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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. A Constructive Mean-Field Analysis of Multi-Population Neural Networks with Random Synaptic Weights and Stochastic Inputs

    PubMed Central

    Faugeras, Olivier; Touboul, Jonathan; Cessac, Bruno

    2008-01-01

    We deal with the problem of bridging the gap between two scales in neuronal modeling. At the first (microscopic) scale, neurons are considered individually and their behavior described by stochastic differential equations that govern the time variations of their membrane potentials. They are coupled by synaptic connections acting on their resulting activity, a nonlinear function of their membrane potential. At the second (mesoscopic) scale, interacting populations of neurons are described individually by similar equations. The equations describing the dynamical and the stationary mean-field behaviors are considered as functional equations on a set of stochastic processes. Using this new point of view allows us to prove that these equations are well-posed on any finite time interval and to provide a constructive method for effectively computing their unique solution. This method is proved to converge to the unique solution and we characterize its complexity and convergence rate. We also provide partial results for the stationary problem on infinite time intervals. These results shed some new light on such neural mass models as the one of Jansen and Rit (1995): their dynamics appears as a coarse approximation of the much richer dynamics that emerges from our analysis. Our numerical experiments confirm that the framework we propose and the numerical methods we derive from it provide a new and powerful tool for the exploration of neural behaviors at different scales. PMID:19255631

  3. 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

  4. Independent optical excitation of distinct neural populations.

    PubMed

    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

    2014-03-01

    Optogenetic tools enable 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 study of how different synapses or pathways interact to encode information in the brain. Here we describe two channelrhodopsins, Chronos and Chrimson, discovered through sequencing and physiological characterization of opsins from over 100 species of alga. Chrimson's excitation spectrum is red shifted by 45 nm relative to previous channelrhodopsins and can enable experiments in which red light is preferred. We show minimal visual system-mediated behavioral interference when using Chrimson in neurobehavioral studies in Drosophila melanogaster. Chronos has faster kinetics than previous channelrhodopsins yet is effectively more light sensitive. Together these two reagents enable two-color activation of neural spiking and downstream synaptic transmission in independent neural populations without detectable cross-talk in mouse brain slice.

  5. 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

  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. Neural population densities shape network correlations

    NASA Astrophysics Data System (ADS)

    Lefebvre, Jérémie; Perkins, Theodore J.

    2012-02-01

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

  8. A thesaurus for a neural population code.

    PubMed

    Ganmor, Elad; Segev, Ronen; Schneidman, Elad

    2015-09-08

    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.

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

    PubMed

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

    2012-01-01

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

  10. Realization problem of multi-layer cellular neural networks.

    PubMed

    Ban, Jung-Chao; Chang, Chih-Hung

    2015-10-01

    This paper investigates whether the output space of a multi-layer cellular neural network can be realized via a single layer cellular neural network in the sense of the existence of finite-to-one map from one output space to the other. Whenever such realization exists, the phenomena exhibited in the output space of the revealed single layer cellular neural network is at most a constant multiple of the phenomena exhibited in the output space of the original multi-layer cellular neural network. Meanwhile, the computation complexity of a single layer system is much less than the complexity of a multi-layer system. Namely, one can trade the precision of the results for the execution time. We remark that a routine extension of the proposed methodology in this paper can be applied to the substitution of hidden spaces although the detailed illustration is omitted.

  11. A thesaurus for a neural population code

    PubMed Central

    Ganmor, Elad; Segev, Ronen; Schneidman, Elad

    2015-01-01

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

  12. 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

    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

  14. 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.

  15. Neural networks within multi-core optic fibers.

    PubMed

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

    2016-07-07

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

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

    NASA Astrophysics Data System (ADS)

    Sapozhnikova, Elena P.

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

  17. 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

  18. Rapid three dimensional two photon neural population scanning.

    PubMed

    Schuck, Renaud; Quicke, Peter; Copeland, Caroline; Garasto, Stefania; Annecchino, Luca A; Hwang, June Kyu; Schultz, Simon R

    2015-08-01

    Recording the activity of neural populations at high sampling rates is a fundamental requirement for understanding computation in neural circuits. Two photon microscopy provides one promising approach towards this. However, neural circuits are three dimensional, and functional imaging in two dimensions fails to capture the 3D nature of neural dynamics. Electrically tunable lenses (ETLs) provide a simple and cheap method to extend laser scanning microscopy into the relatively unexploited third dimension. We have therefore incorporated them into our Adaptive Spiral Scanning (SSA) algorithm, which calculates kinematically efficient scanning strategies using radially modulated spiral paths. We characterised the response of the ETL, incorporated its dynamics using MATLAB models of the SSA algorithm and tested the models on populations of Izhikevich neurons of varying size and density. From this, we show that our algorithms can theoretically at least achieve sampling rates of 36.2Hz compared to 21.6Hz previously reported for 3D scanning techniques. PMID:26737626

  19. Neural network for interpretation of multi-meaning Chinese words

    NASA Astrophysics Data System (ADS)

    He, Qianhua; Xu, Bingzheng

    1994-03-01

    We proposed a neural network that can interpret multi-meaning Chinese words correctly by using context information. The self-organized network, designed for translating Chinese to English, builds a context according to key words of the processed text and utilizes it to interpret multi-meaning words correctly. The network is generated automatically basing on a Chinese-English dictionary and a knowledge-base of weights, and can adapt to the change of contexts. Simulation experiments have proved that the network worked as expected.

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

    PubMed

    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.

  1. 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

  2. Simultaneous Multi-plane Imaging of Neural Circuits.

    PubMed

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

    2016-01-20

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

  3. 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

  4. 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

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

    PubMed

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

    2015-10-01

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

  6. Attention selects informative neural populations in human V1.

    PubMed

    Verghese, Preeti; Kim, Yee-Joon; Wade, Alex R

    2012-11-14

    In a neural population driven by a simple grating stimulus, different subpopulations are maximally informative about changes to the grating's orientation and contrast. In theory, observers should attend to the optimal subpopulation when switching between orientation and contrast discrimination tasks. Here we used source-imaged, steady-state visual evoked potentials and visual psychophysics to determine whether this is the case. Observers fixated centrally while static targets were presented bilaterally along with a cue indicating task type (contrast or orientation modulation detection) and task location (left or right). Changes in neuronal activity were measured by quantifying frequency-tagged responses from flickering "reporter" gratings surrounding the targets. To determine the orientation tuning of attentionally modulated neurons, we measured responses for three different probe-reporter angles: 0, 20, and 45°. We estimated frequency-tagged cortical activity using a minimum norm inverse procedure combined with realistic MR-derived head models and retinotopically mapped visual areas. Estimates of neural activity from regions of interest centered on V1 showed that attention to a spatial location clearly increased the amplitude of the neural response in that location. More importantly, the pattern of modulation depended on the task. For orientation discrimination, attentional modulation showed a sharp peak in the population tuned 20° from the target orientation, whereas for contrast discrimination the enhancement was more broadly tuned. Similar tuning functions for orientation and contrast discrimination were obtained from psychophysical adaptation studies. These findings indicate that humans attend selectively to the most informative neural population and that these populations change depending on the nature of the task. PMID:23152620

  7. 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

  8. Measuring Fisher information accurately in correlated neural populations.

    PubMed

    Kanitscheider, Ingmar; Coen-Cagli, Ruben; Kohn, Adam; Pouget, Alexandre

    2015-06-01

    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.

  9. 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.

  10. Mapping a complete neural population in the retina.

    PubMed

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

    2012-10-24

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

  11. 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.

  12. 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

  13. 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

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

    PubMed

    Shahamiri, Seyed Reza; Salim, Siti Salwah Binti

    2014-09-01

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

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

    PubMed

    Bays, Paul M

    2014-03-01

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

  16. 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

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

    PubMed

    Zhou, Liqun; Zhang, Yanyan

    2016-01-01

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

  18. 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.

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

    PubMed

    Peng, Qiuyan; Shi, Bertram E

    2014-08-01

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

  20. 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.

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

    PubMed

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

    2012-01-01

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

  2. 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

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

    PubMed

    Soltic, Snjezana; Kasabov, Nikola

    2010-12-01

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

  5. Neural network analysis of the information content in population responses from human periodontal receptors

    NASA Astrophysics Data System (ADS)

    Edin, Benoni B.; Trulsson, Mats

    1992-07-01

    Understanding of the information processing in some sensory systems is hampered for several reasons. First, some of these systems may depend on several receptor types with different characteristics, and the crucial features of natural stimuli encoded by the receptors are rarely known with certainty. Second, the functional output of sensory processing is often not well defined. The human tooth is endowed with several types of sensory receptors. Among these, the mechanoreceptors located in the periodontal ligaments have been implicated in force encoding during chewing and biting. Individual receptors cannot, however, code unambiguously either the direction or the magnitude of the applied forces. Neuronal responses recorded in single human nerve fibers from periodontal receptors were fed to multi-layered feed-forward networks. The networks were trained with error back-propagation to identify specific features of the force stimuli that evoked the receptor responses. It was demonstrated that population responses in periodontal receptors contain information about both the point of attack and the direction of applied forces. It is concluded that networks may provide a powerful tool to investigate the information content in responses from biological receptor populations. As such, specific hypotheses with respect to information processing may be tested using neural networks also in sensory systems less well understood than, for instance, the visual system.

  6. 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.

  7. In vitro neural differentiation of CD34 (+) stem cell populations in hair follicles by three different neural induction protocols.

    PubMed

    Najafzadeh, Nowruz; Sagha, Mohsen; Heydari Tajaddod, Shirin; Golmohammadi, Mohammad Ghasem; Massahi Oskoui, Nasim; Deldadeh Moghaddam, Maryam

    2015-02-01

    Differentiation of hair follicle stem cells (HFSCs) into neurons and glial cells represents a promising cell-based therapy for neurodegenerative diseases. The hair follicle bulge area is reported as a putative source of new stem cell population for many years. In vitro studies have implicated neural differentiation of HFSCs. Here, we report the identification and purification of CD34 (+) cells from hair follicle by magnetic activated cell sorting (MACS). We next determined the cytotoxic effects of all-trans retinoic acid (RA) by using cell viability assays. Moreover, the neural differentiation potential of CD34 (+) cells was evaluated in the presence of RA, serum-free condition, and neural differentiation medium (NDM) treatments by using immunocytochemistry and reverse transcription polymerase chain reaction (RT-PCR). Our results showed that the isolated CD34 (+) stem cells were 12% of the total cells in the bulge area, and the neural cells derived from the stem cells expressed nestin, microtubule-associated protein 2 (MAP2), and glial fibrillary acidic protein (GFAP). Interestingly, all the neural induction media supported neuronal differentiation most effectively, but treatment with serum-free medium significantly increased the number of GFAP-positive glial cells. Moreover, increasing RA concentration (≥10 μM) leads to increased cell death in the cells, but a lower concentration of RA (1 μM) treatment results in a decrease in CD34-expressing stem cells. These findings show an instructive neuronal effect of three neural induction media in HFSCs, indicating the important role of this induction media in the specification of the stem cells toward a neural phenotype.

  8. Two developmentally distinct populations of neural crest cells contribute to the zebrafish heart.

    PubMed

    Cavanaugh, Ann M; Huang, Jie; Chen, Jau-Nian

    2015-08-15

    Cardiac neural crest cells are essential for outflow tract remodeling in animals with divided systemic and pulmonary circulatory systems, but their contributions to cardiac development in animals with a single-loop circulatory system are less clear. Here we genetically labeled neural crest cells and examined their contribution to the developing zebrafish heart. We identified two populations of neural crest cells that contribute to distinct compartments of zebrafish cardiovascular system at different developmental stages. A stream of neural crest cells migrating through pharyngeal arches 1 and 2 integrates into the myocardium of the primitive heart tube between 24 and 30 h post fertilization and gives rise to cardiomyocytes. A second wave of neural crest cells migrating along aortic arch 6 envelops the endothelium of the ventral aorta and invades the bulbus arteriosus after three days of development. Interestingly, while inhibition of FGF signaling has no effect on the integration of neural crest cells to the primitive heart tube, it prevents these cells from contributing to the outflow tract, demonstrating disparate responses of neural crest cells to FGF signaling. Furthermore, neural crest ablation in zebrafish leads to multiple cardiac defects, including reduced heart rate, defective myocardial maturation and a failure to recruit progenitor cells from the second heart field. These findings add to our understanding of the contribution of neural crest cells to the developing heart and provide insights into the requirement for these cells in cardiac maturation.

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

    PubMed

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

    2015-06-01

    Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identifica- tion of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neu- ral network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder. PMID:26485974

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

    PubMed

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

    2015-06-01

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

  11. 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

  12. Bilingualism provides a neural reserve for aging populations.

    PubMed

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

    2015-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-08-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

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

  15. 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

  16. 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.

  17. 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.

  18. Neural network predictions of acoustical parameters in multi-purpose performance halls.

    PubMed

    Cheung, L Y; Tang, S K

    2013-09-01

    A detailed binaural sound measurement was carried out in two multi-purpose performance halls of different seating capacities and designs in Hong Kong in the present study. The effectiveness of using neural network in the predictions of the acoustical properties using a limited number of measurement points was examined. The root-mean-square deviation from measurements, statistical parameter distribution matching, and the results of a t-test for vanishing mean difference between simulations and measurements were adopted as the evaluation criteria for the neural network performance. The audience locations relative to the sound source were used as the inputs to the neural network. Results show that the neural network training scheme using nine uniformly located measurement points in each specific hall area is the best choice regardless of the hall setting and design. It is also found that the neural network prediction of hall spaciousness does not require a large amount of training data, but the accuracy of the reverberance related parameter predictions increases with increasing volume of training data.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2015-08-01

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

  1. 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

  2. Average activity of excitatory and inhibitory neural populations

    NASA Astrophysics Data System (ADS)

    Roulet, Javier; Mindlin, Gabriel B.

    2016-09-01

    We develop an extension of the Ott-Antonsen method [E. Ott and T. M. Antonsen, Chaos 18(3), 037113 (2008)] that allows obtaining the mean activity (spiking rate) of a population of excitable units. By means of the Ott-Antonsen method, equations for the dynamics of the order parameters of coupled excitatory and inhibitory populations of excitable units are obtained, and their mean activities are computed. Two different excitable systems are studied: Adler units and theta neurons. The resulting bifurcation diagrams are compared with those obtained from studying the phenomenological Wilson-Cowan model in some regions of the parameter space. Compatible behaviors, as well as higher dimensional chaotic solutions, are observed. We study numerical simulations to further validate the equations.

  3. 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.

  4. 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.

  5. 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.

  6. Modeling collective & intelligent decision making of multi-cellular populations.

    PubMed

    Shin, Yong-Jun; Mahrou, Bahareh

    2014-01-01

    In the presence of unpredictable disturbances and uncertainties, cells intelligently achieve their goals by sharing information via cell-cell communication and making collective decisions, which are more reliable compared to individual decisions. Inspired by adaptive sensor network algorithms studied in communication engineering, we propose that a multi-cellular adaptive network can convert unreliable decisions by individual cells into a more reliable cell-population decision. It is demonstrated using the effector T helper (a type of immune cell) population, which plays a critical role in initiating immune reactions in response to invading foreign agents (e.g., viruses, bacteria, etc.). While each individual cell follows a simple adaptation rule, it is the combined coordination among multiple cells that leads to the manifestation of "self-organizing" decision making via cell-cell communication.

  7. 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.

  8. 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.

  9. 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

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

    PubMed

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

    2015-01-01

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

  11. 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.

  12. Pharmacodynamic population analysis in chronic renal failure using artificial neural networks--a comparative study.

    PubMed

    Gaweda, Adam E; Jacobs, Alfred A; Brier, Michael E; Zurada, Jacek M

    2003-01-01

    This work presents a pharmacodynamic population analysis in chronic renal failure patients using Artificial Neural Networks (ANNs). In pursuit of an effective and cost-efficient strategy for drug delivery in patients with renal failure, two different types of ANN are applied to perform drug dose-effect modeling and their performance compared. Applied in a clinical environment, such models will allow for prediction of patient response to the drug at the effect site and, subsequently, for adjusting the dosing regimen.

  13. 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

  14. 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

  15. 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

  16. Modelling neural informational propagation and functional auditory sensory memory with temporal multi-scale operators.

    PubMed

    Serman, Maja; Serman, Nikola; Griffith, Niall J L

    2007-08-01

    In this paper we prove that both diffusion and the leaky integrators cascade based transport mechanisms have as their inherent property the effect of temporal multi-scaling. The two transport mechanisms are modeled not as convolution based algorithms but as causal physical processes. This implies that propagation of information through a neural map may act as a mechanism for achieving temporal multi-scale analysis in the auditory system. Specifically, we are interested in the effects of such a transport process on the formation and the dynamics of auditory sensory memory. Two temporal models of information propagation are discussed and compared in terms of their ability to model auditory sensory memory effects and the biological plausibility of their structure: the causal diffusion based operator (CD) and the leaky integrator cascade based operator (LINC). We show that temporal multi-scale representations achieved by both models exhibit the effects similar to those of auditory sensory memory (filtering, time delay and binding of information). As regards higher-level functions of auditory sensory memory such as change detection, the LINC operator seems to be a biologically more plausible solution for modeling temporal cortical processing.

  17. Workshop on neural networks

    SciTech Connect

    Uhrig, R.E.; Emrich, M.L.

