normal craniofacial morphogenesis and is highly conserved among vertebrates. Neural crest cell patterning is intimately connected to the early segmentation of the neural tube, such that neural crest cells migrate in discrete segregated streams. Recent advances in live embryo imaging have begun to reveal the complex behaviour of neural crest cells which involve intricate cell-cell and cell-environment interactions. Despite the overall similarity in neural crest cell migration between distinct vertebrates species there are important mechanistic differences. Apoptosis for example, is important for neural crest cell patterning in chick embryos but not in mouse, frog or fish embryos. In this paper we highlight the potential evolutionary significance of such interspecies differences in jaw development and evolution. Developmental Dynamics 229:14-29, 2004. PMID:14699574

  10. Multi-state analysis of the impacts of avian pox on a population of Serins (Serinus serinus): The importance of estimating recapture rates

    USGS Publications Warehouse

    Senar, J.C.; Conroy, M.J.

    2004-01-01

    Disease is one of the evolutionary forces shaping populations. Recent studies have shown that epidemics like avian pox, malaria, or mycoplasmosis have affected passerine population dynamics, being responsible for the decline of some populations or disproportionately killing males and larger individuals and thus selecting for specific morphotypes. However, few studies have estimated the effects of an epidemic by following individual birds using the capture-recapture approach. Because avian pox can be diagnosed by direct examination of the birds, we are here able to analyze, using multistate models, the development and consequences of an avian pox epidemic affecting in 1996, a population of Serins (Serinus serinus) in northeastern Spain. The epidemics lasted from June to the end of November of 1996, with a maximum apparent prevalence rate > 30% in October. However, recapture rate of sick birds was very high (0.81, range 0.37-0.93) compared to that of healthy birds (0.21, range 0.020-32), which highly inflated apparent prevalence rate. This was additionally supported by the low predicted transition from the state of being uninfected to the state of being infected (0.03, SE 0.03). Once infected, Serin avian pox was very virulent with (15-day) survival rate of infected birds being of only 0.46 (SE 0.17) compared to that of healthy ones (0.87, SE 0.03). Probability of recovery from disease, provided that the bird survived the first two weeks, however, was very high (0.65, SE 0.25). The use of these estimates together with a simple model, allowed us to predict an asymptotic increase to prevalence of about 4% by the end of the outbreak period, followed by a sharp decline, with the only remaining infestations being infected birds that had not yet recovered. This is in contrast to the apparent prevalence of pox and stresses the need to estimate recapture rates when estimating population dynamics parameters. ?? 2004 Museu de Cie??ncies Naturals.

  11. Discovery of transcription factors and other candidate regulators of neural crest development

    PubMed Central

    Adams, MS; Gammill, LS; Bronner-Fraser, M

    2011-01-01

    Neural crest cells migrate long distances and form divergent derivatives in vertebrate embryos. Despite previous efforts to identify genes upregulated in neural crest populations, transcription factors have proved to be elusive due to relatively low expression levels and often transient expression. We screened newly induced neural crest cells for early target genes with the aim of identifying transcriptional regulators and other developmentally important genes. This yielded numerous candidate regulators, including fourteen transcription factors, many of which were not previously associated with neural crest development. Quantitative real-time PCR confirmed upregulation of several transcription factors in newly induced neural crest populations in vitro. In a secondary screen by in situ hybridization, we verified the expression of >100 genes in the neural crest. We note that several of the transcription factors and other genes from the screen are expressed in other migratory cell populations and have been implicated in diverse forms of cancer. PMID:18351660

  12. Dynamics of neural cryptography

    SciTech Connect

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-15

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  13. MULTI-WAVELENGTH STUDY OF A COMPLETE IRAC 3.6 {mu}m SELECTED GALAXY SAMPLE: A FAIR CENSUS OF RED AND BLUE POPULATIONS AT REDSHIFTS 0.4-1.2

    SciTech Connect

    Huang, J.-S.; Faber, S. M.; Koo, D.; Rigopoulou, D.; Magdis, G.; Newman, J.; Shu, C.; Luo, Z.; Ashby, M. L. N.; Wang, T.; Willner, S. P.; Fazio, G. G.; Barmby, P.; Coil, A.; Zheng, X. Z.

