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Sample records for derivative based neural

  1. DESIGN OF LOW CYTOTOXICITY DIARYLANILINE DERIVATIVES BASED ON QSAR RESULTS: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELLING

    Directory of Open Access Journals (Sweden)

    Ihsanul Arief

    2016-11-01

    Full Text Available Study on cytotoxicity of diarylaniline derivatives by using quantitative structure-activity relationship (QSAR has been done. The structures and cytotoxicities of  diarylaniline derivatives were obtained from the literature. Calculation of molecular and electronic parameters was conducted using Austin Model 1 (AM1, Parameterized Model 3 (PM3, Hartree-Fock (HF, and density functional theory (DFT methods.  Artificial neural networks (ANN analysis used to produce the best equation with configuration of input data-hidden node-output data = 5-8-1, value of r2 = 0.913; PRESS = 0.069. The best equation used to design and predict new diarylaniline derivatives.  The result shows that compound N1-(4′-Cyanophenyl-5-(4″-cyanovinyl-2″,6″-dimethyl-phenoxy-4-dimethylether benzene-1,2-diamine is the best-proposed compound with cytotoxicity value (CC50 of 93.037 μM.

  2. Neural network modelling of antifungal activity of a series of oxazole derivatives based on in silico pharmacokinetic parameters

    Directory of Open Access Journals (Sweden)

    Kovačević Strahinja Z.

    2013-01-01

    Full Text Available In the present paper, the antifungal activity of a series of benzoxazole and oxazolo[ 4,5-b]pyridine derivatives was evaluated against Candida albicans by using quantitative structure-activity relationships chemometric methodology with artificial neural network (ANN regression approach. In vitro antifungal activity of the tested compounds was presented by minimum inhibitory concentration expressed as log(1/cMIC. In silico pharmacokinetic parameters related to absorption, distribution, metabolism and excretion (ADME were calculated for all studied compounds by using PreADMET software. A feedforward back-propagation ANN with gradient descent learning algorithm was applied for modelling of the relationship between ADME descriptors (blood-brain barrier penetration, plasma protein binding, Madin-Darby cell permeability and Caco-2 cell permeability and experimental log(1/cMIC values. A 4-6-1 ANN was developed with the optimum momentum and learning rates of 0.3 and 0.05, respectively. An excellent correlation between experimental antifungal activity and values predicted by the ANN was obtained with a correlation coefficient of 0.9536. [Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. 172014

  3. Efficient Transduction of Feline Neural Progenitor Cells for Delivery of Glial Cell Line-Derived Neurotrophic Factor Using a Feline Immunodeficiency Virus-Based Lentiviral Construct

    Directory of Open Access Journals (Sweden)

    X. Joann You

    2011-01-01

    Full Text Available Work has shown that stem cell transplantation can rescue or replace neurons in models of retinal degenerative disease. Neural progenitor cells (NPCs modified to overexpress neurotrophic factors are one means of providing sustained delivery of therapeutic gene products in vivo. To develop a nonrodent animal model of this therapeutic strategy, we previously derived NPCs from the fetal cat brain (cNPCs. Here we use bicistronic feline lentiviral vectors to transduce cNPCs with glial cell-derived neurotrophic factor (GDNF together with a GFP reporter gene. Transduction efficacy is assessed, together with transgene expression level and stability during induction of cellular differentiation, together with the influence of GDNF transduction on growth and gene expression profile. We show that GDNF overexpressing cNPCs expand in vitro, coexpress GFP, and secrete high levels of GDNF protein—before and after differentiation—all qualities advantageous for use as a cell-based approach in feline models of neural degenerative disease.

  4. Chronic behavior evaluation of a micro-machined neural implant with optimized design based on an experimentally derived model.

    Science.gov (United States)

    Andrei, Alexandru; Welkenhuysen, Marleen; Ameye, Lieveke; Nuttin, Bart; Eberle, Wolfgang

    2011-01-01

    Understanding the mechanical interactions between implants and the surrounding tissue is known to have an important role for improving the bio-compatibility of such devices. Using a recently developed model, a particular micro-machined neural implant design aiming the reduction of insertion forces dependence on the insertion speed was optimized. Implantations with 10 and 100 μm/s insertion speeds showed excellent agreement with the predicted behavior. Lesion size, gliosis (GFAP), inflammation (ED1) and neuronal cells density (NeuN) was evaluated after 6 week of chronic implantation showing no insertion speed dependence.

  5. A voltage-sensitive dye-based assay for the identification of differentiated neurons derived from embryonic neural stem cell cultures.

    Directory of Open Access Journals (Sweden)

    Richardson N Leão

    Full Text Available BACKGROUND: Pluripotent and multipotent stem cells hold great therapeutical promise for the replacement of degenerated tissue in neurological diseases. To fulfill that promise we have to understand the mechanisms underlying the differentiation of multipotent cells into specific types of neurons. Embryonic stem cell (ESC and embryonic neural stem cell (NSC cultures provide a valuable tool to study the processes of neural differentiation, which can be assessed using immunohistochemistry, gene expression, Ca(2+-imaging or electrophysiology. However, indirect methods such as protein and gene analysis cannot provide direct evidence of neuronal functionality. In contrast, direct methods such as electrophysiological techniques are well suited to produce direct evidence of neural functionality but are limited to the study of a few cells on a culture plate. METHODOLOGY/PRINCIPAL FINDINGS: In this study we describe a novel method for the detection of action potential-capable neurons differentiated from embryonic NSC cultures using fast voltage-sensitive dyes (VSD. We found that the use of extracellularly applied VSD resulted in a more detailed labeling of cellular processes compared to calcium indicators. In addition, VSD changes in fluorescence translated precisely to action potential kinetics as assessed by the injection of simulated slow and fast sodium currents using the dynamic clamp technique. We further demonstrate the use of a finite element model of the NSC culture cover slip for optimizing electrical stimulation parameters. CONCLUSIONS/SIGNIFICANCE: Our method allows for a repeatable fast and accurate stimulation of neurons derived from stem cell cultures to assess their differentiation state, which is capable of monitoring large amounts of cells without harming the overall culture.

  6. Neural Based Orthogonal Data Fitting The EXIN Neural Networks

    CERN Document Server

    Cirrincione, Giansalvo

    2008-01-01

    Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

  7. Neural network representation and learning of mappings and their derivatives

    Science.gov (United States)

    White, Halbert; Hornik, Kurt; Stinchcombe, Maxwell; Gallant, A. Ronald

    1991-01-01

    Discussed here are recent theorems proving that artificial neural networks are capable of approximating an arbitrary mapping and its derivatives as accurately as desired. This fact forms the basis for further results establishing the learnability of the desired approximations, using results from non-parametric statistics. These results have potential applications in robotics, chaotic dynamics, control, and sensitivity analysis. An example involving learning the transfer function and its derivatives for a chaotic map is discussed.

  8. Chitosan derived co-spheroids of neural stem cells and mesenchymal stem cells for neural regeneration.

    Science.gov (United States)

    Han, Hao-Wei; Hsu, Shan-Hui

    2017-10-01

    Chitosan has been considered as candidate biomaterials for neural applications. The effective treatment of neurodegeneration or injury to the central nervous system (CNS) is still in lack nowadays. Adult neural stem cells (NSCs) represents a promising cell source to treat the CNS diseases but they are limited in number. Here, we developed the core-shell spheroids of NSCs (shell) and mesenchymal stem cells (MSCs, core) by co-culturing cells on the chitosan surface. The NSCs in chitosan derived co-spheroids displayed a higher survival rate than those in NSC homo-spheroids. The direct interaction of NSCs with MSCs in the co-spheroids increased the Notch activity and differentiation tendency of NSCs. Meanwhile, the differentiation potential of MSCs in chitosan derived co-spheroids was significantly enhanced toward neural lineages. Furthermore, NSC homo-spheroids and NSC/MSC co-spheroids derived on chitosan were evaluated for their in vivo efficacy by the embryonic and adult zebrafish brain injury models. The locomotion activity of zebrafish receiving chitosan derived NSC homo-spheroids or NSC/MSC co-spheroids was partially rescued in both models. Meanwhile, the higher survival rate was observed in the group of adult zebrafish implanted with chitosan derived NSC/MSC co-spheroids as compared to NSC homo-spheroids. These evidences indicate that chitosan may provide an extracellular matrix-like environment to drive the interaction and the morphological assembly between NSCs and MSCs and promote their neural differentiation capacities, which can be used for neural regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Regeneration of neural crest derivatives in the Xenopus tadpole tail

    Directory of Open Access Journals (Sweden)

    Slack Jonathan MW

    2007-05-01

    Full Text Available Abstract Background After amputation of the Xenopus tadpole tail, a functionally competent new tail is regenerated. It contains spinal cord, notochord and muscle, each of which has previously been shown to derive from the corresponding tissue in the stump. The regeneration of the neural crest derivatives has not previously been examined and is described in this paper. Results Labelling of the spinal cord by electroporation, or by orthotopic grafting of transgenic tissue expressing GFP, shows that no cells emigrate from the spinal cord in the course of regeneration. There is very limited regeneration of the spinal ganglia, but new neurons as well as fibre tracts do appear in the regenerated spinal cord and the regenerated tail also contains abundant peripheral innervation. The regenerated tail contains a normal density of melanophores. Cell labelling experiments show that melanophores do not arise from the spinal cord during regeneration, nor from the mesenchymal tissues of the skin, but they do arise by activation and proliferation of pre-existing melanophore precursors. If tails are prepared lacking melanophores, then the regenerates also lack them. Conclusion On regeneration there is no induction of a new neural crest similar to that seen in embryonic development. However there is some regeneration of neural crest derivatives. Abundant melanophores are regenerated from unpigmented precursors, and, although spinal ganglia are not regenerated, sufficient sensory systems are produced to enable essential functions to continue.

  10. Directed differentiation of porcine epiblast-derived neural progenitor cells into neurons and glia

    DEFF Research Database (Denmark)

    Rasmussen, Mikkel Aabech; Hall, Vanessa Jane; Carter, T.F.

    2011-01-01

    Neural progenitor cells (NPCs) are promising candidates for cell-based therapy of neurodegenerative diseases; however, safety concerns must be addressed through transplantation studies in large animal models, such as the pig. The aim of this study was to derive NPCs from porcine blastocysts...

  11. Design of a decoupled AP1000 reactor core control system using digital proportional–integral–derivative (PID) control based on a quasi-diagonal recurrent neural network (QDRNN)

    International Nuclear Information System (INIS)

    Wei, Xinyu; Wang, Pengfei; Zhao, Fuyu

    2016-01-01

    Highlights: • We establish a disperse dynamic model for AP1000 reactor core. • A digital PID control based on QDRNN is used to design a decoupling control system. • The decoupling performance is verified and discussed. • The decoupling control system is simulated under the load following operation. - Abstract: The control system of the AP1000 reactor core uses the mechanical shim (MSHIM) strategy, which includes a power control subsystem and an axial power distribution control subsystem. To address the strong coupling between the two subsystems, an interlock between the two subsystems is used, which can only alleviate but not eliminate the coupling. Therefore, sometimes the axial offset (AO) cannot be controlled tightly, and the flexibility of load-following operation is limited. Thus, the decoupling of the original AP1000 reactor core control system is the focus of this paper. First, a two-node disperse dynamic model is established for the AP1000 reactor core to use PID control. Then, a digital PID control system based on a quasi-diagonal recurrent neural network (QDRNN) is designed to decouple the original system. Finally, the decoupling of the control system is verified by the step signal and load-following condition. The results show that the designed control system can decouple the original system as expected and the AO can be controlled much more tightly. Moreover, the flexibility of the load following is increased.

  12. Design of a decoupled AP1000 reactor core control system using digital proportional–integral–derivative (PID) control based on a quasi-diagonal recurrent neural network (QDRNN)

    Energy Technology Data Exchange (ETDEWEB)

    Wei, Xinyu, E-mail: xyuwei@mail.xjtu.edu.cn; Wang, Pengfei, E-mail: pengfeixiaoli@yahoo.cn; Zhao, Fuyu, E-mail: fuyuzhao_xj@163.com

    2016-08-01

    Highlights: • We establish a disperse dynamic model for AP1000 reactor core. • A digital PID control based on QDRNN is used to design a decoupling control system. • The decoupling performance is verified and discussed. • The decoupling control system is simulated under the load following operation. - Abstract: The control system of the AP1000 reactor core uses the mechanical shim (MSHIM) strategy, which includes a power control subsystem and an axial power distribution control subsystem. To address the strong coupling between the two subsystems, an interlock between the two subsystems is used, which can only alleviate but not eliminate the coupling. Therefore, sometimes the axial offset (AO) cannot be controlled tightly, and the flexibility of load-following operation is limited. Thus, the decoupling of the original AP1000 reactor core control system is the focus of this paper. First, a two-node disperse dynamic model is established for the AP1000 reactor core to use PID control. Then, a digital PID control system based on a quasi-diagonal recurrent neural network (QDRNN) is designed to decouple the original system. Finally, the decoupling of the control system is verified by the step signal and load-following condition. The results show that the designed control system can decouple the original system as expected and the AO can be controlled much more tightly. Moreover, the flexibility of the load following is increased.

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

    Science.gov (United States)

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

    2016-01-01

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

  14. Neural stem cell-derived exosomes mediate viral entry

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    Sims B

    2014-10-01

    Full Text Available Brian Sims,1,2,* Linlin Gu,3,* Alexandre Krendelchtchikov,3 Qiana L Matthews3,4 1Division of Neonatology, Department of Pediatrics, 2Department of Cell, Developmental, and Integrative Biology, 3Division of Infectious Diseases, Department of Medicine, 4Center for AIDS Research, University of Alabama at Birmingham, Birmingham, AL, USA *These authors contributed equally to this work Background: Viruses enter host cells through interactions of viral ligands with cellular receptors. Viruses can also enter cells in a receptor-independent fashion. Mechanisms regarding the receptor-independent viral entry into cells have not been fully elucidated. Exosomal trafficking between cells may offer a mechanism by which viruses can enter cells.Methods: To investigate the role of exosomes on cellular viral entry, we employed neural stem cell-derived exosomes and adenovirus type 5 (Ad5 for the proof-of-principle study. Results: Exosomes significantly enhanced Ad5 entry in Coxsackie virus and adenovirus receptor (CAR-deficient cells, in which Ad5 only had very limited entry. The exosomes were shown to contain T-cell immunoglobulin mucin protein 4 (TIM-4, which binds phosphatidylserine. Treatment with anti-TIM-4 antibody significantly blocked the exosome-mediated Ad5 entry.Conclusion: Neural stem cell-derived exosomes mediated significant cellular entry of Ad5 in a receptor-independent fashion. This mediation may be hampered by an antibody specifically targeting TIM-4 on exosomes. This set of results will benefit further elucidation of virus/exosome pathways, which would contribute to reducing natural viral infection by developing therapeutic agents or vaccines. Keywords: neural stem cell-derived exosomes, adenovirus type 5, TIM-4, viral entry, phospholipids

  15. Modeling Cerebrovascular Pathophysiology in Amyloid-β Metabolism using Neural-Crest-Derived Smooth Muscle Cells

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    Christine Cheung

    2014-10-01

    Full Text Available Summary: There is growing recognition of cerebrovascular contributions to neurodegenerative diseases. In the walls of cerebral arteries, amyloid-beta (Aβ accumulation is evident in a majority of aged people and patients with cerebral amyloid angiopathy. Here, we leverage human pluripotent stem cells to generate vascular smooth muscle cells (SMCs from neural crest progenitors, recapitulating brain-vasculature-specific attributes of Aβ metabolism. We confirm that the lipoprotein receptor, LRP1, functions in our neural-crest-derived SMCs to mediate Aβ uptake and intracellular lysosomal degradation. Hypoxia significantly compromises the contribution of SMCs to Aβ clearance by suppressing LRP1 expression. This enabled us to develop an assay of Aβ uptake by using the neural crest-derived SMCs with hypoxia as a stress paradigm. We then tested several vascular protective compounds in a high-throughput format, demonstrating the value of stem-cell-based phenotypic screening for novel therapeutics and drug repurposing, aimed at alleviating amyloid burden. : The contribution of blood vessel pathologies to neurodegenerative disorders is relatively neglected, partly due to inadequate human tissues for research. By using human stem cells, Cheung et al. establish a method of generating vascular smooth muscle cells (SMCs from neural crest progenitors, the primary precursors that give rise to brain blood vessels. These stem-cell-derived SMCs display defective amyloid processing under chronic hypoxia, a phenomenon well documented in the cerebral vasculatures of aged people and patients with Alzheimer’s disease.

  16. Memristor-based neural networks

    International Nuclear Information System (INIS)

    Thomas, Andy

    2013-01-01

    The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  18. A novel word spotting method based on recurrent neural networks.

    Science.gov (United States)

    Frinken, Volkmar; Fischer, Andreas; Manmatha, R; Bunke, Horst

    2012-02-01

    Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.

  19. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  20. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

  1. Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives.

    Science.gov (United States)

    Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Pardo-Vazquez, Jose L; Leboran, Victor; Molenberghs, Geert; Faes, Christel; Acuña, Carlos

    2011-06-30

    It is well established that neural activity is stochastically modulated over time. Therefore, direct comparisons across experimental conditions and determination of change points or maximum firing rates are not straightforward. This study sought to compare temporal firing probability curves that may vary across groups defined by different experimental conditions. Odds-ratio (OR) curves were used as a measure of comparison, and the main goal was to provide a global test to detect significant differences of such curves through the study of their derivatives. An algorithm is proposed that enables ORs based on generalized additive models, including factor-by-curve-type interactions to be flexibly estimated. Bootstrap methods were used to draw inferences from the derivatives curves, and binning techniques were applied to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to study premotor ventral cortex neural activity associated with decision-making. The proposed statistical procedures proved very useful in revealing the neural activity correlates of decision-making in a visual discrimination task. Copyright © 2011 John Wiley & Sons, Ltd.

  2. Adipose tissue-derived stem cells in neural regenerative medicine.

    Science.gov (United States)

    Yeh, Da-Chuan; Chan, Tzu-Min; Harn, Horng-Jyh; Chiou, Tzyy-Wen; Chen, Hsin-Shui; Lin, Zung-Sheng; Lin, Shinn-Zong

    2015-01-01

    Adipose tissue-derived stem cells (ADSCs) have two essential characteristics with regard to regenerative medicine: the convenient and efficient generation of large numbers of multipotent cells and in vitro proliferation without a loss of stemness. The implementation of clinical trials has prompted widespread concern regarding safety issues and has shifted research toward the therapeutic efficacy of stem cells in dealing with neural degeneration in cases such as stroke, amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, Huntington's disease, cavernous nerve injury, and traumatic brain injury. Most existing studies have reported that cell therapies may be able to replenish lost cells and promote neuronal regeneration, protect neuronal survival, and play a role in overcoming permanent paralysis and loss of sensation and the recovery of neurological function. The mechanisms involved in determining therapeutic capacity remain largely unknown; however, this concept can still be classified in a methodical manner by citing current evidence. Possible mechanisms include the following: 1) the promotion of angiogenesis, 2) the induction of neuronal differentiation and neurogenesis, 3) reductions in reactive gliosis, 4) the inhibition of apoptosis, 5) the expression of neurotrophic factors, 6) immunomodulatory function, and 7) facilitating neuronal integration. In this study, several human clinical trials using ADSCs for neuronal disorders were investigated. It is suggested that ADSCs are one of the choices among various stem cells for translating into clinical application in the near future.

  3. Unfolding code for neutron spectrometry based on neural nets technology

    International Nuclear Information System (INIS)

    Ortiz R, J. M.; Vega C, H. R.

    2012-10-01

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the R obust Design of Artificial Neural Networks Methodology . The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

  4. Unfolding code for neutron spectrometry based on neural nets technology

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M.; Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Apdo. Postal 336, 98000 Zacatecas (Mexico)

    2012-10-15

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the {sup R}obust Design of Artificial Neural Networks Methodology{sup .} The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a {sup 6}Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

  5. A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

    Science.gov (United States)

    Hong, Xia

    2006-07-01

    In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

  6. File list: Unc.PSC.05.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available Unc.PSC.05.AllAg.hESC_derived_neural_cells hg19 Unclassified Pluripotent stem cell hESC derived neural... cells SRX378284 http://dbarchive.biosciencedbc.jp/kyushu-u/hg19/assembled/Unc.PSC.05.AllAg.hESC_derived_neural_cells.bed ...

  7. File list: Unc.PSC.20.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available Unc.PSC.20.AllAg.mESC_derived_neural_cells mm9 Unclassified Pluripotent stem cell mESC derived neural...,SRX213761,SRX213757 http://dbarchive.biosciencedbc.jp/kyushu-u/mm9/assembled/Unc.PSC.20.AllAg.mESC_derived_neural_cells.bed ...

  8. File list: ALL.PSC.05.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available ALL.PSC.05.AllAg.hESC_derived_neural_cells hg19 All antigens Pluripotent stem cell hESC derived neural...RX729682,SRX729698,SRX729709 http://dbarchive.biosciencedbc.jp/kyushu-u/hg19/assembled/ALL.PSC.05.AllAg.hESC_derived_neural_cells.bed ...

  9. File list: Pol.PSC.05.AllAg.iPS_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available Pol.PSC.05.AllAg.iPS_derived_neural_cells hg19 RNA polymerase Pluripotent stem cell iPS derived neural... cells http://dbarchive.biosciencedbc.jp/kyushu-u/hg19/assembled/Pol.PSC.05.AllAg.iPS_derived_neural_cells.bed ...

  10. File list: NoD.PSC.10.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available NoD.PSC.10.AllAg.mESC_derived_neural_cells mm9 No description Pluripotent stem cell mESC derived neural... cells SRX440736,SRX440731 http://dbarchive.biosciencedbc.jp/kyushu-u/mm9/assembled/NoD.PSC.10.AllAg.mESC_derived_neural_cells.bed ...

  11. File list: His.PSC.20.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  12. File list: His.PSC.10.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  13. File list: Oth.PSC.05.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  14. File list: NoD.PSC.10.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  15. File list: InP.PSC.05.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  16. File list: Unc.PSC.50.AllAg.iPS_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  17. File list: His.PSC.05.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  18. File list: Pol.PSC.20.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  19. File list: Oth.PSC.50.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  20. File list: His.PSC.50.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  1. File list: NoD.PSC.05.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available NoD.PSC.05.AllAg.hESC_derived_neural_cells hg19 No description Pluripotent stem cell hESC derived neural...edbc.jp/kyushu-u/hg19/assembled/NoD.PSC.05.AllAg.hESC_derived_neural_cells.bed ...

  2. File list: ALL.PSC.05.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  3. File list: Oth.PSC.05.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  4. File list: Pol.PSC.20.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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  5. File list: NoD.PSC.05.AllAg.iPS_derived_neural_cells [Chip-atlas[Archive

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  6. File list: InP.PSC.20.AllAg.iPS_derived_neural_cells [Chip-atlas[Archive

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  7. File list: His.PSC.20.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  8. File list: His.PSC.05.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  9. File list: NoD.PSC.20.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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    Full Text Available NoD.PSC.20.AllAg.hESC_derived_neural_crests hg19 No description Pluripotent stem cell hESC derived neural... crests http://dbarchive.biosciencedbc.jp/kyushu-u/hg19/assembled/NoD.PSC.20.AllAg.hESC_derived_neural_crests.bed ...

  10. File list: His.PSC.20.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  11. File list: DNS.PSC.05.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

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  12. File list: NoD.PSC.05.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

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  13. File list: NoD.PSC.50.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

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  14. File list: NoD.PSC.20.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

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  15. File list: Unc.PSC.20.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available Unc.PSC.20.AllAg.hESC_derived_neural_crests hg19 Unclassified Pluripotent stem cell hESC derived neural... crests SRX059366 http://dbarchive.biosciencedbc.jp/kyushu-u/hg19/assembled/Unc.PSC.20.AllAg.hESC_derived_neural_crests.bed ...

  16. File list: ALL.PSC.20.AllAg.iPS_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  17. File list: NoD.PSC.10.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

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  18. File list: Pol.PSC.05.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available Pol.PSC.05.AllAg.hESC_derived_neural_crests hg19 RNA polymerase Pluripotent stem cell hESC derived neural... crests http://dbarchive.biosciencedbc.jp/kyushu-u/hg19/assembled/Pol.PSC.05.AllAg.hESC_derived_neural_crests.bed ...

  19. File list: Unc.PSC.05.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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    Full Text Available Unc.PSC.05.AllAg.hESC_derived_neural_crests hg19 Unclassified Pluripotent stem cell hESC derived neural... crests SRX059366 http://dbarchive.biosciencedbc.jp/kyushu-u/hg19/assembled/Unc.PSC.05.AllAg.hESC_derived_neural_crests.bed ...

  20. File list: DNS.PSC.50.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

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  1. File list: Pol.PSC.05.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

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    Full Text Available Pol.PSC.05.AllAg.mESC_derived_neural_cells mm9 RNA polymerase Pluripotent stem cell mESC derived neural...RX213764,SRX213760 http://dbarchive.biosciencedbc.jp/kyushu-u/mm9/assembled/Pol.PSC.05.AllAg.mESC_derived_neural_cells.bed ...

  2. File list: Pol.PSC.05.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  3. File list: DNS.PSC.20.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available DNS.PSC.20.AllAg.mESC_derived_neural_cells mm9 DNase-seq Pluripotent stem cell mESC derived neural... cells http://dbarchive.biosciencedbc.jp/kyushu-u/mm9/assembled/DNS.PSC.20.AllAg.mESC_derived_neural_cells.bed ...

  4. File list: Pol.PSC.10.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  5. File list: Oth.PSC.05.AllAg.iPS_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  6. File list: DNS.PSC.50.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  7. File list: His.PSC.20.AllAg.iPS_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available His.PSC.20.AllAg.iPS_derived_neural_cells hg19 Histone Pluripotent stem cell iPS derived neural...archive.biosciencedbc.jp/kyushu-u/hg19/assembled/His.PSC.20.AllAg.iPS_derived_neural_cells.bed ...