    1990-01-01

    The topics covered in this report are: Learning, Memory, and Artificial Neural Systems; Emerging Neural Network Technology; Neural Networks; Digital Signal Processing and Neural Networks; Application of Neural Networks to In-Core Fuel Management; Neural Networks in Process Control; Neural Network Applications in Image Processing; Neural Networks for Multi-Sensor Information Fusion; Neural Network Research in Instruments Controls Division; Neural Networks Research in the ORNL Engineering Physics and Mathematics Division; Neural Network Applications for Linear Programming; Neural Network Applications to Signal Processing and Diagnostics; Neural Networks in Filtering and Control; Neural Network Research at Tennessee Technological University; and Global Minima within the Hopfield Hypercube.

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

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

    PubMed

    Fukushima, Kunihiko

    2013-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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

    NASA Astrophysics Data System (ADS)

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

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

  8. 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

  9. 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.

  10. Linear response theory for two neural populations applied to gamma oscillation generation

    NASA Astrophysics Data System (ADS)

    Payeur, Alexandre; Lefebvre, Jérémie; Maler, Leonard; Longtin, André

    2013-03-01

    Linear response theory (LRT) can be used to compute spectral properties of single and populations of stochastic leaky integrate-and-fire neurons. The effects of inputs, both external and from delayed feedback, can be modeled within that theory when the neural function is sufficiently linearized by noise. It has been used to explain experiments where gamma oscillations are induced by spatially correlated stochastic inputs to a network with delayed inhibitory feedback. Here we expand this theory to include two distinct population types. We first show how to deal with homogeneous networks where both types of neurons have identical intrinsic properties. We further tackle the asymmetric case, where noise or bias differ. We also analyze the case where the membrane time constants differ, based on experimental evidence, which requires delicate alterations of the theory. We directly apply the theory to networks of ON and OFF cells in the electrosensory system, which together provide global delayed negative feedback to all cells; however, ON and OFF cells receive external inputs of opposite polarities. Theoretical results are in excellent agreement with numerical simulations of the two population network. In contrast to the case of a single ON cell population with feedback, the more realistic presence of both cell types can significantly reduce the propensity of the delayed feedback network to oscillate for spatially correlated inputs. Our results are further linked to recent predictions from deterministic neural field theory. Among other findings, our work suggests that the observed gamma oscillations could be explained only if the ON and OFF cell feedback pathways are anatomically segregated. Thus our two population LRT can make specific predictions about network topography in specific systems.

  11. 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.

  12. 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.

  13. 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

  14. 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

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

    PubMed

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

    2009-06-01

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

  16. 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

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

    PubMed Central

    Hashemi, Meysam; Hutt, Axel; Sleigh, Jamie

    2014-01-01

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

  18. 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

  19. 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

  20. Her4-positive population in the tectum opticum is proliferating neural precursors in the adult zebrafish brain.

    PubMed

    Jung, Seung-Hyun; Kim, Hyung-Seok; Ryu, Jae-Ho; Gwak, Jung-Woo; Bae, Young-Ki; Kim, Cheol-Hee; Yeo, Sang-Yeob

    2012-06-01

    Previous studies have shown that Notch signaling not only regulates the number of early differentiating neurons, but also maintains proliferating neural precursors in the neural tube. Although it is well known that Notch signaling is closely related to the differentiation of adult neural stem cells, none of transgenic zebrafish provides a tool to figure out the relationship between Notch signaling and the differentiation of neural precursors. The goal of this study was to characterize Her4-positive cells by comparing the expression of a fluorescent Her4 reporter in Tg[her4-dRFP] animals with a GFAP reporter in Tg[gfap-GFP] adult zebrafish. BrdU incorporation indicated that dRFP-positive cells were proliferating and a double labeling assay revealed that a significant fraction of the Her4-dRFP positive population was also GFAP-GFP positive. Our observations suggest that a reporter line with Notch-dependent gene expression can provide a tool to examine proliferating neural precursors and/or neuronal/glial precursors in the development of the adult nervous system to examine the model in which Notch signaling maintains proliferating neural precursors in the neural tube.

  1. 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

  2. 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.

  3. 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

  4. 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

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

    NASA Astrophysics Data System (ADS)

    Lang, Jun; Hao, Zhengchao

    2014-01-01

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

  6. 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.

  7. 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

  8. 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

  9. Multi-layered population structure in Island Southeast Asians

    PubMed Central

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

    2016-01-01

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

  10. 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.

  11. 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.

  12. Prediction of multi-locus inbreeding coefficients and relation to linkage disequilibrium in random mating populations.

    PubMed

    Hill, William G; Weir, Bruce S

    2007-09-01

    An algorithm to predict the level of identity by descent simultaneously at multiple loci is presented, which can in principle be extended to any number of loci. The model assumes a random mating population, with random association of haplotypes. The relationship is shown between coefficients of multi-locus identity or non-identity by descent and moments of multi-locus linkage disequilibrium. Thus, these moments can be computed from the multilocus identity or, using algorithms derived previously to predict the disequilibria moments, vice-versa. The results can be applied to predict multi-locus identity in, for example, gene mapping.

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

    PubMed

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

    2015-06-18

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

  14. 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.

  15. 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.

  16. 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

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

    PubMed

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

    2014-02-12

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

  18. 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…

  19. 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

  20. 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.

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

    PubMed Central

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

    2013-01-01

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

  2. 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

  3. 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’.

  4. Brain tumor detection using scalp eeg with modified Wavelet-ICA and multi layer feed forward neural network.

    PubMed

    Selvam, V Salai; Shenbagadevi, S

    2011-01-01

    Use of scalp EEG for the diagnosis of various cerebral disorders is progressively increasing. Though the advanced neuroimaging techniques such as MRI and CT-SCAN still stay as principal confirmative methods for detecting and localizing brain tumors, the development of automated systems for the detection of brain tumors using the scalp EEG has started attracting the researchers all over the world notably since 2000. This is because of two important facts: (i) cheapness and easiness of methods of recording and analyzing the scalp EEG and (ii) lower risk and possible early detection. This paper presents a method of detecting the brain tumor using the first, second and third order statistics of the scalp EEG with a Modified Wavelet-Independent Component Analysis (MwICA) technique and a multi-layer feed-forward neural network. PMID:22255732

  5. The Advantage of Ambiguity? Enhanced Neural Responses to Multi-Stable Percepts Correlate with the Degree of Perceived Instability

    PubMed Central

    Dyson, Benjamin J.

    2011-01-01

    Artwork can often pique the interest of the viewer or listener as a result of the ambiguity or instability contained within it. Our engagement with uncertain sensory experiences might have its origins in early cortical responses, in that perceptually unstable stimuli might preclude neural habituation and maintain activity in early sensory areas. To assess this idea, participants engaged with an ambiguous visual stimulus wherein two squares alternated with one another, in terms of simultaneously opposing vertical and horizontal locations relative to fixation (i.e., stroboscopic alternating motion; von Schiller, 1933). At each trial, participants were invited to interpret the movement of the squares in one of five ways: traditional vertical or horizontal motion, novel clockwise or counter-clockwise motion, and, a free-view condition in which participants were encouraged to switch the direction of motion as often as possible. Behavioral reports of perceptual stability showed clockwise and counter-clockwise motion to possess an intermediate level of stability compared to relatively stable vertical and horizontal motion, and, relatively unstable motion perceived during free-view conditions. Early visual evoked components recorded at parietal–occipital sites such as C1, P1, and N1 modulated as a function of visual intention. Both at a group and individual level, increased perceptual instability was related to increased negativity in all three of these early visual neural responses. Engagement with increasingly ambiguous input may partly result from the underlying exaggerated neural response to it. The study underscores the utility of combining neuroelectric recording with the presentation of perceptually multi-stable yet physically identical stimuli, in revealing brain activity associated with the purely internal process of interpreting and appreciating the sensory world that surrounds us. PMID:21897812

  6. Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control

    NASA Astrophysics Data System (ADS)

    Chang, Fi-John; Chen, Pin-An; Lu, Ying-Ray; Huang, Eric; Chang, Kai-Yao

    2014-09-01

    Urban flood control is a crucial task, which commonly faces fast rising peak flows resulting from urbanization. To mitigate future flood damages, it is imperative to construct an on-line accurate model to forecast inundation levels during flood periods. The Yu-Cheng Pumping Station located in Taipei City of Taiwan is selected as the study area. Firstly, historical hydrologic data are fully explored by statistical techniques to identify the time span of rainfall affecting the rise of the water level in the floodwater storage pond (FSP) at the pumping station. Secondly, effective factors (rainfall stations) that significantly affect the FSP water level are extracted by the Gamma test (GT). Thirdly, one static artificial neural network (ANN) (backpropagation neural network-BPNN) and two dynamic ANNs (Elman neural network-Elman NN; nonlinear autoregressive network with exogenous inputs-NARX network) are used to construct multi-step-ahead FSP water level forecast models through two scenarios, in which scenario I adopts rainfall and FSP water level data as model inputs while scenario II adopts only rainfall data as model inputs. The results demonstrate that the GT can efficiently identify the effective rainfall stations as important inputs to the three ANNs; the recurrent connections from the output layer (NARX network) impose more effects on the output than those of the hidden layer (Elman NN) do; and the NARX network performs the best in real-time forecasting. The NARX network produces coefficients of efficiency within 0.9-0.7 (scenario I) and 0.7-0.5 (scenario II) in the testing stages for 10-60-min-ahead forecasts accordingly. This study suggests that the proposed NARX models can be valuable and beneficial to the government authority for urban flood control.

  7. 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).

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

    PubMed

    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

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

    PubMed

    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.

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

    PubMed

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

    2015-07-01

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

  11. 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.

  12. 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

  13. 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.

  14. 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

  15. 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

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

    SciTech Connect

    Shamsollahi, P.; Malik, O.P.

    1999-09-01

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

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

    PubMed

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

    2015-09-01

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

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

    PubMed

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

    2015-09-01

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

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

    PubMed Central

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

    2015-01-01

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

  20. 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.

  1. 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

  2. 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

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

    PubMed Central

    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. Clinical trial registration: NCT00912041. DOI: http://dx.doi.org/10.7554/eLife.07436.001 PMID:26099302

  4. Fluorescent probes as a tool for cell population tracking in spontaneously active neural networks derived from human pluripotent stem cells.

    PubMed

    Mäkinen, M; Joki, T; Ylä-Outinen, L; Skottman, H; Narkilahti, S; Aänismaa, R

    2013-04-30

    Applications such as 3D cultures and tissue modelling require cell tracking with non-invasive methods. In this work, the suitability of two fluorescent probes, CellTracker, CT, and long chain carbocyanine dye, DiD, was investigated for long-term culturing of labeled human pluripotent stem cell-derived neural cells. We found that these dyes did not affect the cell viability. However, proliferation was decreased in DiD labeled cell population. With both dyes the labeling was stable up to 4 weeks. CT and DiD labeled cells could be co-cultured and, importantly, these mixed populations had their normal ability to form spontaneous electrical network activity. In conclusion, human neural cells can be successfully labeled with these two fluorescent probes without significantly affecting the cell characteristics. These labeled cells could be utilized further in e.g. building controlled neuronal networks for neurotoxicity screening platforms, combining cells with biomaterials for 3D studies, and graft development. PMID:23473797

  5. 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

  6. Neural Autoantibody Evaluation in Functional Gastrointestinal Disorders: A Population-Based Case–Control Study

    PubMed Central

    Pittock, Sean J.; Lennon, Vanda A.; Dege, Carissa L.; Talley, Nicholas J.; Richard Locke, G.

    2011-01-01

    Background Our goal is to investigate the serum profile of neural autoantibodies in community-based patients with irritable bowel syndrome (IBS) or functional dyspepsia. The pathogenesis of functional gastrointestinal (GI) disorders, including IBS and dyspepsia, are unknown. Theories range from purely psychological to autoimmune alterations in GI tract neuromuscular function. Methods The study subjects, based in Olmsted County, MN, reported symptoms of functional dyspepsia or IBS (n = 69), or were asymptomatic controls (n = 64). Their coded sera were screened for antibodies targeting neuronal, glial, and muscle autoantigens. Results The prevalence of neural autoantibodies with functional GI disorders did not differ significantly from controls (17% vs. 13%; P = 0.43). In no case was a neuronal or glial nuclear autoantibody or enteric neuronal autoantibody identified. Neuronal cation channel antibodies were identified in 9% of cases (voltage-gated potassium channel [VGKC] in one dyspepsia case and one IBS case, ganglionic acetylcholine receptor [AChR] in four IBS cases) and in 6% of controls (ganglionic AChR in one, voltage-gated calcium channel [VGCC], N-type, in two and VGKC in one; P = 0.36). The frequency of glutamic acid decarboxylase-65 (GAD65) autoantibodies was similar in cases (10%) and controls (5%; P = 0.23). Conclusions Our data do not support neural autoimmunity as the basis for most IBS or functional dyspepsia cases. PMID:21181442

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

    PubMed Central

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

    2015-01-01

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

  8. 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

  9. 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

  10. 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.

  11. 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

  12. Multi-aspect target discrimination using hidden Markov models and neural networks.

    PubMed

    Robinson, Marc; Azimi-Sadjadi, Mahmood R; Salazar, Jaime

    2005-03-01

    This paper presents a new multi-aspect pattern classification method using hidden Markov models (HMMs). Models are defined for each class, with the probability found by each model determining class membership. Each HMM model is enhanced by the use of a multilayer perception (MLP) network to generate emission probabilities. This hybrid system uses the MLP to find the probability of a state for an unknown pattern and the HMM to model the process underlying the state transitions. A new batch gradient descent-based method is introduced for optimal estimation of the transition and emission probabilities. A prediction method in conjunction with HMM model is also presented that attempts to improve the computation of transition probabilities by using the previous states to predict the next state. This method exploits the correlation information between consecutive aspects. These algorithms are then implemented and benchmarked on a multi-aspect underwater target classification problem using a realistic sonar data set collected in different bottom conditions.

  13. 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

  14. Analysis of select folate pathway genes, PAX3, and human T in a Midwestern neural tube defect population.

    PubMed

    Trembath, D; Sherbondy, A L; Vandyke, D C; Shaw, G M; Todoroff, K; Lammer, E J; Finnell, R H; Marker, S; Lerner, G; Murray, J C

    1999-05-01

    Neural tube defects (NTDs) are a common birth defect, seen in approximately 1/1,000 births in the United States. NTDs are considered a complex trait where several genes, interacting with environmental factors, create the phenotype. Using a Midwestern NTD population consisting of probands, parents, and siblings from Iowa, Minnesota, and Nebraska, we analyzed a range of candidate genes, including 5,10-methylenetetrahydrofolate reductase (MTHFR), folate receptors-alpha (FOLR1; hereafter abbreviated "FR-alpha") and -beta (FOLR2; hereafter, "FR-beta"), methionine synthase (hereinafter, "MS"), T, the human homolog of the murine Brachyury gene, and the paired-box homeotic gene 3 (PAX3), for association with NTDs. We were unable to demonstrate an association using a previously described Ala-->Val mutation in MTHFR and the majority of our NTD populations. However, we discovered a silent polymorphism in exon 6 of MTHFR which conserved a serine residue and which showed significant association with NTDs in our Iowa population. Analysis of exon 7 of MTHFR then demonstrated an Ala-->Glu mutation which was significantly associated with our Iowa NTD population; however, we could not replicate this result either in a combined Minnesota/ Nebraska or in a California NTD population. Using polymorphic markers for MS, FR-beta, T, and PAX3, we were unable to demonstrate linkage disequilibrium with our NTD populations. A mutation search of FR-alpha revealed one proband with a de novo silent mutation of the stop codon. This work provides a new panel of genetic variants for studies of folate metabolism and supports, in some NTD populations, an association between MTHFR and NTDs.

  15. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-01-01

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

  17. 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

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

    PubMed Central

    Zhang, Yanling; Liu, Aizhi; Sun, Changyin

    2016-01-01

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

  19. 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

  20. 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.

  1. 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

  2. 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.

  3. [The blind source separation method based on self-organizing map neural network and convolution kernel compensation for multi-channel sEMG signals].

    PubMed

    Ning, Yong; Zhu, Shan'an; Zhao, Yuming

    2015-02-01

    A new method based on convolution kernel compensation (CKC) for decomposing multi-channel surface electromyogram (sEMG) signals is proposed in this paper. Unsupervised learning and clustering function of self-organizing map (SOM) neural network are employed in this method. An initial innervations pulse train (IPT) is firstly estimated, some time instants corresponding to the highest peaks from the initial IPT are clustered by SOM neural network. Then the final IPT can be obtained from the observations corresponding to these time instants. In this paper, the proposed method was tested on the simulated signal, the influence of signal to noise ratio (SNR), the number of groups clustered by SOM and the number of highest peaks selected from the initial pulse train on the number of reconstructed sources and the pulse accuracy were studied, and the results show that the proposed approach is effective in decomposing multi-channel sEMG signals. PMID:25997257

  4. Differentiation of human epidermal neural crest stem cells (hEPI-NCSC) into virtually homogenous populations of dopaminergic neurons.