    2013-03-20

    We present a multi-wavelength study of a 3.6 {mu}m selected galaxy sample in the Extended Groth Strip (EGS). The sample is complete for galaxies with stellar mass >10{sup 9.5} M{sub Sun} and redshift 0.4 < z < 1.2. In this redshift range, the Infrared Array Camera 3.6 {mu}m band measures the rest-frame near-infrared band, permitting nearly unbiased selection with respect to both quiescent and star-forming galaxies. The numerous spectroscopic redshifts available in the EGS are used to train an artificial neural network to estimate photometric redshifts. The distribution of photometric redshift errors is Gaussian with standard deviation {approx}0.025(1 + z), and the fraction of redshift failures (>3{sigma} errors) is about 3.5%. A new method of validation based on pair statistics confirms the estimate of standard deviation even for galaxies lacking spectroscopic redshifts. Basic galaxy properties measured include rest-frame U - B colors, B- and K-band absolute magnitudes, and stellar masses. We divide the sample into quiescent and star-forming galaxies according to their rest-frame U - B colors and 24-3.6 {mu}m flux density ratios and derive rest K-band luminosity functions and stellar mass functions for quiescent, star-forming, and all galaxies. The results show that massive, quiescent galaxies were in place by z Almost-Equal-To 1, but lower mass galaxies generally ceased their star formation at later epochs.

  14. Multifunctional hybrid optical/digital neural net

    NASA Astrophysics Data System (ADS)

    Casasent, David P.

    1990-08-01

    A multi-functional hybrid neural net is described. It is hybrid since it uses a digital hardware Hecht-Nielsen Corporation (HNC) neural net for adaptive learning and an optical neural net for on-line processing/classification. It is also hybrid in its combination of pattern recognition and neural net techniques. The system is multi-functional. It can function as an optimization and adaptive pattern recognition neural net as well as an auto and heteroassociative processor. I . W. JTRODUCTION Neural nets (NNs) have recently received enormous attention [1 -2] with increasing attention to the use of optical processors and a variety of new learning algorithms. Section 2 describes our hybrid NN with attention to Its fabrication and the role for optical and digital processors. Section 3 details Its use as an associative processor. Section 4 highlights is use in 3 optimization NN problems (a mixture NN a multitarget tracker (MTT) NN and a matrix inversion NN). Section 5 briefly notes it use as a production NN system and symbolic NN. Section 6 describes its use as an adaptive pattern recognition (PR) NN (that marries PR and NN techniques). 2. HYBRID ARCHITECTURE Figure 1 shows our basic hybrid NN [3]. The optical portion of the system is a matrix-vector (M-V) processor whose vector output P3 is the product of the vector at P1 and the matrix at P2. An HNC digital hardware NN is used during learning determine the interconnection weights forP2. If P2 is a spatial light modulator (SLM) its contents can be updated (using gated learning) from thedigital NN. The operations in most adaptive PR NN learning algorithms are sufficiently complex thatthey are best implemented digitally. In addition the learning operations required are often not well suited for optical realization for optimization NNs the weights are fixed and in adaptive learning learning is off-line and once completed the weights can often be fixed. Four gates are shown that determine the final output or the new P1

  15. Robust Reference Intervals for Serum Kappa and Lambda Free Light Chains from a Multi Centre Study Population from Hyderabad, India: Myeloma Diagnostic Implications.

    PubMed

    Mohammed, Noorjahan; Chandran, Priscilla Abraham; Kandregula, Madhavi; Mattaparthi, Ratna Deepika; Gundeti, Sadasivudu; Volturi, Jyotsna; Darapuneni, Radhika; Raju, Sree Bhushan; Dattatreya, Palanki Satya

    2016-01-01

    The International Myeloma Working Group considers the serum free light chain (SFLC) assay to be an adjunct to traditional tests. Apart from the FLC ratio, the absolute values of individual free light chains also are gaining importance as they appear to be more relevant in certain clinical settings. Automated assays are available for their determination. As laboratories put new test systems into use catering to different disease populations, they are required by accreditation and certification bodies to verify or establish performance specifications, including reference intervals (RIs) representative of their population. Our aim was to establish local RIs for SFLC in a multicentre representative healthy population using a robust method. There was no significant relationship between SFLC levels and age, gender and creatinine levels. The 95% RI for κSFLC was 4.81 to 33.86mg/L, for ? SFLC was 5.19 to 23.67mg/L and for κ/?SFLC was 0.36 to 2.33, significantly higher than the values given by the manufacturer. The κ/? SFLC ratio at 2.23, covering 100% of the data, showed 72% sensitivity (95% CI=39.0 - 94.0), 100% specificity (95% CI=71.5 - 100.0), 100% PPV (95% CI=21.5 - 100.0), 95% NPV (95% CI=75.4 - 99.9), and 79% accuracy (95% CI=56.0 - 93.0). In the patient group, kit RI for κ /? SFLC ratio classified 45.5% (n=5) as positive vs 9.1% (n=1) positive by the study RI, while the kit RI for kappa FLC classified 90.9% (n=10) as positive vs 54.5% (n=6) , indicating increased probability of false positive test results with the kit RI when applied to our patient population. Appropriate and specific reference intervals and criteria values result in fewer false-positive and false-negative results which means fewer wrong or missed diagnoses. PMID:27268638

  16. Assessment of the vulnerability and the resilience of the population at risk of multi-hazard: a support to geo-risk management in Central Africa