  8. File list: His.PSC.50.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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  9. File list: Oth.PSC.05.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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  10. File list: Oth.PSC.50.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

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  11. File list: Pol.PSC.10.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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  12. File list: DNS.PSC.10.AllAg.iPS_derived_neural_cells [Chip-atlas[Archive

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  13. File list: Pol.PSC.50.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

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  14. File list: Pol.PSC.50.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

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  15. File list: InP.PSC.50.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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  16. File list: NoD.PSC.20.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

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  17. File list: Oth.PSC.10.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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  18. File list: Pol.PSC.10.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

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  19. File list: His.PSC.50.AllAg.iPS_derived_neural_cells [Chip-atlas[Archive

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  20. File list: DNS.PSC.10.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

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  1. File list: InP.PSC.05.AllAg.hESC_derived_neural_cells [Chip-atlas[Archive

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    Full Text Available InP.PSC.05.AllAg.hESC_derived_neural_cells hg19 Input control Pluripotent stem cell hESC derived neural...RX698183,SRX729711,SRX729701 http://dbarchive.biosciencedbc.jp/kyushu-u/hg19/assembled/InP.PSC.05.AllAg.hESC_derived_neural_cells.bed ...

  2. File list: ALL.PSC.50.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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    Full Text Available ALL.PSC.50.AllAg.hESC_derived_neural_crests hg19 All antigens Pluripotent stem cell hESC derived neural...X1091539,SRX059364 http://dbarchive.biosciencedbc.jp/kyushu-u/hg19/assembled/ALL.PSC.50.AllAg.hESC_derived_neural_crests.bed ...

  3. File list: Oth.PSC.50.AllAg.hESC_derived_neural_crests [Chip-atlas[Archive

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  4. The Temporal Derivative of Expected Utility: A Neural Mechanism for Dynamic Decision-making

    Science.gov (United States)

    Zhang, Xian; Hirsch, Joy

    2012-01-01

    Real world tasks involving moving targets, such as driving a vehicle, are performed based on continuous decisions thought to depend upon the temporal derivative of the expected utility (∂V/∂t), where the expected utility (V) is the effective value of a future reward. However, those neural mechanisms that underlie dynamic decision-making are not well understood. This study investigates human neural correlates of both V and ∂V/∂t using fMRI and a novel experimental paradigm based on a pursuit-evasion game optimized to isolate components of dynamic decision processes. Our behavioral data show that players of the pursuit-evasion game adopt an exponential discounting function, supporting the expected utility theory. The continuous functions of V and ∂V/∂t were derived from the behavioral data and applied as regressors in fMRI analysis, enabling temporal resolution that exceeded the sampling rate of image acquisition, hyper-temporal resolution, by taking advantage of numerous trials that provide rich and independent manipulation of those variables. V and ∂V/∂t were each associated with distinct neural activity. Specifically, ∂V/∂t was associated with anterior and posterior cingulate cortices, superior parietal lobule, and ventral pallidum, whereas V was primarily associated with supplementary motor, pre and post central gyri, cerebellum, and thalamus. The association between the ∂V/∂t and brain regions previously related to decision-making is consistent with the primary role of the temporal derivative of expected utility in dynamic decision-making. PMID:22963852

  5. The temporal derivative of expected utility: a neural mechanism for dynamic decision-making.

    Science.gov (United States)

    Zhang, Xian; Hirsch, Joy

    2013-01-15

    Real world tasks involving moving targets, such as driving a vehicle, are performed based on continuous decisions thought to depend upon the temporal derivative of the expected utility (∂V/∂t), where the expected utility (V) is the effective value of a future reward. However, the neural mechanisms that underlie dynamic decision-making are not well understood. This study investigates human neural correlates of both V and ∂V/∂t using fMRI and a novel experimental paradigm based on a pursuit-evasion game optimized to isolate components of dynamic decision processes. Our behavioral data show that players of the pursuit-evasion game adopt an exponential discounting function, supporting the expected utility theory. The continuous functions of V and ∂V/∂t were derived from the behavioral data and applied as regressors in fMRI analysis, enabling temporal resolution that exceeded the sampling rate of image acquisition, hyper-temporal resolution, by taking advantage of numerous trials that provide rich and independent manipulation of those variables. V and ∂V/∂t were each associated with distinct neural activity. Specifically, ∂V/∂t was associated with anterior and posterior cingulate cortices, superior parietal lobule, and ventral pallidum, whereas V was primarily associated with supplementary motor, pre and post central gyri, cerebellum, and thalamus. The association between the ∂V/∂t and brain regions previously related to decision-making is consistent with the primary role of the temporal derivative of expected utility in dynamic decision-making. Copyright © 2012 Elsevier Inc. All rights reserved.

  6. UNMANNED AIR VEHICLE STABILIZATION BASED ON NEURAL NETWORK REGULATOR

    Directory of Open Access Journals (Sweden)

    S. S. Andropov

    2016-09-01

    Full Text Available A problem of stabilizing for the multirotor unmanned aerial vehicle in an environment with external disturbances is researched. A classic proportional-integral-derivative controller is analyzed, its flaws are outlined: inability to respond to changing of external conditions and the need for manual adjustment of coefficients. The paper presents an adaptive adjustment method for coefficients of the proportional-integral-derivative controller based on neural networks. A neural network structure, its input and output data are described. Neural networks with three layers are used to create an adaptive stabilization system for the multirotor unmanned aerial vehicle. Training of the networks is done with the back propagation method. Each neural network produces regulator coefficients for each angle of stabilization as its output. A method for network training is explained. Several graphs of transition process on different stages of learning, including processes with external disturbances, are presented. It is shown that the system meets stabilization requirements with sufficient number of iterations. Described adjustment method for coefficients can be used in remote control of unmanned aerial vehicles, operating in the changing environment.

  7. Neural network based multiscale image restoration approach

    Science.gov (United States)

    de Castro, Ana Paula A.; da Silva, José D. S.

    2007-02-01

    This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.

  8. Adipose stromal cells contain phenotypically distinct adipogenic progenitors derived from neural crest.

    Directory of Open Access Journals (Sweden)

    Yoshihiro Sowa

    Full Text Available Recent studies have shown that adipose-derived stromal/stem cells (ASCs contain phenotypically and functionally heterogeneous subpopulations of cells, but their developmental origin and their relative differentiation potential remain elusive. In the present study, we aimed at investigating how and to what extent the neural crest contributes to ASCs using Cre-loxP-mediated fate mapping. ASCs harvested from subcutaneous fat depots of either adult P0-Cre/or Wnt1-Cre/Floxed-reporter mice contained a few neural crest-derived ASCs (NCDASCs. This subpopulation of cells was successfully expanded in vitro under standard culture conditions and their growth rate was comparable to non-neural crest derivatives. Although NCDASCs were positive for several mesenchymal stem cell markers as non-neural crest derivatives, they exhibited a unique bipolar or multipolar morphology with higher expression of markers for both neural crest progenitors (p75NTR, Nestin, and Sox2 and preadipocytes (CD24, CD34, S100, Pref-1, GATA2, and C/EBP-delta. NCDASCs were able to differentiate into adipocytes with high efficiency but their osteogenic and chondrogenic potential was markedly attenuated, indicating their commitment to adipogenesis. In vivo, a very small proportion of adipocytes were originated from the neural crest. In addition, p75NTR-positive neural crest-derived cells were identified along the vessels within the subcutaneous adipose tissue, but they were negative for mural and endothelial markers. These results demonstrate that ASCs contain neural crest-derived adipocyte-restricted progenitors whose phenotype is distinct from that of non-neural crest derivatives.

  9. Neural differentiation potential of human bone marrow-derived mesenchymal stromal cells: misleading marker gene expression

    Directory of Open Access Journals (Sweden)

    Montzka Katrin

    2009-03-01

    Full Text Available Abstract Background In contrast to pluripotent embryonic stem cells, adult stem cells have been considered to be multipotent, being somewhat more restricted in their differentiation capacity and only giving rise to cell types related to their tissue of origin. Several studies, however, have reported that bone marrow-derived mesenchymal stromal cells (MSCs are capable of transdifferentiating to neural cell types, effectively crossing normal lineage restriction boundaries. Such reports have been based on the detection of neural-related proteins by the differentiated MSCs. In order to assess the potential of human adult MSCs to undergo true differentiation to a neural lineage and to determine the degree of homogeneity between donor samples, we have used RT-PCR and immunocytochemistry to investigate the basal expression of a range of neural related mRNAs and proteins in populations of non-differentiated MSCs obtained from 4 donors. Results The expression analysis revealed that several of the commonly used marker genes from other studies like nestin, Enolase2 and microtubule associated protein 1b (MAP1b are already expressed by undifferentiated human MSCs. Furthermore, mRNA for some of the neural-related transcription factors, e.g. Engrailed-1 and Nurr1 were also strongly expressed. However, several other neural-related mRNAs (e.g. DRD2, enolase2, NFL and MBP could be identified, but not in all donor samples. Similarly, synaptic vesicle-related mRNA, STX1A could only be detected in 2 of the 4 undifferentiated donor hMSC samples. More significantly, each donor sample revealed a unique expression pattern, demonstrating a significant variation of marker expression. Conclusion The present study highlights the existence of an inter-donor variability of expression of neural-related markers in human MSC samples that has not previously been described. This donor-related heterogeneity might influence the reproducibility of transdifferentiation protocols as

  10. GABA and Gap Junctions in the Development of Synchronized Activity in Human Pluripotent Stem Cell-Derived Neural Networks

    Directory of Open Access Journals (Sweden)

    Meeri Eeva-Liisa Mäkinen

    2018-03-01

    Full Text Available The electrical activity of the brain arises from single neurons communicating with each other. However, how single neurons interact during early development to give rise to neural network activity remains poorly understood. We studied the emergence of synchronous neural activity in human pluripotent stem cell (hPSC-derived neural networks simultaneously on a single-neuron level and network level. The contribution of gamma-aminobutyric acid (GABA and gap junctions to the development of synchronous activity in hPSC-derived neural networks was studied with GABA agonist and antagonist and by blocking gap junctional communication, respectively. We characterized the dynamics of the network-wide synchrony in hPSC-derived neural networks with high spatial resolution (calcium imaging and temporal resolution microelectrode array (MEA. We found that the emergence of synchrony correlates with a decrease in very strong GABA excitation. However, the synchronous network was found to consist of a heterogeneous mixture of synchronously active cells with variable responses to GABA, GABA agonists and gap junction blockers. Furthermore, we show how single-cell distributions give rise to the network effect of GABA, GABA agonists and gap junction blockers. Finally, based on our observations, we suggest that the earliest form of synchronous neuronal activity depends on gap junctions and a decrease in GABA induced depolarization but not on GABAA mediated signaling.

  11. GABA and Gap Junctions in the Development of Synchronized Activity in Human Pluripotent Stem Cell-Derived Neural Networks

    Science.gov (United States)

    Mäkinen, Meeri Eeva-Liisa; Ylä-Outinen, Laura; Narkilahti, Susanna

    2018-01-01

    The electrical activity of the brain arises from single neurons communicating with each other. However, how single neurons interact during early development to give rise to neural network activity remains poorly understood. We studied the emergence of synchronous neural activity in human pluripotent stem cell (hPSC)-derived neural networks simultaneously on a single-neuron level and network level. The contribution of gamma-aminobutyric acid (GABA) and gap junctions to the development of synchronous activity in hPSC-derived neural networks was studied with GABA agonist and antagonist and by blocking gap junctional communication, respectively. We characterized the dynamics of the network-wide synchrony in hPSC-derived neural networks with high spatial resolution (calcium imaging) and temporal resolution microelectrode array (MEA). We found that the emergence of synchrony correlates with a decrease in very strong GABA excitation. However, the synchronous network was found to consist of a heterogeneous mixture of synchronously active cells with variable responses to GABA, GABA agonists and gap junction blockers. Furthermore, we show how single-cell distributions give rise to the network effect of GABA, GABA agonists and gap junction blockers. Finally, based on our observations, we suggest that the earliest form of synchronous neuronal activity depends on gap junctions and a decrease in GABA induced depolarization but not on GABAA mediated signaling. PMID:29559893

  12. Niche-dependent development of functional neuronal networks from embryonic stem cell-derived neural populations

    Directory of Open Access Journals (Sweden)

    Siebler Mario

    2009-08-01

    Full Text Available Abstract Background The present work was performed to investigate the ability of two different embryonic stem (ES cell-derived neural precursor populations to generate functional neuronal networks in vitro. The first ES cell-derived neural precursor population was cultivated as free-floating neural aggregates which are known to form a developmental niche comprising different types of neural cells, including neural precursor cells (NPCs, progenitor cells and even further matured cells. This niche provides by itself a variety of different growth factors and extracellular matrix proteins that influence the proliferation and differentiation of neural precursor and progenitor cells. The second population was cultivated adherently in monolayer cultures to control most stringently the extracellular environment. This population comprises highly homogeneous NPCs which are supposed to represent an attractive way to provide well-defined neuronal progeny. However, the ability of these different ES cell-derived immature neural cell populations to generate functional neuronal networks has not been assessed so far. Results While both precursor populations were shown to differentiate into sufficient quantities of mature NeuN+ neurons that also express GABA or vesicular-glutamate-transporter-2 (vGlut2, only aggregate-derived neuronal populations exhibited a synchronously oscillating network activity 2–4 weeks after initiating the differentiation as detected by the microelectrode array technology. Neurons derived from homogeneous NPCs within monolayer cultures did merely show uncorrelated spiking activity even when differentiated for up to 12 weeks. We demonstrated that these neurons exhibited sparsely ramified neurites and an embryonic vGlut2 distribution suggesting an inhibited terminal neuronal maturation. In comparison, neurons derived from heterogeneous populations within neural aggregates appeared as fully mature with a dense neurite network and punctuated

  13. Neural assembly models derived through nano-scale measurements.

    Energy Technology Data Exchange (ETDEWEB)

    Fan, Hongyou; Branda, Catherine; Schiek, Richard Louis; Warrender, Christina E.; Forsythe, James Chris

    2009-09-01

    This report summarizes accomplishments of a three-year project focused on developing technical capabilities for measuring and modeling neuronal processes at the nanoscale. It was successfully demonstrated that nanoprobes could be engineered that were biocompatible, and could be biofunctionalized, that responded within the range of voltages typically associated with a neuronal action potential. Furthermore, the Xyce parallel circuit simulator was employed and models incorporated for simulating the ion channel and cable properties of neuronal membranes. The ultimate objective of the project had been to employ nanoprobes in vivo, with the nematode C elegans, and derive a simulation based on the resulting data. Techniques were developed allowing the nanoprobes to be injected into the nematode and the neuronal response recorded. To the authors's knowledge, this is the first occasion in which nanoparticles have been successfully employed as probes for recording neuronal response in an in vivo animal experimental protocol.

  14. Invariant moments based convolutional neural networks for image analysis

    Directory of Open Access Journals (Sweden)

    Vijayalakshmi G.V. Mahesh

    2017-01-01

    Full Text Available The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution kernels coefficients derived from the zernike moments by varying the moment order. The performance of the proposed method was compared with the convolutional neural network architecture that used random kernels as initial training parameters. The multilevel configuration of zernike moments was significant in extracting the shape information suitable for hierarchical feature learning to carry out image analysis and classification. Furthermore the results showed an outstanding performance of zernike moment based kernels in terms of the computation time and classification accuracy.

  15. Andrographolide Promotes Neural Differentiation of Rat Adipose Tissue-Derived Stromal Cells through Wnt/β-Catenin Signaling Pathway

    Directory of Open Access Journals (Sweden)

    Yan Liang

    2017-01-01

    Full Text Available Adipose tissue-derived stromal cells (ADSCs are a high-yield source of pluripotent stem cells for use in cell-based therapies. We explored the effect of andrographolide (ANDRO, one of the ingredients of the medicinal herb extract on the neural differentiation of rat ADSCs and associated molecular mechanisms. We observed that rat ADSCs were small and spindle-shaped and expressed multiple stem cell markers including nestin. They were multipotent as evidenced by adipogenic, osteogenic, chondrogenic, and neural differentiation under appropriate conditions. The proportion of cells exhibiting neural-like morphology was higher, and neurites developed faster in the ANDRO group than in the control group in the same neural differentiation medium. Expression levels of the neural lineage markers MAP2, tau, GFAP, and β-tubulin III were higher in the ANDRO group. ANDRO induced a concentration-dependent increase in Wnt/β-catenin signaling as evidenced by the enhanced expression of nuclear β-catenin and the inhibited form of GSK-3β (pSer9. Thus, this study shows for the first time how by enhancing the neural differentiation of ADSCs we expect that ANDRO pretreatment may increase the efficacy of adult stem cell transplantation in nervous system diseases, but more exploration is needed.

  16. Neural differentiation of adipose-derived stem cells isolated from GFP transgenic mice

    International Nuclear Information System (INIS)

    Fujimura, Juri; Ogawa, Rei; Mizuno, Hiroshi; Fukunaga, Yoshitaka; Suzuki, Hidenori

    2005-01-01

    Taking advantage of homogeneously marked cells from green fluorescent protein (GFP) transgenic mice, we have recently reported that adipose-derived stromal cells (ASCs) could differentiate into mesenchymal lineages in vitro. In this study, we performed neural induction using ASCs from GFP transgenic mice and were able to induce these ASCs into neuronal and glial cell lineages. Most of the neurally induced cells showed bipolar or multipolar appearance morphologically and expressed neuronal markers. Electron microscopy revealed their neuronal morphology. Some cells also showed glial phenotypes, as shown immunocytochemically. The present study clearly shows that ASCs derived from GFP transgenic mice differentiate into neural lineages in vitro, suggesting that these cells might provide an ideal source for further neural stem cell research with possible therapeutic application for neurological disorders

  17. Prediction based chaos control via a new neural network

    International Nuclear Information System (INIS)

    Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

    2008-01-01

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

  18. File list: InP.PSC.20.AllAg.mESC_derived_neural_cells [Chip-atlas[Archive

    Lifescience Database Archive (English)

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  5. Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter.

    Science.gov (United States)

    Rigatos, Gerasimos

    2014-12-01

    A synchronizing control scheme for coupled neural oscillators of the FitzHugh-Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane's voltage variations for the two neurons. To compensate for disturbances that affect the neurons' model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons' model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments.

  6. Analysis of neural networks through base functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.

    Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

  7. Human induced pluripotent stem cell-derived models to investigate human cytomegalovirus infection in neural cells.

    Directory of Open Access Journals (Sweden)

    Leonardo D'Aiuto

    Full Text Available Human cytomegalovirus (HCMV infection is one of the leading prenatal causes of congenital mental retardation and deformities world-wide. Access to cultured human neuronal lineages, necessary to understand the species specific pathogenic effects of HCMV, has been limited by difficulties in sustaining primary human neuronal cultures. Human induced pluripotent stem (iPS cells now provide an opportunity for such research. We derived iPS cells from human adult fibroblasts and induced neural lineages to investigate their susceptibility to infection with HCMV strain Ad169. Analysis of iPS cells, iPS-derived neural stem cells (NSCs, neural progenitor cells (NPCs and neurons suggests that (i iPS cells are not permissive to HCMV infection, i.e., they do not permit a full viral replication cycle; (ii Neural stem cells have impaired differentiation when infected by HCMV; (iii NPCs are fully permissive for HCMV infection; altered expression of genes related to neural metabolism or neuronal differentiation is also observed; (iv most iPS-derived neurons are not permissive to HCMV infection; and (v infected neurons have impaired calcium influx in response to glutamate.

  8. Hepatocyte growth factor/scatter factor-MET signaling in neural crest-derived melanocyte development.

    Science.gov (United States)

    Kos, L; Aronzon, A; Takayama, H; Maina, F; Ponzetto, C; Merlino, G; Pavan, W

    1999-02-01

    The mechanisms governing development of neural crest-derived melanocytes, and how alterations in these pathways lead to hypopigmentation disorders, are not completely understood. Hepatocyte growth factor/scatter factor (HGF/SF) signaling through the tyrosine-kinase receptor, MET, is capable of promoting the proliferation, increasing the motility, and maintaining high tyrosinase activity and melanin synthesis of melanocytes in vitro. In addition, transgenic mice that ubiquitously overexpress HGF/SF demonstrate hyperpigmentation in the skin and leptomenigenes and develop melanomas. To investigate whether HGF/ SF-MET signaling is involved in the development of neural crest-derived melanocytes, transgenic embryos, ubiquitously overexpressing HGF/SF, were analyzed. In HGF/SF transgenic embryos, the distribution of melanoblasts along the characteristic migratory pathway was not affected. However, additional ectopically localized melanoblasts were also observed in the dorsal root ganglia and neural tube, as early as 11.5 days post coitus (p.c.). We utilized an in vitro neural crest culture assay to further explore the role of HGF/SF-MET signaling in neural crest development. HGF/SF added to neural crest cultures increased melanoblast number, permitted differentiation into pigmented melanocytes, promoted melanoblast survival, and could replace mast-cell growth factor/Steel factor (MGF) in explant cultures. To examine whether HGF/SF-MET signaling is required for the proper development of melanocytes, embryos with a targeted Met null mutation (Met-/-) were analysed. In Met-/- embryos, melanoblast number and location were not overtly affected up to 14 days p.c. These results demonstrate that HGF/SF-MET signaling influences, but is not required for, the initial development of neural crest-derived melanocytes in vivo and in vitro.

  9. Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

    African Journals Online (AJOL)

    A two-layer feed-forward neural network with Hyperbolic tangent sigmoid ... The neural network model when subjected to test, using the validation input data; ... Proportional Integral Derivative (PID) Controller is used to control the neural ...

  10. Robust synchronization of delayed neural networks based on adaptive control and parameters identification

    International Nuclear Information System (INIS)

    Zhou Jin; Chen Tianping; Xiang Lan

    2006-01-01

    This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique

  11. Financial Derivatives (Based on Two Supports Evaluation

    Directory of Open Access Journals (Sweden)

    Tiberiu Socaciu

    2016-07-01

    Full Text Available In this paper we build a PDE like Black-Scholes equation in hypothesis of a financial derivative that is dependent on two supports (usual is dependent only on one support, like amoption based on gold, when national currency has a great float.Keywords: Financial derivatives, derivatives evaluation, derivatives based on two supports, extended Itō like lemma.

  12. Enhanced growth of neural networks on conductive cellulose-derived nanofibrous scaffolds

    International Nuclear Information System (INIS)

    Kuzmenko, Volodymyr; Kalogeropoulos, Theodoros; Thunberg, Johannes; Johannesson, Sara; Hägg, Daniel; Enoksson, Peter; Gatenholm, Paul

    2016-01-01

    The problem of recovery from neurodegeneration needs new effective solutions. Tissue engineering is viewed as a prospective approach for solving this problem since it can help to develop healthy neural tissue using supportive scaffolds. This study presents effective and sustainable tissue engineering methods for creating biomaterials from cellulose that can be used either as scaffolds for the growth of neural tissue in vitro or as drug screening models. To reach this goal, nanofibrous electrospun cellulose mats were made conductive via two different procedures: carbonization and addition of multi-walled carbon nanotubes. The resulting scaffolds were much more conductive than untreated cellulose material and were used to support growth and differentiation of SH-SY5Y neuroblastoma cells. The cells were evaluated by scanning electron microscopy and confocal microscopy methods over a period of 15 days at different time points. The results showed that the cellulose-derived conductive scaffolds can provide support for good cell attachment, growth and differentiation. The formation of a neural network occurred within 10 days of differentiation, which is a promising length of time for SH-SY5Y neuroblastoma cells. - Highlights: • The conductive scaffolds for neural tissue engineering are derived from cellulose. • The scaffolds are used to support growth and differentiation of SH-SY5Y cells. • Distinctive cell differentiation occurs within 10 days on conductive scaffolds. • Electrical conductivity and nanotopography improve neural network formation.

  13. Prolonged Expansion Induces Spontaneous Neural Progenitor Differentiation from Human Gingiva-Derived Mesenchymal Stem Cells.

    Science.gov (United States)

    Rajan, Thangavelu Soundara; Scionti, Domenico; Diomede, Francesca; Piattelli, Adriano; Bramanti, Placido; Mazzon, Emanuela; Trubiani, Oriana

    2017-12-01

    Neural crest-derived mesenchymal stem cells (MSCs) obtained from dental tissues received considerable interest in regenerative medicine, particularly in nerve regeneration owing to their embryonic origin and ease of harvest. Proliferation efficacy and differentiation capacity into diverse cell lineages propose dental MSCs as an in vitro tool for disease modeling. In this study, we investigated the spontaneous differentiation efficiency of dental MSCs obtained from human gingiva tissue (hGMSCs) into neural progenitor cells after extended passaging. At passage 41, the morphology of hGMSCs changed from typical fibroblast-like shape into sphere-shaped cells with extending processes. Next-generation transcriptomics sequencing showed increased expression of neural progenitor markers such as NES, MEIS2, and MEST. In addition, de novo expression of neural precursor genes, such as NRN1, PHOX2B, VANGL2, and NTRK3, was noticed in passage 41. Immunocytochemistry results showed suppression of neurogenesis repressors TP53 and p21, whereas Western blot results revealed the expression of neurotrophic factors BDNF and NT3 at passage 41. Our results showed the spontaneous efficacy of hGMSCs to differentiate into neural precursor cells over prolonged passages and that these cells may assist in producing novel in vitro disease models that are associated with neural development.

  14. Differentiation defect in neural crest-derived smooth muscle cells in patients with aortopathy associated with bicuspid aortic valves

    Directory of Open Access Journals (Sweden)

    Jiao Jiao

    2016-08-01

    Full Text Available Individuals with bicuspid aortic valves (BAV are at a higher risk of developing thoracic aortic aneurysms (TAA than patients with trileaflet aortic valves (TAV. The aneurysms associated with BAV most commonly involve the ascending aorta and spare the descending aorta. Smooth muscle cells (SMCs in the ascending and descending aorta arise from neural crest (NC and paraxial mesoderm (PM, respectively. We hypothesized defective differentiation of the neural crest stem cells (NCSCs-derived SMCs but not paraxial mesoderm cells (PMCs-derived SMCs contributes to the aortopathy associated with BAV. When induced pluripotent stem cells (iPSCs from BAV/TAA patients were differentiated into NCSC-derived SMCs, these cells demonstrated significantly decreased expression of marker of SMC differentiation (MYH11 and impaired contraction compared to normal control. In contrast, the PMC-derived SMCs were similar to control cells in these aspects. The NCSC-SMCs from the BAV/TAA also showed decreased TGF-β signaling based on phosphorylation of SMAD2, and increased mTOR signaling. Inhibition of mTOR pathway using rapamycin rescued the aberrant differentiation. Our data demonstrates that decreased differentiation and contraction of patient's NCSC-derived SMCs may contribute to that aortopathy associated with BAV.