    PubMed

    Narytnyk, Alla; Verdon, Bernard; Loughney, Andrew; Sweeney, Michele; Clewes, Oliver; Taggart, Michael J; Sieber-Blum, Maya

    2014-04-01

    Here we provide a protocol for the directed differentiation of hEPI-NCSC into midbrain dopaminergic neurons, which degenerate in Parkinson's disease. hEPI-NCSC are neural crest-derived multipotent stem cells that persist into adulthood in the bulge of hair follicles. The experimental design is distinctly different from conventional protocols for embryonic stem cells and induced pluripotent stem (iPS) cells. It includes pre-differentiation of the multipotent hEPI-NCSC into neural stem cell-like cells, followed by ventralizing, patterning, continued exposure to the TGFβ receptor inhibitor, SB431542, and at later stages of differentiation the presence of the WNT inhibitor, IWP-4. All cells expressed A9 midbrain dopaminergic neuron progenitor markers with gene expression levels comparable to those in normal human substantia nigra. The current study shows for the first time that virtually homogeneous populations of dopaminergic neurons can be derived ex vivo from somatic stem cells without the need for purification, with useful timeliness and high efficacy. This novel development is an important first step towards the establishment of fully functional dopaminergic neurons from an ontologically relevant stem cell type, hEPI-NCSC. PMID:24399192

  5. 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

  6. 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

  7. 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.

  8. Neural population evidence of functional heterogeneity along the CA3 transverse axis: Pattern completion vs. pattern separation

    PubMed Central

    Lee, Heekyung; Wang, Cheng; Deshmukh, Sachin S.; Knierim, James J.

    2015-01-01

    Summary Classical theories of associative memory model CA3 as a homogeneous attractor network because of its strong recurrent circuitry. However, anatomical gradients suggest a functional diversity along the CA3 transverse axis. We examined the neural population coherence along this axis, when the local and global spatial reference frames were put in conflict with each other. Proximal CA3 (near the dentate gyrus), where the recurrent collaterals are the weakest, showed degraded representations, similar to the pattern separation shown by the dentate gyrus. Distal CA3 (near CA2), where the recurrent collaterals are the strongest, maintained coherent representations in the conflict situation, resembling the classic attractor network system. CA2 also maintained coherent representations. This dissociation between proximal and distal CA3 provides strong evidence that the recurrent collateral system underlies the associative network functions of CA3, with a separate role of proximal CA3 in pattern separation. PMID:26298276

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

    PubMed Central

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

    2009-01-01

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

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

    PubMed Central

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

    2012-01-01

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

  11. 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

  12. 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…

  13. 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.

  14. 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.

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

    PubMed Central

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

    2016-01-01

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

  16. 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

  17. 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

  18. A Multi-Step Assessment Scheme for Seismic Network Site Selection in Densely Populated Areas

    NASA Astrophysics Data System (ADS)

    Plenkers, Katrin; Husen, Stephan; Kraft, Toni

    2015-10-01

    We developed a multi-step assessment scheme for improved site selection during seismic network installation in densely populated areas. Site selection is a complex process where different aspects (seismic background noise, geology, and financing) have to be taken into account. In order to improve this process, we developed a step-wise approach that allows quantifying the quality of a site by using, in addition to expert judgement and test measurements, two weighting functions as well as reference stations. Our approach ensures that the recording quality aimed for is reached and makes different sites quantitatively comparable to each other. Last but not least, it is an easy way to document the decision process, because all relevant parameters are listed, quantified, and weighted.

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

    PubMed

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

    2009-08-01

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

  20. 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

  1. 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.

  2. 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

  3. 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

    PubMed Central

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

    2013-01-01

    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 AI and rostral 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. PMID:23283406

  4. 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.

  5. 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

  6. A category-free neural population supports evolving demands during decision-making.

    PubMed

    Raposo, David; Kaufman, Matthew T; Churchland, Anne K

    2014-12-01

    The posterior parietal cortex (PPC) receives diverse inputs and is involved in a dizzying array of behaviors. These many behaviors could rely on distinct categories of neurons specialized to represent particular variables or could rely on a single population of PPC neurons that is leveraged in different ways. To distinguish these possibilities, we evaluated rat PPC neurons recorded during multisensory decisions. Newly designed tests revealed that task parameters and temporal response features were distributed randomly across neurons, without evidence of categories. This suggests that PPC neurons constitute a dynamic network that is decoded according to the animal's present needs. To test for an additional signature of a dynamic network, we compared moments when behavioral demands differed: decision and movement. Our new state-space analysis revealed that the network explored different dimensions during decision and movement. These observations suggest that a single network of neurons can support the evolving behavioral demands of decision-making. PMID:25383902

  7. 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

  8. 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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Flowering time is one of the major adaptive traits in domestication of maize and an important selection criterion in breeding. To detect more maize flowering time variants we evaluated flowering time traits using an extremely large multi- genetic background population that contained more than 8000 l...

  10. 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

  11. Effect of blood pressure on the retinal vasculature in a multi-ethnic Asian population.

    PubMed

    Jeganathan, V Swetha E; Sabanayagam, Charumathi; Tai, E Shyong; Lee, Jeannette; Sun, Cong; Kawasaki, Ryo; Nagarajan, Sangeetha; Huey-Shi, Maisie Ho; Sandar, Mya; Wong, Tien Yin

    2009-11-01

    Blood pressure has a significant effect on retinal arterioles. There are few data on whether this effect varies by race/ethnicity. We examined the relationship of blood pressure and retinal vascular caliber in a multi-ethnic Asian population. The study is population-based and cross sectional in design. A total of 3749 Chinese, Malay and Indian participants aged > or =24 years residing in Singapore were included in the study. Retinal vascular caliber was measured using a computer program from digital retinal photographs. The associations of retinal vascular caliber with blood pressure and hypertension in each racial/ethnic group were analyzed. The main outcome measures are retinal arteriolar caliber and venular caliber. The results show that retinal arterioles were narrower in persons with uncontrolled/untreated hypertension (140.0 microm) as compared with persons with controlled hypertension (142.1 microm, P=0.0001) and those with no hypertension (146.0 microm, P<0.0001). On controlling for age, gender, body mass index, lipids and smoking, each 10 mm Hg increase in mean arterial blood pressure was associated with a 3.1 microm decrease in arteriolar caliber (P<0.0001), with a similar magnitude seen in all three racial/ethnic groups: 3.1 microm in Chinese, 2.8 microm in Malays and 3.2 microm in Indians (P<0.0001 for all). Each 10 mm Hg increase in mean arterial blood pressure was associated with a 1.8 microm increase in venular caliber (P<0.0001); furthermore, the magnitude of this effect was similar across the three racial/ethnic groups. The effect of blood pressure on the retinal vasculature was similar across three major racial/ethnic groups in Asia. PMID:19713968

  12. Estimating ancestral proportions in a multi-ethnic US sample: implications for studies of admixed populations.

    PubMed

    Levran, Orna; Awolesi, Olaoluwakitan; Shen, Pei-Hong; Adelson, Miriam; Kreek, Mary Jeanne

    2012-07-05

    This study was designed to determine the ancestral composition of a multi-ethnic sample collected for studies of drug addictions in New York City and Las Vegas, and to examine the reliability of self-identified ethnicity and three-generation family history data. Ancestry biographical scores for seven clusters corresponding to world major geographical regions were obtained using STRUCTURE, based on genotypes of 168 ancestry informative markers (AIMs), for a sample of 1,291 African Americans (AA), European Americans (EA), and Hispanic Americans (HA) along with data from 1,051 HGDP-CEPH 'diversity panel' as a reference. Self-identified ethnicity and family history data, obtained in an interview, were accurate in identifying the individual major ancestry in the AA and the EA samples (approximately 99% and 95%, respectively) but were not useful for the HA sample and could not predict the extent of admixture in any group. The mean proportions of the combined clusters corresponding to European and Middle Eastern populations in the AA sample, revealed by AIMs analysis, were 0.13. The HA subjects, predominantly Puerto Ricans, showed a highly variable hybrid contribution pattern of clusters corresponding to Europe (0.27), Middle East (0.27), Africa (0.20), and Central Asia (0.14). The effect of admixture on allele frequencies is demonstrated for two single-nucleotide polymorphisms (118A > G, 17 C > T) of the mu opioid receptor gene (OPRM1). This study reiterates the importance of AIMs in defining ancestry, especially in admixed populations.

  13. Desynchronization of Noisy Multi-cellular Clocks Underlies the Population-level Singularity Behavior of Mammalian Circadian Clock

    NASA Astrophysics Data System (ADS)

    Kobayashi, Tetsuya J.; Ukai, Hideki; Ueda, Hiroki R.

    2007-07-01

    The singularity behavior of circadian clocks defined as the suppression of circadian oscillation by critical perturbation is one of the intriguing dynamical properties of circadian rhythms. Although the singularity behaviors have been observed in various organisms, its mechanism has not yet been elucidated, because the hierarchical structure of multi-cell-level circadian clocks exists behind the organism-level circadian rhythm. In vitro light-responsible circadian system is indispensable for extracting the underlying mechanism of the singularity behavior behind the hierarchical structure of multi-cell organisms. To obtain such in vitro system, we synthetically constructed light-responsible mammalian clock cells by exogenously introducing a photo-responsible receptor. By using this synthetic system and population-level high-throughput promoter activity assay, we found that a light pulse with critical timing and strength can induce population-level singularity behavior of the light-responsible mammalian clock cells. Subsequent single-cell measurement revealed that desynchronization of multi-cellular clocks underlies the population-level singularity. A mathematical model consistently explains our population-level and single-cell-level experimental data, and also demonstrates that the synchronization and desynchronization of cellular clocks is the underlying mechanism of population-level response of circadian clocks to external perturbation. In addition, our model suggests that fluctuation in single-cell-level behavior of the clock cells is the key determinant of the observable singularity behavior.

  14. 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

  15. 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

  16. Development of the Multi-Trait Personality Inventory (MTPI): comparison among four Chinese populations.

    PubMed

    Cheung, P C; Conger, A J; Hau, K T; Lew, W J; Lau, S

    1992-12-01

    Anemic approach was adopted to develop a culture-specific instrument for the assessment of Chinese personality. The Multi-Trial Personality Inventory (MTPI) was administered to 1,673 men and 944 women in four major Chinese populations. It was found that Chinese in mainland China, Taiwan, Hong Kong, and the United States possess some common traits deeply rooted in the Chinese culture characterized by Confucian thoughts (e.g., self-discipline and moderation) and some additional traits nurtured by their respective environments. Consequently, findings of this study lent support to the hypothesis that, in spite of superficial discontinuities, there are basic continuities in the personality traits of mainland and overseas Chinese. The cross-cultural differences in personality were examined from a political-social perspective and also explained with a cultural-ecological model. In the development of the MTPI, a new methodology that relies on forming factor-consistent clusters was employed to deal successfully with the problem of complex factor space.

  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. 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

  19. 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.

  20. Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population.

    PubMed

    Hozé, C; Fritz, S; Phocas, F; Boichard, D; Ducrocq, V; Croiseau, P

    2014-01-01

    Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed

  1. 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

  2. Critical analysis of dimension reduction by a moment closure method in a population density approach to neural network modeling.

    PubMed

    Ly, Cheng; Tranchina, Daniel

    2007-08-01

    Computational techniques within the population density function (PDF) framework have provided time-saving alternatives to classical Monte Carlo simulations of neural network activity. Efficiency of the PDF method is lost as the underlying neuron model is made more realistic and the number of state variables increases. In a detailed theoretical and computational study, we elucidate strengths and weaknesses of dimension reduction by a particular moment closure method (Cai, Tao, Shelley, & McLaughlin, 2004; Cai, Tao, Rangan, & McLaughlin, 2006) as applied to integrate-and-fire neurons that receive excitatory synaptic input only. When the unitary postsynaptic conductance event has a single-exponential time course, the evolution equation for the PDF is a partial differential integral equation in two state variables, voltage and excitatory conductance. In the moment closure method, one approximates the conditional kth centered moment of excitatory conductance given voltage by the corresponding unconditioned moment. The result is a system of k coupled partial differential equations with one state variable, voltage, and k coupled ordinary differential equations. Moment closure at k = 2 works well, and at k = 3 works even better, in the regime of high dynamically varying synaptic input rates. Both closures break down at lower synaptic input rates. Phase-plane analysis of the k = 2 problem with typical parameters proves, and reveals why, no steady-state solutions exist below a synaptic input rate that gives a firing rate of 59 s(1) in the full 2D problem. Closure at k = 3 fails for similar reasons. Low firing-rate solutions can be obtained only with parameters for the amplitude or kinetics (or both) of the unitary postsynaptic conductance event that are on the edge of the physiological range. We conclude that this dimension-reduction method gives ill-posed problems for a wide range of physiological parameters, and we suggest future directions. PMID:17571938

  3. 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

  4. 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.

  5. Stellar populations of galaxies in the ALHAMBRA survey up to z ~ 1. I. MUFFIT: A multi-filter fitting code for stellar population diagnostics

    NASA Astrophysics Data System (ADS)

    Díaz-García, L. A.; Cenarro, A. J.; López-Sanjuan, C.; Ferreras, I.; Varela, J.; Viironen, K.; Cristóbal-Hornillos, D.; Moles, M.; Marín-Franch, A.; Arnalte-Mur, P.; Ascaso, B.; Cerviño, M.; González Delgado, R. M.; Márquez, I.; Masegosa, J.; Molino, A.; Pović, M.; Alfaro, E.; Aparicio-Villegas, T.; Benítez, N.; Broadhurst, T.; Cabrera-Caño, J.; Castander, F. J.; Cepa, J.; Fernández-Soto, A.; Husillos, C.; Infante, L.; Aguerri, J. A. L.; Martínez, V. J.; del Olmo, A.; Perea, J.; Prada, F.; Quintana, J. M.; Gruel, N.

    2015-10-01

    Aims: We present MUFFIT, a new generic code optimized to retrieve the main stellar population parameters of galaxies in photometric multi-filter surveys, and check its reliability and feasibility with real galaxy data from the ALHAMBRA survey. Methods: Making use of an error-weighted χ2-test, we compare the multi-filter fluxes of galaxies with the synthetic photometry of mixtures of two single stellar populations at different redshifts and extinctions, to provide the most likely range of stellar population parameters (mainly ages and metallicities), extinctions, redshifts, and stellar masses. To improve the diagnostic reliability, MUFFIT identifies and removes from the analysis those bands that are significantly affected by emission lines. The final parameters and their uncertainties are derived by a Monte Carlo method, using the individual photometric uncertainties in each band. Finally, we discuss the accuracies, degeneracies, and reliability of MUFFIT using both simulated and real galaxies from ALHAMBRA, comparing with results from the literature. Results: MUFFIT is a precise and reliable code to derive stellar population parameters of galaxies in ALHAMBRA. Using the results from photometric-redshift codes as input, MUFFIT improves the photometric-redshift accuracy by ~10-20%. MUFFIT also detects nebular emissions in galaxies, providing physical information about their strengths. The stellar masses derived from MUFFIT show excellent agreement with the COSMOS and SDSS values. In addition, the retrieved age-metallicity locus for a sample of z ≤ 0.22 early-type galaxies in ALHAMBRA at different stellar mass bins are in very good agreement with the ones from SDSS spectroscopic diagnostics. Moreover, a one-to-one comparison between the redshifts, ages, metallicities, and stellar masses derived spectroscopically for SDSS and by MUFFIT for ALHAMBRA reveals good qualitative agreements in all the parameters, hence reinforcing the strengths of multi-filter galaxy data

  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. Accurate, multi-kb reads resolve complex populations and detect rare microorganisms.

    PubMed

    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; Banfield, Jillian F

    2015-04-01

    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.

  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. 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

  11. High precision spatial and temporal control of neural circuitry using a semi-automated multi-wavelength nanopatterning system

    NASA Astrophysics Data System (ADS)

    Mitnala, Sandhya; Huebshman, Michael; Herold, Christian; Herz, Joachim; Garner, Harold

    2009-02-01

    It has been one of the most discussed and intriguing topics -the quest to control neural circuitry as a precursor to decoding the operations of the human brain and manipulating its diseased state. Electrophysiology has created a gateway to control this circuitry with high precision. However, it is not practical to apply these techniques to living systems because these techniques are invasive and lack the spatial resolution necessary to properly address various neural cell components, cell assemblies or even tissues. Here we describe a new instrument that has the potential to replace the conventional patch clamping technique, the workhorse of neural physiology. A Digital Light Processing system from Texas Instruments and an Olympus IX71 inverted microscope were combined to achieve neuronal control at a subcellular spatial resolution. Accompanying these two technologies can be almost any light source, and for these experiments a pair of pulsed light sources that produced two pulse trains at different wavelengths tuned to activate or inactivate selectively the ChR2 and NpHR channels that were cloned to express light sensitive versions in neurons. Fura- 2 ratiometric fluorescent dye would be used to read-out calcium activity. The Pulsed light sources and a filter wheel are under computer control using a National Instruments digital control board and a CCD camera used to acquire real time cellular responses to the spatially controlled pulsed light channel activation would be controlled and synchronized using NI LabVIEW software. This will provide for a millisecond precision temporal control of neural circuitry. Thus this technology could provide researchers with an optical tool to control the neural circuitry both spatially and temporally with high precision.