    NASA Astrophysics Data System (ADS)

    Michellier, Caroline; Kervyn, François; Tréfon, Théodore; Wolff, Eléonore

    2013-04-01

    GeoRisCA is a project which aims at studying the geo-risk in the Kivu region (DRC, Rwanda, Burundi), in order to support risk management. The approach developed in GeoRisCA combines methodologies from various disciplines, which will allow the analyses of seismic, volcanic and mass-movement hazards and the vulnerability assessment of the threatened elements. Vulnerability is a complex concept which is commonly defined as the susceptibility of the population, the infrastructures and the natural ecosystems to suffer from damages if a hazard occurs. The densely populated area extended from the North Kivu province in Democratic Republic of the Congo (DRC) to North Burundi and East Rwanda is vulnerable to several geohazards, such as landslides triggered by geodynamical processes (climate, seismicity, volcanism) and possibly worsen by anthropic actions. Located in the East African rift valley, the region is also characterized by a strong seismicity, with increasing people and infrastructure exposed. In addition, east DRC hosts the two most active African volcanoes: Nyiragongo and Nyamulagira. Their activity can have serious impacts, as in 2002 when Nyiragongo directly endangers the ~800.000 inhabitants of Goma city, located ~15 km to the south. Linked to passive volcanic degassing, SO2 and CO2 discharge may also increase the population vulnerability(morbidity, mortality). Focusing specifically on this region, the vulnerability assessment methodology developed in GeoRisCA takes into account "exposure to perturbations" and "adaptive capacity or resilience" of the vulnerable systems. On one hand, the exposure is identified as the potential degree of loss of a given element or set of elements at risk; i.e., the susceptibility of people, infrastructures and buildings with respect to a hazard (social vulnerability). It focuses mainly on land use, and on demographic and socio-economic factors that increase or attenuate the impacts of hazards events on local populations. On the

  17. Neural and cognitive characteristics of extraordinary altruists.

    PubMed

    Marsh, Abigail A; Stoycos, Sarah A; Brethel-Haurwitz, Kristin M; Robinson, Paul; VanMeter, John W; Cardinale, Elise M

    2014-10-21

    Altruistic behavior improves the welfare of another individual while reducing the altruist's welfare. Humans' tendency to engage in altruistic behaviors is unevenly distributed across the population, and individual variation in altruistic tendencies may be genetically mediated. Although neural endophenotypes of heightened or extreme antisocial behavior tendencies have been identified in, for example, studies of psychopaths, little is known about the neural mechanisms that support heightened or extreme prosocial or altruistic tendencies. In this study, we used structural and functional magnetic resonance imaging to assess a population of extraordinary altruists: altruistic kidney donors who volunteered to donate a kidney to a stranger. Such donations meet the most stringent definitions of altruism in that they represent an intentional behavior that incurs significant costs to the donor to benefit an anonymous, nonkin other. Functional imaging and behavioral tasks included face-emotion processing paradigms that reliably distinguish psychopathic individuals from controls. Here we show that extraordinary altruists can be distinguished from controls by their enhanced volume in right amygdala and enhanced responsiveness of this structure to fearful facial expressions, an effect that predicts superior perceptual sensitivity to these expressions. These results mirror the reduced amygdala volume and reduced responsiveness to fearful facial expressions observed in psychopathic individuals. Our results support the possibility of a neural basis for extraordinary altruism. We anticipate that these findings will expand the scope of research on biological mechanisms that promote altruistic behaviors to include neural mechanisms that support affective and social responsiveness. PMID:25225374

  18. Neural and cognitive characteristics of extraordinary altruists

    PubMed Central

    Marsh, Abigail A.; Stoycos, Sarah A.; Brethel-Haurwitz, Kristin M.; Robinson, Paul; VanMeter, John W.; Cardinale, Elise M.

    2014-01-01

    Altruistic behavior improves the welfare of another individual while reducing the altruist’s welfare. Humans’ tendency to engage in altruistic behaviors is unevenly distributed across the population, and individual variation in altruistic tendencies may be genetically mediated. Although neural endophenotypes of heightened or extreme antisocial behavior tendencies have been identified in, for example, studies of psychopaths, little is known about the neural mechanisms that support heightened or extreme prosocial or altruistic tendencies. In this study, we used structural and functional magnetic resonance imaging to assess a population of extraordinary altruists: altruistic kidney donors who volunteered to donate a kidney to a stranger. Such donations meet the most stringent definitions of altruism in that they represent an intentional behavior that incurs significant costs to the donor to benefit an anonymous, nonkin other. Functional imaging and behavioral tasks included face-emotion processing paradigms that reliably distinguish psychopathic individuals from controls. Here we show that extraordinary altruists can be distinguished from controls by their enhanced volume in right amygdala and enhanced responsiveness of this structure to fearful facial expressions, an effect that predicts superior perceptual sensitivity to these expressions. These results mirror the reduced amygdala volume and reduced responsiveness to fearful facial expressions observed in psychopathic individuals. Our results support the possibility of a neural basis for extraordinary altruism. We anticipate that these findings will expand the scope of research on biological mechanisms that promote altruistic behaviors to include neural mechanisms that support affective and social responsiveness. PMID:25225374