  15. Comprehensive heat transfer correlation for water/ethylene glycol-based graphene (nitrogen-doped graphene) nanofluids derived by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

    Science.gov (United States)

    Savari, Maryam; Moghaddam, Amin Hedayati; Amiri, Ahmad; Shanbedi, Mehdi; Ayub, Mohamad Nizam Bin

    2017-10-01

    Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer and fluid flow characteristics of a car radiator and followed by comparing with those of the experimental results for testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene and nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled with respect to the variation of inlet temperature, Reynolds number, Prandtl number and concentration, which were defined as the input (design) variables. To reach reliable results, we divided these data into train and test sections to accomplish modeling. Artificial networks were instructed by a major part of experimental data. The other part of primary data which had been considered for testing the appropriateness of the models was entered into artificial network models. Finally, predictad results were compared to the experimental data to evaluate validity. Confronted with high-level of validity confirmed that the proposed modeling procedure by BPNN with one hidden layer and five neurons is efficient and it can be expanded for all water/ethylene glycol-based carbon nanostructures nanofluids. Finally, we expanded our data collection from model and could present a fundamental correlation for calculating Nusselt number of the water/ethylene glycol-based nanofluids including graphene or nitrogen-doped graphene.

  16. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    Science.gov (United States)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  17. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  18. Neural Network Classifier Based on Growing Hyperspheres

    Czech Academy of Sciences Publication Activity Database

    Jiřina Jr., Marcel; Jiřina, Marcel

    2000-01-01

    Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics

  19. Enhanced Neural Cell Adhesion and Neurite Outgrowth on Graphene-Based Biomimetic Substrates

    Directory of Open Access Journals (Sweden)

    Suck Won Hong

    2014-01-01

    Full Text Available Neural cell adhesion and neurite outgrowth were examined on graphene-based biomimetic substrates. The biocompatibility of carbon nanomaterials such as graphene and carbon nanotubes (CNTs, that is, single-walled and multiwalled CNTs, against pheochromocytoma-derived PC-12 neural cells was also evaluated by quantifying metabolic activity (with WST-8 assay, intracellular oxidative stress (with ROS assay, and membrane integrity (with LDH assay. Graphene films were grown by using chemical vapor deposition and were then coated onto glass coverslips by using the scooping method. Graphene sheets were patterned on SiO2/Si substrates by using photolithography and were then covered with serum for a neural cell culture. Both types of CNTs induced significant dose-dependent decreases in the viability of PC-12 cells, whereas graphene exerted adverse effects on the neural cells just at over 62.5 ppm. This result implies that graphene and CNTs, even though they were the same carbon-based nanomaterials, show differential influences on neural cells. Furthermore, graphene-coated or graphene-patterned substrates were shown to substantially enhance the adhesion and neurite outgrowth of PC-12 cells. These results suggest that graphene-based substrates as biomimetic cues have good biocompatibility as well as a unique surface property that can enhance the neural cells, which would open up enormous opportunities in neural regeneration and nanomedicine.

  20. Nonlinear control strategy based on using a shape-tunable neural controller

    Energy Technology Data Exchange (ETDEWEB)

    Chen, C.; Peng, S. [Feng Chia Univ, Taichung (Taiwan, Province of China). Department of chemical Engineering; Chang, W. [Feng Chia Univ, Taichung (Taiwan, Province of China). Department of Automatic Control

    1997-08-01

    In this paper, a nonlinear control strategy based on using a shape-tunable neural network is developed for adaptive control of nonlinear processes. Based on the steepest descent method, a learning algorithm that enables the neural controller to possess the ability of automatic controller output range adjustment is derived. The novel feature of automatic output range adjustment provides the neural controller more flexibility and capability, and therefore the scaling procedure, which is usually unavoidable for the conventional fixed-shape neural controllers, becomes unnecessary. The advantages and effectiveness of the proposed nonlinear control strategy are demonstrated through the challenge problem of controlling an open-loop unstable nonlinear continuous stirred tank reactor (CSTR). 14 refs., 11 figs.

  1. Optical-Correlator Neural Network Based On Neocognitron

    Science.gov (United States)

    Chao, Tien-Hsin; Stoner, William W.

    1994-01-01

    Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

  2. High content screening of defined chemical libraries using normal and glioma-derived neural stem cell lines.

    Science.gov (United States)

    Danovi, Davide; Folarin, Amos A; Baranowski, Bart; Pollard, Steven M

    2012-01-01

    Small molecules with potent biological effects on the fate of normal and cancer-derived stem cells represent both useful research tools and new drug leads for regenerative medicine and oncology. Long-term expansion of mouse and human neural stem cells is possible using adherent monolayer culture. These cultures represent a useful cellular resource to carry out image-based high content screening of small chemical libraries. Improvements in automated microscopy, desktop computational power, and freely available image processing tools, now means that such chemical screens are realistic to undertake in individual academic laboratories. Here we outline a cost effective and versatile time lapse imaging strategy suitable for chemical screening. Protocols are described for the handling and screening of human fetal Neural Stem (NS) cell lines and their malignant counterparts, Glioblastoma-derived neural stem cells (GNS). We focus on identification of cytostatic and cytotoxic "hits" and discuss future possibilities and challenges for extending this approach to assay lineage commitment and differentiation. Copyright © 2012 Elsevier Inc. All rights reserved.

  3. Neural network-based QSAR and insecticide discovery: spinetoram

    Science.gov (United States)

    Sparks, Thomas C.; Crouse, Gary D.; Dripps, James E.; Anzeveno, Peter; Martynow, Jacek; DeAmicis, Carl V.; Gifford, James

    2008-06-01

    Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry.

  4. Topological derivatives of eigenvalues and neural networks in identification of imperfections

    International Nuclear Information System (INIS)

    Grzanek, M; Nowakowski, A; Sokolowski, J

    2008-01-01

    Numerical method for identification of imperfections is devised for elliptic spectral problems. The neural networks are employed for numerical solution. The topological derivatives of eigenvalues are used in the learning procedure of the neural networks. The topological derivatives of eigenvalues are determined by the methods of asymptotic analysis in singularly perturbed geometrical domains. The convergence of the numerical method in a probabilistic setting is analysed. The method is presented for the identification of small singular perturbations of the boundary of geometrical domain, however the framework is general and can be used for numerical solutions of inverse problems in the presence of small imperfections in the interior of the domain. Some numerical results are given for elliptic spectral problem in two spatial dimensions.

  5. Purification of human induced pluripotent stem cell-derived neural precursors using magnetic activated cell sorting.

    Science.gov (United States)

    Rodrigues, Gonçalo M C; Fernandes, Tiago G; Rodrigues, Carlos A V; Cabral, Joaquim M S; Diogo, Maria Margarida

    2015-01-01

    Neural precursor (NP) cells derived from human induced pluripotent stem cells (hiPSCs), and their neuronal progeny, will play an important role in disease modeling, drug screening tests, central nervous system development studies, and may even become valuable for regenerative medicine treatments. Nonetheless, it is challenging to obtain homogeneous and synchronously differentiated NP populations from hiPSCs, and after neural commitment many pluripotent stem cells remain in the differentiated cultures. Here, we describe an efficient and simple protocol to differentiate hiPSC-derived NPs in 12 days, and we include a final purification stage where Tra-1-60+ pluripotent stem cells (PSCs) are removed using magnetic activated cell sorting (MACS), leaving the NP population nearly free of PSCs.

  6. Effectiveness of firefly algorithm based neural network in time series ...

    African Journals Online (AJOL)

    Effectiveness of firefly algorithm based neural network in time series forecasting. ... In the experiments, three well known time series were used to evaluate the performance. Results obtained were compared with ... Keywords: Time series, Artificial Neural Network, Firefly Algorithm, Particle Swarm Optimization, Overfitting ...

  7. Induced Pluripotent Stem Cell-Derived Neural Cells Survive and Mature in the Nonhuman Primate Brain

    Directory of Open Access Journals (Sweden)

    Marina E. Emborg

    2013-03-01

    Full Text Available The generation of induced pluripotent stem cells (iPSCs opens up the possibility for personalized cell therapy. Here, we show that transplanted autologous rhesus monkey iPSC-derived neural progenitors survive for up to 6 months and differentiate into neurons, astrocytes, and myelinating oligodendrocytes in the brains of MPTP-induced hemiparkinsonian rhesus monkeys with a minimal presence of inflammatory cells and reactive glia. This finding represents a significant step toward personalized regenerative therapies.

  8. Efficient and Rapid Derivation of Primitive Neural Stem Cells and Generation of Brain Subtype Neurons From Human Pluripotent Stem Cells

    OpenAIRE

    Yan, Yiping; Shin, Soojung; Jha, Balendu Shekhar; Liu, Qiuyue; Sheng, Jianting; Li, Fuhai; Zhan, Ming; Davis, Janine; Bharti, Kapil; Zeng, Xianmin; Rao, Mahendra; Malik, Nasir; Vemuri, Mohan C.

    2013-01-01

    This study developed a highly efficient serum-free pluripotent stem cell (PSC) neural induction medium that can induce human PSCs into primitive neural stem cells (NSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. This method of primitive NSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

  9. Machine learning of radial basis function neural network based on Kalman filter: Introduction

    Directory of Open Access Journals (Sweden)

    Vuković Najdan L.

    2014-01-01

    Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.

  10. Neural-net based real-time economic dispatch for thermal power plants

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.; Milosevic, B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1996-12-01

    This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

  11. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  12. Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model.

    Directory of Open Access Journals (Sweden)

    Jan Tønnesen

    Full Text Available Intrastriatal grafts of stem cell-derived dopamine (DA neurons induce behavioral recovery in animal models of Parkinson's disease (PD, but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral mesencephalon of tyrosine hydroxylase-GFP transgenic mice were expanded as neurospheres and transplanted into organotypic cultures of wild type mouse striatum. Differentiated GFP-labeled DA neurons in the grafts exhibited mature neuronal properties, including spontaneous firing of action potentials, presence of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation or inhibition of grafted cells and host neurons using channelrhodopsin-2 (ChR2 and halorhodopsin (NpHR, respectively, revealed complex, bi-directional synaptic interactions between grafted cells and host neurons and extensive synaptic connectivity within the graft. Our data demonstrate for the first time using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying behavioral recovery as well as adverse effects following stem cell-based DA cell replacement strategies in PD.

  13. Comparative performance analysis of human iPSC-derived and primary neural progenitor cells (NPC grown as neurospheres in vitro

    Directory of Open Access Journals (Sweden)

    Maxi Hofrichter

    2017-12-01

    hiPSC-NPCs-derived neurospheres seem to be useful for DNT evaluation representing early neural development in vitro. More system characterization by compound testing is needed to gain higher confidence in this method.

  14. Face recognition based on improved BP neural network

    Directory of Open Access Journals (Sweden)

    Yue Gaili

    2017-01-01

    Full Text Available In order to improve the recognition rate of face recognition, face recognition algorithm based on histogram equalization, PCA and BP neural network is proposed. First, the face image is preprocessed by histogram equalization. Then, the classical PCA algorithm is used to extract the features of the histogram equalization image, and extract the principal component of the image. And then train the BP neural network using the trained training samples. This improved BP neural network weight adjustment method is used to train the network because the conventional BP algorithm has the disadvantages of slow convergence, easy to fall into local minima and training process. Finally, the BP neural network with the test sample input is trained to classify and identify the face images, and the recognition rate is obtained. Through the use of ORL database face image simulation experiment, the analysis results show that the improved BP neural network face recognition method can effectively improve the recognition rate of face recognition.

  15. neural network based load frequency control for restructuring power

    African Journals Online (AJOL)

    2012-03-01

    Mar 1, 2012 ... the system in the back propagation chain used in controller training. For this application, .... The partial derivative of E with respect to ele- ments of Γ, for example W, ... Ki = any non-negative value. Figure 7: Neural Network ...

  16. Target recognition based on convolutional neural network

    Science.gov (United States)

    Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

    2017-11-01

    One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

  17. Design of a universal two-layered neural network derived from the PLI theory

    Science.gov (United States)

    Hu, Chia-Lun J.

    2004-05-01

    The if-and-only-if (IFF) condition that a set of M analog-to-digital vector-mapping relations can be learned by a one-layered-feed-forward neural network (OLNN) is that all the input analog vectors dichotomized by the i-th output bit must be positively, linearly independent, or PLI. If they are not PLI, then the OLNN just cannot learn no matter what learning rules is employed because the solution of the connection matrix does not exist mathematically. However, in this case, one can still design a parallel-cascaded, two-layered, perceptron (PCTLP) to acheive this general mapping goal. The design principle of this "universal" neural network is derived from the major mathematical properties of the PLI theory - changing the output bits of the dependent relations existing among the dichotomized input vectors to make the PLD relations PLI. Then with a vector concatenation technique, the required mapping can still be learned by this PCTLP system with very high efficiency. This paper will report in detail the mathematical derivation of the general design principle and the design procedures of the PCTLP neural network system. It then will be verified in general by a practical numerical example.

  18. DWI-Based Neural Fingerprinting Technology: A Preliminary Study on Stroke Analysis

    Directory of Open Access Journals (Sweden)

    Chenfei Ye

    2014-01-01

    Full Text Available Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI and diffusion tensor imaging (DTI. Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

  19. DWI-based neural fingerprinting technology: a preliminary study on stroke analysis.

    Science.gov (United States)

    Ye, Chenfei; Ma, Heather Ting; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo

    2014-01-01

    Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

  20. Neural bases of congenital amusia in tonal language speakers.

    Science.gov (United States)

    Zhang, Caicai; Peng, Gang; Shao, Jing; Wang, William S-Y

    2017-03-01

    Congenital amusia is a lifelong neurodevelopmental disorder of fine-grained pitch processing. In this fMRI study, we examined the neural bases of congenial amusia in speakers of a tonal language - Cantonese. Previous studies on non-tonal language speakers suggest that the neural deficits of congenital amusia lie in the music-selective neural circuitry in the right inferior frontal gyrus (IFG). However, it is unclear whether this finding can generalize to congenital amusics in tonal languages. Tonal language experience has been reported to shape the neural processing of pitch, which raises the question of how tonal language experience affects the neural bases of congenital amusia. To investigate this question, we examined the neural circuitries sub-serving the processing of relative pitch interval in pitch-matched Cantonese level tone and musical stimuli in 11 Cantonese-speaking amusics and 11 musically intact controls. Cantonese-speaking amusics exhibited abnormal brain activities in a widely distributed neural network during the processing of lexical tone and musical stimuli. Whereas the controls exhibited significant activation in the right superior temporal gyrus (STG) in the lexical tone condition and in the cerebellum regardless of the lexical tone and music conditions, no activation was found in the amusics in those regions, which likely reflects a dysfunctional neural mechanism of relative pitch processing in the amusics. Furthermore, the amusics showed abnormally strong activation of the right middle frontal gyrus and precuneus when the pitch stimuli were repeated, which presumably reflect deficits of attending to repeated pitch stimuli or encoding them into working memory. No significant group difference was found in the right IFG in either the whole-brain analysis or region-of-interest analysis. These findings imply that the neural deficits in tonal language speakers might differ from those in non-tonal language speakers, and overlap partly with the

  1. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Science.gov (United States)

    Ziv, Omer; Zaritsky, Assaf; Yaffe, Yakey; Mutukula, Naresh; Edri, Reuven; Elkabetz, Yechiel

    2015-10-01

    Neural stem cells (NSCs) are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture) and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM)--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII) reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  2. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Directory of Open Access Journals (Sweden)

    Omer Ziv

    2015-10-01

    Full Text Available Neural stem cells (NSCs are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  3. Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.

    Science.gov (United States)

    Zhang, Wei; Huang, Tingwen; He, Xing; Li, Chuandong

    2017-11-01

    In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Neural differentiation of adipose-derived stem cells by indirect co-culture with Schwann cells

    Directory of Open Access Journals (Sweden)

    Li Xiaojie

    2009-01-01

    Full Text Available To investigate whether adipose-derived stem cells (ADSCs could be subject to neural differentiation induced only by Schwann cell (SC factors, we co-cultured ADSCs and SCs in transwell culture dishes. Immunoassaying, Western blot analysis, and RT-PCR were performed (1, 3, 7, 14 d and the co-cultured ADSCs showed gene and protein expression of S-100, Nestin, and GFAP. Further, qRT-PCR disclosed relative quantitative differences in the above three gene expressions. We think ADSCs can undergo induced neural differentiation by being co-cultured with SCs, and such differentia­tions begin 1 day after co-culture, become apparent after 7 days, and thereafter remain stable till the 14th day.

  5. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  6. Research on Fault Diagnosis Method Based on Rule Base Neural Network

    Directory of Open Access Journals (Sweden)

    Zheng Ni

    2017-01-01

    Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

  7. H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

    Science.gov (United States)

    Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

    2018-03-01

    This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  8. Pinning synchronization of memristor-based neural networks with time-varying delays.

    Science.gov (United States)

    Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng

    2017-09-01

    In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Human embryonic stem cell-derived neurons adopt and regulate the activity of an established neural network

    Science.gov (United States)

    Weick, Jason P.; Liu, Yan; Zhang, Su-Chun

    2011-01-01

    Whether hESC-derived neurons can fully integrate with and functionally regulate an existing neural network remains unknown. Here, we demonstrate that hESC-derived neurons receive unitary postsynaptic currents both in vitro and in vivo and adopt the rhythmic firing behavior of mouse cortical networks via synaptic integration. Optical stimulation of hESC-derived neurons expressing Channelrhodopsin-2 elicited both inhibitory and excitatory postsynaptic currents and triggered network bursting in mouse neurons. Furthermore, light stimulation of hESC-derived neurons transplanted to the hippocampus of adult mice triggered postsynaptic currents in host pyramidal neurons in acute slice preparations. Thus, hESC-derived neurons can participate in and modulate neural network activity through functional synaptic integration, suggesting they are capable of contributing to neural network information processing both in vitro and in vivo. PMID:22106298

  10. A neutron spectrum unfolding computer code based on artificial neural networks

    International Nuclear Information System (INIS)

    Ortiz-Rodríguez, J.M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J.M.; Vega-Carrillo, H.R.

    2014-01-01

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding

  11. The neural bases for valuing social equality.

    Science.gov (United States)

    Aoki, Ryuta; Yomogida, Yukihito; Matsumoto, Kenji

    2015-01-01

    The neural basis of how humans value and pursue social equality has become a major topic in social neuroscience research. Although recent studies have identified a set of brain regions and possible mechanisms that are involved in the neural processing of equality of outcome between individuals, how the human brain processes equality of opportunity remains unknown. In this review article, first we describe the importance of the distinction between equality of outcome and equality of opportunity, which has been emphasized in philosophy and economics. Next, we discuss possible approaches for empirical characterization of human valuation of equality of opportunity vs. equality of outcome. Understanding how these two concepts are distinct and interact with each other may provide a better explanation of complex human behaviors concerning fairness and social equality. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  12. pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays.

    Science.gov (United States)

    Wang, Fen; Chen, Yuanlong; Liu, Meichun

    2018-02-01

    Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Induced pluripotent stem cell-derived neural cells survive and mature in the nonhuman primate brain.

    Science.gov (United States)

    Emborg, Marina E; Liu, Yan; Xi, Jiajie; Zhang, Xiaoqing; Yin, Yingnan; Lu, Jianfeng; Joers, Valerie; Swanson, Christine; Holden, James E; Zhang, Su-Chun

    2013-03-28

    The generation of induced pluripotent stem cells (iPSCs) opens up the possibility for personalized cell therapy. Here, we show that transplanted autologous rhesus monkey iPSC-derived neural progenitors survive for up to 6 months and differentiate into neurons, astrocytes, and myelinating oligodendrocytes in the brains of MPTP-induced hemiparkinsonian rhesus monkeys with a minimal presence of inflammatory cells and reactive glia. This finding represents a significant step toward personalized regenerative therapies. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Larger bases and mixed analog/digital neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    The paper overviews results dealing with the approximation capabilities of neural networks, and bounds on the size of threshold gate circuits. Based on an explicit numerical algorithm for Kolmogorov`s superpositions the authors show that minimum size neural networks--for implementing any Boolean function--have the identity function as the activation function. Conclusions and several comments on the required precision are ending the paper.

  15. Adaptive PID control based on orthogonal endocrine neural networks.

    Science.gov (United States)

    Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D

    2016-12-01

    A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. [Investigation of neural stem cell-derived donor contribution in the inner ear following blastocyst injection].

    Science.gov (United States)

    Volkenstein, S; Brors, D; Hansen, S; Mlynski, R; Dinger, T C; Müller, A M; Dazert, S

    2008-03-01

    Utilising the enormous proliferation and multi-lineage differentiation potentials of somatic stem cells represents a possible therapeutical strategy for diseases of non-regenerative tissues like the inner ear. In the current study, the possibility of murine neural stem cells to contribute to the developing inner ear following blastocyst injection was investigated. Fetal brain-derived neural stem cells from the embryonic day 14 cortex of male mice were isolated and expanded for four weeks in neurobasal media supplemented with bFGF and EGF. Neural stem cells of male animals were harvested, injected into blastocysts and the blastocysts were transferred into pseudo-pregnant foster animals. Each blastocyst was injected with 5-15 microspheres growing from single cell suspension from neurospheres dissociated the day before. The resulting mice were investigated six months POST PARTUM for the presence of donor cells. Brainstem evoked response audiometry (BERA) was performed in six animals. To visualize donor cells Lac-Z staining was performed on sliced cochleas of two animals. In addition, the cochleas of four female animals were isolated and genomic DNA of the entire cochlea was analyzed for donor contribution by Y-chromosome-specific PCR. All animals had normal thresholds in brainstem evoked response audiometry. The male-specific PCR product indicating the presence of male donor cells were detected in the cochleas of three of the four female animals investigated. In two animals, male donor cells were detected unilateral, in one animal bilateral. The results suggest that descendants of neural stem cells are detectable in the inner ear after injection into blastocysts and possess the ability to integrate into the developing inner ear without obvious loss in hearing function.

  17. Resting State EEG-based biometrics for individual identification using convolutional neural networks.

    Science.gov (United States)

    Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

    2015-08-01

    Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

  18. Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

    Science.gov (United States)

    Wang, Leimin; Shen, Yi; Zhang, Guodong

    Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

  19. Numeral eddy current sensor modelling based on genetic neural network

    International Nuclear Information System (INIS)

    Yu Along

    2008-01-01

    This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

  20. Wind power prediction based on genetic neural network

    Science.gov (United States)

    Zhang, Suhan

    2017-04-01

    The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

  1. Learning in neural networks based on a generalized fluctuation theorem

    Science.gov (United States)

    Hayakawa, Takashi; Aoyagi, Toshio

    2015-11-01

    Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

  2. multi scale analysis of a function by neural networks elementary derivatives functions

    International Nuclear Information System (INIS)

    Chikhi, A.; Gougam, A.; Chafa, F.

    2006-01-01

    Recently, the wavelet network has been introduced as a special neural network supported by the wavelet theory . Such networks constitute a tool for function approximation problems as it has been already proved in reference . Our present work deals with this model, treating a multi scale analysis of a function. We have then used a linear expansion of a given function in wavelets, neglecting the usual translation parameters. We investigate two training operations. The first one consists on an optimization of the output synaptic layer, the second one, optimizing the output function with respect to scale parameters. We notice a temporary merging of the scale parameters leading to some interesting results : new elementary derivatives units emerge, representing a new elementary task, which is the derivative of the output task

  3. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  4. A chemically defined substrate for the expansion and neuronal differentiation of human pluripotent stem cell-derived neural progenitor cells

    Directory of Open Access Journals (Sweden)

    Yihuan Tsai

    2015-07-01

    Full Text Available Due to the limitation of current pharmacological therapeutic strategies, stem cell therapies have emerged as a viable option for treating many incurable neurological disorders. Specifically, human pluripotent stem cell (hPSC-derived neural progenitor cells (hNPCs, a multipotent cell population that is capable of near indefinite expansion and subsequent differentiation into the various cell types that comprise the central nervous system (CNS, could provide an unlimited source of cells for such cell-based therapies. However the clinical application of these cells will require (i defined, xeno-free conditions for their expansion and neuronal differentiation and (ii scalable culture systems that enable their expansion and neuronal differentiation in numbers sufficient for regenerative medicine and drug screening purposes. Current extracellular matrix protein (ECMP-based substrates for the culture of hNPCs are expensive, difficult to isolate, subject to batch-to-batch variations, and, therefore, unsuitable for clinical application of hNPCs. Using a high-throughput array-based screening approach, we identified a synthetic polymer, poly(4-vinyl phenol (P4VP, that supported the long-term proliferation and self-renewal of hNPCs. The hNPCs cultured on P4VP maintained their characteristic morphology, expressed high levels of markers of multipotency, and retained their ability to differentiate into neurons. Such chemically defined substrates will eliminate critical roadblocks for the utilization of hNPCs for human neural regenerative repair, disease modeling, and drug discovery.

  5. Human neural progenitors derived from integration-free iPSCs for SCI therapy

    Directory of Open Access Journals (Sweden)

    Ying Liu

    2017-03-01

    Full Text Available As a potentially unlimited autologous cell source, patient induced pluripotent stem cells (iPSCs provide great capability for tissue regeneration, particularly in spinal cord injury (SCI. However, despite significant progress made in translation of iPSC-derived neural progenitor cells (NPCs to clinical settings, a few hurdles remain. Among them, non-invasive approach to obtain source cells in a timely manner, safer integration-free delivery of reprogramming factors, and purification of NPCs before transplantation are top priorities to overcome. In this study, we developed a safe and cost-effective pipeline to generate clinically relevant NPCs. We first isolated cells from patients' urine and reprogrammed them into iPSCs by non-integrating Sendai viral vectors, and carried out experiments on neural differentiation. NPCs were purified by A2B5, an antibody specifically recognizing a glycoganglioside on the cell surface of neural lineage cells, via fluorescence activated cell sorting. Upon further in vitro induction, NPCs were able to give rise to neurons, oligodendrocytes and astrocytes. To test the functionality of the A2B5+ NPCs, we grafted them into the contused mouse thoracic spinal cord. Eight weeks after transplantation, the grafted cells survived, integrated into the injured spinal cord, and differentiated into neurons and glia. Our specific focus on cell source, reprogramming, differentiation and purification method purposely addresses timing and safety issues of transplantation to SCI models. It is our belief that this work takes one step closer on using human iPSC derivatives to SCI clinical settings.

  6. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non......-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

  7. Diagnostics of Nuclear Reactor Accidents Based on Particle Swarm Optimization Trained Neural Networks

    International Nuclear Information System (INIS)

    Abdel-Aal, M.M.Z.