  12. Multi-dimensional reliability assessment of fractal signature analysis in an outpatient sports medicine population.

    PubMed

    Jarraya, Mohamed; Guermazi, Ali; Niu, Jingbo; Duryea, Jeffrey; Lynch, John A; Roemer, Frank W

    2015-11-01

    The aim of this study has been to test reproducibility of fractal signature analysis (FSA) in a young, active patient population taking into account several parameters including intra- and inter-reader placement of regions of interest (ROIs) as well as various aspects of projection geometry. In total, 685 patients were included (135 athletes and 550 non-athletes, 18-36 years old). Regions of interest (ROI) were situated beneath the medial tibial plateau. The reproducibility of texture parameters was evaluated using intraclass correlation coefficients (ICC). Multi-dimensional assessment included: (1) anterior-posterior (A.P.) vs. posterior-anterior (P.A.) (Lyon-Schuss technique) views on 102 knees; (2) unilateral (single knee) vs. bilateral (both knees) acquisition on 27 knees (acquisition technique otherwise identical; same A.P. or P.A. view); (3) repetition of the same image acquisition on 46 knees (same A.P. or P.A. view, and same unitlateral or bilateral acquisition); and (4) intra- and inter-reader reliability with repeated placement of the ROIs in the subchondral bone area on 99 randomly chosen knees. ICC values on the reproducibility of texture parameters for A.P. vs. P.A. image acquisitions for horizontal and vertical dimensions combined were 0.72 (95% confidence interval (CI) 0.70-0.74) ranging from 0.47 to 0.81 for the different dimensions. For unilateral vs. bilateral image acquisitions, the ICCs were 0.79 (95% CI 0.76-0.82) ranging from 0.55 to 0.88. For the repetition of the identical view, the ICCs were 0.82 (95% CI 0.80-0.84) ranging from 0.67 to 0.85. Intra-reader reliability was 0.93 (95% CI 0.92-0.94) and inter-observer reliability was 0.96 (95% CI 0.88-0.99). A decrease in reliability was observed with increasing voxel sizes. Our study confirms excellent intra- and inter-reader reliability for FSA, however, results seem to be affected by acquisition technique, which has not been previously recognized.

  13. Multi-criteria decision making development of ion chromatographic method for determination of inorganic anions in oilfield waters based on artificial neural networks retention model.

    PubMed

    Stefanović, Stefica Cerjan; Bolanča, Tomislav; Luša, Melita; Ukić, Sime; Rogošić, Marko

    2012-02-24

    This paper describes the development of ad hoc methodology for determination of inorganic anions in oilfield water, since their composition often significantly differs from the average (concentration of components and/or matrix). Therefore, fast and reliable method development has to be performed in order to ensure the monitoring of desired properties under new conditions. The method development was based on computer assisted multi-criteria decision making strategy. The used criteria were: maximal value of objective functions used, maximal robustness of the separation method, minimal analysis time, and maximal retention distance between two nearest components. Artificial neural networks were used for modeling of anion retention. The reliability of developed method was extensively tested by the validation of performance characteristics. Based on validation results, the developed method shows satisfactory performance characteristics, proving the successful application of computer assisted methodology in the described case study.

  14. Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network.

    PubMed

    Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

    2009-06-01

    Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly's halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support.

  15. Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression - A quantitative structure property relationship approach.

    PubMed

    Parinet, Julien; Julien, Maxime; Nun, Pierrick; Robins, Richard J; Remaud, Gerald; Höhener, Patrick

    2015-09-01

    We aim at predicting the effect of structure and isotopic substitutions on the equilibrium vapour pressure isotope effect of various organic compounds (alcohols, acids, alkanes, alkenes and aromatics) at intermediate temperatures. We attempt to explore quantitative structure property relationships by using artificial neural networks (ANN); the multi-layer perceptron (MLP) and compare the performances of it with multi-linear regression (MLR). These approaches are based on the relationship between the molecular structure (organic chain, polar functions, type of functions, type of isotope involved) of the organic compounds, and their equilibrium vapour pressure. A data set of 130 equilibrium vapour pressure isotope effects was used: 112 were used in the training set and the remaining 18 were used for the test/validation dataset. Two sets of descriptors were tested, a set with all the descriptors: number of(12)C, (13)C, (16)O, (18)O, (1)H, (2)H, OH functions, OD functions, CO functions, Connolly Solvent Accessible Surface Area (CSA) and temperature and a reduced set of descriptors. The dependent variable (the output) is the natural logarithm of the ratios of vapour pressures (ln R), expressed as light/heavy as in classical literature. Since the database is rather small, the leave-one-out procedure was used to validate both models. Considering higher determination coefficients and lower error values, it is concluded that the multi-layer perceptron provided better results compared to multi-linear regression. The stepwise regression procedure is a useful tool to reduce the number of descriptors. To our knowledge, a Quantitative Structure Property Relationship (QSPR) approach for isotopic studies is novel.

  16. Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression - A quantitative structure property relationship approach.

    PubMed

    Parinet, Julien; Julien, Maxime; Nun, Pierrick; Robins, Richard J; Remaud, Gerald; Höhener, Patrick

    2015-09-01

    We aim at predicting the effect of structure and isotopic substitutions on the equilibrium vapour pressure isotope effect of various organic compounds (alcohols, acids, alkanes, alkenes and aromatics) at intermediate temperatures. We attempt to explore quantitative structure property relationships by using artificial neural networks (ANN); the multi-layer perceptron (MLP) and compare the performances of it with multi-linear regression (MLR). These approaches are based on the relationship between the molecular structure (organic chain, polar functions, type of functions, type of isotope involved) of the organic compounds, and their equilibrium vapour pressure. A data set of 130 equilibrium vapour pressure isotope effects was used: 112 were used in the training set and the remaining 18 were used for the test/validation dataset. Two sets of descriptors were tested, a set with all the descriptors: number of(12)C, (13)C, (16)O, (18)O, (1)H, (2)H, OH functions, OD functions, CO functions, Connolly Solvent Accessible Surface Area (CSA) and temperature and a reduced set of descriptors. The dependent variable (the output) is the natural logarithm of the ratios of vapour pressures (ln R), expressed as light/heavy as in classical literature. Since the database is rather small, the leave-one-out procedure was used to validate both models. Considering higher determination coefficients and lower error values, it is concluded that the multi-layer perceptron provided better results compared to multi-linear regression. The stepwise regression procedure is a useful tool to reduce the number of descriptors. To our knowledge, a Quantitative Structure Property Relationship (QSPR) approach for isotopic studies is novel. PMID:25559176

  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. 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

  19. Assessing the Relative Impact of Distinct Ionospheric Outflow Populations on Geospace Dynamics using Multi-Fluid Global MHD simulations

    NASA Astrophysics Data System (ADS)

    Brambles, O.; Lotko, W.; Ouellette, J.; Zhang, B.; Lyon, J.; Wiltberger, M. J.

    2014-12-01

    Satellite observations and numerical modeling studies have demonstrated that ionospheric ion outflows of different species, source locations and energies populate and interact with distinct regions of the magnetosphere, and therefore can have profoundly different impacts on the coupled solar wind-magnetosphere-ionosphere (SWMI) system. In previous modeling studies, multi-fluid global simulations of the SWMI interaction typically use one fluid to model the solar wind and a second fluid to represent the outflowing ions. These studies are limited as they are incapable of tracking multiple, distinct ionosphere-sourced ion populations. Either significant ion populations and their influence must be excluded from the simulation or multiple ion populations must be combined into a single fluid. In this study, a multi-fluid adaption of the Lyon-Fedder-Mobarry (MFLFM) model that is capable of including numerous separate fluids is used to: (1) evaluate how different outflowing ion populations propagate in the magnetosphere and enter the tail, (2) determine their resulting magnetospheric distribution, and (3) calculate their relative impacts on SWMI coupling. The outflow flux for each population is regulated using causally driven models based on empirical data. These models include specifications for transversely accelerated O+ originating from the cusp and nightside auroral region, H+ polar wind outflow and the plasmasphere. The outflow distributions and hemispheric outflow flux resulting from these models, and their resulting composition in the magnetosphere are validated using satellite data. The effects of each individual ion source on dayside reconnection, electrodynamic magnetosphere-ionosphere coupling and magnetotail processes are evaluated. Among other effects, we find that ionospheric ions that are entrained directly into the warm plasma cloak are more effective at reducing the dayside reconnection potential than ions that are transported further downtail and are

  20. 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.

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

    USGS Publications Warehouse

    Converse, Sarah J.; Royle, J. Andrew; Gitzen, Robert A.; Millspaugh, Joshua J.; Cooper, Andrew B.; Licht, Daniel S.

    2012-01-01

    An ecological monitoring program should be viewed as a component of a larger framework designed to advance science and/or management, rather than as a stand-alone activity. Monitoring targets (the ecological variables of interest; e.g. abundance or occurrence of a species) should be set based on the needs of that framework (Nichols and Williams 2006; e.g. Chapters 2–4). Once such monitoring targets are set, the subsequent step in monitoring design involves consideration of the field and analytical methods that will be used to measure monitoring targets with adequate accuracy and precision. Long-term monitoring programs will involve replication of measurements over time, and possibly over space; that is, one location or each of multiple locations will be monitored multiple times, producing a collection of site visits (replicates). Clearly this replication is important for addressing spatial and temporal variability in the ecological resources of interest (Chapters 7–10), but it is worth considering how this replication can further be exploited to increase the effectiveness of monitoring. In particular, defensible monitoring of the majority of animal, and to a lesser degree plant, populations and communities will generally require investigators to account for imperfect detection (Chapters 4, 18). Raw indices of population state variables, such as abundance or occupancy (sensu MacKenzie et al. 2002), are rarely defensible when detection probabilities are < 1, because in those cases detection may vary over time and space in unpredictable ways. Myriad authors have discussed the risks inherent in making inference from monitoring data while failing to correct for differences in detection, resulting in indices that have an unknown relationship to the parameters of interest (e.g. Nichols 1992, Anderson 2001, MacKenzie et al. 2002, Williams et al. 2002, Anderson 2003, White 2005, Kéry and Schmidt 2008). While others have argued that indices may be preferable in some

  2. Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

    NASA Technical Reports Server (NTRS)

    Roberts, J. Brent; Robertson, Franklin R.; Clayson, Carol Anne

    2012-01-01

    Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

  3. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

    PubMed Central

    Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

    2016-01-01

    Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition. PMID:27527175

  4. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

    PubMed

    Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

    2016-01-01

    Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition. PMID:27527175

  5. 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.

  6. 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

  7. 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

  8. 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...

  9. [Population].

    PubMed

    1979-01-01

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

  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.

  11. An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

    PubMed Central

    Segura, Vincent; Vilhjálmsson, Bjarni J.; Platt, Alexander; Korte, Arthur; Seren, Ümit; Long, Quan; Nordborg, Magnus

    2012-01-01

    Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable. PMID:22706313

  12. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations.

    PubMed

    Segura, Vincent; Vilhjálmsson, Bjarni J; Platt, Alexander; Korte, Arthur; Seren, Ümit; Long, Quan; Nordborg, Magnus

    2012-06-17

    Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.

  13. The Lockman Hole Project: A Multi-frequency Study of the Faint Radio Population down to LOFAR bands

    NASA Astrophysics Data System (ADS)

    Guglielmino, G.; Prandoni, I.; Morganti, R.; Heald, G.; Mahony, E.; van Bemmel, I.

    2014-07-01

    We are performing a multi-frequency radio analysis of a well-known deep field: the Lockman Hole, which is one of the best studied sky regions in different wavebands. This will provide us with important complementary data (for example redshifts) to the radio data, allowing us to characterize the physical and evolutionary properties of the various classes of sources composing the faint radio population. LOFAR imaging of the Lockman Hole can play an important role in this project, allowing, for the very first time, to observe the sub-mJy source population at very low frequencies (30-200 MHz), where self-absorption phenomena are expected to be very important. Here we present some preliminary results.

  14. Habit, custom, and power: a multi-level theory of population health.

    PubMed

    Zimmerman, Frederick J

    2013-03-01

    In multi-level theory, individual behavior flows from cognitive habits, either directly through social referencing, rules of thumb, or automatic behaviors; or indirectly through the shaping of rationality itself by framing or heuristics. Although behavior does not arise from individually rational optimization, it generally appears to be rational, because the cognitive habits that guide behavior evolve toward optimality. However, power imbalances shaped by particular social, political, and economic structures can distort this evolution, leading to individual behavior that fails to maximize individual or social well-being. Replacing the dominant rational-choice paradigm with a multi-level theoretical paradigm involving habit, custom, and power will enable public health to engage in rigorous new areas of research.

  15. 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.

  16. 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.

  17. 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...

  18. 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.

  19. 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…

  20. 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

  1. A neural model of how the brain represents and compares multi-digit numbers: spatial and categorical processes.

    PubMed

    Grossberg, Stephen; Repin, Dmitry V

    2003-10-01

    Both animals and humans represent and compare numerical quantities, but only humans have evolved multi-digit place-value number systems. This article develops a Spatial Number Network, or SpaN, model to explain how these shared numerical capabilities are computed using a spatial representation of number quantities in the Where cortical processing stream, notably the inferior parietal cortex. Multi-digit numerical representations that obey a place-value principle are proposed to arise through learned interactions between categorical language representations in the What cortical processing stream and the Where spatial representation. Learned semantic categories that symbolize separate digits, as well as place markers like 'ty,' 'hundred,' and 'thousand,' are associated through learning with the corresponding spatial locations of the Where representation. Such What-to-Where auditory-to-visual learning generates place-value numbers as an emergent property, and may be compared with other examples of multi-modal cross-modality learning, including synesthesia. The model quantitatively simulates error rates in quantification and numerical comparison tasks, and reaction times for number priming and numerical assessment and comparison tasks. In the Where cortical process, transient responses to inputs are integrated before they activate an ordered spatial map that selectively responds to the number of events in a sequence and exhibits Weber law properties. Numerical comparison arises from activity pattern changes across the spatial map that define a 'directional comparison wave.' Variants of these model mechanisms have elsewhere been used to explain data about other Where stream phenomena, such as motion perception, spatial attention, and target tracking. The model is compared with other models of numerical representation.

  2. 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

  3. 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

  4. Multi-compartmental biomaterial scaffolds for patterning neural tissue organoids in models of neurodevelopment and tissue regeneration

    PubMed Central

    McMurtrey, Richard J

    2016-01-01

    Biomaterials are becoming an essential tool in the study and application of stem cell research. Various types of biomaterials enable three-dimensional culture of stem cells, and, more recently, also enable high-resolution patterning and organization of multicellular architectures. Biomaterials also hold potential to provide many additional advantages over cell transplants alone in regenerative medicine. This article describes novel designs for functionalized biomaterial constructs that guide tissue development to targeted regional identities and structures. Such designs comprise compartmentalized regions in the biomaterial structure that are functionalized with molecular factors that form concentration gradients through the construct and guide stem cell development, axis patterning, and tissue architecture, including rostral/caudal, ventral/dorsal, or medial/lateral identities of the central nervous system. The ability to recapitulate innate developmental processes in a three-dimensional environment and under specific controlled conditions has vital application to advanced models of neurodevelopment and for repair of specific sites of damaged or diseased neural tissue. PMID:27766141

  5. [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.

  6. Population.

    ERIC Educational Resources Information Center

    International Planned Parenthood Federation, London (England).

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

  7. Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury.

    PubMed

    Llorens-Bobadilla, Enric; Zhao, Sheng; Baser, Avni; Saiz-Castro, Gonzalo; Zwadlo, Klara; Martin-Villalba, Ana

    2015-09-01

    Heterogeneous pools of adult neural stem cells (NSCs) contribute to brain maintenance and regeneration after injury. The balance of NSC activation and quiescence, as well as the induction of lineage-specific transcription factors, may contribute to diversity of neuronal and glial fates. To identify molecular hallmarks governing these characteristics, we performed single-cell sequencing of an unbiased pool of adult subventricular zone NSCs. This analysis identified a discrete, dormant NSC subpopulation that already expresses distinct combinations of lineage-specific transcription factors during homeostasis. Dormant NSCs enter a primed-quiescent state before activation, which is accompanied by downregulation of glycolytic metabolism, Notch, and BMP signaling and a concomitant upregulation of lineage-specific transcription factors and protein synthesis. In response to brain ischemia, interferon gamma signaling induces dormant NSC subpopulations to enter the primed-quiescent state. This study unveils general principles underlying NSC activation and lineage priming and opens potential avenues for regenerative medicine in the brain. PMID:26235341

  8. 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.

  9. 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.

  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. 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

  12. 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

  13. 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

  14. Counting the cost of diabetic hospital admissions from a multi-ethnic population in Trinidad.

    PubMed

    Gulliford, M C; Ariyanayagam-Baksh, S M; Bickram, L; Picou, D; Mahabir, D

    1995-12-01

    Many middle-income countries are experiencing an increase in diabetes mellitus but patterns of morbidity and resource use from diabetes in developing countries have not been well described. We evaluated hospital admission with diabetes among different ethnic groups in Trinidad. We compiled a register of all patients with diabetes admitted to adult medical, general surgical, and ophthalmology wards at Port of Spain Hospital, Trinidad. During 26 weeks, 1447 patients with diabetes had 1722 admissions. Annual admission rates, standardized to the World Population, for the catchment population aged 30-64 years were 1031 (95% CI 928 to 1134) per 100,000 in men and 1354 (1240 to 1468) per 100,000 in women. Compared with the total population, admission rates were 33% higher in the Indian origin population and 47% lower in those of mixed ethnicity. The age-standardized rate of amputation with diabetes in the general population aged 30-64 years was 54 (37 to 71) per 100,000. The hospital admission fatality rate was 8.9% (95%CI 7.6% to 10.2%). Mortality was associated with increasing age, admission with hyperglycaemia, elevated serum creatinine, cardiac failure or stroke and with lower-limb amputation during admission. Diabetes accounted for 13.6% of hospital admissions and 23% of hospital bed occupancy. Admissions associated with disorders of blood glucose control or foot problems accounted for 52% of diabetic hospital bed occupancy. The annual cost of admissions with diabetes was conservatively estimated at TT+ 10.66 million (UK 1.24 million pounds). In this community diabetes admission rates were high and varied according to the prevalence of diabetes. Admissions, fatalities and resource use were associated with acute and chronic complications of diabetes. Investing in better quality preventive clinical care for diabetes might provide an economically advantageous policy for countries like Trinidad and Tobago.