  19. Designer Self-Assembling Peptide Nanofiber Scaffolds for Adult Mouse Neural Stem Cell 3-Dimensional Cultures

    PubMed Central

    Gelain, Fabrizio; Bottai, Daniele; Vescovi, Angleo; Zhang, Shuguang

    2006-01-01

    Biomedical researchers have become increasingly aware of the limitations of conventional 2-dimensional tissue cell culture systems, including coated Petri dishes, multi-well plates and slides, to fully address many critical issues in cell biology, cancer biology and neurobiology, such as the 3-D microenvironment, 3-D gradient diffusion, 3-D cell migration and 3-D cell-cell contact interactions. In order to fully understand how cells behave in the 3-D body, it is important to develop a well-controlled 3-D cell culture system where every single ingredient is known. Here we report the development of a 3-D cell culture system using a designer peptide nanofiber scaffold with mouse adult neural stem cells. We attached several functional motifs, including cell adhesion, differentiation and bone marrow homing motifs, to a self-assembling peptide RADA16 (Ac-RADARADARADARADA-COHN2). These functionalized peptides undergo self-assembly into a nanofiber structure similar to Matrigel. During cell culture, the cells were fully embedded in the 3-D environment of the scaffold. Two of the peptide scaffolds containing bone marrow homing motifs significantly enhanced the neural cell survival without extra soluble growth and neurotrophic factors to the routine cell culture media. In these designer scaffolds, the cell populations with β-Tubulin+, GFAP+ and Nestin+ markers are similar to those found in cell populations cultured on Matrigel. The gene expression profiling array experiments showed selective gene expression, possibly involved in neural stem cell adhesion and differentiation. Because the synthetic peptides are intrinsically pure and a number of desired function cellular motifs are easy to incorporate, these designer peptide nanofiber scaffolds provide a promising controlled 3-D culture system for diverse tissue cells, and are useful as well for general molecular and cell biology. PMID:17205123

  20. Isolation and propagation of neural stem cells in caprine (Capra hircus).

    PubMed

    Agarwal, Pranjali; Kumar, Manish; Kumar, Kuldeep; Singh, Renu; Mahapatra, Puspendra Saswat; Kumar, Ajay; Bhure, Sanjeev Kumar; Malakar, Dhruba; Sarkar, Mihir; Bag, Sadhan

    2014-08-01

    Neural stem cells (NSCs) can self-renew and give rise to neurons, astrocytes and oligodendrocytes; they are found in the nervous system of mammalian organisms, representing a promising resource for both fundamental research and therapeutics. There have been few investigations on NSCs in the livestock species. Therefore, we have successfully isolated and characterised NSCs from the foetal brain of a small domestic animal, the goat (called GNSCs). These cells from the foetal brain showed self-renewal, rapid proliferation with a population doubling time of 88 h, were morphologically homogeneous and maintained normal chromosome throughout the culture period. The cells expressed NSC-specific markers (Sox2, Pax6 and Mushashi), but were negative for CD34 and CD45. They were capable of multi-differentiation into neurons, astrocytes, oligodendrocytes, as well as adipocytes and osteocytes. The availability of such cells may hold great interest for basic and applied neuroscience. PMID:24687727

  1. Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease.

    PubMed Central

    Buzsáki, György; Watson, Brendon O.

    2012-01-01

    The perpetual activity of the cerebral cortex is largely supported by the variety of oscillations the brain generates, spanning a number of frequencies and anatomical locations, as well as behavioral correlates. First, we review findings from animal studies showing that most forms of brain rhythms are inhibition-based, producing rhythmic volleys of inhibitory inputs to principal cell populations, thereby providing alternating temporal windows of relatively reduced and enhanced excitability in neuronal networks. These inhibition-based mechanisms offer natural temporal frames to group or “chunk” neuronal activity into cell assemblies and sequences of assemblies, with more complex multi-oscillation interactions creating syntactical rules for the effective exchange of information among cortical networks. We then review recent studies in human psychiatric patients demonstrating a variety alterations in neural oscillations across all major psychiatric diseases, and suggest possible future research directions and treatment approaches based on the fundamental properties of brain rhythms. PMID:23393413

  2. Dogs leaving the ICU carry a very large multi-drug resistant enterococcal population with capacity for biofilm formation and horizontal gene transfer.

    PubMed

    Ghosh, Anuradha; Dowd, Scot E; Zurek, Ludek

    2011-01-01