    2004-01-01

    Automation in large, complex systems such as chemical plants, electrical power generation, aerospace and nuclear plants has been steadily increasing in the recent past. automated diagnosis and control forms a necessary part of these systems,this contains thousands of alarms processing in every component, subsystem and system. so the accurate and speed of diagnosis of faults is an important factors in operation and maintaining their health and continued operation and in reducing of repair and recovery time. using of artificial intelligence facilitates the alarm classifications and faults diagnosis to control any abnormal events during the operation cycle of the plant. thesis work uses the artificial neural network as a powerful classification tool. the work basically is has two components, the first is to effectively train the neural network using particle swarm optimization, which non-derivative based technique. to achieve proper training of the neural network to fault classification problem and comparing this technique to already existing techniques

  8. Neural Crest-Derived Mesenchymal Cells Require Wnt Signaling for Their Development and Drive Invagination of the Telencephalic Midline

    Science.gov (United States)

    Choe, Youngshik; Zarbalis, Konstantinos S.; Pleasure, Samuel J.

    2014-01-01

    Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs) leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis. PMID:24516524

  9. Neural crest-derived mesenchymal cells require Wnt signaling for their development and drive invagination of the telencephalic midline.

    Directory of Open Access Journals (Sweden)

    Youngshik Choe

    Full Text Available Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis.

  10. Neural network-based sensor signal accelerator.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    2000-10-16

    A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher-speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.

  11. The neural bases of orthographic working memory

    Directory of Open Access Journals (Sweden)

    Jeremy Purcell

    2014-04-01

    First, these results reveal a neurotopography of OWM lesion sites that is well-aligned with results from neuroimaging of orthographic working memory in neurally intact participants (Rapp & Dufor, 2011. Second, the dorsal neurotopography of the OWM lesion overlap is clearly distinct from what has been reported for lesions associated with either lexical or sublexical deficits (e.g., Henry, Beeson, Stark, & Rapcsak, 2007; Rapcsak & Beeson, 2004; these have, respectively, been identified with the inferior occipital/temporal and superior temporal/inferior parietal regions. These neurotopographic distinctions support the claims of the computational distinctiveness of long-term vs. working memory operations. The specific lesion loci raise a number of questions to be discussed regarding: (a the selectivity of these regions and associated deficits to orthographic working memory vs. working memory more generally (b the possibility that different lesion sub-regions may correspond to different components of the OWM system.

  12. Microfluidic systems for stem cell-based neural tissue engineering.

    Science.gov (United States)

    Karimi, Mahdi; Bahrami, Sajad; Mirshekari, Hamed; Basri, Seyed Masoud Moosavi; Nik, Amirala Bakhshian; Aref, Amir R; Akbari, Mohsen; Hamblin, Michael R

    2016-07-05

    Neural tissue engineering aims at developing novel approaches for the treatment of diseases of the nervous system, by providing a permissive environment for the growth and differentiation of neural cells. Three-dimensional (3D) cell culture systems provide a closer biomimetic environment, and promote better cell differentiation and improved cell function, than could be achieved by conventional two-dimensional (2D) culture systems. With the recent advances in the discovery and introduction of different types of stem cells for tissue engineering, microfluidic platforms have provided an improved microenvironment for the 3D-culture of stem cells. Microfluidic systems can provide more precise control over the spatiotemporal distribution of chemical and physical cues at the cellular level compared to traditional systems. Various microsystems have been designed and fabricated for the purpose of neural tissue engineering. Enhanced neural migration and differentiation, and monitoring of these processes, as well as understanding the behavior of stem cells and their microenvironment have been obtained through application of different microfluidic-based stem cell culture and tissue engineering techniques. As the technology advances it may be possible to construct a "brain-on-a-chip". In this review, we describe the basics of stem cells and tissue engineering as well as microfluidics-based tissue engineering approaches. We review recent testing of various microfluidic approaches for stem cell-based neural tissue engineering.

  13. Neural stem cells induce bone-marrow-derived mesenchymal stem cells to generate neural stem-like cells via juxtacrine and paracrine interactions

    International Nuclear Information System (INIS)

    Alexanian, Arshak R.

    2005-01-01

    Several recent reports suggest that there is far more plasticity that previously believed in the developmental potential of bone-marrow-derived cells (BMCs) that can be induced by extracellular developmental signals of other lineages whose nature is still largely unknown. In this study, we demonstrate that bone-marrow-derived mesenchymal stem cells (MSCs) co-cultured with mouse proliferating or fixed (by paraformaldehyde or methanol) neural stem cells (NSCs) generate neural stem cell-like cells with a higher expression of Sox-2 and nestin when grown in NS-A medium supplemented with N2, NSC conditioned medium (NSCcm) and bFGF. These neurally induced MSCs eventually differentiate into β-III-tubulin and GFAP expressing cells with neuronal and glial morphology when grown an additional week in Neurobasal/B27 without bFGF. We conclude that juxtacrine interaction between NSCs and MSCs combined with soluble factors released from NSCs are important for generation of neural-like cells from bone-marrow-derived adherent MSCs

  14. Neural network based electron identification in the ZEUS calorimeter

    International Nuclear Information System (INIS)

    Abramowicz, H.; Caldwell, A.; Sinkus, R.

    1995-01-01

    We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach. (orig.)

  15. Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays.

    Science.gov (United States)

    Li, Hongfei; Jiang, Haijun; Hu, Cheng

    2016-03-01

    In this paper, we investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, boundedness and ultimate boundedness of solutions of memristor-based BAM neural networks are guaranteed by Chain rule and inequalities technique. Moreover, a new method involving Yoshizawa-like theorem is favorably employed to acquire the existence of periodic solution. By applying the theory of set-valued maps and functional differential inclusions, an available Lyapunov functional and some new testable algebraic criteria are derived for ensuring the uniqueness and global exponential stability of periodic solution of memristor-based BAM neural networks. The obtained results expand and complement some previous work on memristor-based BAM neural networks. Finally, a numerical example is provided to show the applicability and effectiveness of our theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Constitutively active Notch1 converts cranial neural crest-derived frontonasal mesenchyme to perivascular cells in vivo

    Directory of Open Access Journals (Sweden)

    Sophie R. Miller

    2017-03-01

    Full Text Available Perivascular/mural cells originate from either the mesoderm or the cranial neural crest. Regardless of their origin, Notch signalling is necessary for their formation. Furthermore, in both chicken and mouse, constitutive Notch1 activation (via expression of the Notch1 intracellular domain is sufficient in vivo to convert trunk mesoderm-derived somite cells to perivascular cells, at the expense of skeletal muscle. In experiments originally designed to investigate the effect of premature Notch1 activation on the development of neural crest-derived olfactory ensheathing glial cells (OECs, we used in ovo electroporation to insert a tetracycline-inducible NotchΔE construct (encoding a constitutively active mutant of mouse Notch1 into the genome of chicken cranial neural crest cell precursors, and activated NotchΔE expression by doxycycline injection at embryonic day 4. NotchΔE-targeted cells formed perivascular cells within the frontonasal mesenchyme, and expressed a perivascular marker on the olfactory nerve. Hence, constitutively activating Notch1 is sufficient in vivo to drive not only somite cells, but also neural crest-derived frontonasal mesenchyme and perhaps developing OECs, to a perivascular cell fate. These results also highlight the plasticity of neural crest-derived mesenchyme and glia.

  17. Neural Representation. A Survey-Based Analysis of the Notion

    Directory of Open Access Journals (Sweden)

    Oscar Vilarroya

    2017-08-01

    Full Text Available The word representation (as in “neural representation”, and many of its related terms, such as to represent, representational and the like, play a central explanatory role in neuroscience literature. For instance, in “place cell” literature, place cells are extensively associated with their role in “the representation of space.” In spite of its extended use, we still lack a clear, universal and widely accepted view on what it means for a nervous system to represent something, on what makes a neural activity a representation, and on what is re-presented. The lack of a theoretical foundation and definition of the notion has not hindered actual research. My aim here is to identify how active scientists use the notion of neural representation, and eventually to list a set of criteria, based on actual use, that can help in distinguishing between genuine or non-genuine neural-representation candidates. In order to attain this objective, I present first the results of a survey of authors within two domains, place-cell and multivariate pattern analysis (MVPA research. Based on the authors’ replies, and on a review of neuroscientific research, I outline a set of common properties that an account of neural representation seems to require. I then apply these properties to assess the use of the notion in two domains of the survey, place-cell and MVPA studies. I conclude by exploring a shift in the notion of representation suggested by recent literature.

  18. Derivation of GFDM Based on OFDM Principles

    Energy Technology Data Exchange (ETDEWEB)

    Hussein Moradi; Behrouz Farhang-Boroujeny

    2015-06-01

    This paper starts with discussing the principle based on which the celebrated orthogonal frequency division multiplexing (OFDM) signals are constructed. It then extends the same principle to construct the newly introduced generalized frequency division multiplexing (GFDM) signals. This novel derivation sheds light on some interesting properties of GFDM. In particular, our derivation seamlessly leads to an implementation of GFDM transmitter which has significantly lower complexity than what has been reported so far. Our derivation also facilitates a trivial understanding of how GFDM (similar to OFDM) can be applied in MIMO channels.

  19. ADAM10 is essential for cranial neural crest-derived maxillofacial bone development

    Energy Technology Data Exchange (ETDEWEB)

    Tan, Yu, E-mail: tanyu2048@163.com; Fu, Runqing, E-mail: furunqing@sjtu.edu.cn; Liu, Jiaqiang, E-mail: liujqmj@163.com; Wu, Yong, E-mail: wyonger@gmail.com; Wang, Bo, E-mail: wb228@126.com; Jiang, Ning, E-mail: 179639060@qq.com; Nie, Ping, E-mail: nieping1011@sina.com; Cao, Haifeng, E-mail: 0412chf@163.com; Yang, Zhi, E-mail: wcums1981@163.com; Fang, Bing, E-mail: fangbing@sjtu.edu.cn

    2016-07-08

    Growth disorders of the craniofacial bones may lead to craniofacial deformities. The majority of maxillofacial bones are derived from cranial neural crest cells via intramembranous bone formation. Any interruption of the craniofacial skeleton development process might lead to craniofacial malformation. A disintegrin and metalloprotease (ADAM)10 plays an essential role in organ development and tissue integrity in different organs. However, little is known about its function in craniofacial bone formation. Therefore, we investigated the role of ADAM10 in the developing craniofacial skeleton, particularly during typical mandibular bone development. First, we showed that ADAM10 was expressed in a specific area of the craniofacial bone and that the expression pattern dynamically changed during normal mouse craniofacial development. Then, we crossed wnt1-cre transgenic mice with adam10-flox mice to generate ADAM10 conditional knockout mice. The stereomicroscopic, radiographic, and von Kossa staining results showed that conditional knockout of ADAM10 in cranial neural crest cells led to embryonic death, craniofacial dysmorphia and bone defects. Furthermore, we demonstrated that impaired mineralization could be triggered by decreased osteoblast differentiation, increased cell death. Overall, these findings show that ADAM10 plays an essential role in craniofacial bone development. -- Highlights: •We firstly reported that ADAM10 was essentially involved in maxillofacial bone development. •ADAM10 cKO mice present craniofacial dysmorphia and bone defects. •Impaired osteoblast differentiation,proliferation and apoptosis underlie the bone deformity.

  20. ADAM10 is essential for cranial neural crest-derived maxillofacial bone development

    International Nuclear Information System (INIS)

    Tan, Yu; Fu, Runqing; Liu, Jiaqiang; Wu, Yong; Wang, Bo; Jiang, Ning; Nie, Ping; Cao, Haifeng; Yang, Zhi; Fang, Bing

    2016-01-01

    Growth disorders of the craniofacial bones may lead to craniofacial deformities. The majority of maxillofacial bones are derived from cranial neural crest cells via intramembranous bone formation. Any interruption of the craniofacial skeleton development process might lead to craniofacial malformation. A disintegrin and metalloprotease (ADAM)10 plays an essential role in organ development and tissue integrity in different organs. However, little is known about its function in craniofacial bone formation. Therefore, we investigated the role of ADAM10 in the developing craniofacial skeleton, particularly during typical mandibular bone development. First, we showed that ADAM10 was expressed in a specific area of the craniofacial bone and that the expression pattern dynamically changed during normal mouse craniofacial development. Then, we crossed wnt1-cre transgenic mice with adam10-flox mice to generate ADAM10 conditional knockout mice. The stereomicroscopic, radiographic, and von Kossa staining results showed that conditional knockout of ADAM10 in cranial neural crest cells led to embryonic death, craniofacial dysmorphia and bone defects. Furthermore, we demonstrated that impaired mineralization could be triggered by decreased osteoblast differentiation, increased cell death. Overall, these findings show that ADAM10 plays an essential role in craniofacial bone development. -- Highlights: •We firstly reported that ADAM10 was essentially involved in maxillofacial bone development. •ADAM10 cKO mice present craniofacial dysmorphia and bone defects. •Impaired osteoblast differentiation,proliferation and apoptosis underlie the bone deformity.

  1. Neural Cell Chip Based Electrochemical Detection of Nanotoxicity.

    Science.gov (United States)

    Kafi, Md Abdul; Cho, Hyeon-Yeol; Choi, Jeong Woo

    2015-07-02

    Development of a rapid, sensitive and cost-effective method for toxicity assessment of commonly used nanoparticles is urgently needed for the sustainable development of nanotechnology. A neural cell with high sensitivity and conductivity has become a potential candidate for a cell chip to investigate toxicity of environmental influences. A neural cell immobilized on a conductive surface has become a potential tool for the assessment of nanotoxicity based on electrochemical methods. The effective electrochemical monitoring largely depends on the adequate attachment of a neural cell on the chip surfaces. Recently, establishment of integrin receptor specific ligand molecules arginine-glycine-aspartic acid (RGD) or its several modifications RGD-Multi Armed Peptide terminated with cysteine (RGD-MAP-C), C(RGD)₄ ensure farm attachment of neural cell on the electrode surfaces either in their two dimensional (dot) or three dimensional (rod or pillar) like nano-scale arrangement. A three dimensional RGD modified electrode surface has been proven to be more suitable for cell adhesion, proliferation, differentiation as well as electrochemical measurement. This review discusses fabrication as well as electrochemical measurements of neural cell chip with particular emphasis on their use for nanotoxicity assessments sequentially since inception to date. Successful monitoring of quantum dot (QD), graphene oxide (GO) and cosmetic compound toxicity using the newly developed neural cell chip were discussed here as a case study. This review recommended that a neural cell chip established on a nanostructured ligand modified conductive surface can be a potential tool for the toxicity assessments of newly developed nanomaterials prior to their use on biology or biomedical technologies.

  2. Neural Cell Chip Based Electrochemical Detection of Nanotoxicity

    Directory of Open Access Journals (Sweden)

    Md. Abdul Kafi

    2015-07-01

    Full Text Available Development of a rapid, sensitive and cost-effective method for toxicity assessment of commonly used nanoparticles is urgently needed for the sustainable development of nanotechnology. A neural cell with high sensitivity and conductivity has become a potential candidate for a cell chip to investigate toxicity of environmental influences. A neural cell immobilized on a conductive surface has become a potential tool for the assessment of nanotoxicity based on electrochemical methods. The effective electrochemical monitoring largely depends on the adequate attachment of a neural cell on the chip surfaces. Recently, establishment of integrin receptor specific ligand molecules arginine-glycine-aspartic acid (RGD or its several modifications RGD-Multi Armed Peptide terminated with cysteine (RGD-MAP-C, C(RGD4 ensure farm attachment of neural cell on the electrode surfaces either in their two dimensional (dot or three dimensional (rod or pillar like nano-scale arrangement. A three dimensional RGD modified electrode surface has been proven to be more suitable for cell adhesion, proliferation, differentiation as well as electrochemical measurement. This review discusses fabrication as well as electrochemical measurements of neural cell chip with particular emphasis on their use for nanotoxicity assessments sequentially since inception to date. Successful monitoring of quantum dot (QD, graphene oxide (GO and cosmetic compound toxicity using the newly developed neural cell chip were discussed here as a case study. This review recommended that a neural cell chip established on a nanostructured ligand modified conductive surface can be a potential tool for the toxicity assessments of newly developed nanomaterials prior to their use on biology or biomedical technologies.

  3. Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses.

    Science.gov (United States)

    Zamani, Majid; Demosthenous, Andreas

    2014-07-01

    Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting.

  4. Neural Network Based Models for Fusion Applications

    Science.gov (United States)

    Meneghini, Orso; Tema Biwole, Arsene; Luda, Teobaldo; Zywicki, Bailey; Rea, Cristina; Smith, Sterling; Snyder, Phil; Belli, Emily; Staebler, Gary; Canty, Jeff

    2017-10-01

    Whole device modeling, engineering design, experimental planning and control applications demand models that are simultaneously physically accurate and fast. This poster reports on the ongoing effort towards the development and validation of a series of models that leverage neural-­network (NN) multidimensional regression techniques to accelerate some of the most mission critical first principle models for the fusion community, such as: the EPED workflow for prediction of the H-Mode and Super H-Mode pedestal structure the TGLF and NEO models for the prediction of the turbulent and neoclassical particle, energy and momentum fluxes; and the NEO model for the drift-kinetic solution of the bootstrap current. We also applied NNs on DIII-D experimental data for disruption prediction and quantifying the effect of RMPs on the pedestal and ELMs. All of these projects were supported by the infrastructure provided by the OMFIT integrated modeling framework. Work supported by US DOE under DE-SC0012656, DE-FG02-95ER54309, DE-FC02-04ER54698.

  5. Pharmacological rescue of mitochondrial deficits in iPSC-derived neural cells from patients with familial Parkinson's disease

    DEFF Research Database (Denmark)

    Cooper, Oliver; Seo, Hyemyung; Andrabi, Shaida

    2012-01-01

    , or the LRRK2 kinase inhibitor GW5074. Analysis of mitochondrial responses in iPSC-derived neural cells from PD patients carrying different mutations provides insight into convergence of cellular disease mechanisms between different familial forms of PD and highlights the importance of oxidative stress...... of reactive oxygen species, mitochondrial respiration, proton leakage, and intraneuronal movement of mitochondria. Cellular vulnerability associated with mitochondrial dysfunction in iPSC-derived neural cells from familial PD patients and at-risk individuals could be rescued with coenzyme Q(10), rapamycin...

  6. The Effects of Low-Dose Bisphenol A and Bisphenol F on Neural Differentiation of a Fetal Brain-Derived Neural Progenitor Cell Line.

    Science.gov (United States)

    Fujiwara, Yuki; Miyazaki, Wataru; Koibuchi, Noriyuki; Katoh, Takahiko

    2018-01-01

    Environmental chemicals are known to disrupt the endocrine system in humans and to have adverse effects on several organs including the developing brain. Recent studies indicate that exposure to environmental chemicals during gestation can interfere with neuronal differentiation, subsequently affecting normal brain development in newborns. Xenoestrogen, bisphenol A (BPA), which is widely used in plastic products, is one such chemical. Adverse effects of exposure to BPA during pre- and postnatal periods include the disruption of brain function. However, the effect of BPA on neural differentiation remains unclear. In this study, we explored the effects of BPA or bisphenol F (BPF), an alternative compound for BPA, on neural differentiation using ReNcell, a human fetus-derived neural progenitor cell line. Maintenance in growth factor-free medium initiated the differentiation of ReNcell to neuronal cells including neurons, astrocytes, and oligodendrocytes. We exposed the cells to BPA or BPF for 3 days from the period of initiation and performed real-time PCR for neural markers such as β III-tubulin and glial fibrillary acidic protein (GFAP), and Olig2. The β III-tubulin mRNA level decreased in response to BPA, but not BPF, exposure. We also observed that the number of β III-tubulin-positive cells in the BPA-exposed group was less than that of the control group. On the other hand, there were no changes in the MAP2 mRNA level. These results indicate that BPA disrupts neural differentiation in human-derived neural progenitor cells, potentially disrupting brain development.

  7. The Effects of Low-Dose Bisphenol A and Bisphenol F on Neural Differentiation of a Fetal Brain-Derived Neural Progenitor Cell Line

    Directory of Open Access Journals (Sweden)

    Yuki Fujiwara

    2018-02-01

    Full Text Available Environmental chemicals are known to disrupt the endocrine system in humans and to have adverse effects on several organs including the developing brain. Recent studies indicate that exposure to environmental chemicals during gestation can interfere with neuronal differentiation, subsequently affecting normal brain development in newborns. Xenoestrogen, bisphenol A (BPA, which is widely used in plastic products, is one such chemical. Adverse effects of exposure to BPA during pre- and postnatal periods include the disruption of brain function. However, the effect of BPA on neural differentiation remains unclear. In this study, we explored the effects of BPA or bisphenol F (BPF, an alternative compound for BPA, on neural differentiation using ReNcell, a human fetus-derived neural progenitor cell line. Maintenance in growth factor-free medium initiated the differentiation of ReNcell to neuronal cells including neurons, astrocytes, and oligodendrocytes. We exposed the cells to BPA or BPF for 3 days from the period of initiation and performed real-time PCR for neural markers such as β III-tubulin and glial fibrillary acidic protein (GFAP, and Olig2. The β III-tubulin mRNA level decreased in response to BPA, but not BPF, exposure. We also observed that the number of β III-tubulin-positive cells in the BPA-exposed group was less than that of the control group. On the other hand, there were no changes in the MAP2 mRNA level. These results indicate that BPA disrupts neural differentiation in human-derived neural progenitor cells, potentially disrupting brain development.

  8. Fractional-order gradient descent learning of BP neural networks with Caputo derivative.

    Science.gov (United States)

    Wang, Jian; Wen, Yanqing; Gou, Yida; Ye, Zhenyun; Chen, Hua

    2017-05-01

    Fractional calculus has been found to be a promising area of research for information processing and modeling of some physical systems. In this paper, we propose a fractional gradient descent method for the backpropagation (BP) training of neural networks. In particular, the Caputo derivative is employed to evaluate the fractional-order gradient of the error defined as the traditional quadratic energy function. The monotonicity and weak (strong) convergence of the proposed approach are proved in detail. Two simulations have been implemented to illustrate the performance of presented fractional-order BP algorithm on three small datasets and one large dataset. The numerical simulations effectively verify the theoretical observations of this paper as well. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Generation of highly purified neural stem cells from human adipose-derived mesenchymal stem cells by Sox1 activation.

    Science.gov (United States)

    Feng, Nianhua; Han, Qin; Li, Jing; Wang, Shihua; Li, Hongling; Yao, Xinglei; Zhao, Robert Chunhua

    2014-03-01

    Neural stem cells (NSCs) are ideal candidates in stem cell-based therapy for neurodegenerative diseases. However, it is unfeasible to get enough quantity of NSCs for clinical application. Generation of NSCs from human adipose-derived mesenchymal stem cells (hAD-MSCs) will provide a solution to this problem. Currently, the differentiation of hAD-MSCs into highly purified NSCs with biological functions is rarely reported. In our study, we established a three-step NSC-inducing protocol, in which hAD-MSCs were induced to generate NSCs with high purity after sequentially cultured in the pre-inducing medium (Step1), the N2B27 medium (Step2), and the N2B27 medium supplement with basic fibroblast growth factor and epidermal growth factor (Step3). These hAD-MSC-derived NSCs (adNSCs) can form neurospheres and highly express Sox1, Pax6, Nestin, and Vimentin; the proportion was 96.1% ± 1.3%, 96.8% ± 1.7%, 96.2% ± 1.3%, and 97.2% ± 2.5%, respectively, as detected by flow cytometry. These adNSCs can further differentiate into astrocytes, oligodendrocytes, and functional neurons, which were able to generate tetrodotoxin-sensitive sodium current. Additionally, we found that the neural differentiation of hAD-MSCs were significantly suppressed by Sox1 interference, and what's more, Step1 was a key step for the following induction, probably because it was associated with the initiation and nuclear translocation of Sox1, an important transcriptional factor for neural development. Finally, we observed that bone morphogenetic protein signal was inhibited, and Wnt/β-catenin signal was activated during inducing process, and both signals were related with Sox1 expression. In conclusion, we successfully established a three-step inducing protocol to derive NSCs from hAD-MSCs with high purity by Sox1 activation. These findings might enable to acquire enough autologous transplantable NSCs for the therapy of neurodegenerative diseases in clinic.

  10. Large-scale generation of human iPSC-derived neural stem cells/early neural progenitor cells and their neuronal differentiation.

    Science.gov (United States)

    D'Aiuto, Leonardo; Zhi, Yun; Kumar Das, Dhanjit; Wilcox, Madeleine R; Johnson, Jon W; McClain, Lora; MacDonald, Matthew L; Di Maio, Roberto; Schurdak, Mark E; Piazza, Paolo; Viggiano, Luigi; Sweet, Robert; Kinchington, Paul R; Bhattacharjee, Ayantika G; Yolken, Robert; Nimgaonka, Vishwajit L; Nimgaonkar, Vishwajit L

    2014-01-01

    Induced pluripotent stem cell (iPSC)-based technologies offer an unprecedented opportunity to perform high-throughput screening of novel drugs for neurological and neurodegenerative diseases. Such screenings require a robust and scalable method for generating large numbers of mature, differentiated neuronal cells. Currently available methods based on differentiation of embryoid bodies (EBs) or directed differentiation of adherent culture systems are either expensive or are not scalable. We developed a protocol for large-scale generation of neuronal stem cells (NSCs)/early neural progenitor cells (eNPCs) and their differentiation into neurons. Our scalable protocol allows robust and cost-effective generation of NSCs/eNPCs from iPSCs. Following culture in neurobasal medium supplemented with B27 and BDNF, NSCs/eNPCs differentiate predominantly into vesicular glutamate transporter 1 (VGLUT1) positive neurons. Targeted mass spectrometry analysis demonstrates that iPSC-derived neurons express ligand-gated channels and other synaptic proteins and whole-cell patch-clamp experiments indicate that these channels are functional. The robust and cost-effective differentiation protocol described here for large-scale generation of NSCs/eNPCs and their differentiation into neurons paves the way for automated high-throughput screening of drugs for neurological and neurodegenerative diseases.

  11. Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control.

    Science.gov (United States)

    Li, Xiaofan; Fang, Jian-An; Li, Huiyuan

    2017-09-01

    This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. A Markovian event-based framework for stochastic spiking neural networks.