  15. Multi-objective optimal design of online PID controllers using model predictive control based on the group method of data handling-type neural networks

    NASA Astrophysics Data System (ADS)

    Majdabadi-Farahani, V.; Hanif, M.; Gholaminezhad, I.; Jamali, A.; Nariman-Zadeh, N.

    2014-10-01

    In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of Δ u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.

  16. 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.

  17. 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

  18. On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification.

    PubMed

    Yang, Huijuan; Sakhavi, Siavash; Ang, Kai Keng; Guan, Cuntai

    2015-01-01

    Learning the deep structures and unknown correlations is important for the detection of motor imagery of EEG signals (MI-EEG). This study investigates the use of convolutional neural networks (CNNs) for the classification of multi-class MI-EEG signals. Augmented common spatial pattern (ACSP) features are generated based on pair-wise projection matrices, which covers various frequency ranges. We propose a frequency complementary feature map selection (FCMS) scheme by constraining the dependency among frequency bands. Experiments are conducted on BCI competition IV dataset IIa with 9 subjects. Averaged cross-validation accuracy of 68.45% and 69.27% is achieved for FCMS and all feature maps, respectively, which is significantly higher (4.53% and 5.34%) than random map selection and higher (1.44% and 2.26%) than filter-bank CSP (FBCSP). The results demonstrate that the CNNs are capable of learning discriminant, deep structure features for EEG classification without relying on the handcrafted features. PMID:26736829

  19. Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory.

    PubMed

    Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Xu, Jing; Zheng, Kehong

    2015-11-13

    In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system.

  20. 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

  1. A Review of the Development and Application of Generic Multi-Attribute Utility Instruments for Paediatric Populations.

    PubMed

    Chen, Gang; Ratcliffe, Julie

    2015-10-01

    Multi-attribute utility instruments (MAUIs) are increasingly being used as a means of quantifying utility for the calculation of quality-adjusted life-years within the context of cost utility analysis. Traditionally, MAUIs have been developed and applied in adult populations. However, increasingly, researchers in health economics and other disciplines are recognising the importance of the measurement and valuation of health in both children and adolescents. Presently, there are nine generic MAUIs available internationally that have been used in paediatric populations: the Quality of Well-Being Scale (QWB), the Health Utility Index Mark 2 (HUI2), the HUI3, the Sixteen-dimensional measure of health-related quality of life (HRQoL) (16D), the Seventeen-dimensional measure of HRQoL (17D), the Assessment of Quality of Life 6-Dimension (AQoL-6D) Adolescent, the Child Health Utility 9D (CHU9D), the EQ-5D Youth version (EQ-5D-Y) and the Adolescent Health Utility Measure (AHUM). This paper critically reviews the development and application of the above nine MAUIs and discusses the specific challenges of health utility measurement in children and adolescents. Areas for further research relating to the development and application of generic MAUIs in paediatric populations are highlighted.

  2. 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

  3. 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).

  4. 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.

  5. 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

  6. 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.

  7. Strategies for monitoring and managing mass populations of toxic cyanobacteria in recreational waters: a multi-interdisciplinary approach

    PubMed Central

    2009-01-01

    Mass populations of toxin-producing cyanobacteria commonly develop in fresh-, brackish- and marine waters and effective strategies for monitoring and managing cyanobacterial health risks are required to safeguard animal and human health. A multi-interdisciplinary study, including two UK freshwaters with a history of toxic cyanobacterial blooms, was undertaken to explore different approaches for the identification, monitoring and management of potentially-toxic cyanobacteria and their associated risks. The results demonstrate that (i) cyanobacterial bloom occurrence can be predicted at a local- and national-scale using process-based and statistical models; (ii) cyanobacterial concentration and distribution in waterbodies can be monitored using remote sensing, but minimum detection limits need to be evaluated; (iii) cyanotoxins may be transferred to spray-irrigated root crops; and (iv) attitudes and perceptions towards risks influence the public's preferences and willingness-to-pay for cyanobacterial health risk reductions in recreational waters. PMID:20102578

  8. Near scale-free dynamics in neural population activity of waking/sleeping rats revealed by multiscale analysis.

    PubMed

    Safonov, Leonid A; Isomura, Yoshikazu; Kang, Siu; Struzik, Zbigniew R; Fukai, Tomoki; Câteau, Hideyuki

    2010-09-28

    A neuron embedded in an intact brain, unlike an isolated neuron, participates in network activity at various spatial resolutions. Such multiple scale spatial dynamics is potentially reflected in multiple time scales of temporal dynamics. We identify such multiple dynamical time scales of the inter-spike interval (ISI) fluctuations of neurons of waking/sleeping rats by means of multiscale analysis. The time scale of large non-Gaussianity in the ISI fluctuations, measured with the Castaing method, ranges up to several minutes, markedly escaping the low-pass filtering characteristics of neurons. A comparison between neural activity during waking and sleeping reveals that non-Gaussianity is stronger during waking than sleeping throughout the entire range of scales observed. We find a remarkable property of near scale independence of the magnitude correlations as the primary cause of persistent non-Gaussianity. Such scale-invariance of correlations is characteristic of multiplicative cascade processes and raises the possibility of the existence of a scale independent memory preserving mechanism.

  9. 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

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

    USGS Publications Warehouse

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

    2011-01-01

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

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

    PubMed

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

    2013-02-01

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

  12. Murine Whole-Organ Immune Cell Populations Revealed by Multi-epitope-Ligand Cartography

    PubMed Central

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

    2013-01-01

    Multi-epitope-ligand cartography (MELC) is an innovative high-throughput fluorescence microscopy–based method. A tissue section is analyzed through a repeated cycling of (1) incubation with a fluorophore-labeled antibody, (2) fluorescence imaging, and (3) soft bleaching. This method allows staining of the same tissue section with up to 100 fluorescent markers and to analyze their toponomic expression using further image processing and pixel-precise overlay of the corresponding images. In this study, we adapted this method to identify a large panel of murine leukocyte subpopulations in a whole frozen section of a peripheral lymph node. Using the resulting antibody library, we examined non-inflamed versus inflamed tissues of brain and spinal cord in the experimental autoimmune encephalomyelitis (EAE) model. The presence and activity of specific leukocyte subpopulations (different T cell subpopulations, dendritic cells, macrophages, etc.) could be assessed and the cellular localizations and the corresponding activation status in situ were investigated. The results were then correlated with quantitative RT-PCR. PMID:23160665

  13. Multiphase multi-velocity discrete population balance model of fragmenting particulate flows

    NASA Astrophysics Data System (ADS)

    Panchagnula, Mahesh; Rayapati, Prasad; Peddieson, John

    2008-11-01

    Fragmenting particulate flows are studied using discrete population balance modeling. The range of particle sizes is divided into N classes with each size class being allowed to behave as an individual fluid-like phase. The particulate phases are embedded in a continuous phase with which they share a pressure field and are coupled through drag forces. The particulate material is therefore modeled as a mixture of N+1 inter-penetrating continua. The fragmentation process is modeled using the population balance approach which allows for parent size-class particles to break up into any of the smaller daughter size-classes following a pre-defined breakage phenomenology. The accompanying mass and momentum exchange between the size-classes is modeled as source terms in the conservation equations. The model is applied to a micro-centrifuge flow field. We show here that the larger particles, while being encouraged to break up are also preferentially transported towards the walls of the centrifuge, owing to the swirl induced radial pressure gradient. By experimenting with various breakage phenomenologies, we show that the classical log-normal particle size distribution can be recovered in the long time limit for all breakage phenomenologies but the short time evolution of the particle size distribution is sensitive to that choice.

  14. 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

  15. 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.

  16. 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

  17. A population density approach that facilitates large-scale modeling of neural networks: extension to slow inhibitory synapses.

    PubMed

    Nykamp, D Q; Tranchina, D

    2001-03-01

    A previously developed method for efficiently simulating complex networks of integrate-and-fire neurons was specialized to the case in which the neurons have fast unitary postsynaptic conductances. However, inhibitory synaptic conductances are often slower than excitatory ones for cortical neurons, and this difference can have a profound effect on network dynamics that cannot be captured with neurons that have only fast synapses. We thus extend the model to include slow inhibitory synapses. In this model, neurons are grouped into large populations of similar neurons. For each population, we calculate the evolution of a probability density function (PDF), which describes the distribution of neurons over state-space. The population firing rate is given by the flux of probability across the threshold voltage for firing an action potential. In the case of fast synaptic conductances, the PDF was one-dimensional, as the state of a neuron was completely determined by its transmembrane voltage. An exact extension to slow inhibitory synapses increases the dimension of the PDF to two or three, as the state of a neuron now includes the state of its inhibitory synaptic conductance. However, by assuming that the expected value of a neuron's inhibitory conductance is independent of its voltage, we derive a reduction to a one-dimensional PDF and avoid increasing the computational complexity of the problem. We demonstrate that although this assumption is not strictly valid, the results of the reduced model are surprisingly accurate. PMID:11244554

  18. Computing with neural synchrony.

    PubMed

    Brette, Romain

    2012-01-01

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

  19. 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

  20. 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

  1. 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

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

    PubMed

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

    2000-01-01

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

  3. 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.

  4. 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

  5. Variation in human β-defensin genes: new insights from a multi-population study

    PubMed Central

    Mehlotra, Rajeev K.; Zimmerman, Peter A.; Weinberg, Aaron; Jurevic, Richard J.

    2012-01-01

    Summary Human β-defensin 2 (hBD-2) and hBD-3, encoded by DEFB4 and DEFB103A, respectively, have shown anti-HIV activity, and both genes exhibit copy number variation (CNV). Although the role of hBD-1, encoded by DEFB1, in HIV-1 infection is less clear, single nucleotide polymorphisms (SNPs) in DEFB1 may influence viral loads and disease progression. We examined the distribution of DEFB1 SNPs and DEFB4/103A CNV, and the relationship between DEFB1 SNPs and DEFB4/103A CNV using samples from two HIV/AIDS cohorts from the United States (n = 150) and five diverse populations from the Coriell Cell Repositories (n = 46). We determined the frequencies of 10 SNPs in DEFB1 by using a post-PCR, oligonucleotide ligation detection reaction-fluorescent microsphere assay, and CNV in DEFB4/103A by real-time quantitative PCR. There were noticeable differences in the frequencies of DEFB1 SNP alleles and haplotypes among various racial/ethnic groups. The DEFB4/103A copy numbers varied from 2 to 8 (median, 4), and there was a significant difference between the copy numbers of self-identified whites and blacks in the US cohorts (Mann-Whitney U test p = 0.04). A significant difference was observed in the distribution of DEFB4/103A CNV among DEFB1 -52G/A and -390T/A genotypes (Kruskal-Wallis p = 0.017 and 0.026, respectively), while not in the distribution of DEFB4/103A CNV among -52G/A_-44C/G_-20G/A diplotypes. These observations provide additional insights for further investigating the complex interplay between β-defensin genetic polymorphisms and susceptibility to, or the progression or severity of, HIV infection/disease. PMID:23194186

  6. 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

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

    PubMed Central

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

    2016-01-01

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

  8. 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.

  9. Determinants of Posterior Corneal Biometric Measurements in a Multi-Ethnic Asian Population

    PubMed Central

    Ang, Marcus; Chong, Wesley; Huang, Huiqi; Wong, Tien Yin; He, Ming-Guang; Aung, Tin; Mehta, Jodhbir S.

    2014-01-01

    Purpose To describe the corneal and anterior segment determinants of posterior corneal arc length (PCAL) and posterior corneal curvature (PCC). Methods Cross-sectional, population-based study of 1069 subjects (1069 eyes) aged 40–80 years, from three major Asian ethnic groups. All underwent anterior segment optical coherence tomography imaging and analysis with Zhongshan Angle Assessment Program. Our main outcome measures were determinants of PCAL and PCC using adjusted, multivariate linear regression analysis, adjusted for confounders to obtain the estimated marginal means (EMM) with standard error (SE). Results The overall mean (± SD) of PCC was: 6.51±0.39 mm; and PCAL was: 12.52±0.59 mm. Malays had a relatively longer PCAL (EMM = 12.74 mm, SE = 0.04 mm) than Chinese (EMM = 12.48 mm, SE = 0.03 mm, P<0.001), and Indians (EMM = 12.42 mm, SE = 0.03 mm, P<0.001). Anterior segment parameters had weak-moderate correlations with PCAL, which included: anterior chamber depth (ACD) (r = 0.55, P<0.001), PCC (r = 0.27, P<0.001), anterior corneal curvature (ACC) (r = 0.14, P<0.001) and central corneal thickness (CCT) (r = −0.07, P = 0.023). In multivariate analysis, anterior segment parameters explained only 37.6% of the variance of PCAL, with ACD being the most important determinant (partial R2  = 0.300; P<0.001). The determinants of PCC included ACC, PCAL and CCT (explaining 72.1% variation of PCC), with ACC being the most important determinant (partial R2  = 0.683; P<0.001). Conclusion There was moderate correlation of PCAL with ACD, but anterior segment parameters accounted for only a small proportion of the variation in PCAL. The significant differences in PCAL and PCC amongst different Asian ethnic groups suggests that there is a need to consider this factor when planning for anterior segment surgeries such as endothelial keratoplasty. PMID:25006679

  10. 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

  11. Closed-loop firing rate regulation of two interacting excitatory and inhibitory neural populations of the basal ganglia.

    PubMed

    Haidar, Ihab; Pasillas-Lépine, William; Chaillet, Antoine; Panteley, Elena; Palfi, Stéphane; Senova, Suhan

    2016-02-01

    This paper develops a new closed-loop firing rate regulation strategy for a population of neurons in the subthalamic nucleus, derived using a model-based analysis of the basal ganglia. The system is described using a firing rate model, in order to analyse the generation of beta-band oscillations. On this system, a proportional regulation of the firing rate reduces the gain of the subthalamo-pallidal loop in the parkinsonian case, thus impeding pathological oscillation generation. A filter with a well-chosen frequency is added to this proportional scheme, in order to avoid a potential instability of the feedback loop due to actuation and measurement delays. Our main result is a set of conditions on the parameters of the stimulation strategy that guarantee both its stability and a prescribed delay margin. A discussion on the applicability of the proposed method and a complete set of mathematical proofs is included.

  12. The neural decoding toolbox

    PubMed Central

    Meyers, Ethan M.

    2013-01-01

    Population decoding is a powerful way to analyze neural data, however, currently only a small percentage of systems neuroscience researchers use this method. In order to increase the use of population decoding, we have created the Neural Decoding Toolbox (NDT) which is a Matlab package that makes it easy to apply population decoding analyses to neural activity. The design of the toolbox revolves around four abstract object classes which enables users to interchange particular modules in order to try different analyses while keeping the rest of the processing stream intact. The toolbox is capable of analyzing data from many different types of recording modalities, and we give examples of how it can be used to decode basic visual information from neural spiking activity and how it can be used to examine how invariant the activity of a neural population is to stimulus transformations. Overall this toolbox will make it much easier for neuroscientists to apply population decoding analyses to their data, which should help increase the pace of discovery in neuroscience. PMID:23734125

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

    PubMed

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

    2008-03-01

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

  14. '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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  16. 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.

  17. '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

  18. 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

  19. 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

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  2. 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

  3. Licit prescription drug use in a Swedish population according to age, gender and socioeconomic status after adjusting for level of multi-morbidity

    PubMed Central

    2012-01-01

    Background There is a great variability in licit prescription drug use in the population and among patients. Factors other than purely medical ones have proven to be of importance for the prescribing of licit drugs. For example, individuals with a high age, female gender and low socioeconomic status are more likely to use licit prescription drugs. However, these results have not been adjusted for multi-morbidity level. In this study we investigate the odds of using licit prescription drugs among individuals in the population and the rate of licit prescription drug use among patients depending on gender, age and socioeconomic status after adjustment for multi-morbidity level. Methods The study was carried out on the total population aged 20 years or older in Östergötland county with about 400 000 inhabitants in year 2006. The Johns Hopkins ACG Case-mix was used as a proxy for the individual level of multi-morbidity in the population to which we have related the odds ratio for individuals and incidence rate ratio (IRR) for patients of using licit prescription drugs, defined daily doses (DDDs) and total costs of licit prescription drugs after adjusting for age, gender and socioeconomic factors (educational and income level). Results After adjustment for multi-morbidity level male individuals had less than half the odds of using licit prescription drugs (OR 0.41 (95% CI 0.40-0.42)) compared to female individuals. Among the patients, males had higher total costs (IRR 1.14 (95% CI 1.13-1.15)). Individuals above 80 years had nine times the odds of using licit prescription drugs (OR 9.09 (95% CI 8.33-10.00)) despite adjustment for multi-morbidity. Patients in the highest education and income level had the lowest DDDs (IRR 0.78 (95% CI 0.76-0.80), IRR 0.73 (95% CI 0.71-0.74)) after adjustment for multi-morbidity level. Conclusions This paper shows that there is a great variability in licit prescription drug use associated with gender, age and socioeconomic status

  4. 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

  5. Evidence that a late-emerging population of trunk neural crest cells forms the plastron bones in the turtle Trachemys scripta.