    Science.gov (United States)

    Touboul, Jonathan D; Faugeras, Olivier D

    2011-11-01

    In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

  13. Iris double recognition based on modified evolutionary neural network

    Science.gov (United States)

    Liu, Shuai; Liu, Yuan-Ning; Zhu, Xiao-Dong; Huo, Guang; Liu, Wen-Tao; Feng, Jia-Kai

    2017-11-01

    Aiming at multicategory iris recognition under illumination and noise interference, this paper proposes a method of iris double recognition based on a modified evolutionary neural network. An equalization histogram and Laplace of Gaussian operator are used to process the iris to suppress illumination and noise interference and Haar wavelet to convert the iris feature to binary feature encoding. Calculate the Hamming distance for the test iris and template iris , and compare with classification threshold, determine the type of iris. If the iris cannot be identified as a different type, there needs to be a secondary recognition. The connection weights in back-propagation (BP) neural network use modified evolutionary neural network to adaptively train. The modified neural network is composed of particle swarm optimization with mutation operator and BP neural network. According to different iris libraries in different circumstances of experimental results, under illumination and noise interference, the correct recognition rate of this algorithm is higher, the ROC curve is closer to the coordinate axis, the training and recognition time is shorter, and the stability and the robustness are better.

  14. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

    Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

  15. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

  16. RBF neural network based H∞ H∞ H∞ synchronization for ...

    Indian Academy of Sciences (India)

    Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an H ∞ norm constraint. It is shown that finding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved ...

  17. Detecting danger labels with RAM-based neural networks

    DEFF Research Database (Denmark)

    Jørgensen, T.M.; Christensen, S.S.; Andersen, A.W.

    1996-01-01

    An image processing system for the automatic location of danger labels on the back of containers is presented. The system uses RAM-based neural networks to locate and classify labels after a pre-processing step involving specially designed non-linear edge filters and RGB-to-HSV conversion. Result...

  18. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  19. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    user

    Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total ..... Genetic algorithm-based self-learning fuzzy PI controller for shunt active filter, ... Verification of global optimality of the OFC active power filters by means of ...

  20. Neural network-based retrieval from software reuse repositories

    Science.gov (United States)

    Eichmann, David A.; Srinivas, Kankanahalli

    1992-01-01

    A significant hurdle confronts the software reuser attempting to select candidate components from a software repository - discriminating between those components without resorting to inspection of the implementation(s). We outline an approach to this problem based upon neural networks which avoids requiring the repository administrators to define a conceptual closeness graph for the classification vocabulary.

  1. Numerical Analysis of Modeling Based on Improved Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Shao Jie

    2014-01-01

    Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.

  2. SU-8-based microneedles for in vitro neural applications

    International Nuclear Information System (INIS)

    Altuna, Ane; Tijero, María; Berganzo, Javier; Salido, Rafa; Fernández, Luis J; Gabriel, Gemma; Guimerá, Anton; Villa, Rosa; Menéndez de la Prida, Liset

    2010-01-01

    This paper presents novel design, fabrication, packaging and the first in vitro neural activity recordings of SU-8-based microneedles. The polymer SU-8 was chosen because it provides excellent features for the fabrication of flexible and thin probes. A microprobe was designed in order to allow a clean insertion and to minimize the damage caused to neural tissue during in vitro applications. In addition, a tetrode is patterned at the tip of the needle to obtain fine-scale measurements of small neuronal populations within a radius of 100 µm. Impedance characterization of the electrodes has been carried out to demonstrate their viability for neural recording. Finally, probes are inserted into 400 µm thick hippocampal slices, and simultaneous action potentials with peak-to-peak amplitudes of 200–250 µV are detected.

  3. A neutron spectrum unfolding computer code based on artificial neural networks

    Science.gov (United States)

    Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2014-02-01

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

  4. Long-term culture and differentiation of CNS precursors derived from anterior human neural rosettes following exposure to ventralizing factors

    International Nuclear Information System (INIS)

    Colleoni, Silvia; Galli, Cesare; Giannelli, Serena G.; Armentero, Marie-Therese; Blandini, Fabio; Broccoli, Vania; Lazzari, Giovanna

    2010-01-01

    In this study we demonstrated that neural rosettes derived from human ES cells can give rise either to neural crest precursors, following expansion in presence of bFGF and EGF, or to dopaminergic precursors after exposure to ventralizing factors Shh and FGF8. Both regionalised precursors are capable of extensive proliferation and differentiation towards the corresponding terminally differentiated cell types. In particular, peripheral neurons, cartilage, bone, smooth muscle cells and also pigmented cells were obtained from neural crest precursors while tyrosine hydroxylase and Nurr1 positive dopaminergic neurons were derived from FGF8 and Shh primed rosette cells. Gene expression and immunocytochemistry analyses confirmed the expression of dorsal and neural crest genes such as Sox10, Slug, p75, FoxD3, Pax7 in neural precursors from bFGF-EGF exposed rosettes. By contrast, priming of rosettes with FGF8 and Shh induced the expression of dopaminergic markers Engrailed1, Pax2, Pitx3, floor plate marker FoxA2 and radial glia markers Blbp and Glast, the latter in agreement with the origin of dopaminergic precursors from floor plate radial glia. Moreover, in vivo transplant of proliferating Shh/FGF8 primed precursors in parkinsonian rats demonstrated engraftment and terminal dopaminergic differentiation. In conclusion, we demonstrated the derivation of long-term self-renewing precursors of selected regional identity as potential cell reservoirs for cell therapy applications, such as CNS degenerative diseases, or for the development of toxicological tests.

  5. Long-term culture and differentiation of CNS precursors derived from anterior human neural rosettes following exposure to ventralizing factors

    Energy Technology Data Exchange (ETDEWEB)

    Colleoni, Silvia, E-mail: silviacolleoni@avantea.it [Laboratorio di Tecnologie della Riproduzione, Avantea, Via Porcellasco 7/f, 26100 Cremona (Italy); Galli, Cesare [Laboratorio di Tecnologie della Riproduzione, Avantea, Via Porcellasco 7/f, 26100 Cremona (Italy); Dipartimento Clinico Veterinario, Universita di Bologna, Via Tolara di Sopra 50, 40064 Ozzano Emilia (Italy); Giannelli, Serena G. [Stem Cells and Neurogenesis Unit, Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan (Italy); Armentero, Marie-Therese; Blandini, Fabio [Laboratory of Functional Neurochemistry, Interdepartmental Research Center for Parkinson' s Disease, Neurological Institute C. Mondino, Via Mondino 2, 27100 Pavia (Italy); Broccoli, Vania, E-mail: broccoli.vania@hsr.it [Stem Cells and Neurogenesis Unit, Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, 20132 Milan (Italy); Lazzari, Giovanna, E-mail: giovannalazzari@avantea.it [Laboratorio di Tecnologie della Riproduzione, Avantea, Via Porcellasco 7/f, 26100 Cremona (Italy)

    2010-04-15

    In this study we demonstrated that neural rosettes derived from human ES cells can give rise either to neural crest precursors, following expansion in presence of bFGF and EGF, or to dopaminergic precursors after exposure to ventralizing factors Shh and FGF8. Both regionalised precursors are capable of extensive proliferation and differentiation towards the corresponding terminally differentiated cell types. In particular, peripheral neurons, cartilage, bone, smooth muscle cells and also pigmented cells were obtained from neural crest precursors while tyrosine hydroxylase and Nurr1 positive dopaminergic neurons were derived from FGF8 and Shh primed rosette cells. Gene expression and immunocytochemistry analyses confirmed the expression of dorsal and neural crest genes such as Sox10, Slug, p75, FoxD3, Pax7 in neural precursors from bFGF-EGF exposed rosettes. By contrast, priming of rosettes with FGF8 and Shh induced the expression of dopaminergic markers Engrailed1, Pax2, Pitx3, floor plate marker FoxA2 and radial glia markers Blbp and Glast, the latter in agreement with the origin of dopaminergic precursors from floor plate radial glia. Moreover, in vivo transplant of proliferating Shh/FGF8 primed precursors in parkinsonian rats demonstrated engraftment and terminal dopaminergic differentiation. In conclusion, we demonstrated the derivation of long-term self-renewing precursors of selected regional identity as potential cell reservoirs for cell therapy applications, such as CNS degenerative diseases, or for the development of toxicological tests.

  6. Neural activity in relation to clinically derived personality syndromes in depression using a psychodynamic fMRI paradigm

    Directory of Open Access Journals (Sweden)

    Svenja eTaubner

    2013-12-01

    Full Text Available Objective: The heterogeneity between patients with depression cannot be captured adequately with existing descriptive systems of diagnosis and neurobiological models of depression. Furthermore, considering the highly individual nature of depression, the application of general stimuli in past research efforts may not capture the essence of the disorder. This study aims to identify subtypes of depression by using empirically-derived personality-syndromes, and to explore neural correlates of the derived personality syndromes.Method: In the present exploratory study an individually tailored and psychodynamically based fMRI paradigm using dysfunctional relationship patterns was presented to 20 chronically depressed patients. Results from the Shedler-Westen-Assessment-Procedure (SWAP-200 were analyzed by Q-factor analysis to identify clinically relevant subgroups of depression and related brain activation.Results: The principle component analysis of SWAP-200 items from all 20 patients lead to a 2-factor solution: Depressive Personality and Emotional-Hostile-Externalizing Personality. Both factors were used in a whole-brain correlational analysis but only the second factor yielded significant positive correlations in four regions: A large cluster in the right orbitofrontal cortex (OFC, the left ventral striatum, a small cluster in the left temporal pole and another small cluster in the right middle frontal gyrus. Discussion: The degree to which patients with depression score high on the factor Emotional-Hostile-Externalizing Personality correlated with relatively higher activity in three key areas involved in emotion processing, evaluation of reward/punishment, negative cognitions, depressive pathology and social knowledge (OFC, ventral striatum, temporal pole. Results may contribute to an alternative description of neural correlates of depression showing differential brain activation dependent on the extent of specific personality syndromes in

  7. A neutron spectrum unfolding code based on generalized regression artificial neural networks

    International Nuclear Information System (INIS)

    Ortiz R, J. M.; Martinez B, M. R.; Castaneda M, R.; Solis S, L. O.; Vega C, H. R.

    2015-10-01

    The most delicate part of neutron spectrometry, is the unfolding process. Then derivation of the spectral information is not simple because the unknown is not given directly as result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, as the optimum selection of the network topology and the long training time. Compared to BPNN, is usually much faster to train a generalized regression neural network (GRNN). That is mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum. In addition, often are more accurate than BPNN in prediction. These characteristics make GRNN be of great interest in the neutron spectrometry domain. In this work is presented a computational tool based on GRNN, capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. (Author)

  8. A neutron spectrum unfolding code based on generalized regression artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M.; Martinez B, M. R.; Castaneda M, R.; Solis S, L. O. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Av. Ramon Lopez Velarde 801, Col. Centro, 98000 Zacatecas, Zac. (Mexico); Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas, Zac. (Mexico)

    2015-10-15

    The most delicate part of neutron spectrometry, is the unfolding process. Then derivation of the spectral information is not simple because the unknown is not given directly as result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, as the optimum selection of the network topology and the long training time. Compared to BPNN, is usually much faster to train a generalized regression neural network (GRNN). That is mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum. In addition, often are more accurate than BPNN in prediction. These characteristics make GRNN be of great interest in the neutron spectrometry domain. In this work is presented a computational tool based on GRNN, capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a {sup 6}LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. (Author)

  9. Integrative analysis of genes and miRNA alterations in human embryonic stem cells-derived neural cells after exposure to silver nanoparticles.

    Science.gov (United States)

    Oh, Jung-Hwa; Son, Mi-Young; Choi, Mi-Sun; Kim, Soojin; Choi, A-Young; Lee, Hyang-Ae; Kim, Ki-Suk; Kim, Janghwan; Song, Chang Woo; Yoon, Seokjoo

    2016-05-15

    Given the rapid growth of engineered and customer products made of silver nanoparticles (Ag NPs), understanding their biological and toxicological effects on humans is critically important. The molecular developmental neurotoxic effects associated with exposure to Ag NPs were analyzed at the physiological and molecular levels, using an alternative cell model: human embryonic stem cell (hESC)-derived neural stem/progenitor cells (NPCs). In this study, the cytotoxic effects of Ag NPs (10-200μg/ml) were examined in these hESC-derived NPCs, which have a capacity for neurogenesis in vitro, at 6 and 24h. The results showed that Ag NPs evoked significant toxicity in hESC-derived NPCs at 24h in a dose-dependent manner. In addition, Ag NPs induced cell cycle arrest and apoptosis following a significant increase in oxidative stress in these cells. To further clarify the molecular mechanisms of the toxicological effects of Ag NPs at the transcriptional and post-transcriptional levels, the global expression profiles of genes and miRNAs were analyzed in hESC-derived NPCs after Ag NP exposure. The results showed that Ag NPs induced oxidative stress and dysfunctional neurogenesis at the molecular level in hESC-derived NPCs. Based on this hESC-derived neural cell model, these findings have increased our understanding of the molecular events underlying developmental neurotoxicity induced by Ag NPs in humans. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. Hypoxia Epigenetically Confers Astrocytic Differentiation Potential on Human Pluripotent Cell-Derived Neural Precursor Cells

    Directory of Open Access Journals (Sweden)

    Tetsuro Yasui

    2017-06-01

    Full Text Available Human neural precursor cells (hNPCs derived from pluripotent stem cells display a high propensity for neuronal differentiation, but they require long-term culturing to differentiate efficiently into astrocytes. The mechanisms underlying this biased fate specification of hNPCs remain elusive. Here, we show that hypoxia confers astrocytic differentiation potential on hNPCs through epigenetic gene regulation, and that this was achieved by cooperation between hypoxia-inducible factor 1α and Notch signaling, accompanied by a reduction of DNA methylation level in the promoter region of a typical astrocyte-specific gene, Glial fibrillary acidic protein. Furthermore, we found that this hypoxic culture condition could be applied to rapid generation of astrocytes from Rett syndrome patient-derived hNPCs, and that these astrocytes impaired neuronal development. Thus, our findings shed further light on the molecular mechanisms regulating hNPC differentiation and provide attractive tools for the development of therapeutic strategies for treating astrocyte-mediated neurological disorders.

  11. An Aminopropyl Carbazole Derivative Induces Neurogenesis by Increasing Final Cell Division in Neural Stem Cells.

    Science.gov (United States)

    Shin, Jae-Yeon; Kong, Sun-Young; Yoon, Hye Jin; Ann, Jihyae; Lee, Jeewoo; Kim, Hyun-Jung

    2015-07-01

    P7C3 and its derivatives, 1-(3,6-dibromo-9H-carbazol-9-yl)-3-(p-tolylamino)propan-2-ol (1) and N-(3-(3,6-dibromo-9H-carbazol-9-yl)-2-hydroxypropyl)-N-(3-methoxyphenyl)-4-methylbenzenesulfonamide (2), were previously reported to increase neurogenesis in rat neural stem cells (NSCs). Although P7C3 is known to increase neurogenesis by protecting newborn neurons, it is not known whether its derivatives also have protective effects to increase neurogenesis. In the current study, we examined how 1 induces neurogenesis. The treatment of 1 in NSCs increased numbers of cells in the absence of epidermal growth factor (EGF) and fibroblast growth factor 2 (FGF2), while not affecting those in the presence of growth factors. Compound 1 did not induce astrocytogenesis during NSC differentiation. 5-Bromo-2'-deoxyuridine (BrdU) pulsing experiments showed that 1 significantly enhanced BrdU-positive neurons. Taken together, our data suggest that 1 promotes neurogenesis by the induction of final cell division during NSC differentiation.

  12. GDNF facilitates differentiation of the adult dentate gyrus-derived neural precursor cells into astrocytes via STAT3

    Energy Technology Data Exchange (ETDEWEB)

    Boku, Shuken, E-mail: shuboku@med.hokudai.ac.jp [Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo (Japan); Nakagawa, Shin [Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo (Japan); Takamura, Naoki [Pharmaceutical Laboratories, Dainippon Sumitomo Pharma Co. Ltd., Osaka (Japan); Kato, Akiko [Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo (Japan); Takebayashi, Minoru [Department of Psychiatry, National Hospital Organization Kure Medical Center, Kure (Japan); Hisaoka-Nakashima, Kazue [Department of Pharmacology, Hiroshima University Graduate School of Biomedical Sciences, Hiroshima (Japan); Omiya, Yuki; Inoue, Takeshi; Kusumi, Ichiro [Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo (Japan)

    2013-05-17

    Highlights: •GDNF has no effect on ADP proliferation and apoptosis. •GDNF increases ADP differentiation into astrocyte. •A specific inhibitor of STAT3 decreases the astrogliogenic effect of GDNF. •STAT3 knockdown by lentiviral shRNA vector also decreases the astrogliogenic effect of GDNF. •GDNF increases the phosphorylation of STAT3. -- Abstract: While the pro-neurogenic actions of antidepressants in the adult hippocampal dentate gyrus (DG) are thought to be one of the mechanisms through which antidepressants exert their therapeutic actions, antidepressants do not increase proliferation of neural precursor cells derived from the adult DG. Because previous studies showed that antidepressants increase the expression and secretion of glial cell line-derived neurotrophic factor (GDNF) in C6 glioma cells derived from rat astrocytes and GDNF increases neurogenesis in adult DG in vivo, we investigated the effects of GDNF on the proliferation, differentiation and apoptosis of cultured neural precursor cells derived from the adult DG. Data showed that GDNF facilitated the differentiation of neural precursor cells into astrocytes but had no effect on their proliferation or apoptosis. Moreover, GDNF increased the phosphorylation of STAT3, and both a specific inhibitor of STAT3 and lentiviral shRNA for STAT3 decreased their differentiation into astrocytes. Taken together, our findings suggest that GDNF facilitates astrogliogenesis from neural precursor cells in adult DG through activating STAT3 and that this action might indirectly affect neurogenesis.

  13. GDNF facilitates differentiation of the adult dentate gyrus-derived neural precursor cells into astrocytes via STAT3

    International Nuclear Information System (INIS)

    Boku, Shuken; Nakagawa, Shin; Takamura, Naoki; Kato, Akiko; Takebayashi, Minoru; Hisaoka-Nakashima, Kazue; Omiya, Yuki; Inoue, Takeshi; Kusumi, Ichiro

    2013-01-01

    Highlights: •GDNF has no effect on ADP proliferation and apoptosis. •GDNF increases ADP differentiation into astrocyte. •A specific inhibitor of STAT3 decreases the astrogliogenic effect of GDNF. •STAT3 knockdown by lentiviral shRNA vector also decreases the astrogliogenic effect of GDNF. •GDNF increases the phosphorylation of STAT3. -- Abstract: While the pro-neurogenic actions of antidepressants in the adult hippocampal dentate gyrus (DG) are thought to be one of the mechanisms through which antidepressants exert their therapeutic actions, antidepressants do not increase proliferation of neural precursor cells derived from the adult DG. Because previous studies showed that antidepressants increase the expression and secretion of glial cell line-derived neurotrophic factor (GDNF) in C6 glioma cells derived from rat astrocytes and GDNF increases neurogenesis in adult DG in vivo, we investigated the effects of GDNF on the proliferation, differentiation and apoptosis of cultured neural precursor cells derived from the adult DG. Data showed that GDNF facilitated the differentiation of neural precursor cells into astrocytes but had no effect on their proliferation or apoptosis. Moreover, GDNF increased the phosphorylation of STAT3, and both a specific inhibitor of STAT3 and lentiviral shRNA for STAT3 decreased their differentiation into astrocytes. Taken together, our findings suggest that GDNF facilitates astrogliogenesis from neural precursor cells in adult DG through activating STAT3 and that this action might indirectly affect neurogenesis

  14. The image recognition based on neural network and Bayesian decision

    Science.gov (United States)

    Wang, Chugege

    2018-04-01

    The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

  15. Deep Neural Network-Based Chinese Semantic Role Labeling

    Institute of Scientific and Technical Information of China (English)

    ZHENG Xiaoqing; CHEN Jun; SHANG Guoqiang

    2017-01-01

    A recent trend in machine learning is to use deep architec-tures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network-based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six-step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part-of-speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network - based model is designed and optimized, particularly for smart phone applications. Ex-periment results on all the NLP sub - tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requir-ing real-time response, highlighting the potential of the pro-posed solution for practical NLP systems.

  16. A chemically defined substrate for the expansion and neuronal differentiation of human pluripotent stem cell-derived neural progenitor cells.

    Science.gov (United States)

    Tsai, Yihuan; Cutts, Josh; Kimura, Azuma; Varun, Divya; Brafman, David A

    2015-07-01

    Due to the limitation of current pharmacological therapeutic strategies, stem cell therapies have emerged as a viable option for treating many incurable neurological disorders. Specifically, human pluripotent stem cell (hPSC)-derived neural progenitor cells (hNPCs), a multipotent cell population that is capable of near indefinite expansion and subsequent differentiation into the various cell types that comprise the central nervous system (CNS), could provide an unlimited source of cells for such cell-based therapies. However the clinical application of these cells will require (i) defined, xeno-free conditions for their expansion and neuronal differentiation and (ii) scalable culture systems that enable their expansion and neuronal differentiation in numbers sufficient for regenerative medicine and drug screening purposes. Current extracellular matrix protein (ECMP)-based substrates for the culture of hNPCs are expensive, difficult to isolate, subject to batch-to-batch variations, and, therefore, unsuitable for clinical application of hNPCs. Using a high-throughput array-based screening approach, we identified a synthetic polymer, poly(4-vinyl phenol) (P4VP), that supported the long-term proliferation and self-renewal of hNPCs. The hNPCs cultured on P4VP maintained their characteristic morphology, expressed high levels of markers of multipotency, and retained their ability to differentiate into neurons. Such chemically defined substrates will eliminate critical roadblocks for the utilization of hNPCs for human neural regenerative repair, disease modeling, and drug discovery. Copyright © 2015. Published by Elsevier B.V.

  17. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  18. Production of hemizygous and homozygous embryonic stem cell-derived neural progenitor cells from the transgenic alszheimer göttingen minipis

    DEFF Research Database (Denmark)

    Hall, Vanessa Jane; Jacobsen, J.; Gunnarsson, A.

    2011-01-01

    Production of hemizygous and homozygous embryonic stem cell-derived neural progenitor cells from the transgenic alszheimer göttingen minipis......Production of hemizygous and homozygous embryonic stem cell-derived neural progenitor cells from the transgenic alszheimer göttingen minipis...

  19. Efficient and rapid derivation of primitive neural stem cells and generation of brain subtype neurons from human pluripotent stem cells.

    Science.gov (United States)

    Yan, Yiping; Shin, Soojung; Jha, Balendu Shekhar; Liu, Qiuyue; Sheng, Jianting; Li, Fuhai; Zhan, Ming; Davis, Janine; Bharti, Kapil; Zeng, Xianmin; Rao, Mahendra; Malik, Nasir; Vemuri, Mohan C

    2013-11-01

    Human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are unique cell sources for disease modeling, drug discovery screens, and cell therapy applications. The first step in producing neural lineages from hPSCs is the generation of neural stem cells (NSCs). Current methods of NSC derivation involve the time-consuming, labor-intensive steps of an embryoid body generation or coculture with stromal cell lines that result in low-efficiency derivation of NSCs. In this study, we report a highly efficient serum-free pluripotent stem cell neural induction medium that can induce hPSCs into primitive NSCs (pNSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. The pNSCs expressed the neural stem cell markers Pax6, Sox1, Sox2, and Nestin; were negative for Oct4; could be expanded for multiple passages; and could be differentiated into neurons, astrocytes, and oligodendrocytes, in addition to the brain region-specific neuronal subtypes GABAergic, dopaminergic, and motor neurons. Global gene expression of the transcripts of pNSCs was comparable to that of rosette-derived and human fetal-derived NSCs. This work demonstrates an efficient method to generate expandable pNSCs, which can be further differentiated into central nervous system neurons and glia with temporal, spatial, and positional cues of brain regional heterogeneity. This method of pNSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

  20. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

  1. Healthy human CSF promotes glial differentiation of hESC-derived neural cells while retaining spontaneous activity in existing neuronal networks

    Directory of Open Access Journals (Sweden)

    Heikki Kiiski

    2013-05-01

    The possibilities of human pluripotent stem cell-derived neural cells from the basic research tool to a treatment option in regenerative medicine have been well recognized. These cells also offer an interesting tool for in vitro models of neuronal networks to be used for drug screening and neurotoxicological studies and for patient/disease specific in vitro models. Here, as aiming to develop a reductionistic in vitro human neuronal network model, we tested whether human embryonic stem cell (hESC-derived neural cells could be cultured in human cerebrospinal fluid (CSF in order to better mimic the in vivo conditions. Our results showed that CSF altered the differentiation of hESC-derived neural cells towards glial cells at the expense of neuronal differentiation. The proliferation rate was reduced in CSF cultures. However, even though the use of CSF as the culture medium altered the glial vs. neuronal differentiation rate, the pre-existing spontaneous activity of the neuronal networks persisted throughout the study. These results suggest that it is possible to develop fully human cell and culture-based environments that can further be modified for various in vitro modeling purposes.

  2. A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

    Directory of Open Access Journals (Sweden)

    Yong Tao

    2016-01-01

    Full Text Available A sliding mode control method based on radial basis function (RBF neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.

  3. Sensitivity of hiPSC-derived neural stem cells (NSC) to Pyrroloquinoline quinone depends on their developmental stage.

    Science.gov (United States)

    Augustyniak, J; Lenart, J; Zychowicz, M; Lipka, G; Gaj, P; Kolanowska, M; Stepien, P P; Buzanska, L

    2017-12-01

    Pyrroloquinoline quinone (PQQ) is a factor influencing on the mitochondrial biogenesis. In this study the PQQ effect on viability, total cell number, antioxidant capacity, mitochondrial biogenesis and differentiation potential was investigated in human induced Pluripotent Stem Cells (iPSC) - derived: neural stem cells (NSC), early neural progenitors (eNP) and neural progenitors (NP). Here we demonstrated that sensitivity to PQQ is dependent upon its dose and neural stage of development. Induction of the mitochondrial biogenesis by PQQ at three stages of neural differentiation was evaluated at mtDNA, mRNA and protein level. Changes in NRF1, TFAM and PPARGC1A gene expression were observed at all developmental stages, but only at eNP were correlated with the statistically significant increase in the mtDNA copy numbers and enhancement of SDHA, COX-1 protein level. Thus, the "developmental window" of eNP for PQQ-evoked mitochondrial biogenesis is proposed. This effect was independent of high antioxidant capacity of PQQ, which was confirmed in all tested cell populations, regardless of the stage of hiPSC neural differentiation. Furthermore, a strong induction of GFAP, with down regulation of MAP2 gene expression upon PQQ treatment was observed. This indicates a possibility of shifting the balance of cell differentiation in the favor of astroglia, but more research is needed at this point. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Integrative analysis of genes and miRNA alterations in human embryonic stem cells-derived neural cells after exposure to silver nanoparticles

    International Nuclear Information System (INIS)

    Oh, Jung-Hwa; Son, Mi-Young; Choi, Mi-Sun; Kim, Soojin; Choi, A-young; Lee, Hyang-Ae; Kim, Ki-Suk; Kim, Janghwan; Song, Chang Woo; Yoon, Seokjoo

    2016-01-01

    Given the rapid growth of engineered and customer products made of silver nanoparticles (Ag NPs), understanding their biological and toxicological effects on humans is critically important. The molecular developmental neurotoxic effects associated with exposure to Ag NPs were analyzed at the physiological and molecular levels, using an alternative cell model: human embryonic stem cell (hESC)-derived neural stem/progenitor cells (NPCs). In this study, the cytotoxic effects of Ag NPs (10–200 μg/ml) were examined in these hESC-derived NPCs, which have a capacity for neurogenesis in vitro, at 6 and 24 h. The results showed that Ag NPs evoked significant toxicity in hESC-derived NPCs at 24 h in a dose-dependent manner. In addition, Ag NPs induced cell cycle arrest and apoptosis following a significant increase in oxidative stress in these cells. To further clarify the molecular mechanisms of the toxicological effects of Ag NPs at the transcriptional and post-transcriptional levels, the global expression profiles of genes and miRNAs were analyzed in hESC-derived NPCs after Ag NP exposure. The results showed that Ag NPs induced oxidative stress and dysfunctional neurogenesis at the molecular level in hESC-derived NPCs. Based on this hESC-derived neural cell model, these findings have increased our understanding of the molecular events underlying developmental neurotoxicity induced by Ag NPs in humans. - Highlights: • This system served as a suitable model for developmental neurotoxicity testing. • Ag NPs induce the apoptosis in human neural cells by ROS generation. • Genes for development of neurons were dysregulated in response to Ag NPs. • Molecular events during early developmental neurotoxicity were proposed.

  5. Integrative analysis of genes and miRNA alterations in human embryonic stem cells-derived neural cells after exposure to silver nanoparticles

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Jung-Hwa [Korea Institute of Toxicology (KIT), Daejeon 34114 (Korea, Republic of); Department of human and environmental toxicology, University of Science & Technology, Daejeon 34113 (Korea, Republic of); Son, Mi-Young [Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahangno, Yuseong-gu, Daejeon 34141 (Korea, Republic of); Department of functional genomics, University of Science & Technology, 217 Gajungro, Yuseong-gu, Daejeon 34113 (Korea, Republic of); Choi, Mi-Sun; Kim, Soojin; Choi, A-young [Korea Institute of Toxicology (KIT), Daejeon 34114 (Korea, Republic of); Lee, Hyang-Ae; Kim, Ki-Suk [Korea Institute of Toxicology (KIT), Daejeon 34114 (Korea, Republic of); Department of human and environmental toxicology, University of Science & Technology, Daejeon 34113 (Korea, Republic of); Kim, Janghwan [Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahangno, Yuseong-gu, Daejeon 34141 (Korea, Republic of); Department of functional genomics, University of Science & Technology, 217 Gajungro, Yuseong-gu, Daejeon 34113 (Korea, Republic of); Song, Chang Woo, E-mail: cwsong@kitox.re.kr [Korea Institute of Toxicology (KIT), Daejeon 34114 (Korea, Republic of); Department of human and environmental toxicology, University of Science & Technology, Daejeon 34113 (Korea, Republic of); Yoon, Seokjoo, E-mail: sjyoon@kitox.re.kr [Korea Institute of Toxicology (KIT), Daejeon 34114 (Korea, Republic of); Department of human and environmental toxicology, University of Science & Technology, Daejeon 34113 (Korea, Republic of)

    2016-05-15

    Given the rapid growth of engineered and customer products made of silver nanoparticles (Ag NPs), understanding their biological and toxicological effects on humans is critically important. The molecular developmental neurotoxic effects associated with exposure to Ag NPs were analyzed at the physiological and molecular levels, using an alternative cell model: human embryonic stem cell (hESC)-derived neural stem/progenitor cells (NPCs). In this study, the cytotoxic effects of Ag NPs (10–200 μg/ml) were examined in these hESC-derived NPCs, which have a capacity for neurogenesis in vitro, at 6 and 24 h. The results showed that Ag NPs evoked significant toxicity in hESC-derived NPCs at 24 h in a dose-dependent manner. In addition, Ag NPs induced cell cycle arrest and apoptosis following a significant increase in oxidative stress in these cells. To further clarify the molecular mechanisms of the toxicological effects of Ag NPs at the transcriptional and post-transcriptional levels, the global expression profiles of genes and miRNAs were analyzed in hESC-derived NPCs after Ag NP exposure. The results showed that Ag NPs induced oxidative stress and dysfunctional neurogenesis at the molecular level in hESC-derived NPCs. Based on this hESC-derived neural cell model, these findings have increased our understanding of the molecular events underlying developmental neurotoxicity induced by Ag NPs in humans. - Highlights: • This system served as a suitable model for developmental neurotoxicity testing. • Ag NPs induce the apoptosis in human neural cells by ROS generation. • Genes for development of neurons were dysregulated in response to Ag NPs. • Molecular events during early developmental neurotoxicity were proposed.

  6. Artificial Neural Network Based Optical Character Recognition

    OpenAIRE

    Vivek Shrivastava; Navdeep Sharma

    2012-01-01

    Optical Character Recognition deals in recognition and classification of characters from an image. For the recognition to be accurate, certain topological and geometrical properties are calculated, based on which a character is classified and recognized. Also, the Human psychology perceives characters by its overall shape and features such as strokes, curves, protrusions, enclosures etc. These properties, also called Features are extracted from the image by means of spatial pixel-...

  7. Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model

    DEFF Research Database (Denmark)

    Tønnesen, Jan; Parish, Clare L; Sørensen, Andreas T

    2011-01-01

    Intrastriatal grafts of stem cell-derived dopamine (DA) neurons induce behavioral recovery in animal models of Parkinson's disease (PD), but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral...... of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation...... using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying...

  8. Cryptography based on neural networks - analytical results

    International Nuclear Information System (INIS)

    Rosen-Zvi, Michal; Kanter, Ido; Kinzel, Wolfgang

    2002-01-01

    The mutual learning process between two parity feed-forward networks with discrete and continuous weights is studied analytically, and we find that the number of steps required to achieve full synchronization between the two networks in the case of discrete weights is finite. The synchronization process is shown to be non-self-averaging and the analytical solution is based on random auxiliary variables. The learning time of an attacker that is trying to imitate one of the networks is examined analytically and is found to be much longer than the synchronization time. Analytical results are found to be in agreement with simulations. (letter to the editor)

  9. Recursive Neural Networks Based on PSO for Image Parsing

    Directory of Open Access Journals (Sweden)

    Guo-Rong Cai

    2013-01-01

    Full Text Available This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO and Recursive Neural Networks (RNNs. State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

  10. Cognitive disorder and changes in cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor following brain injury

    Institute of Scientific and Technical Information of China (English)

    Weiliang Zhao; Dezhi Kang; Yuanxiang Lin

    2008-01-01

    BACKGROUND: Learning and memory damage is one of the most permanent and the severest symptoms of traumatic brain injury; it can seriously influence the normal life and work of patients. Some research has demonstrated that cognitive disorder is closely related to nicotine cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor. OBJECTIVE: To summarize the cognitive disorder and changes in nicotine cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor following brain injury. RETRIEVAL STRATEGY: A computer-based online search was conducted in PUBMED for English language publications containing the key words "brain injured, cognitive handicap, acetylcholine, N-methyl-D aspartate receptors, neural cell adhesion molecule, brain-derived neurotrophic factor" from January 2000 to December 2007. There were 44 papers in total. Inclusion criteria: ① articles about changes in nicotine cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor following brain injury; ② articles in the same researching circle published in authoritative journals or recently published. Exclusion criteria: duplicated articles.LITERATURE EVALUATION: References were mainly derived from research on changes in these four factors following brain injury. The 20 included papers were clinical or basic experimental studies. DATA SYNTHESIS: After craniocerebral injury, changes in these four factors in brain were similar to those during recovery from cognitive disorder, to a certain degree. Some data have indicated that activation of nicotine cholinergic receptors, N-methyl-D aspartate receptors, neural cell adhesion molecule, and brain-derived neurotrophic factor could greatly improve cognitive disorder following brain injury. However, there are still a lot of questions remaining; for example, how do these

  11. Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

    Science.gov (United States)

    Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

    2018-06-01

    This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  12. A recurrent neural network based on projection operator for extended general variational inequalities.

    Science.gov (United States)

    Liu, Qingshan; Cao, Jinde

    2010-06-01

    Based on the projection operator, a recurrent neural network is proposed for solving extended general variational inequalities (EGVIs). Sufficient conditions are provided to ensure the global convergence of the proposed neural network based on Lyapunov methods. Compared with the existing neural networks for variational inequalities, the proposed neural network is a modified version of the general projection neural network existing in the literature and capable of solving the EGVI problems. In addition, simulation results on numerical examples show the effectiveness and performance of the proposed neural network.

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

    Science.gov (United States)

    Patel, Maulika S; Mazumdar, Himanshu S

    2014-11-21

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

  14. Artificial Neural Network Based Model of Photovoltaic Cell

    Directory of Open Access Journals (Sweden)

    Messaouda Azzouzi

    2017-03-01

    Full Text Available This work concerns the modeling of a photovoltaic system and the prediction of the sensitivity of electrical parameters (current, power of the six types of photovoltaic cells based on voltage applied between terminals using one of the best known artificial intelligence technique which is the Artificial Neural Networks. The results of the modeling and prediction have been well shown as a function of number of iterations and using different learning algorithms to obtain the best results. 

  15. Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin

    Directory of Open Access Journals (Sweden)

    William Taube Navaraj

    2017-09-01

    Full Text Available This paper presents novel Neural Nanowire Field Effect Transistors (υ-NWFETs based hardware-implementable neural network (HNN approach for tactile data processing in electronic skin (e-skin. The viability of Si nanowires (NWs as the active material for υ-NWFETs in HNN is explored through modeling and demonstrated by fabricating the first device. Using υ-NWFETs to realize HNNs is an interesting approach as by printing NWs on large area flexible substrates it will be possible to develop a bendable tactile skin with distributed neural elements (for local data processing, as in biological skin in the backplane. The modeling and simulation of υ-NWFET based devices show that the overlapping areas between individual gates and the floating gate determines the initial synaptic weights of the neural network - thus validating the working of υ-NWFETs as the building block for HNN. The simulation has been further extended to υ-NWFET based circuits and neuronal computation system and this has been validated by interfacing it with a transparent tactile skin prototype (comprising of 6 × 6 ITO based capacitive tactile sensors array integrated on the palm of a 3D printed robotic hand. In this regard, a tactile data coding system is presented to detect touch gesture and the direction of touch. Following these simulation studies, a four-gated υ-NWFET is fabricated with Pt/Ti metal stack for gates, source and drain, Ni floating gate, and Al2O3 high-k dielectric layer. The current-voltage characteristics of fabricated υ-NWFET devices confirm the dependence of turn-off voltages on the (synaptic weight of each gate. The presented υ-NWFET approach is promising for a neuro-robotic tactile sensory system with distributed computing as well as numerous futuristic applications such as prosthetics, and electroceuticals.

  16. Control of GMA Butt Joint Welding Based on Neural Networks

    DEFF Research Database (Denmark)

    Christensen, Kim Hardam; Sørensen, Torben

    2004-01-01

    This paper presents results from an experimentally based research on Gas Metal Arc Welding (GMAW), controlled by the artificial neural network (ANN) technology. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a high degree of quality......-linear least square error minimization, has been used with the back-propagation algorithm for training the network, while a Bayesian regularization technique has been successfully applied for minimizing the risk of inexpedient over-training....

  17. Plasmid-based generation of induced neural stem cells from adult human fibroblasts

    Directory of Open Access Journals (Sweden)

    Philipp Capetian

    2016-10-01

    Full Text Available Direct reprogramming from somatic to neural cell types has become an alternative to induced pluripotent stem cells. Most protocols employ viral expression systems, posing the risk of random genomic integration. Recent developments led to plasmid-based protocols, lowering this risk. However, these protocols either relied on continuous presence of a variety of small molecules or were only able to reprogram murine cells. We therefore established a reprogramming protocol based on vectors containing the Epstein-Barr virus (EBV-derived oriP/EBNA1 as well as the defined expression factors Oct3/4, Sox2, Klf4, L-myc, Lin28, and a small hairpin directed against p53. We employed a defined neural medium in combination with the neurotrophins bFGF, EGF and FGF4 for cultivation without the addition of small molecules. After reprogramming, cells demonstrated a temporary increase in the expression of endogenous Oct3/4. We obtained induced neural stem cells (iNSC 30 days after transfection. In contrast to previous results, plasmid vectors as well as a residual expression of reprogramming factors remained detectable in all cell lines. Cells showed a robust differentiation into neuronal (72% and glial cells (9% astrocytes, 6% oligodendrocytes. Despite the temporary increase of pluripotency-associated Oct3/4 expression during reprogramming, we did not detect pluripotent stem cells or non-neural cells in culture (except occasional residual fibroblasts. Neurons showed electrical activity and functional glutamatergic synapses. Our results demonstrate that reprogramming adult human fibroblasts to iNSC by plasmid vectors and basic neural medium without small molecules is possible and feasible. However, a full set of pluripotency-associated transcription factors may indeed result in the acquisition of a transient (at least partial pluripotent intermediate during reprogramming. In contrast to previous reports, the EBV-based plasmid system remained present and active inside

  18. Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

    Science.gov (United States)

    Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

    2015-11-01

    This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

    Directory of Open Access Journals (Sweden)

    Manjunath Patel Gowdru Chandrashekarappa

    2014-01-01

    Full Text Available The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN and genetic algorithm neural network (GA-NN. The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.

  20. Derivation of corneal endothelial cell-like cells from rat neural crest cells in vitro.

    Directory of Open Access Journals (Sweden)

    Chengqun Ju

    Full Text Available The aim of this study was to investigate the feasibility of inducing rat neural crest cells (NCC to differentiate to functional corneal endothelial cell (CEC-like cells in vitro. Rat NCC were induced with adult CEC-derived conditioned medium. Immunofluorescence, flow cytometry and real time RT-PCR assay were used to detect expression of the corneal endothelium differentiation marker N-cadherin and transcription factors FoxC1 and Pitx2. CFDA SE-labeled CEC-like cells were transplanted to the corneal endothelium of a rat corneal endothelium deficiency model, and an eye-down position was maintained for 24 hours to allow cell attachment. The animals were observed for as long as 2 months after surgery and underwent clinical and histological examination. Spindle-like NCC turned to polygonal CEC-like after induction and expressed N-cadherin, FoxC1, Pitx2, zonula occludens-1 and sodium-potassium pump Na(+/K(+ ATPase. The corneas of the experimental group were much clearer than those of the control group and the mean corneal thickness in the experimental group was significantly less than in the control group7, 14, 21 and 28 days after surgery. Confocal microscopy through focusing and histological analysis confirmed that green fluorescence-positive CEC-like cells formed a monolayer covering the Descemet's membrane in the experimental group. In conclusion, CEC-like cells derived from NCCs displayed characters of native CEC, and the induction protocol provides guidance for future human CEC induction from NCC.

  1. Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging.

    Science.gov (United States)

    Gençay, R; Qi, M

    2001-01-01

    We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.

  2. hiPSC-derived neural stem cells from patients with schizophrenia induce an impaired angiogenesis.

    Science.gov (United States)

    Casas, Bárbara S; Vitória, Gabriela; do Costa, Marcelo N; Madeiro da Costa, Rodrigo; Trindade, Pablo; Maciel, Renata; Navarrete, Nelson; Rehen, Stevens K; Palma, Verónica

    2018-02-22

    Schizophrenia is a neurodevelopmental disease characterized by cerebral connectivity impairment and loss of gray matter. It was described in adult schizophrenia patients (SZP) that concentration of VEGFA, a master angiogenic factor, is decreased. Recent evidence suggests cerebral hypoperfusion related to a dysfunctional Blood Brain Barrier (BBB) in SZP. Since neurogenesis and blood-vessel formation occur in a coincident and coordinated fashion, a defect in neurovascular development could result in increased vascular permeability and, therefore, in poor functionality of the SZP's neurons. Here, we characterized the conditioned media (CM) of human induced Pluripotent Stem Cells (hiPSC)-derived Neural Stem Cells of SZP (SZP NSC) versus healthy subjects (Ctrl NSC), and its impact on angiogenesis. Our results reveal that SZP NSC have an imbalance in the secretion and expression of several angiogenic factors, among them non-canonical neuro-angiogenic guidance factors. SZP NSC migrated less and their CM was less effective in inducing migration and angiogenesis both in vitro and in vivo. Since SZP originates during embryonic brain development, our findings suggest a defective crosstalk between NSC and endothelial cells (EC) during the formation of the neuro-angiogenic niche.

  3. Neurally Derived Tissues in Xenopus laevis Embryos Exhibit a Consistent Bioelectrical Left-Right Asymmetry

    Directory of Open Access Journals (Sweden)

    Vaibhav P. Pai

    2012-01-01

    Full Text Available Consistent left-right asymmetry in organ morphogenesis is a fascinating aspect of bilaterian development. Although embryonic patterning of asymmetric viscera, heart, and brain is beginning to be understood, less is known about possible subtle asymmetries present in anatomically identical paired structures. We investigated two important developmental events: physiological controls of eye development and specification of neural crest derivatives, in Xenopus laevis embryos. We found that the striking hyperpolarization of transmembrane potential (Vmem demarcating eye induction usually occurs in the right eye field first. This asymmetry is randomized by perturbing visceral left-right patterning, suggesting that eye asymmetry is linked to mechanisms establishing primary laterality. Bilateral misexpression of a depolarizing channel mRNA affects primarily the right eye, revealing an additional functional asymmetry in the control of eye patterning by Vmem. The ATP-sensitive K+ channel subunit transcript, SUR1, is asymmetrically expressed in the eye primordia, thus being a good candidate for the observed physiological asymmetries. Such subtle asymmetries are not only seen in the eye: consistent asymmetry was also observed in the migration of differentiated melanocytes on the left and right sides. These data suggest that even anatomically symmetrical structures may possess subtle but consistent laterality and interact with other developmental left-right patterning pathways.

  4. Immunization with a Neural-Derived Peptide Protects the Spinal Cord from Apoptosis after Traumatic Injury

    Directory of Open Access Journals (Sweden)

    Roxana Rodríguez-Barrera

    2013-01-01

    Full Text Available Apoptosis is one of the most destructive mechanisms that develop after spinal cord (SC injury. Immunization with neural-derived peptides (INDPs such as A91 has shown to reduce the deleterious proinflammatory response and the amount of harmful compounds produced after SC injury. With the notion that the aforementioned elements are apoptotic inducers, we hypothesized that INDPs would reduce apoptosis after SC injury. In order to test this assumption, adult rats were subjected to SC contusion and immunized either with A91 or phosphate buffered saline (PBS; control group. Seven days after injury, animals were euthanized to evaluate the number of apoptotic cells at the injury site. Apoptosis was evaluated using DAPI and TUNEL techniques; caspase-3 activity was also evaluated. To further elucidate the mechanisms through which A91 exerts this antiapoptotic effects we quantified tumor necrosis factor-alpha (TNF-α. To also demonstrate that the decrease in apoptotic cells correlated with a functional improvement, locomotor recovery was evaluated. Immunization with A91 significantly reduced the number of apoptotic cells and decreased caspase-3 activity and TNF-α concentration. Immunization with A91 also improved the functional recovery of injured rats. The present study shows the beneficial effect of INDPs on preventing apoptosis and provides more evidence on the neuroprotective mechanisms exerted by this strategy.

  5. Artificial Neural Network for Production of Antioxidant Peptides Derived from Bighead Carp Muscles with Alcalase

    Directory of Open Access Journals (Sweden)

    Lin Li

    2006-01-01

    Full Text Available Controlled enzymatic modification proteins are currently being used as good sources of bioactive protein ingredients, and hydrolysates derived from bighead carp muscles may serve as antioxidants through the control of the processing-related parameters. The antioxidant ability was evaluated with regard to the scavenging effect on free radical DPPH·, OH· and O2 ·–. Due to the robustness, fault tolerance, high computational speed and self--learning ability, artificial neural network (ANN can be employed to build a predictive model for hydrolysis and optimize the hydrolysis variables: pH, temperature, hydrolysis time, muscle/water ratio and enzyme/substrate ratio (E/S for the production of antioxidant peptides. Optimum conditions to achieve the maximum antioxidant ability were obtained. The hydrolysates, which scavenged most effectively the DPPH·, OH· and O2 ·–, were hydrolyzed for 4.8 h with an activity of alcalase of 4.8 AU/kg, for 6 h with 3.84 AU/kg and for 4.3 h with 4.8 AU/kg, at pH=7.5 and 60 °C. Their respective muscle/water ratio was 1:1.9, 1:1.4 and 1:1. The present study confirmed that ANN could be used to simulate the hydrolysis process and predict hydrolysis conditions under which the hydrolysates could show the most effective scavenging ability on DPPH·, OH· and O2 ·–.

  6. Characterization and molecular profiling of PSEN1 familial Alzheimer's disease iPSC-derived neural progenitors.

    Directory of Open Access Journals (Sweden)

    Andrew A Sproul

    Full Text Available Presenilin 1 (PSEN1 encodes the catalytic subunit of γ-secretase, and PSEN1 mutations are the most common cause of early onset familial Alzheimer's disease (FAD. In order to elucidate pathways downstream of PSEN1, we characterized neural progenitor cells (NPCs derived from FAD mutant PSEN1 subjects. Thus, we generated induced pluripotent stem cells (iPSCs from affected and unaffected individuals from two families carrying PSEN1 mutations. PSEN1 mutant fibroblasts, and NPCs produced greater ratios of Aβ42 to Aβ40 relative to their control counterparts, with the elevated ratio even more apparent in PSEN1 NPCs than in fibroblasts. Molecular profiling identified 14 genes differentially-regulated in PSEN1 NPCs relative to control NPCs. Five of these targets showed differential expression in late onset AD/Intermediate AD pathology brains. Therefore, in our PSEN1 iPSC model, we have reconstituted an essential feature in the molecular pathogenesis of FAD, increased generation of Aβ42/40, and have characterized novel expression changes.

  7. Data systems and computer science: Neural networks base R/T program overview

    Science.gov (United States)

    Gulati, Sandeep

    1991-01-01

    The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

  8. The neuro-glial properties of adipose-derived adult stromal (ADAS) cells are not regulated by Notch 1 and are not derived from neural crest lineage.

    Science.gov (United States)

    Wrage, Philip C; Tran, Thi; To, Khai; Keefer, Edward W; Ruhn, Kelly A; Hong, John; Hattangadi, Supriya; Treviño, Isaac; Tansey, Malú G

    2008-01-16

    We investigated whether adipose-derived adult stromal (ADAS) are of neural crest origin and the extent to which Notch 1 regulates their growth and differentiation. Mouse ADAS cells cultured in media formulated for neural stem cells (NSC) displayed limited capacity for self-renewal, clonogenicity, and neurosphere formation compared to NSC from the subventricular zone in the hippocampus. Although ADAS cells expressed Nestin, GFAP, NSE and Tuj1 in vitro, exposure to NSC differentiation supplements did not induce mature neuronal marker expression. In contrast, in mesenchymal stem cell (MSC) media, ADAS cells retained their ability to proliferate and differentiate beyond 20 passages and expressed high levels of Nestin. In neuritizing cocktails, ADAS cells extended processes, downregulated Nestin expression, and displayed depolarization-induced Ca(2+) transients but no spontaneous or evoked neural network activity on Multi-Electrode Arrays. Deletion of Notch 1 in ADAS cell cultures grown in NSC proliferation medium did not significantly alter their proliferative potential in vitro or the differentiation-induced downregulation of Nestin. Co-culture of ADAS cells with fibroblasts that stably expressed the Notch ligand Jagged 1 or overexpression of the Notch intracellular domain (NICD) did not alter ADAS cell growth, morphology, or cellular marker expression. ADAS cells did not display robust expression of neural crest transcription factors or genes (Sox, CRABP2, and TH); and lineage tracing analyses using Wnt1-Cre;Rosa26R-lacZ or -EYFP reporter mice confirmed that fewer than 2% of the ADAS cell population derived from a Wnt1-positive population during development. In summary, although media formulations optimized for MSCs or NSCs enable expansion of mouse ADAS cells in vitro, we find no evidence that these cells are of neural crest origin, that they can undergo robust terminal differentiation into functionally mature neurons, and that Notch 1 is likely to be a key

  9. Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays

    Science.gov (United States)

    2017-01-01

    Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don’t include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results. PMID:28931066

  10. Neural Network Control-Based Drive Design of Servomotor and Its Application to Automatic Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang

    2015-01-01

    Full Text Available An automatic guided vehicle (AGV is extensively used for productions in a flexible manufacture system with high efficiency and high flexibility. A servomotor-based AGV is designed and implemented in this paper. In order to steer the AGV to go along a predefined path with corner or arc, the conventional proportional-integral-derivative (PID control is used in the system. However, it is difficult to tune PID gains at various conditions. As a result, the neural network (NN control is considered to assist the PID control for gain tuning. The experimental results are first provided to verify the correctness of the neural network plus PID control for 400 W-motor control system. Secondly, the AGV includes two sets of the designed motor systems and CAN BUS transmission so that it can move along the straight line and curve paths shown in the taped videos.

  11. A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2015-01-01

    Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.