    PubMed

    Cebra-Thomas, Judith A; Betters, Erin; Yin, Melinda; Plafkin, Callie; McDow, Kendra; Gilbert, Scott F

    2007-01-01

    The origin of the turtle plastron is not known, but these nine bones have been homologized to the exoskeletal components of the clavicles, the interclavicular bone, and gastralia. Earlier evidence from our laboratory showed that the bone-forming cells of the plastron were positive for HNK-1 and PDGFRalpha, two markers of the skeletogenic neural crest. This study looks at the embryonic origin of these plastron-forming cells. We show that the HNK-1+ cells are also positive for p75 and FoxD3, confirming their neural crest identity, and that they originate from the dorsal neural tube of stage 17 turtle embryos, several days after the original wave of neural crest cells have migrated and differentiated. DiI studies show that these are migratory cells, and they can be observed in the lateral regions of the embryo and can be seen forming intramembranous bone in the ventral (plastron) regions. Before migrating ventrally, these late-emerging neural crest cells reside for over a week in a carapacial staging area above the neural tube and vertebrae. It is speculated that this staging area is where they lose the inability to form skeletal cells. PMID:17501750

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. [Multi-scale and multi-parameter spatial distribution patterns of Seriphidium terrae-albae and Artemisia songarica populations in Gurbantunggut Desert of Northeast China].

    PubMed

    Ye, Tao; Yuan-Ming, Zhang; Xiao-Bo, Wu

    2013-11-01

    The researches on the plant population spatial pattern were mostly based on 0-D plant point (0-D IND) or 0-D plant count, and only a few was based on the 2-D projective cover (2-D PC) and 3-D aboveground biomass (3-D AGB reflected by canopy volume). Until now, the plant population spatial distribution patterns incarnated by these parameters were still unclear. Taking the widely distributed small semi-shrubs Seriphidium terrae-albae and Artemisia songarica in Gurbantunggut Desert of Northwest China as test objects, this paper studied the IND, PC, and AGB of each individual at two sampling plots. Through six-scale division of plot coordinate system with GIS, and by using aggregation analysis, coefficient of variation (CV) , and a scaling exponent between the CV and six scales, the characteristics of the population spatial distribution patterns with the above mentioned parameters were comparatively analyzed. At all scales, the IND (except for the S. terrae-albae population at 0.5 m scale) and the AGB of the two shrubs all presented a clumped distribution, and the aggregation intensity increased' with increasing scale. However, the PC had a uniform distribution (except for the A. songarica population at 5 and 8 m scales). With increasing scale, the CV values of the two shrubs decreased. The absolute value of scaling exponent (k value) of the IND was higher than those of the PC and AGB, and there was no significant difference in the k values between the PC and AGB, indicating that the scale variation scope of the struc- tural complexity of the IND was larger than that of the PC and AGB. The k value of each parameter for S. terrae-albae was higher than that for A. songarica, which could be related to the populations' interspecific relationship and plant size. In sum, the IND and AGB had similar spatial patterns, while the PC and AGB had almost same spatial pattern complexity and scale change characteristics. PMID:24564127

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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

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

    PubMed

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

    2014-08-01

    Here, we exploit the spatial separation of temporal events of neural differentiation in the elongating chick body axis to provide the first analysis of transcriptome change in progressively more differentiated neural cell populations in vivo. Microarray data, validated against direct RNA sequencing, identified: (1) a gene cohort characteristic of the multi-potent stem zone epiblast, which contains neuro-mesodermal progenitors that progressively generate the spinal cord; (2) a major transcriptome re-organisation as cells then adopt a neural fate; and (3) increasing diversity as neural patterning and neuron production begin. Focussing on the transition from multi-potent to neural state cells, we capture changes in major signalling pathways, uncover novel Wnt and Notch signalling dynamics, and implicate new pathways (mevalonate pathway/steroid biogenesis and TGFβ). This analysis further predicts changes in cellular processes, cell cycle, RNA-processing and protein turnover as cells acquire neural fate. We show that these changes are conserved across species and provide biological evidence for reduced proteasome efficiency and a novel lengthening of S phase. This latter step may provide time for epigenetic events to mediate large-scale transcriptome re-organisation; consistent with this, we uncover simultaneous downregulation of major chromatin modifiers as the neural programme is established. We further demonstrate that transcription of one such gene, HDAC1, is dependent on FGF signalling, making a novel link between signals that control neural differentiation and transcription of a core regulator of chromatin organisation. Our work implicates new signalling pathways and dynamics, cellular processes and epigenetic modifiers in neural differentiation in vivo, identifying multiple new potential cellular and molecular mechanisms that direct differentiation. PMID:25063452

  2. 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

  3. 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.

  4. Binary Populations in Milky Way Satellite Galaxies: Constraints from Multi-epoch Data in the Carina, Fornax, Sculptor, and Sextans Dwarf Spheroidal Galaxies

    NASA Astrophysics Data System (ADS)

    Minor, Quinn E.

    2013-12-01

    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^{+0.28}_{-0.05}, 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^{+0.13}_{-0.09}, 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-1 or smaller, presently attainable only by a high-resolution spectrograph.

  5. 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.

  6. 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

  7. 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

  8. 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

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

    PubMed Central

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

    2011-01-01

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

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

    PubMed

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

    2011-01-01

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

  11. 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.

  12. 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

  13. 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…

  14. 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

  15. 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

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

    PubMed Central

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

    2016-01-01

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

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. 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

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

    PubMed

    Miller, Joshua H

    2011-03-28

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

  3. Population based evaluation of a multi-parametric steroid profiling on administered endogenous steroids in single low dose.

    PubMed

    Van Renterghem, Pieter; Van Eenoo, Peter; Delbeke, Frans T

    2010-12-12

    Steroid profiling provides valuable information to detect doping with endogenous steroids. Apart from the traditionally monitored steroids, minor metabolites can play an important role to increase the specificity and efficiency of current detection methods. The applicability of several minor steroid metabolites was tested on administration studies with low doses of oral testosterone (T), T gel, dihydrotestosterone (DHT) gel and oral dehydroepiandrosterone (DHEA). The collected data for all monitored parameters were evaluated with the respective population based reference ranges. Besides the traditional markers T/E, T and DHT, minor metabolites 4-OH-Adion and 6α-OH-Adion were found as most sensitive metabolites to detect oral T administration. The most sensitive metabolites for the detection of DHEA were identified as 16α-OH-DHEA and 7β-OH-DHEA but longest detection up to three days (after oral administration of 50 mg) was obtained with non-specific 5β-steroids and its ratios. Steroids applied as a gel had longer effects on the metabolism but were generally not detectable with universal decision criteria. It can be concluded that population based reference ranges show limited overall performance in detecting misuse of small doses of natural androgens. Although some minor metabolites provide additional information for the oral testosterone and DHEA formulations, the topical administered steroids could not be detected for all volunteers using universal reference limits. Application of other population based threshold limits did not lead to longer detection times. PMID:20688095

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

    PubMed

    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.

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

    PubMed 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

  6. Epigenetic Regulation in Neural Crest Development

    PubMed Central

    Hu, Na; Strobl-Mazzulla, Pablo H.; Bronner, Marianne E.

    2014-01-01

    The neural crest is a migratory and multipotent cell population that plays a crucial many aspects of embryonic development. In all vertebrate embryos, these cells emerge from the dorsal neural tube then migrate long distances to different regions of the body, where they contribute to formation of many cell types and structures. These include much of the peripheral nervous system, craniofacial skeleton, smooth muscle, and pigmentation of the skin. The best-studied regulatory events guiding neural crest development are mediated by transcription factors and signaling molecules. In recent years, however, growing evidence supports an important role for epigenetic regulation as an additional mechanism for controlling the timing and level of gene expression at different stages of neural crest development. Here, we summarize the process of neural crest formation, with focus on the role of epigenetic regulation in neural crest specification, migration, and differentiation as well as in neural crest related birth defects and diseases. PMID:25446277

  7. 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

  8. 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

  9. 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.

  10. 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.

  11. 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

    2016-06-01

    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.

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

    PubMed

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

    2013-01-01

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

  13. 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

  14. 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

  15. Retinoic Acid Upregulates Ret and Induces Chain Migration and Population Expansion in Vagal Neural Crest Cells to Colonise the Embryonic Gut

    PubMed Central

    Simkin, Johanna E.; Zhang, Dongcheng; Rollo, Benjamin N.; Newgreen, Donald F.

    2013-01-01

    Vagal neural crest cells (VNCCs) arise in the hindbrain, and at (avian) embryonic day (E) 1.5 commence migration through paraxial tissues to reach the foregut as chains of cells 1–2 days later. They then colonise the rest of the gut in a rostrocaudal wave. The chains of migrating cells later resolve into the ganglia of the enteric nervous system. In organ culture, E4.5 VNCCs resident in the gut (termed enteric or ENCC) which have previously encountered vagal paraxial tissues, rapidly colonised aneural gut tissue in large numbers as chains of cells. Within the same timeframe, E1.5 VNCCs not previously exposed to paraxial tissues provided very few cells that entered the gut mesenchyme, and these never formed chains, despite their ability to migrate in paraxial tissue and in conventional cell culture. Exposing VNCCs in vitro to paraxial tissue normally encountered en route to the foregut conferred enteric migratory ability. VNCC after passage through paraxial tissue developed elements of retinoic acid signalling such as Retinoic Acid Binding Protein 1 expression. The paraxial tissue's ability to promote gut colonisation was reproduced by the addition of retinoic acid, or the synthetic retinoid Am80, to VNCCs (but not to trunk NCCs) in organ culture. The retinoic acid receptor antagonist CD 2665 strongly reduced enteric colonisation by E1.5 VNCC and E4.5 ENCCs, at a concentration suggesting RARα signalling. By FACS analysis, retinoic acid application to vagal neural tube and NCCs in vitro upregulated Ret; a Glial-derived-neurotrophic-factor receptor expressed by ENCCs which is necessary for normal enteric colonisation. This shows that early VNCC, although migratory, are incapable of migrating in appropriate chains in gut mesenchyme, but can be primed for this by retinoic acid. This is the first instance of the characteristic form of NCC migration, chain migration, being attributed to the application of a morphogen. PMID:23717535

  16. 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

  17. Neural invasion in gastric carcinoma.

    PubMed Central

    Mori, M; Adachi, Y; Kamakura, T; Ikeda, Y; Maehara, Y; Sugimachi, K

    1995-01-01

    AIMS--To determine whether neural invasion in advanced gastric cancer is of clinicopathological significance. METHODS--The study population comprised 121 cases of primary advanced gastric carcinoma. Two paraffin wax embedded blocks taken from the central tissue slice in each primary tumour were used. For definitive recognition of neural invasion, immunostaining for S-100 protein was applied to one slide; the other slide was stained with haematoxylin and eosin. RESULTS--Neural invasion was recognised in 34 of 121 (28%) primary gastric carcinomas. There were significant differences in tumour size, depth of tumour invasion, stage, and curability between patients with and without neural invasion. The five year survival rates of patients with and without neural invasion were 10 and 50%, respectively. Multivariate analysis, however, demonstrated that neural invasion was not an independent prognostic factor. CONCLUSIONS--Neural invasion could be an additional useful factor for providing information about the malignant potential of gastric carcinoma. This may be analogous to vessel permeation which is thought to be important, but is not an independent prognostic factor. Images PMID:7745113

  18. 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.

  19. 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

  20. Preventive oral health behaviors in a multi-cultural population: the North York Oral Health Promotion Survey.

    PubMed

    Payne, B J; Locker, D

    1994-02-01

    To examine the preventive oral health behavior levels of randomly-selected dentate and edentulous adults, age 18 and over, a mail survey was conducted in North York, Ontario, a multicultural suburb of Metropolitan Toronto (n = 1,050). High optimal levels of at least daily tooth brushing were reported by the majority of the dentate (96 per cent). Lower rates were evident for yearly preventive visiting (69 per cent), daily flossing (22 per cent), daily use of an interdental device (25 per cent), not snacking between meals (12 per cent) and eating one or no cariogenic foods on the previous day (36 per cent). Logistic regression results indicated higher levels on an additive index of oral preventive behaviors for females, those having a higher education and non-Italian respondents. Edentulous respondents reported high daily denture cleaning rates (87 per cent), but less frequent night removal (51 per cent), checking for oral lesions (68 per cent) and preventive visiting (12 per cent). Oral disease is one of the most common and costly chronic disorders affecting modern populations. However, unlike most other chronic diseases, it is largely preventable. These data indicate a clear need for determined oral health promotion efforts to inform and encourage increased levels of preventive behaviors in addition to tooth and denture brushing, particularly among specific sociodemographic and ethnic groups.

  1. Multi-level sexual selection: individual and family-level selection for mating success in a historical human population.

    PubMed

    Moorad, Jacob A

    2013-06-01

    Precopulatory sexual selection is the association between fitness and traits associated with mate acquisition. Although sexual selection is generally recognized to be a powerful evolutionary force, most investigations are limited to characters belonging to individuals. A broader multilevel perspective acknowledges that individual fitness can be affected by aspects of mating success that are characters of groups, such as families. Parental mating success in polygynous or polyandrous human societies may exemplify traits under group-level sexual selection. Using fitness measures that account for age-structure, I measure multilevel selection for mate number over 55 years in a human population with declining rates of polygyny. Sexual selection had three components: individual-level selection for ever-mating (whether an individual mated) and individual- and family-level selection for polyandry and polygyny. Family- and individual-level selection for polygyny was equally strong, three times stronger than family-level selection for polyandry and more than an order of magnitude stronger than individual-level selection for polyandry. However, individual-level selection for polyandry and polygyny was more effective at explaining relative fitness variance than family-level selection. Selection for ever-mating was the most important source of sexual selection for fitness; variation for ever-mating explained 23% of relative fitness variance.

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

    PubMed Central

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

    2016-01-01

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

  3. Intergenerational family relations and life satisfaction among three elderly population groups in transition in the Israeli multi-cultural society.

    PubMed

    Katz, Ruth

    2009-03-01

    The study aims to illuminate the links between personal and familial resources and wellbeing of elders 65+ in three population groups in Israel: kibbutz members, new immigrants from the former Soviet Union and Arabs-all of whom are undergoing different types of personal, social and economic transitions. About 70 respondents in each group were interviewed regarding life satisfaction, familial relations based on the paradigm of intergenerational family solidarity and personal resources (socio-demographic and physical functioning). The main conclusions of this study are: the lives of the elderly immigrants are much more disruptive by the transitional migration processes they are undergoing and this affects their well-being which was much lower than the other two groups. Additionally they received more help from the family. Family solidarity, mainly opportunity structures and emotional bonds were especially strong among the Arabs, with the lowest level of conflict. The Arab elderly were also different from the other two groups in the lower level of help they provided to their adult children, probably due to their more limited level of personal resources and the differing social expectations. The majority of respondents acknowledged some degree of filial obligations, although much lower among kibbutz members. Personal resources (physical functioning and financial adequacy) had the strongest effect on life satisfaction in all three groups. The dimensions of family solidarity played a less dominant role. The discussion highlights the distinctive family culture of the three groups, the transition they face, and their differential resources with some policy recommendations.

  4. An analysis of the population genetics of potential multi-drug resistance in Wuchereria bancrofti due to combination chemotherapy.

    PubMed

    Schwab, A E; Churcher, T S; Schwab, A J; Basáñez, M-G; Prichard, R K

    2007-07-01

    Currently, annual mass treatments with albendazole (ABZ) plus ivermectin (IVM) or diethylcarbamazine (DEC) are administered under the Global Programme to Eliminate Lymphatic Filariasis (GPELF). Drug resistance against both ABZ and IVM is prevalent in nematodes of veterinary importance, raising awareness that if anthelmintic resistance were to develop among Wuchereria bancrofti populations, this would jeopardize GPELF's goals. Genetic structure was incorporated into an existing transmission dynamics model for lymphatic filariasis (LF) to investigate the potential development of concurrent resistance to ABZ and IVM. The resultant models explore the impact of different inheritance modes of resistance to ABZ and IVM on the likely risk of treatment failure under our model assumptions. Results indicate that under ABZ+IVM combination, selection for resistance to one drug is enhanced if resistance to the other drug is already present. Excess parasite homozygosity may increase selection for dominant IVM resistance via enhancing the frequency of recessive ABZ resistance. The model predicts that if multiple resistance genes are associated with different efficacy properties of a drug combination, then examining changes at single loci may be misleading. Sampling schemes in genetic epidemiological surveys investigating the frequency of an allele under selection should consider host age, as individuals of different ages may acquire parasites at different rates.

  5. 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.

  6. 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

  7. 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

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

    USGS Publications Warehouse

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

    2015-01-01

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

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

    PubMed

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

    2013-12-01

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

  11. 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

  12. A holistic approach to education programs in thalassemia for a multi-ethnic population: consideration of perspectives, attitudes, and perceived needs.