  12. Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

    Science.gov (United States)

    Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

    2017-01-01

    Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

  13. Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

    Directory of Open Access Journals (Sweden)

    Chuan Chen

    Full Text Available Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs with both discrete delay and distributed delay (mixed delays. By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

  14. Task-dependent neural bases of perceiving emotionally expressive targets

    Directory of Open Access Journals (Sweden)

    Jamil eZaki

    2012-08-01

    Full Text Available Social cognition is fundamentally interpersonal: individuals’ behavior and dispositions critically affect their interaction partners’ information processing. However, cognitive neuroscience studies, partially because of methodological constraints, have remained largely perceiver-centric: focusing on the abilities, motivations, and goals of social perceivers while largely ignoring interpersonal effects. Here, we address this knowledge gap by examining the neural bases of perceiving emotionally expressive and inexpressive social targets. Sixteen perceivers were scanned using fMRI while they watched targets discussing emotional autobiographical events. Perceivers continuously rated each target’s emotional state or eye-gaze direction. The effects of targets’ emotional expressivity on perceiver’s brain activity depended on task set: when perceivers explicitly attended to targets’ emotions, expressivity predicted activity in neural structures—including medial prefrontal and posterior cingulate cortex—associated with drawing inferences about mental states. When perceivers instead attended to targets’ eye-gaze, target expressivity predicted activity in regions—including somatosensory cortex, fusiform gyrus, and motor cortex—associated with monitoring sensorimotor states and biological motion. These findings suggest that expressive targets affect information processing in manner that depends on perceivers’ goals. More broadly, these data provide an early step towards understanding the neural bases of interpersonal social cognition.

  15. Chinese Sentence Classification Based on Convolutional Neural Network

    Science.gov (United States)

    Gu, Chengwei; Wu, Ming; Zhang, Chuang

    2017-10-01

    Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

  16. Parametric Jominy profiles predictor based on neural networks

    Directory of Open Access Journals (Sweden)

    Valentini, R.

    2005-12-01

    Full Text Available The paper presents a method for the prediction of the Jominy hardness profiles of steels for microalloyed Boron steel which is based on neural networks. The Jominy profile has been parameterized and the parameters, which are a sort of "compact representation" of the profile itself, are linked to the steel chemical composition through a neural network. Numerical results are presented and discussed.

    El trabajo presenta un método de estimación de perfiles de dureza Jominy para aceros microaleados al boro basado en redes neuronales. Los parámetros de perfil Jominy, que constituyen una especie de "representación compacta" del perfil mismo, son determinados y puestos en relación con la composición química del acero mediante una red neuronal. Los resultados numéricos son expuestos y discutidos.

  17. A Gain-Scheduling PI Control Based on Neural Networks

    Directory of Open Access Journals (Sweden)

    Stefania Tronci

    2017-01-01

    Full Text Available This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR, considering both single-input single-output (SISO and multi-input multi-output (MIMO control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

  18. ID card number detection algorithm based on convolutional neural network

    Science.gov (United States)

    Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan

    2018-04-01

    In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.

  19. Battery Performance Modelling ad Simulation: a Neural Network Based Approach

    Science.gov (United States)

    Ottavianelli, Giuseppe; Donati, Alessandro

    2002-01-01

    This project has developed on the background of ongoing researches within the Control Technology Unit (TOS-OSC) of the Special Projects Division at the European Space Operations Centre (ESOC) of the European Space Agency. The purpose of this research is to develop and validate an Artificial Neural Network tool (ANN) able to model, simulate and predict the Cluster II battery system's performance degradation. (Cluster II mission is made of four spacecraft flying in tetrahedral formation and aimed to observe and study the interaction between sun and earth by passing in and out of our planet's magnetic field). This prototype tool, named BAPER and developed with a commercial neural network toolbox, could be used to support short and medium term mission planning in order to improve and maximise the batteries lifetime, determining which are the future best charge/discharge cycles for the batteries given their present states, in view of a Cluster II mission extension. This study focuses on the five Silver-Cadmium batteries onboard of Tango, the fourth Cluster II satellite, but time restrains have allowed so far to perform an assessment only on the first battery. In their most basic form, ANNs are hyper-dimensional curve fits for non-linear data. With their remarkable ability to derive meaning from complicated or imprecise history data, ANN can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. ANNs learn by example, and this is why they can be described as an inductive, or data-based models for the simulation of input/target mappings. A trained ANN can be thought of as an "expert" in the category of information it has been given to analyse, and this expert can then be used, as in this project, to provide projections given new situations of interest and answer "what if" questions. The most appropriate algorithm, in terms of training speed and memory storage requirements, is clearly the Levenberg

  20. Derivative based sensitivity analysis of gamma index

    Directory of Open Access Journals (Sweden)

    Biplab Sarkar

    2015-01-01

    Full Text Available Originally developed as a tool for patient-specific quality assurance in advanced treatment delivery methods to compare between measured and calculated dose distributions, the gamma index (γ concept was later extended to compare between any two dose distributions. It takes into effect both the dose difference (DD and distance-to-agreement (DTA measurements in the comparison. Its strength lies in its capability to give a quantitative value for the analysis, unlike other methods. For every point on the reference curve, if there is at least one point in the evaluated curve that satisfies the pass criteria (e.g., δDD = 1%, δDTA = 1 mm, the point is included in the quantitative score as "pass." Gamma analysis does not account for the gradient of the evaluated curve - it looks at only the minimum gamma value, and if it is <1, then the point passes, no matter what the gradient of evaluated curve is. In this work, an attempt has been made to present a derivative-based method for the identification of dose gradient. A mathematically derived reference profile (RP representing the penumbral region of 6 MV 10 cm × 10 cm field was generated from an error function. A general test profile (GTP was created from this RP by introducing 1 mm distance error and 1% dose error at each point. This was considered as the first of the two evaluated curves. By its nature, this curve is a smooth curve and would satisfy the pass criteria for all points in it. The second evaluated profile was generated as a sawtooth test profile (STTP which again would satisfy the pass criteria for every point on the RP. However, being a sawtooth curve, it is not a smooth one and would be obviously poor when compared with the smooth profile. Considering the smooth GTP as an acceptable profile when it passed the gamma pass criteria (1% DD and 1 mm DTA against the RP, the first and second order derivatives of the DDs (δD', δD" between these two curves were derived and used as the

  1. Bone Marrow-Derived, Neural-Like Cells Have the Characteristics of Neurons to Protect the Peripheral Nerve in Microenvironment

    Directory of Open Access Journals (Sweden)

    Shi-lei Guo

    2015-01-01

    Full Text Available Effective repair of peripheral nerve defects is difficult because of the slow growth of new axonal growth. We propose that “neural-like cells” may be useful for the protection of peripheral nerve destructions. Such cells should prolong the time for the disintegration of spinal nerves, reduce lesions, and improve recovery. But the mechanism of neural-like cells in the peripheral nerve is still unclear. In this study, bone marrow-derived neural-like cells were used as seed cells. The cells were injected into the distal end of severed rabbit peripheral nerves that were no longer integrated with the central nervous system. Electromyography (EMG, immunohistochemistry, and transmission electron microscopy (TEM were employed to analyze the development of the cells in the peripheral nerve environment. The CMAP amplitude appeared during the 5th week following surgery, at which time morphological characteristics of myelinated nerve fiber formation were observed. Bone marrow-derived neural-like cells could protect the disintegration and destruction of the injured peripheral nerve.

  2. Optical supervised filtering technique based on Hopfield neural network

    Science.gov (United States)

    Bal, Abdullah

    2004-11-01

    Hopfield neural network is commonly preferred for optimization problems. In image segmentation, conventional Hopfield neural networks (HNN) are formulated as a cost-function-minimization problem to perform gray level thresholding on the image histogram or the pixels' gray levels arranged in a one-dimensional array [R. Sammouda, N. Niki, H. Nishitani, Pattern Rec. 30 (1997) 921-927; K.S. Cheng, J.S. Lin, C.W. Mao, IEEE Trans. Med. Imag. 15 (1996) 560-567; C. Chang, P. Chung, Image and Vision comp. 19 (2001) 669-678]. In this paper, a new high speed supervised filtering technique is proposed for image feature extraction and enhancement problems by modifying the conventional HNN. The essential improvement in this technique is to use 2D convolution operation instead of weight-matrix multiplication. Thereby, neural network based a new filtering technique has been obtained that is required just 3 × 3 sized filter mask matrix instead of large size weight coefficient matrix. Optical implementation of the proposed filtering technique is executed easily using the joint transform correlator. The requirement of non-negative data for optical implementation is provided by bias technique to convert the bipolar data to non-negative data. Simulation results of the proposed optical supervised filtering technique are reported for various feature extraction problems such as edge detection, corner detection, horizontal and vertical line extraction, and fingerprint enhancement.

  3. A developmental perspective on the neural bases of human empathy.

    Science.gov (United States)

    Tousignant, Béatrice; Eugène, Fanny; Jackson, Philip L

    2017-08-01

    While empathy has been widely studied in philosophical and psychological literatures, recent advances in social neuroscience have shed light on the neural correlates of this complex interpersonal phenomenon. In this review, we provide an overview of brain imaging studies that have investigated the neural substrates of human empathy. Based on existing models of the functional architecture of empathy, we review evidence of the neural underpinnings of each main component, as well as their development from infancy. Although early precursors of affective sharing and self-other distinction appear to be present from birth, recent findings also suggest that even higher-order components of empathy such as perspective-taking and emotion regulation demonstrate signs of development during infancy. This merging of developmental and social neuroscience literature thus supports the view that ontogenic development of empathy is rooted in early infancy, well before the emergence of verbal abilities. With age, the refinement of top-down mechanisms may foster more appropriate empathic responses, thus promoting greater altruistic motivation and prosocial behaviors. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Neural bases of ingroup altruistic motivation in soccer fans.

    Science.gov (United States)

    Bortolini, Tiago; Bado, Patrícia; Hoefle, Sebastian; Engel, Annerose; Zahn, Roland; de Oliveira Souza, Ricardo; Dreher, Jean-Claude; Moll, Jorge

    2017-11-23

    Humans have a strong need to belong to social groups and a natural inclination to benefit ingroup members. Although the psychological mechanisms behind human prosociality have extensively been studied, the specific neural systems bridging group belongingness and altruistic motivation remain to be identified. Here, we used soccer fandom as an ecological framing of group membership to investigate the neural mechanisms underlying ingroup altruistic behaviour in male fans using event-related functional magnetic resonance. We designed an effort measure based on handgrip strength to assess the motivation to earn money (i) for oneself, (ii) for anonymous ingroup fans, or (iii) for a neutral group of anonymous non-fans. While overlapping valuation signals in the medial orbitofrontal cortex (mOFC) were observed for the three conditions, the subgenual cingulate cortex (SCC) exhibited increased functional connectivity with the mOFC as well as stronger hemodynamic responses for ingroup versus outgroup decisions. These findings indicate a key role for the SCC, a region previously implicated in altruistic decisions and group affiliation, in dovetailing altruistic motivations with neural valuation systems in real-life ingroup behaviour.

  5. The Artificial Neural Networks Based on Scalarization Method for a Class of Bilevel Biobjective Programming Problem

    Science.gov (United States)

    Chen, Zhong; Liu, June; Li, Xiong

    2017-01-01

    A two-stage artificial neural network (ANN) based on scalarization method is proposed for bilevel biobjective programming problem (BLBOP). The induced set of the BLBOP is firstly expressed as the set of minimal solutions of a biobjective optimization problem by using scalar approach, and then the whole efficient set of the BLBOP is derived by the proposed two-stage ANN for exploring the induced set. In order to illustrate the proposed method, seven numerical examples are tested and compared with results in the classical literature. Finally, a practical problem is solved by the proposed algorithm. PMID:29312446

  6. The postischemic environment differentially impacts teratoma or tumor formation after transplantation of human embryonic stem cell-derived neural progenitors

    DEFF Research Database (Denmark)

    Seminatore, Christine; Polentes, Jerome; Ellman, Ditte

    2010-01-01

    Risk of tumorigenesis is a major obstacle to human embryonic and induced pluripotent stem cell therapy. Likely linked to the stage of differentiation of the cells at the time of implantation, formation of teratoma/tumors can also be influenced by factors released by the host tissue. We have...... analyzed the relative effects of the stage of differentiation and the postischemic environment on the formation of adverse structures by transplanted human embryonic stem cell-derived neural progenitors....

  7. Neural Differentiation of Human Adipose Tissue-Derived Stem Cells Involves Activation of the Wnt5a/JNK Signalling

    Directory of Open Access Journals (Sweden)

    Sujeong Jang

    2015-01-01

    Full Text Available Stem cells are a powerful resource for cell-based transplantation therapies, but understanding of stem cell differentiation at the molecular level is not clear yet. We hypothesized that the Wnt pathway controls stem cell maintenance and neural differentiation. We have characterized the transcriptional expression of Wnt during the neural differentiation of hADSCs. After neural induction, the expressions of Wnt2, Wnt4, and Wnt11 were decreased, but the expression of Wnt5a was increased compared with primary hADSCs in RT-PCR analysis. In addition, the expression levels of most Fzds and LRP5/6 ligand were decreased, but not Fzd3 and Fzd5. Furthermore, Dvl1 and RYK expression levels were downregulated in NI-hADSCs. There were no changes in the expression of ß-catenin and GSK3ß. Interestingly, Wnt5a expression was highly increased in NI-hADSCs by real time RT-PCR analysis and western blot. Wnt5a level was upregulated after neural differentiation and Wnt3, Dvl2, and Naked1 levels were downregulated. Finally, we found that the JNK expression was increased after neural induction and ERK level was decreased. Thus, this study shows for the first time how a single Wnt5a ligand can activate the neural differentiation pathway through the activation of Wnt5a/JNK pathway by binding Fzd3 and Fzd5 and directing Axin/GSK-3ß in hADSCs.

  8. Decreased neural precursor cell pool in NADPH oxidase 2-deficiency: From mouse brain to neural differentiation of patient derived iPSC

    Directory of Open Access Journals (Sweden)

    Zeynab Nayernia

    2017-10-01

    Full Text Available There is emerging evidence for the involvement of reactive oxygen species (ROS in the regulation of stem cells and cellular differentiation. Absence of the ROS-generating NADPH oxidase NOX2 in chronic granulomatous disease (CGD patients, predominantly manifests as immune deficiency, but has also been associated with decreased cognition. Here, we investigate the role of NOX enzymes in neuronal homeostasis in adult mouse brain and in neural cells derived from human induced pluripotent stem cells (iPSC. High levels of NOX2 were found in mouse adult neurogenic regions. In NOX2-deficient mice, neurogenic regions showed diminished redox modifications, as well as decrease in neuroprecursor numbers and in expression of genes involved in neural differentiation including NES, BDNF and OTX2. iPSC from healthy subjects and patients with CGD were used to study the role of NOX2 in human in vitro neuronal development. Expression of NOX2 was low in undifferentiated iPSC, upregulated upon neural induction, and disappeared during neuronal differentiation. In human neurospheres, NOX2 protein and ROS generation were polarized within the inner cell layer of rosette structures. NOX2 deficiency in CGD-iPSCs resulted in an abnormal neural induction in vitro, as revealed by a reduced expression of neuroprogenitor markers (NES, BDNF, OTX2, NRSF/REST, and a decreased generation of mature neurons. Vector-mediated NOX2 expression in NOX2-deficient iPSCs rescued neurogenesis. Taken together, our study provides novel evidence for a regulatory role of NOX2 during early stages of neurogenesis in mouse and human.

  9. Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

    Directory of Open Access Journals (Sweden)

    Hiroyuki Akama

    Full Text Available In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF. This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk and co-occurrence adjustment (degree balance and distribution. We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

  10. The Reference Ability Neural Network Study: Life-time stability of reference-ability neural networks derived from task maps of young adults.

    Science.gov (United States)

    Habeck, C; Gazes, Y; Razlighi, Q; Steffener, J; Brickman, A; Barulli, D; Salthouse, T; Stern, Y

    2016-01-15

    Analyses of large test batteries administered to individuals ranging from young to old have consistently yielded a set of latent variables representing reference abilities (RAs) that capture the majority of the variance in age-related cognitive change: Episodic Memory, Fluid Reasoning, Perceptual Processing Speed, and Vocabulary. In a previous paper (Stern et al., 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20-80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging. We used a multivariate approach, linear indicator regression, to derive a unique covariance pattern or Reference Ability Neural Network (RANN) for each of the 4 RAs. The RANNs were derived from the neural task data of 64 younger adults of age 30 and below. We then prospectively applied the RANNs to fMRI data from the remaining sample of 227 adults of age 31 and above in order to classify each subject-task map into one of the 4 possible reference domains. Overall classification accuracy across subjects in the sample age 31 and above was 0.80±0.18. Classification accuracy by RA domain was also good, but variable; memory: 0.72±0.32; reasoning: 0.75±0.35; speed: 0.79±0.31; vocabulary: 0.94±0.16. Classification accuracy was not associated with cross-sectional age, suggesting that these networks, and their specificity to the respective reference domain, might remain intact throughout the age range. Higher mean brain volume was correlated with increased overall classification accuracy; better overall performance on the tasks in the scanner was also associated with classification accuracy. For the RANN network scores, we observed for each RANN that a higher score was associated with a higher corresponding classification accuracy for that reference ability. Despite the absence of behavioral performance information in the

  11. Efficient derivation of multipotent neural stem/progenitor cells from non-human primate embryonic stem cells.

    Directory of Open Access Journals (Sweden)

    Hiroko Shimada

    Full Text Available The common marmoset (Callithrix jacchus is a small New World primate that has been used as a non-human primate model for various biomedical studies. We previously demonstrated that transplantation of neural stem/progenitor cells (NS/PCs derived from mouse and human embryonic stem cells (ESCs and induced pluripotent stem cells (iPSCs promote functional locomotor recovery of mouse spinal cord injury models. However, for the clinical application of such a therapeutic approach, we need to evaluate the efficacy and safety of pluripotent stem cell-derived NS/PCs not only by xenotransplantation, but also allotransplantation using non-human primate models to assess immunological rejection and tumorigenicity. In the present study, we established a culture method to efficiently derive NS/PCs as neurospheres from common marmoset ESCs. Marmoset ESC-derived neurospheres could be passaged repeatedly and showed sequential generation of neurons and astrocytes, similar to that of mouse ESC-derived NS/PCs, and gave rise to functional neurons as indicated by calcium imaging. Although marmoset ESC-derived NS/PCs could not differentiate into oligodendrocytes under default culture conditions, these cells could abundantly generate oligodendrocytes by incorporating additional signals that recapitulate in vivo neural development. Moreover, principal component analysis of microarray data demonstrated that marmoset ESC-derived NS/PCs acquired similar gene expression profiles to those of fetal brain-derived NS/PCs by repeated passaging. Therefore, marmoset ESC-derived NS/PCs may be useful not only for accurate evaluation by allotransplantation of NS/PCs into non-human primate models, but are also applicable to analysis of iPSCs established from transgenic disease model marmosets.

  12. Real-Time Inverse Optimal Neural Control for Image Based Visual Servoing with Nonholonomic Mobile Robots

    Directory of Open Access Journals (Sweden)

    Carlos López-Franco

    2015-01-01

    Full Text Available We present an inverse optimal neural controller for a nonholonomic mobile robot with parameter uncertainties and unknown external disturbances. The neural controller is based on a discrete-time recurrent high order neural network (RHONN trained with an extended Kalman filter. The reference velocities for the neural controller are obtained with a visual sensor. The effectiveness of the proposed approach is tested by simulations and real-time experiments.

  13. Neural Network Based Finite-Time Stabilization for Discrete-Time Markov Jump Nonlinear Systems with Time Delays

    Directory of Open Access Journals (Sweden)

    Fei Chen

    2013-01-01

    Full Text Available This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabilization of the closed-loop system. A numerical example is illustrated to verify the efficiency of the proposed technique.

  14. Neural Network Based Sensory Fusion for Landmark Detection

    Science.gov (United States)

    Kumbla, Kishan -K.; Akbarzadeh, Mohammad R.

    1997-01-01

    NASA is planning to send numerous unmanned planetary missions to explore the space. This requires autonomous robotic vehicles which can navigate in an unstructured, unknown, and uncertain environment. Landmark based navigation is a new area of research which differs from the traditional goal-oriented navigation, where a mobile robot starts from an initial point and reaches a destination in accordance with a pre-planned path. The landmark based navigation has the advantage of allowing the robot to find its way without communication with the mission control station and without exact knowledge of its coordinates. Current algorithms based on landmark navigation however pose several constraints. First, they require large memories to store the images. Second, the task of comparing the images using traditional methods is computationally intensive and consequently real-time implementation is difficult. The method proposed here consists of three stages, First stage utilizes a heuristic-based algorithm to identify significant objects. The second stage utilizes a neural network (NN) to efficiently classify images of the identified objects. The third stage combines distance information with the classification results of neural networks for efficient and intelligent navigation.

  15. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  16. A neutron spectrum unfolding code based on generalized regression artificial neural networks

    International Nuclear Information System (INIS)

    Rosario Martinez-Blanco, Ma. del

    2016-01-01

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a "6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. - Highlights: • Main drawback of neutron spectrometry with BPNN is network topology optimization. • Compared to BPNN, it’s usually much faster to train a (GRNN). • GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest. • This computational code, automates the pre-processing, training

  17. Heparan Sulfate Proteoglycans as Drivers of Neural Progenitors Derived From Human Mesenchymal Stem Cells.

    Science.gov (United States)

    Okolicsanyi, Rachel K; Oikari, Lotta E; Yu, Chieh; Griffiths, Lyn R; Haupt, Larisa M

    2018-01-01

    Background: Due to their relative ease of isolation and their high ex vivo and in vitro expansive potential, human mesenchymal stem cells (hMSCs) are an attractive candidate for therapeutic applications in the treatment of brain injury and neurological diseases. Heparan sulfate proteoglycans (HSPGs) are a family of ubiquitous proteins involved in a number of vital cellular processes including proliferation and stem cell lineage differentiation. Methods: Following the determination that hMSCs maintain neural potential throughout extended in vitro expansion, we examined the role of HSPGs in mediating the neural potential of hMSCs. hMSCs cultured in basal conditions (undifferentiated monolayer cultures) were found to co-express neural markers and HSPGs throughout expansion with modulation of the in vitro niche through the addition of exogenous HS influencing cellular HSPG and neural marker expression. Results: Conversion of hMSCs into hMSC Induced Neurospheres (hMSC IN) identified distinctly localized HSPG staining within the spheres along with altered gene expression of HSPG core protein and biosynthetic enzymes when compared to undifferentiated hMSCs. Conclusion: Comparison of markers of pluripotency, neural self-renewal and neural lineage specification between hMSC IN, hMSC and human neural stem cell (hNSC H9) cultures suggest that in vitro generated hMSC IN may represent an intermediary neurogenic cell type, similar to a common neural progenitor cell. In addition, this data demonstrates HSPGs and their biosynthesis machinery, are associated with hMSC IN formation. The identification of specific HSPGs driving hMSC lineage-specification will likely provide new markers to allow better use of hMSCs in therapeutic applications and improve our understanding of human neurogenesis.

  18. QSAR Study of Insecticides of Phthalamide Derivatives Using Multiple Linear Regression and Artificial Neural Network Methods

    Directory of Open Access Journals (Sweden)

    Adi Syahputra

    2014-03-01

    Full Text Available Quantitative structure activity relationship (QSAR for 21 insecticides of phthalamides containing hydrazone (PCH was studied using multiple linear regression (MLR, principle component regression (PCR and artificial neural network (ANN. Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84. Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl-5-chloro-N’-((5-methylthiophen-2-ylmethylene benzohydrazide, 2-(decalinecarbamoyl-5-chloro-N’-((thiophen-2-yl-methylene benzohydrazide and 2-(decaline carbamoyl-N’-(4-fluorobenzylidene-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.

  19. Chemical Sensors Based on Cyclodextrin Derivatives.

    Science.gov (United States)

    Ogoshi, Tomoki; Harada, Akira

    2008-08-25

    This review focuses on chemical sensors based on cyclodextrin (CD) derivatives. This has been a field of classical interest, and is now of current interest for numerous scientists. First, typical chemical sensors using chromophore appended CDs are mentioned. Various "turn-off" and "turn-on" fluorescent chemical sensors, in which fluorescence intensity was decreased or increased by complexation with guest molecules, respectively, were synthesized. Dye modified CDs and photoactive metal ion-ligand complex appended CDs, metallocyclodextrins, were also applied for chemical sensors. Furthermore, recent novel approaches to chemical sensing systems using supramolecular structures such as CD dimers, trimers and cooperative binding systems of CDs with the other macrocycle [2]rotaxane and supramolecular polymers consisting of CD units are mentioned. New chemical sensors using hybrids of CDs with p-conjugated polymers, peptides, DNA, nanocarbons and nanoparticles are also described in this review.

  20. ESC-Derived BDNF-Overexpressing Neural Progenitors Differentially Promote Recovery in Huntington's Disease Models by Enhanced Striatal Differentiation

    Directory of Open Access Journals (Sweden)

    Tina Zimmermann

    2016-10-01

    Full Text Available Huntington's disease (HD is characterized by fatal motoric failures induced by loss of striatal medium spiny neurons. Neuronal cell death has been linked to impaired expression and axonal transport of the neurotrophin BDNF (brain-derived neurotrophic factor. By transplanting embryonic stem cell-derived neural progenitors overexpressing BDNF, we combined cell replacement and BDNF supply as a potential HD therapy approach. Transplantation of purified neural progenitors was analyzed in a quinolinic acid (QA chemical and two genetic HD mouse models (R6/2 and N171-82Q on the basis of distinct behavioral parameters, including CatWalk gait analysis. Explicit rescue of motor function by BDNF neural progenitors was found in QA-lesioned mice, whereas genetic mouse models displayed only minor improvements. Tumor formation was absent, and regeneration was attributed to enhanced neuronal and striatal differentiation. In addition, adult neurogenesis was preserved in a BDNF-dependent manner. Our findings provide significant insight for establishing therapeutic strategies for HD to ameliorate neurodegenerative symptoms.

  1. In vitro differentiation of neural cells from human adipose tissue derived stromal cells.

    Science.gov (United States)

    Dave, Shruti D; Patel, Chetan N; Vanikar, Aruna V; Trivedi, Hargovind L

    2018-01-01

    Stem cells, including neural stem cells (NSCs), are endowed with self-renewal capability and hence hold great opportunity for the institution of replacement/protective therapy. We propose a method for in vitro generation of stromal cells from human adipose tissue and their differentiation into neural cells. Ten grams of donor adipose tissue was surgically resected from the abdominal wall of the human donor after the participants' informed consents. The resected adipose tissue was minced and incubated for 1 hour in the presence of an enzyme (collagenase-type I) at 37 0 C followed by its centrifugation. After centrifugation, the supernatant and pellets were separated and cultured in a medium for proliferation at 37 0 C with 5% CO2 for 9-10 days in separate tissue culture dishes for generation of mesenchymal stromal cells (MSC). At the end of the culture, MSC were harvested and analyzed. The harvested MSC were subjected for further culture for their differentiation into neural cells for 5-7 days using differentiation medium mainly comprising of neurobasal medium. At the end of the procedure, culture cells were isolated and studied for expression of transcriptional factor proteins: orthodenticle homolog-2 (OTX-2), beta-III-tubulin (β3-Tubulin), glial-fibrillary acid protein (GFAP) and synaptophysin-β2. In total, 50 neural cells-lines were generated. In vitro generated MSC differentiated neural cells' mean quantum was 5.4 ± 6.9 ml with the mean cell count being, 5.27 ± 2.65 × 10 3/ μl. All of them showed the presence of OTX-2, β3-Tubulin, GFAP, synaptophysin-β2. Neural cells can be differentiated in vitro from MSC safely and effectively. In vitro generated neural cells represent a potential therapy for recovery from spinal cord injuries and neurodegenerative disease.