    PubMed

    Wong, Li Ping; George, Elizabeth; Tan, Jin-Ai Mary Anne

    2011-06-01

    Hemoglobin disorders which include thalassemias are the most common heritable disorders. Effective treatment is available, and these disorders can be avoided as identification of carriers is achievable using simple hematological tests. An in-depth understanding of the awareness, attitudes, perceptions, and screening reservations towards thalassemia is necessary, as Malaysia has a multi-ethnic population with different religious beliefs. A total of 13 focus group discussions (70 participants) with members of the general lay public were conducted between November 2008 and January 2009. Lack of knowledge and understanding about thalassemia leads to general confusions over differences between thalassemia carriers and thalassemia major, inheritance patterns, and the physical and psychologically impact of the disorder in affected individuals and their families. Although most of the participants have not been tested for thalassemia, a large majority expressed willingness to be screened. Views on prenatal diagnosis and termination of fetuses with thalassemia major received mixed opinions from participants with different religions and practices. Perceived stigma and discrimination attached to being a carrier emerged as a vital topic in some group discussions where disparity in the answers exhibited differences in levels of participants' literacy and ethnic origins. The two most common needs identified from the discussion were information and screening facilities. Participants' interest in knowing the severity of the disease and assessing their risk of getting the disorder may imply the health belief model as a possible means of predicting thalassemia public screening services. Findings provide valuable insights for the development of more effective educational, screening, and prenatal diagnostic services in the multi-ethnic Asian society.

  13. 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

  14. Neural dynamics and circuit mechanisms of decision-making.

    PubMed

    Wang, Xiao-Jing

    2012-12-01

    In this review, I briefly summarize current neurobiological studies of decision-making that bear on two general themes. The first focuses on the nature of neural representation and dynamics in a decision circuit. Experimental and computational results suggest that ramping-to-threshold in the temporal domain and trajectory of population activity in the state space represent a duality of perspectives on a decision process. Moreover, a decision circuit can display several different dynamical regimes, such as the ramping mode and the jumping mode with distinct defining properties. The second is concerned with the relationship between biologically-based mechanistic models and normative-type models. A fruitful interplay between experiments and these models at different levels of abstraction have enabled investigators to pose increasingly refined questions and gain new insights into the neural basis of decision-making. In particular, recent work on multi-alternative decisions suggests that deviations from rational models of choice behavior can be explained by established neural mechanisms.

  15. 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

  16. 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.

  17. 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

  18. 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

  19. The neural processing of taste

    PubMed Central

    Lemon, Christian H; Katz, Donald B

    2007-01-01

    Although there have been many recent advances in the field of gustatory neurobiology, our knowledge of how the nervous system is organized to process information about taste is still far from complete. Many studies on this topic have focused on understanding how gustatory neural circuits are spatially organized to represent information about taste quality (e.g., "sweet", "salty", "bitter", etc.). Arguments pertaining to this issue have largely centered on whether taste is carried by dedicated neural channels or a pattern of activity across a neural population. But there is now mounting evidence that the timing of neural events may also importantly contribute to the representation of taste. In this review, we attempt to summarize recent findings in the field that pertain to these issues. Both space and time are variables likely related to the mechanism of the gustatory neural code: information about taste appears to reside in spatial and temporal patterns of activation in gustatory neurons. What is more, the organization of the taste network in the brain would suggest that the parameters of space and time extend to the neural processing of gustatory information on a much grander scale. PMID:17903281

  20. A comparison of outcomes with coronary artery calcium scanning in Unselected Populations - The Multi-Ethnic Study of Atherosclerosis (MESA) and Heinz Nixdorf Recall Study (HNR)

    PubMed Central

    Budoff, MJ; Möhlenkamp, Stefan; McClelland, Robyn; Delaney, Joseph A.; Bauer, Marcus; Jöckel, Heinz Karl; Kälsch, Hagen; Kronmal, Richard; Nasir, Khurram; Lehmann, Nils; Moebus, Susanne; Mukamal, Ken; Erbel, Raimund

    2013-01-01

    Background The Multi-Ethnic Study of Atherosclerosis (MESA) and the Heinz Nixdorf Recall Study (HNR)) differed in regards to informing physicians and patients of the results of their subclinical atherosclerosis. Objective This study investigates whether the association of coronary artery calcium (CAC) with incident non-fatal and fatal cardiovascular (CVD) events is different among these two large, population-based observational studies. Methods All Caucasian subjects aged 45–75 years, free of baseline cardiovascular disease were included (n=2232 in MESA, n=3119 HNR participants). We studied the association between CAC and event rates at 5 years, including hard cardiac events (MI, cardiac death, resuscitated cardiac arrest), and separately added revascularizations, and strokes (fatal and non-fatal) to determine adjusted hazard ratios (HR). Results Both cohorts demonstrated very low CHD (including revascularization) rates with zero calcium (1.13 and 1.16% over 5 years in MESA and HNR respectively) and increasing significantly in both groups with CAC 100–399 (6.71 and 4.52% in MESA and HNR) and CAC >400 (12.5 and 13.54% in MESA and HNR respectively) and demonstrating strong independent predictive values for scores of 100–399 and >400, despite multivariable adjustment for risk factors. Risk factor adjusted five year revascularization rates were nearly identical for HNR and MESA, and generally low for both studies (1.4% [45/3119] for HNR and 1.9% [43/2232] for MESA) over 5 years. Conclusions Across two culturally diverse populations, CAC >400 is a strong predictor of events. High CAC did not determininistically result in revascularization and knowledge of CAC did not increase revascularizations. PMID:23849491

  1. 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

  2. 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.

  3. 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.

  4. A novel, multi-parallel, real-time polymerase chain reaction approach for eight gastrointestinal parasites provides improved diagnostic capabilities to resource-limited at-risk populations.

    PubMed

    Mejia, Rojelio; Vicuña, Yosselin; Broncano, Nely; Sandoval, Carlos; Vaca, Maritza; Chico, Martha; Cooper, Philip J; Nutman, Thomas B

    2013-06-01

    Diagnosis of gastrointestinal parasites has traditionally relied on stool microscopy, which has low diagnostic sensitivity and specificity. We have developed a novel, rapid, high-throughput quantitative multi-parallel real-time polymerase chain reaction (qPCR) platform. Species-specific primers/probes were used for eight common gastrointestinal parasite pathogens: Ascaris lumbricoides, Necator americanus, Ancylostoma duodenale, Giardia lamblia, Cryptosporidium spp., Entamoeba histolytica, Trichuris trichiura, and Strongyloides stercoralis. Stool samples from 400 13-month-old children in rural Ecuador were analyzed and the qPCR was compared with a standard direct wet mount slide for stool microscopy, as were 125 8-14-year-old children before and after anthelmintic treatment. The qPCR showed higher detection rates for all parasites compared with direct microscopy, Ascaris (7.0% versus 5.5%) and for Giardia (31.5% versus 5.8%). Using an enhanced DNA extraction method, we were able to detect T. trichiura DNA. These assays will be useful to refine treatment options for affected populations, ultimately leading to better health outcomes. PMID:23509117

  5. 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.

  6. A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images.

    PubMed

    Lin, Gang; Chawla, Monica K; Olson, Kathy; Barnes, Carol A; Guzowski, John F; Bjornsson, Christopher; Shain, William; Roysam, Badrinath

    2007-09-01

    Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model-based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user-provided training examples. Starting with an initial over-segmentation produced by a gradient-weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi-model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations.

  7. 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

  8. Are symbols useful and culturally acceptable in health-state valuation studies? An exploratory study in a multi-ethnic Asian population

    PubMed Central

    Hwee-Lin, Wee; Li, Shu-Chuen; Zhang, Xu-Hao; Xie, Feng; Feeny, David; Luo, Nan; Cheung, Yin-Bun; Machin, David; Fong, Kok-Yong; Thumboo, Julian

    2008-01-01

    Background Symbols have been used in health state valuation studies to help subjects distinguish the severity of various characteristics of a given health state. Symbols used in such studies need to be evaluated for their cross-cultural appropriateness because a given symbol may have different meanings or acceptability in different cultures, which may affect results of such studies. Objectives To evaluate if using symbols to differentiate health states of different severity is useful and culturally acceptable in a multi-ethnic, urban Asian population. Methods Using in-depth interviews with adult Chinese, Malay, and Indian Singaporeans conducted in English/mother-tongue, subjects were shown a health state with 6 levels (Health Utilities Index 3 vision), each displayed with a symbol, and asked (1a) if symbols were useful in differentiating severity of each level (measured using dichotomous and 0–10 visual analog scale [VAS] scales) or (1b) offensive and (2) to assess 7 alternative sets of symbols. Results Of 63 subjects (91% response rate), 18 (29%) felt symbols were useful in differentiating severity of each level. Reported usefulness of symbols was fair (median VAS score: 3.0, score exceeding 5.0 for 33% of subjects). One Malay subject felt symbols were offensive. Conclusions Use of symbols for health state valuation was culturally acceptable and useful for some subjects. PMID:19920973

  9. 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

  10. 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.

  11. Artificial neural networks in predicting current in electric arc furnaces

    NASA Astrophysics Data System (ADS)

    Panoiu, M.; Panoiu, C.; Iordan, A.; Ghiormez, L.

    2014-03-01

    The paper presents a study of the possibility of using artificial neural networks for the prediction of the current and the voltage of Electric Arc Furnaces. Multi-layer perceptron and radial based functions Artificial Neural Networks implemented in Matlab were used. The study is based on measured data items from an Electric Arc Furnace in an industrial plant in Romania.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. “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

  17. 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

  18. 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

  19. 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.

  20. Neural networks as a possible architecture for the distributed control of space systems

    NASA Technical Reports Server (NTRS)

    Fiesler, E.; Choudry, A.

    1987-01-01

    Researchers attempted to identify the features essential for large, complex, multi-modular multi-functional systems possessing a high level of interconnectivity. These features were studied in the context of neural networks with the aim of arriving at a possible architecture of the distributed control system-specific features of the neural networks and their applicability in space systems.

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

    PubMed Central

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

    2010-01-01

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

  2. A neural net model for multiple memory domains.

    PubMed

    Anninos, P; Kokkinidis, M

    1984-07-01

    Previous studies with neural nets constructed of discrete populations of formal neurons have assumed that all neurons have the same probability of connection with any other neuron in the net. However, in this new study we incorporate the behavior of the neural systems in which the neural connections can be set up by means of chemical markers carried by the individual cells. With this new approach we studied the dynamics of isolated neural nets again as well as the dynamics of neural nets with sustained inputs. Results obtained with this approach show simple and multiple hysteresis phenomena. Such hysteresis loops may be considered to represent the basis for short-term memory.

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. Neural Darwinism and consciousness.

    PubMed

    Seth, Anil K; Baars, Bernard J

    2005-03-01

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

  9. Neural circuits with long-distance axon tracts for determining functional connectivity.

    PubMed

    Tang-Schomer, Min D; Davies, Paul; Graziano, Daniel; Thurber, Amy E; Kaplan, David L

    2014-01-30

    The cortical circuitry in the brain consists of structurally and functionally distinct neuronal assemblies with reciprocal axon connections. To generate cell culture-based systems that emulate axon tract systems of an in vivo neural network, we developed a living neural circuit consisting of compartmentalized neuronal populations connected by arrays of two millimeter-long axon tracts that are integrated on a planar multi-electrode array (MEA). The millimeter-scale node-to-node separation allows for pharmacological and electrophysiological manipulations to simultaneously target multiple neuronal populations. The results show controlled selectivity of dye absorption by neurons in different compartments. MEA-transmitted electrical stimulation of targeted neurons shows ∼46% increase of intracellular calcium levels with 20 Hz stimulation, but ∼22% decrease with 2k Hz stimulation. The unique feature of long distance axons promotes in vivo-like fasciculation. These axon tracts are determined to be inhibitory afferents by showing increased action potential firing of downstream node upon selective application of γ-aminobutyric acid (GABA) to the upstream node. Together, this model demonstrates integrated capabilities for assessing multiple endpoints including axon tract tracing, calcium influx, network architecture and activities. This system can be used as a multi-functional platform for studying axon tract-associated CNS disorders in vitro, such as diffuse axonal injury after brain trauma. PMID:24216177

  10. Neural Circuits with Long-Distance Axon Tracts for Determining Functional Connectivity

    PubMed Central

    Tang-Schomer, Min D.; Davies, Paul; Graziano, Daniel; Thurber, Amy E.; Kaplan, David L.

    2013-01-01

    The cortical circuitry in the brain consists of structurally and functionally distinct neuronal assemblies with reciprocal axon connections. To generate cell culture-based systems that emulate axon tract systems of an in vivo neural network, we developed a living neural circuit consisting of compartmentalized neuronal populations connected by arrays of two millimeter-long axon tracts that are integrated on a planar multi-electrode array (MEA). The millimeter-scale node-to-node separation allows for pharmacological and electrophysiological manipulations to simultaneously target multiple neuronal populations. The results show controlled selectivity of dye absorption by neurons in different compartments. MEA-transmitted electrical stimulation of targeted neurons shows ∼46% increase of intracellular calcium levels with 20 Hz stimulation, but ∼22% decrease with 2k Hz stimulation. The unique feature of long distance axons promotes in vivo-like fasciculation. These axon tracts are determined to be inhibitory afferents by showing increased action potential firing of downstream node upon selective application of γ-aminobutyric acid (GABA) to the upstream node. Together, this model demonstrates integrated capabilities for assessing multiple endpoints including axon tract tracing, calcium influx, network architecture and activities. This system can be used as a multi-functional platform for studying axon tract-associated CNS disorders in vitro, such as diffuse axonal injury after brain trauma. PMID:24216177

  11. Neural circuits with long-distance axon tracts for determining functional connectivity.

    PubMed

    Tang-Schomer, Min D; Davies, Paul; Graziano, Daniel; Thurber, Amy E; Kaplan, David L

    2014-01-30

    The cortical circuitry in the brain consists of structurally and functionally distinct neuronal assemblies with reciprocal axon connections. To generate cell culture-based systems that emulate axon tract systems of an in vivo neural network, we developed a living neural circuit consisting of compartmentalized neuronal populations connected by arrays of two millimeter-long axon tracts that are integrated on a planar multi-electrode array (MEA). The millimeter-scale node-to-node separation allows for pharmacological and electrophysiological manipulations to simultaneously target multiple neuronal populations. The results show controlled selectivity of dye absorption by neurons in different compartments. MEA-transmitted electrical stimulation of targeted neurons shows ∼46% increase of intracellular calcium levels with 20 Hz stimulation, but ∼22% decrease with 2k Hz stimulation. The unique feature of long distance axons promotes in vivo-like fasciculation. These axon tracts are determined to be inhibitory afferents by showing increased action potential firing of downstream node upon selective application of γ-aminobutyric acid (GABA) to the upstream node. Together, this model demonstrates integrated capabilities for assessing multiple endpoints including axon tract tracing, calcium influx, network architecture and activities. This system can be used as a multi-functional platform for studying axon tract-associated CNS disorders in vitro, such as diffuse axonal injury after brain trauma.

  12. 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.

  13. DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

    PubMed Central

    Cowley, Benjamin R.; Kaufman, Matthew T.; Butler, Zachary S.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

    2014-01-01

    Objective Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than three, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance DataHigh was developed to fulfill a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity. PMID:24216250

  14. Parallel architectures and neural networks

    SciTech Connect

    Calianiello, E.R. )

    1989-01-01

    This book covers parallel computer architectures and neural networks. Topics include: neural modeling, use of ADA to simulate neural networks, VLSI technology, implementation of Boltzmann machines, and analysis of neural nets.

  15. 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

  16. 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

  17. 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.

  18. 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

  19. Parallel Consensual Neural Networks

    NASA Technical Reports Server (NTRS)

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

    1993-01-01

    A new neural network architecture is proposed and applied in classification of remote sensing/geographic data from multiple sources. The new architecture is called the parallel consensual neural network and its relation to hierarchical and ensemble neural networks is discussed. The parallel consensual neural network architecture is based on statistical consensus theory. The input data are transformed several times and the different transformed data are applied as if they were independent inputs and are classified using stage neural networks. Finally, the 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. The performance of the consensual neural network architecture is compared to that of a two-layer (one hidden layer) conjugate-gradient backpropagation neural network. The results with the proposed neural network architecture compare favorably in terms of classification accuracy to the backpropagation method.

  20. Neural Tube Defects

    MedlinePlus

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

  1. 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.

  2. 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

  3. 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

  4. Wireless microstimulators for neural prosthetics.

    PubMed

    Sahin, Mesut; Pikov, Victor

    2011-01-01

    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 submillimeter-size floating stimulators. Possible means of energizing such a floating microstimulator, such as optical, acoustic, and electromagnetic waves, are discussed. PMID:21488815

  5. 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.

  6. Electronic neural networks

    SciTech Connect

    Howard, R.E.; Jackel, L.D.; Graf, H.P.

    1988-02-01

    The use of electronic neural networks to handle some complex computing problems is discussed. A simple neural model is shown and discussed in terms of its computational aspects. The use of electronic neural networks in machine pattern recognition and classification and in machine learning is examined. CMOS programmable networks are discussed. 15 references.

  7. 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…

  8. 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.

  9. 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.

  10. 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

  11. Foundations of neural networks

    SciTech Connect

    Simpson, P.K.

    1994-12-31

    Building intelligent systems that can model human behavior has captured the attention of the world for years. So, it is not surprising that a technology such as neural networks has generated great interest. This paper will provide an evolutionary introduction to neural networks by beginning with the key elements and terminology of neural networks, and developing the topologies, learning laws, and recall dynamics from this infrastructure. The perspective taken in this paper is largely that of an engineer, emphasizing the application potential of neural networks and drawing comparisons with other techniques that have similar motivations. As such, mathematics will be relied upon in many of the discussions to make points as precise as possible. The paper begins with a review of what neural networks are and why they are so appealing. A typical neural network is immediately introduced to illustrate several of the key features. With this network as a reference, the evolutionary introduction to neural networks is then pursued. The fundamental elements of a neural network, such as input and output patterns, processing element, connections, and threshold operations, are described, followed by descriptions of neural network topologies, learning algorithms, and recall dynamics. A taxonomy of neural networks is presented that uses two of the key characteristics of learning and recall. Finally, a comparison of neural networks and similar nonneural information processing methods is presented.

  12. Multitask neural network for vision machine systems

    NASA Astrophysics Data System (ADS)

    Gupta, Madan M.; Knopf, George K.

    1991-02-01

    A multi-task dynamic neural network that can be programmed for storing processing and encoding spatio-temporal visual information is presented in this paper. This dynamic neural network called the PNnetwork is comprised of numerous densely interconnected neural subpopulations which reside in one of the two coupled sublayers P or N. The subpopulations in the P-sublayer transmit an excitatory or a positive influence onto all interconnected units whereas the subpopulations in the N-sublayer transmit an inhibitory or negative influence. The dynamical activity generated by each subpopulation is given by a nonlinear first-order system. By varying the coupling strength between these different subpopulations it is possible to generate three distinct modes of dynamical behavior useful for performing vision related tasks. It is postulated that the PN-network can function as a basic programmable processor for novel vision machine systems. 1. 0

  13. Multiresolution dynamic predictor based on neural networks

    NASA Astrophysics Data System (ADS)

    Tsui, Fu-Chiang; Li, Ching-Chung; Sun, Mingui; Sclabassi, Robert J.

    1996-03-01

    We present a multiresolution dynamic predictor (MDP) based on neural networks for multi- step prediction of a time series. The MDP utilizes the discrete biorthogonal wavelet transform to compute wavelet coefficients at several scale levels and recurrent neural networks (RNNs) to form a set of dynamic nonlinear models for prediction of the time series. By employing RNNs in wavelet coefficient space, the MDP is capable of predicting a time series for both the long-term (with coarse resolution) and short-term (with fine resolution). Experimental results have demonstrated the effectiveness of the MDP for multi-step prediction of intracranial pressure (ICP) recorded from head-trauma patients. This approach has applicability to quasi- stationary signals and is suitable for on-line computation.

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

    SciTech Connect

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

    1991-12-31

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

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

    SciTech Connect

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

    1991-01-01

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

  16. 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.

  17. Neural tube closure in Xenopus laevis involves medial migration, directed protrusive activity, cell intercalation and convergent extension.

    PubMed

    Davidson, L A; Keller, R E

    1999-10-01

    We have characterized the cell movements and prospective cell identities as neural folds fuse during neural tube formation in Xenopus laevis. A newly developed whole-mount, two-color fluorescent RNA in situ hybridization method, visualized with confocal microscopy, shows that the dorsal neural tube gene xpax3 and the neural-crest-specific gene xslug are expressed far lateral to the medial site of neural fold fusion and that expression moves medially after fusion. To determine whether cell movements or dynamic changes in gene expression are responsible, we used low-light videomicroscopy followed by fluorescent in situ and confocal microscopy. These methods revealed that populations of prospective neural crest and dorsal neural tube cells near the lateral margin of the neural plate at the start of neurulation move to the dorsal midline using distinctive forms of motility. Before fold fusion, superficial neural cells apically contract, roll the neural plate into a trough and appear to pull the superficial epidermal cell sheet medially. After neural fold fusion, lateral deep neural cells move medially by radially intercalating between other neural cells using two types of motility. The neural crest cells migrate as individual cells toward the dorsal midline using medially directed monopolar protrusions. These movements combine the two lateral populations of neural crest into a single medial population that form the roof of the neural tube. The remaining cells of the dorsal neural tube extend protrusions both medially and laterally bringing about radial intercalation of deep and superficial cells to form a single-cell-layered, pseudostratified neural tube. While ours is the first description of medially directed cell migration during neural fold fusion and re-establishment of the neural tube, these complex cell behaviors may be involved during cavitation of the zebrafish neural keel and secondary neurulation in the posterior axis of chicken and mouse.

  18. A meta-analysis of cardio-metabolic abnormalities in drug naïve, first-episode and multi-episode patients with schizophrenia versus general population controls

    PubMed Central

    Vancampfort, Davy; Wampers, Martien; Mitchell, Alex J; Correll, Christoph U; De Herdt, Amber; Probst, Michel; De Hert, Marc

    2013-01-01

    A meta-analysis was conducted to explore the risk for cardio-metabolic abnormalities in drug naïve, first-episode and multi-episode patients with schizophrenia and age- and gender- or cohort-matched general population controls. Our literature search generated 203 relevant studies, of which 136 were included. The final dataset comprised 185,606 unique patients with schizophrenia, and 28 studies provided data for age- and gender-matched or cohort-matched general population controls (n=3,898,739). We found that multi-episode patients with schizophrenia were at increased risk for abdominal obesity (OR=4.43; CI=2.52-7.82; p<0.001), hypertension (OR=1.36; CI=1.21-1.53; p<0.001), low high-density lipoprotein cholesterol (OR=2.35; CI=1.78-3.10; p<0.001), hypertriglyceridemia (OR=2.73; CI=1.95-3.83; p<0.001), metabolic syndrome (OR=2.35; CI=1.68-3.29; p<0.001), and diabetes (OR=1.99; CI=1.55-2.54; p<0.001), compared to controls. Multi-episode patients with schizophrenia were also at increased risk, compared to first-episode (p<0.001) and drug-naïve (p<0.001) patients, for the above abnormalities, with the exception of hypertension and diabetes. Our data provide further evidence supporting WPA recommendations on screening, follow-up, health education and lifestyle changes in people with schizophrenia. PMID:24096790

  19. 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.

  20. Understanding how lake populations of arctic char are structured and function with special consideration of the potential effects of climate change: a multi-faceted approach.

    PubMed

    Budy, Phaedra; Luecke, Chris

    2014-09-01

    Size dimorphism in fish populations, both its causes and consequences, has been an area of considerable focus; however, uncertainty remains whether size dimorphism is dynamic or stabilizing and about the role of exogenous factors. Here, we explored patterns among empirical vital rates, population structure, abundance and trend, and predicted the effects of climate change on populations of arctic char (Salvelinus alpinus) in two lakes. Both populations cycle dramatically between dominance by small (≤300 mm) and large (>300 mm) char. Apparent survival (Φ) and specific growth rates (SGR) were relatively high (40-96%; SGR range 0.03-1.5%) and comparable to those of conspecifics at lower latitudes. Climate change scenarios mimicked observed patterns of warming and resulted in temperatures closer to optimal for char growth (15.15 °C) and a longer growing season. An increase in consumption rates (28-34%) under climate change scenarios led to much greater growth rates (23-34%). Higher growth rates predicted under climate change resulted in an even greater predicted amplitude of cycles in population structure as well as an increase in reproductive output (Ro) and decrease in generation time (Go). Collectively, these results indicate arctic char populations (not just individuals) are extremely sensitive to small changes in the number of ice-free days. We hypothesize years with a longer growing season, predicted to occur more often under climate change, produce elevated growth rates of small char and act in a manner similar to a "resource pulse," allowing a sub-set of small char to "break through," thus setting the cycle in population structure.

  1. Functional dissection of circuitry in a neural integrator.

    PubMed

    Aksay, Emre; Olasagasti, Itsaso; Mensh, Brett D; Baker, Robert; Goldman, Mark S; Tank, David W

    2007-04-01

    In neural integrators, transient inputs are accumulated into persistent firing rates that are a neural correlate of short-term memory. Integrators often contain two opposing cell populations that increase and decrease sustained firing as a stored parameter value rises. A leading hypothesis for the mechanism of persistence is positive feedback through mutual inhibition between these opposing populations. We tested predictions of this hypothesis in the goldfish oculomotor velocity-to-position integrator by measuring the eye position and firing rates of one population, while pharmacologically silencing the opposing one. In complementary experiments, we measured responses in a partially silenced single population. Contrary to predictions, induced drifts in neural firing were limited to half of the oculomotor range. We built network models with synaptic-input thresholds to demonstrate a new hypothesis suggested by these data: mutual inhibition between the populations does not provide positive feedback in support of integration, but rather coordinates persistent activity intrinsic to each population.

  2. 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

  3. 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

  4. 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.

  5. Multi-genetic marker approach and spatio-temporal analysis suggest there is a single panmictic population of swordfish Xiphias gladius in the Indian Ocean.

    PubMed

    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.

  6. Simultaneous visualization of two Citrus tristeza virus genotypes provides new insights into the structure of multi-component virus populations in a host.

    PubMed

    Bergua, María; Phelan, Dane M; Bak, Aurélie; Bloom, David C; Folimonova, Svetlana Y

    2016-04-01

    Complex Citrus tristeza virus (CTV) populations composed of mixtures of different strains of the virus are commonly found in citrus trees in the field. At present, little is known about how these populations are formed, maintained, and how they are structured within a host. Here we used a novel in situ hybridization approach allowing simultaneous visualization of two different RNA targets with high sensitivity and specificity to examine the distribution of two isolates, T36 and T68-1, representing phylogenetically distinct strains of CTV, in a citrus host in single and mixed infections. Remarkably, in doubly inoculated plants the two virus variants appeared to be well mixed within the infected tissue and showed no spatial segregation. In addition, both CTV variants were often found occupying the same cells. Possible mechanisms involved in shaping CTV populations and the biological significance of the observed lack of structural separation of the individual components are discussed.

  7. 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

  8. Neural stem cells-trends and advances.

    PubMed

    English, Denis; Sharma, Neel K; Sharma, Kaushal; Anand, Akshay

    2013-04-01

    For many years, accepted dogma held that brain is a static organ with no possibility of regeneration of cells in injured or diseased human brain. However, recent preclinical reports have shown regenerative potential of neural stem cells using various injury models. This has resulted in renewed hope for those suffering from spinal cord injury and neural damage. As the potential of stem cell therapy gained impact, these claims, in particular, led to widespread enthusiasm that acute and chronic injury of the nervous system would soon be a problem of the past. The devastation caused by injury or diseases of the brain and spinal cord led to wide premature acceptance that "neural stem cells (NSCs)" derived from embryonic, fetal or adult sources would soon be effective in reversing neural and spinal trauma. However, neural therapy with stem cells has not been realized to its fullest extent. Although, discrete population of regenerative stem cells seems to be present in specific areas of human brain, the function of these cells is unclear. However, similar cells in animals seem to play important role in postnatal growth as well as recovery of neural tissue from injury, anoxia, or disease.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. Neural-Network Simulator

    NASA Technical Reports Server (NTRS)

    Mitchell, Paul H.

    1991-01-01

    F77NNS (FORTRAN 77 Neural Network Simulator) computer program simulates popular back-error-propagation neural network. Designed to take advantage of vectorization when used on computers having this capability, also used on any computer equipped with ANSI-77 FORTRAN Compiler. Problems involving matching of patterns or mathematical modeling of systems fit class of problems F77NNS designed to solve. Program has restart capability so neural network solved in stages suitable to user's resources and desires. Enables user to customize patterns of connections between layers of network. Size of neural network F77NNS applied to limited only by amount of random-access memory available to user.

  14. 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

  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.

  16. 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

  17. An In vivo Multi-Modal Structural Template for Neonatal Piglets Using High Angular Resolution and Population-Based Whole-Brain Tractography

    PubMed Central

    Zhong, Jidan; Chen, David Q.; Walker, Matthew; Waspe, Adam; Looi, Thomas; Piorkowska, Karolina; Drake, James M.; Hodaie, Mojgan

    2016-01-01

    An increasing number of applications use the postnatal piglet model in neuroimaging studies, however, these are based primarily on T1 weighted image templates. There is a growing need for a multimodal structural brain template for a comprehensive depiction of the piglet brain, particularly given the growing applications of diffusion weighted imaging for characterizing tissue microstructures and white matter organization. In this study, we present the first multimodal piglet structural brain template which includes a T1 weighted image with tissue segmentation probability maps, diffusion weighted metric templates with multiple diffusivity maps, and population-based whole-brain fiber tracts for postnatal piglets. These maps provide information about the integrity of white matter that is not available in T1 images alone. The availability of this diffusion weighted metric template will contribute to the structural imaging analysis of the postnatal piglet brain, especially models that are designed for the study of white matter diseases. Furthermore, the population-based whole-brain fiber tracts permit researchers to visualize the white matter connections in the piglet brain across subjects, guiding the delineation of a specific white matter region for structural analysis where current diffusion data is lacking. Researchers are able to augment the tracts by merging tracts from their own data to the population-based fiber tracts and thus improve the confidence of the population-wise fiber distribution. PMID:27729850

  18. 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 ...

  19. Short Term Load Forecasting Using Artificial Neural Networks for the West of Iran

    NASA Astrophysics Data System (ADS)

    Hayati, Mohsen

    In this study, the use of neural networks to study the design of Short-Term Load Forecasting (STLF) Systems for the west of Iran was explored. The three important architectures of neural networks named Multi Layer Perceptron (MLP), Elman Recurrent Neural Network (ERNN) and Radial Basis Function Network (RBFN) to model STLF systems were used. The results show that RBFN networks have the minimum forecasting error and are the best method to model the STLF systems.

  20. 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.

  1. 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

  2. Multi-annual fluctuations in reconstructed historical time-series of a European lobster (Homarus gammarus) population disappear at increased exploitation levels.

    PubMed

    Sundelöf, Andreas; Bartolino, Valerio; Ulmestrand, Mats; Cardinale, Massimiliano

    2013-01-01

    Through the history of ecology, fluctuations of populations have been a dominating topic, and endogenous causes of fluctuations and oscillations have been recognized and studied for more than 80 years. Here we analyzed an historical dataset, covering more than 130 years, of European lobster (Homarus gammarus) catches. The data shows periodic fluctuations, which are first dampened and then disappear over time. The disappearance of the periodicity coincided with a substantial increase in fishing effort and the oscillations have not reappeared in the time series. The shifting baseline syndrome has changed our perception of not only the status of the stock, but also the regulating pressures. We describe the transition of a naturally regulated lobster population into a heavily exploited fisheries controlled stock. This is shown by the incorporation of environmental and endogenous processes in generalized additive models, autocorrelation functions and periodicity analyses of time-series. PMID:23573187

  3. Multi-Annual Fluctuations in Reconstructed Historical Time-Series of a European Lobster (Homarus gammarus) Population Disappear at Increased Exploitation Levels

    PubMed Central

    Sundelöf, Andreas; Bartolino, Valerio; Ulmestrand, Mats; Cardinale, Massimiliano

    2013-01-01

    Through the history of ecology, fluctuations of populations have been a dominating topic, and endogenous causes of fluctuations and oscillations have been recognized and studied for more than 80 years. Here we analyzed an historical dataset, covering more than 130 years, of European lobster (Homarus gammarus) catches. The data shows periodic fluctuations, which are first dampened and then disappear over time. The disappearance of the periodicity coincided with a substantial increase in fishing effort and the oscillations have not reappeared in the time series. The shifting baseline syndrome has changed our perception of not only the status of the stock, but also the regulating pressures. We describe the transition of a naturally regulated lobster population into a heavily exploited fisheries controlled stock. This is shown by the incorporation of environmental and endogenous processes in generalized additive models, autocorrelation functions and periodicity analyses of time-series. PMID:23573187

  4. 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

  5. Statistical Mechanics of Neural Networks

    NASA Astrophysics Data System (ADS)

    Rau, Albrecht

    1992-01-01

    Available from UMI in association with The British Library. Requires signed TDF. In this thesis we study neural networks using tools from the statistical mechanics of systems with quenched disorder. We apply these tools to two structurally different types of networks, feed-forward and feedback networks, whose properties we first review. After reviewing the use of feed-forward networks to infer unknown rules from sets of examples, we demonstrate how practical considerations can be incorporated into the analysis and how, as a consequence, existing learning theories have to be modified. To do so, we analyse the learning of rules which cannot be learnt perfectly due to constraints on the networks used. We present and analyse a model of multi-class classification and mention how it can be used. Finally we give an analytical treatment of a "learning by query" algorithm, for which the rule is extracted from queries which are not random but selected to increase the information gain. In this thesis feedback networks are used as associative memories. Our study centers on an analysis of specific features of the basins of attraction and the structure of weight space of optimized neural networks. We investigate the pattern selectivity of optimized networks, i.e. their ability to differentiate similar but distinct patterns, and show how the basins of attraction may be enlarged using external stimulus fields. Using a new method of analysis we study the weight space organization of optimized neural networks and show how the insights gained can be used to classify different groups of networks.

  6. 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.

  7. 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…

  8. 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