  2. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  3. Decoupling control of vehicle chassis system based on neural network inverse system

    Science.gov (United States)

    Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

    2018-06-01

    Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

  4. FGF8 signaling sustains progenitor status and multipotency of cranial neural crest-derived mesenchymal cells in vivo and in vitro

    Science.gov (United States)

    Shao, Meiying; Liu, Chao; Song, Yingnan; Ye, Wenduo; He, Wei; Yuan, Guohua; Gu, Shuping; Lin, Congxin; Ma, Liang; Zhang, Yanding; Tian, Weidong; Hu, Tao; Chen, YiPing

    2015-01-01

    The cranial neural crest (CNC) cells play a vital role in craniofacial development and regeneration. They are multi-potent progenitors, being able to differentiate into various types of tissues. Both pre-migratory and post-migratory CNC cells are plastic, taking on diverse fates by responding to different inductive signals. However, what sustains the multipotency of CNC cells and derivatives remains largely unknown. In this study, we present evidence that FGF8 signaling is able to sustain progenitor status and multipotency of CNC-derived mesenchymal cells both in vivo and in vitro. We show that augmented FGF8 signaling in pre-migratory CNC cells prevents cell differentiation and organogenesis in the craniofacial region by maintaining their progenitor status. CNC-derived mesenchymal cells with Fgf8 overexpression or control cells in the presence of exogenous FGF8 exhibit prolonged survival, proliferation, and multi-potent differentiation capability in cell cultures. Remarkably, exogenous FGF8 also sustains the capability of CNC-derived mesenchymal cells to participate in organogenesis such as odontogenesis. Furthermore, FGF8-mediated signaling strongly promotes adipogenesis but inhibits osteogenesis of CNC-derived mesenchymal cells in vitro. Our results reveal a specific role for FGF8 in the maintenance of progenitor status and in fate determination of CNC cells, implicating a potential application in expansion and fate manipulation of CNC-derived cells in stem cell-based craniofacial regeneration. PMID:26243590

  5. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  6. Focal Transplantation of Human iPSC-Derived Glial-Rich Neural Progenitors Improves Lifespan of ALS Mice

    Directory of Open Access Journals (Sweden)

    Takayuki Kondo

    2014-08-01

    Full Text Available Transplantation of glial-rich neural progenitors has been demonstrated to attenuate motor neuron degeneration and disease progression in rodent models of mutant superoxide dismutase 1 (SOD1-mediated amyotrophic lateral sclerosis (ALS. However, translation of these results into a clinical setting requires a renewable human cell source. Here, we derived glial-rich neural progenitors from human iPSCs and transplanted them into the lumbar spinal cord of ALS mouse models. The transplanted cells differentiated into astrocytes, and the treated mouse group showed prolonged lifespan. Our data suggest a potential therapeutic mechanism via activation of AKT signal. The results demonstrated the efficacy of cell therapy for ALS by the use of human iPSCs as cell source.

  7. Epigenetic regulation of gene expression in porcine epiblast, hypoblast, trophectoderm and epiblast-derived neural progenitor cells

    DEFF Research Database (Denmark)

    Gao, Yu; Jammes, Helen; Rasmussen, Mikkel Aabech

    2011-01-01

    in this process. In this study, we investigated the relationship between DNA methylation and expression of pluripotency-associated genes (OCT4, NANOG and SOX2), a trophectoderm (TE)-specific gene (ELF5), and genes associated with neural differentiation (SOX2 and VIMENTIN) in porcine Day 10 (E10) epiblast......, hypoblast, and TE as well as in epiblast-derived neural progenitor cells (NPCs). We found that OCT4, NANOG, and SOX2 were highly expressed in the epiblast and hypoblast, while VIMENTIN was only highly expressed in the epiblast. Moreover, low expression of OCT4, NANOG, SOX2 and VIMENTIN was noted in the TE....... Most CpG sites of OCT4, NANOG, SOX2 and VIMENTIN displayed low methylation levels in the epiblast and hypoblast and, strikingly, also in the TE. Hence, the expression patterns of these genes were not directly related to levels of DNA methylation in the TE in contrast to the situation in the mouse...

  8. Reliability analysis of a consecutive r-out-of-n: F system based on neural networks

    International Nuclear Information System (INIS)

    Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada

    2009-01-01

    In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.

  9. Tricyclic antidepressant amitriptyline indirectly increases the proliferation of adult dentate gyrus-derived neural precursors: an involvement of astrocytes.

    Directory of Open Access Journals (Sweden)

    Shuken Boku

    Full Text Available Antidepressants increase the proliferation of neural precursors in adult dentate gyrus (DG, which is considered to be involved in the therapeutic action of antidepressants. However, the mechanism underlying it remains unclear. By using cultured adult rat DG-derived neural precursors (ADP, we have already shown that antidepressants have no direct effects on ADP. Therefore, antidepressants may increase the proliferation of neural precursors in adult DG via unknown indirect mechanism. We have also shown that amitriptyline (AMI, a tricyclic antidepressant, induces the expressions of GDNF, BDNF, FGF2 and VEGF, common neurogenic factors, in primary cultured astrocytes (PCA. These suggest that AMI-induced factors in astrocytes may increase the proliferation of neural precursors in adult DG. To test this hypothesis, we examined the effects of AMI-induced factors and conditioned medium (CM from PCA treated with AMI on ADP proliferation. The effects of CM and factors on ADP proliferation were examined with BrdU immunocytochemistry. AMI had no effect on ADP proliferation, but AMI-treated CM increased it. The receptors of GDNF, BDNF and FGF2, but not VEGF, were expressed in ADP. FGF2 significantly increased ADP proliferation, but not BDNF and GDNF. In addition, both of a specific inhibitor of FGF receptors and anti-FGF2 antibody significantly counteracted the increasing effect of CM on ADP proliferation. In addition, FGF2 in brain is mainly derived from astrocytes that are key components of the neurogenic niches in adult DG. These suggest that AMI may increase ADP proliferation indirectly via PCA and that FGF2 may a potential candidate to mediate such an indirect effect of AMI on ADP proliferation via astrocytes.

  10. Female mice deficient in alpha-fetoprotein show female-typical neural responses to conspecific-derived pheromones.

    Directory of Open Access Journals (Sweden)

    Olivier Brock

    Full Text Available The neural mechanisms controlling sexual behavior are sexually differentiated by the perinatal actions of sex steroid hormones. We recently observed using female mice deficient in alpha-fetoprotein (AFP-KO and which lack the protective actions of AFP against maternal estradiol, that exposure to prenatal estradiol completely defeminized the potential to show lordosis behavior in adulthood. Furthermore, AFP-KO females failed to show any male-directed mate preferences following treatment with estradiol and progesterone, indicating a reduced sexual motivation to seek out the male. In the present study, we asked whether neural responses to male- and female-derived odors are also affected in AFP-KO female mice. Therefore, we compared patterns of Fos, the protein product of the immediate early gene, c-fos, commonly used as a marker of neuronal activation, between wild-type (WT and AFP-KO female mice following exposure to male or estrous female urine. We also tested WT males to confirm the previously observed sex differences in neural responses to male urinary odors. Interestingly, AFP-KO females showed normal, female-like Fos responses, i.e. exposure to urinary odors from male but not estrous female mice induced equivalent levels of Fos protein in the accessory olfactory pathways (e.g. the medial part of the preoptic nucleus, the bed nucleus of the stria terminalis, the amygdala, and the lateral part of the ventromedial hypothalamic nucleus as well as in the main olfactory pathways (e.g. the piriform cortex and the anterior cortical amygdaloid nucleus, as WT females. By contrast, WT males did not show any significant induction of Fos protein in these brain areas upon exposure to either male or estrous female urinary odors. These results thus suggest that prenatal estradiol is not involved in the sexual differentiation of neural Fos responses to male-derived odors.

  11. Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.

    Science.gov (United States)

    Xu, Bin; Yang, Daipeng; Shi, Zhongke; Pan, Yongping; Chen, Badong; Sun, Fuchun

    2017-09-25

    This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

  12. Quantum neural network based machine translator for Hindi to English.

    Science.gov (United States)

    Narayan, Ravi; Singh, V P; Chakraverty, S

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

  13. Sequence memory based on coherent spin-interaction neural networks.

    Science.gov (United States)

    Xia, Min; Wong, W K; Wang, Zhijie

    2014-12-01

    Sequence information processing, for instance, the sequence memory, plays an important role on many functions of brain. In the workings of the human brain, the steady-state period is alterable. However, in the existing sequence memory models using heteroassociations, the steady-state period cannot be changed in the sequence recall. In this work, a novel neural network model for sequence memory with controllable steady-state period based on coherent spininteraction is proposed. In the proposed model, neurons fire collectively in a phase-coherent manner, which lets a neuron group respond differently to different patterns and also lets different neuron groups respond differently to one pattern. The simulation results demonstrating the performance of the sequence memory are presented. By introducing a new coherent spin-interaction sequence memory model, the steady-state period can be controlled by dimension parameters and the overlap between the input pattern and the stored patterns. The sequence storage capacity is enlarged by coherent spin interaction compared with the existing sequence memory models. Furthermore, the sequence storage capacity has an exponential relationship to the dimension of the neural network.

  14. Comparison Of Power Quality Disturbances Classification Based On Neural Network

    Directory of Open Access Journals (Sweden)

    Nway Nway Kyaw Win

    2015-07-01

    Full Text Available Abstract Power quality disturbances PQDs result serious problems in the reliability safety and economy of power system network. In order to improve electric power quality events the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis MRA algorithm and feed forward neural network probabilistic and multilayer feed forward neural network based methodology for automatic classification of eight types of PQ signals flicker harmonics sag swell impulse fluctuation notch and oscillatory will be presented. The wavelet family Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore it can be seen that PNN has better classification accuracy than MLFF.

  15. The Dissolved Oxygen Prediction Method Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Zhong Xiao

    2017-01-01

    Full Text Available The dissolved oxygen (DO is oxygen dissolved in water, which is an important factor for the aquaculture. Using BP neural network method with the combination of purelin, logsig, and tansig activation functions is proposed for the prediction of aquaculture’s dissolved oxygen. The input layer, hidden layer, and output layer are introduced in detail including the weight adjustment process. The breeding data of three ponds in actual 10 consecutive days were used for experiments; these ponds were located in Beihai, Guangxi, a traditional aquaculture base in southern China. The data of the first 7 days are used for training, and the data of the latter 3 days are used for the test. Compared with the common prediction models, curve fitting (CF, autoregression (AR, grey model (GM, and support vector machines (SVM, the experimental results show that the prediction accuracy of the neural network is the highest, and all the predicted values are less than 5% of the error limit, which can meet the needs of practical applications, followed by AR, GM, SVM, and CF. The prediction model can help to improve the water quality monitoring level of aquaculture which will prevent the deterioration of water quality and the outbreak of disease.

  16. Deep Neural Network Based Demand Side Short Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Seunghyoung Ryu

    2016-12-01

    Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

  17. Derivative pricing based on local utility maximization

    OpenAIRE

    Jan Kallsen

    2002-01-01

    This paper discusses a new approach to contingent claim valuation in general incomplete market models. We determine the neutral derivative price which occurs if investors maximize their local utility and if derivative demand and supply are balanced. We also introduce the sensitivity process of a contingent claim. This process quantifies the reliability of the neutral derivative price and it can be used to construct price bounds. Moreover, it allows to calibrate market models in order to be co...

  18. Human iPSC-Derived Neural Progenitors Are an Effective Drug Discovery Model for Neurological mtDNA Disorders.

    Science.gov (United States)

    Lorenz, Carmen; Lesimple, Pierre; Bukowiecki, Raul; Zink, Annika; Inak, Gizem; Mlody, Barbara; Singh, Manvendra; Semtner, Marcus; Mah, Nancy; Auré, Karine; Leong, Megan; Zabiegalov, Oleksandr; Lyras, Ekaterini-Maria; Pfiffer, Vanessa; Fauler, Beatrix; Eichhorst, Jenny; Wiesner, Burkhard; Huebner, Norbert; Priller, Josef; Mielke, Thorsten; Meierhofer, David; Izsvák, Zsuzsanna; Meier, Jochen C; Bouillaud, Frédéric; Adjaye, James; Schuelke, Markus; Wanker, Erich E; Lombès, Anne; Prigione, Alessandro

    2017-05-04

    Mitochondrial DNA (mtDNA) mutations frequently cause neurological diseases. Modeling of these defects has been difficult because of the challenges associated with engineering mtDNA. We show here that neural progenitor cells (NPCs) derived from human induced pluripotent stem cells (iPSCs) retain the parental mtDNA profile and exhibit a metabolic switch toward oxidative phosphorylation. NPCs derived in this way from patients carrying a deleterious homoplasmic mutation in the mitochondrial gene MT-ATP6 (m.9185T>C) showed defective ATP production and abnormally high mitochondrial membrane potential (MMP), plus altered calcium homeostasis, which represents a potential cause of neural impairment. High-content screening of FDA-approved drugs using the MMP phenotype highlighted avanafil, which we found was able to partially rescue the calcium defect in patient NPCs and differentiated neurons. Overall, our results show that iPSC-derived NPCs provide an effective model for drug screening to target mtDNA disorders that affect the nervous system. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

    Science.gov (United States)

    Fechner, Hanna B; Pachur, Thorsten; Schooler, Lael J; Mehlhorn, Katja; Battal, Ceren; Volz, Kirsten G; Borst, Jelmer P

    2016-12-01

    How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  1. Fault diagnosis method for nuclear power plants based on neural networks and voting fusion

    International Nuclear Information System (INIS)

    Zhou Gang; Ge Shengqi; Yang Li

    2010-01-01

    A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs. (authors)

  2. Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

    International Nuclear Information System (INIS)

    Peng Yafu; Hsu, C.-F.

    2009-01-01

    This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

  3. Development of protective autoimmunity by immunization with a neural-derived peptide is ineffective in severe spinal cord injury.

    Directory of Open Access Journals (Sweden)

    Susana Martiñón

    Full Text Available Protective autoimmunity (PA is a physiological response to central nervous system trauma that has demonstrated to promote neuroprotection after spinal cord injury (SCI. To reach its beneficial effect, PA should be boosted by immunizing with neural constituents or neural-derived peptides such as A91. Immunizing with A91 has shown to promote neuroprotection after SCI and its use has proven to be feasible in a clinical setting. The broad applications of neural-derived peptides make it important to determine the main features of this anti-A91 response. For this purpose, adult Sprague-Dawley rats were subjected to a spinal cord contusion (SCC; moderate or severe or a spinal cord transection (SCT; complete or incomplete. Immediately after injury, animals were immunized with PBS or A91. Motor recovery, T cell-specific response against A91 and the levels of IL-4, IFN-γ and brain-derived neurotrophic factor (BDNF released by A91-specific T (T(A91 cells were evaluated. Rats with moderate SCC, presented a better motor recovery after A91 immunization. Animals with moderate SCC or incomplete SCT showed significant T cell proliferation against A91 that was characterized chiefly by the predominant production of IL-4 and the release of BDNF. In contrast, immunization with A91 did not promote a better motor recovery in animals with severe SCC or complete SCT. In fact, T cell proliferation against A91 was diminished in these animals. The present results suggest that the effective development of PA and, consequently, the beneficial effects of immunizing with A91 significantly depend on the severity of SCI. This could mainly be attributed to the lack of T(A91 cells which predominantly showed to have a Th2 phenotype capable of producing BDNF, further promoting neuroprotection.

  4. Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhisheng Zhang

    2016-01-01

    Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

  5. Deep neural network and noise classification-based speech enhancement

    Science.gov (United States)

    Shi, Wenhua; Zhang, Xiongwei; Zou, Xia; Han, Wei

    2017-07-01

    In this paper, a speech enhancement method using noise classification and Deep Neural Network (DNN) was proposed. Gaussian mixture model (GMM) was employed to determine the noise type in speech-absent frames. DNN was used to model the relationship between noisy observation and clean speech. Once the noise type was determined, the corresponding DNN model was applied to enhance the noisy speech. GMM was trained with mel-frequency cepstrum coefficients (MFCC) and the parameters were estimated with an iterative expectation-maximization (EM) algorithm. Noise type was updated by spectrum entropy-based voice activity detection (VAD). Experimental results demonstrate that the proposed method could achieve better objective speech quality and smaller distortion under stationary and non-stationary conditions.

  6. CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking

    Science.gov (United States)

    Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.

    2017-12-01

    We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.

  7. Prediction of flow boiling curves based on artificial neural network

    International Nuclear Information System (INIS)

    Wu Junmei; Xi'an Jiaotong Univ., Xi'an; Su Guanghui

    2007-01-01

    The effects of the main system parameters on flow boiling curves were analyzed by using an artificial neural network (ANN) based on the database selected from the 1960s. The input parameters of the ANN are system pressure, mass flow rate, inlet subcooling, wall superheat and steady/transition boiling, and the output parameter is heat flux. The results obtained by the ANN show that the heat flux increases with increasing inlet sub cooling for all heat transfer modes. Mass flow rate has no significant effects on nucleate boiling curves. The transition boiling and film boiling heat fluxes will increase with an increase of mass flow rate. The pressure plays a predominant role and improves heat transfer in whole boiling regions except film boiling. There are slight differences between the steady and the transient boiling curves in all boiling regions except the nucleate one. (authors)

  8. Neurally based measurement and evaluation of environmental noise

    CERN Document Server

    Soeta, Yoshiharu

    2015-01-01

    This book deals with methods of measurement and evaluation of environmental noise based on an auditory neural and brain-oriented model. The model consists of the autocorrelation function (ACF) and the interaural cross-correlation function (IACF) mechanisms for signals arriving at the two ear entrances. Even when the sound pressure level of a noise is only about 35 dBA, people may feel annoyed due to the aspects of sound quality. These aspects can be formulated by the factors extracted from the ACF and IACF. Several examples of measuring environmental noise—from outdoor noise such as that of aircraft, traffic, and trains, and indoor noise such as caused by floor impact, toilets, and air-conditioning—are demonstrated. According to the noise measurement and evaluation, applications for sound design are discussed. This book provides an excellent resource for students, researchers, and practitioners in a wide range of fields, such as the automotive, railway, and electronics industries, and soundscape, architec...

  9. Multivariate Cryptography Based on Clipped Hopfield Neural Network.

    Science.gov (United States)

    Wang, Jia; Cheng, Lee-Ming; Su, Tong

    2018-02-01

    Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

  10. Deep neural network-based bandwidth enhancement of photoacoustic data.

    Science.gov (United States)

    Gutta, Sreedevi; Kadimesetty, Venkata Suryanarayana; Kalva, Sandeep Kumar; Pramanik, Manojit; Ganapathy, Sriram; Yalavarthy, Phaneendra K

    2017-11-01

    Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

  11. Finger vein recognition based on convolutional neural network

    Directory of Open Access Journals (Sweden)

    Meng Gesi

    2017-01-01

    Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.

  12. Effects of light emitting diode irradiation on neural differentiation of human umbilical cord-derived mesenchymal cells.

    Science.gov (United States)

    Dehghani-Soltani, Samereh; Shojaee, Mohammad; Jalalkamali, Mahshid; Babaee, Abdolreza; Nematollahi-Mahani, Seyed Noureddin

    2017-08-30

    Recently, light emitting diodes (LEDs) have been introduced as a potential physical factor for proliferation and differentiation of various stem cells. Among the mesenchymal stem cells human umbilical cord matrix-derived mesenchymal (hUCM) cells are easily propagated in the laboratory and their low immunogenicity make them more appropriate for regenerative medicine procedures. We aimed at this study to evaluate the effect of red and green light emitted from LED on the neural lineage differentiation of hUCM cells in the presence or absence of retinoic acid (RA). Harvested hUCM cells exhibited mesenchymal and stemness properties. Irradiation of these cells by green and red LED with or without RA pre-treatment successfully differentiated them into neural lineage when the morphology of the induced cells, gene expression pattern (nestin, β-tubulin III and Olig2) and protein synthesis (anti-nestin, anti-β-tubulin III, anti-GFAP and anti-O4 antibodies) was evaluated. These data point for the first time to the fact that LED irradiation and optogenetic technology may be applied for neural differentiation and neuronal repair in regenerative medicine.

  13. Reward-based training of recurrent neural networks for cognitive and value-based tasks.

    Science.gov (United States)

    Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

    2017-01-13

    Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal's internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task.

  14. A simple, xeno-free method for oligodendrocyte generation from human neural stem cells derived from umbilical cord: engagement of gelatinases in cell commitment and differentiation.

    Science.gov (United States)

    Sypecka, Joanna; Ziemka-Nalecz, Małgorzata; Dragun-Szymczak, Patrycja; Zalewska, Teresa

    2017-05-01

    Oligodendrocyte progenitors (OPCs) are ranked among the most likely candidates for cell-based strategies aimed at treating neurodegenerative diseases accompanied by dys/demyelination of the central nervous system (CNS). In this regard, different sources of stem cells are being tested to elaborate xeno-free protocols for efficient generation of OPCs for clinical applications. In the present study, neural stem cells of human umbilical cord blood (HUCB-NSCs) have been used to derive OPCs and subsequently to differentiate them into mature, GalC-expressing oligodendrocytes. Applied components of the extracellular matrix (ECM) and the analogues of physiological substances known to increase glial commitment of neural stem cells have been shown to significantly increase the yield of the resulting OPC fraction. The efficiency of ECM components in promoting oligodendrocyte commitment and differentiation prompted us to investigate the potential role of gelatinases in those processes. Subsequently, endogenous and ECM metalloproteinases (MMPs) activity has been compared with that detected in primary cultures of rat oligodendrocytes in vitro, as well as in rat brains in vivo. The data indicate that gelatinases are engaged in gliogenesis both in vitro and in vivo, although differently, which presumably results from distinct extracellular conditions. In conclusion, the study presents an efficient xeno-free method of deriving oligodendrocyte from HUCB-NSCs and analyses the engagement of MMP-2/MMP-9 in the processes of cell commitment and maturation. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  15. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    Science.gov (United States)

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  16. FGL-functionalized self-assembling nanofiber hydrogel as a scaffold for spinal cord-derived neural stem cells

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jian [Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 (China); Zheng, Jin [Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 (China); Zheng, Qixin, E-mail: zheng-qx@163.com [Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 (China); Wu, Yongchao; Wu, Bin; Huang, Shuai; Fang, Weizhi; Guo, Xiaodong [Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 (China)

    2015-01-01

    A class of designed self-assembling peptide nanofiber scaffolds has been shown to be a good biomimetic material in tissue engineering. Here, we specifically made a new peptide hydrogel scaffold FGLmx by mixing the pure RADA{sub 16} and designer functional peptide RADA{sub 16}-FGL solution, and we analyzed the physiochemical properties of each peptide with atomic force microscopy (AFM) and circular dichroism (CD). In addition, we examined the biocompatibility and bioactivity of FGLmx as well as RADA{sub 16} scaffold on spinal cord-derived neural stem cells (SC-NSCs) isolated from neonatal rats. Our results showed that RADA{sub 16}-FGL displayed a weaker β-sheet structure and FGLmx could self-assemble into nanofibrous morphology. Moreover, we found that FGLmx was not only noncytotoxic to SC-NSCs but also promoted SC-NSC proliferation and migration into the three-dimensional (3-D) scaffold, meanwhile, the adhesion and lineage differentiation of SC-NSCs on FGLmx were similar to that on RADA{sub 16}. Our results indicated that the FGL-functionalized peptide scaffold might be very beneficial for tissue engineering and suggested its further application for spinal cord injury (SCI) repair. - Highlights: • RADA{sub 16} and RADA{sub 16}-FGL peptides were synthesized and characterized. • Rat spinal cord neural stem cells were successfully isolated and characterized. • We provided an induction method for mixed differentiation of neural stem cells. • FGL scaffold had good biocompatibility and bioactivity with neural stem cells.

  17. Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

    Science.gov (United States)

    Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.

    2017-12-01

    We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

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

    Science.gov (United States)

    Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

    2018-01-01

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

  19. Vision-Based Fall Detection with Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Adrián Núñez-Marcos

    2017-01-01

    Full Text Available One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

  20. Neuroprotective effect of transplanted human embryonic stem cell-derived neural precursors in an animal model of multiple sclerosis.

    Directory of Open Access Journals (Sweden)

    Michal Aharonowiz

    Full Text Available BACKGROUND: Multiple sclerosis (MS is an immune mediated demyelinating disease of the central nervous system (CNS. A potential new therapeutic approach for MS is cell transplantation which may promote remyelination and suppress the inflammatory process. METHODS: We transplanted human embryonic stem cells (hESC-derived early multipotent neural precursors (NPs into the brain ventricles of mice induced with experimental autoimmune encephalomyelitis (EAE, the animal model of MS. We studied the effect of the transplanted NPs on the functional and pathological manifestations of the disease. RESULTS: Transplanted hESC-derived NPs significantly reduced the clinical signs of EAE. Histological examination showed migration of the transplanted NPs to the host white matter, however, differentiation to mature oligodendrocytes and remyelination were negligible. Time course analysis of the evolution and progression of CNS inflammation and tissue injury showed an attenuation of the inflammatory process in transplanted animals, which was correlated with the reduction of both axonal damage and demyelination. Co-culture experiments showed that hESC-derived NPs inhibited the activation and proliferation of lymph node-derived T cells in response to nonspecific polyclonal stimuli. CONCLUSIONS: The therapeutic effect of transplantation was not related to graft or host remyelination but was mediated by an immunosuppressive neuroprotective mechanism. The attenuation of EAE by hESC-derived NPs, demonstrated here, may serve as the first step towards further developments of hESC for cell therapy in MS.

  1. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    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

  2. Neural correlates of derived relational responding on tests of stimulus equivalence

    Directory of Open Access Journals (Sweden)

    Cataldo Michael F

    2008-02-01

    Full Text Available Abstract Background An essential component of cognition and language involves the formation of new conditional relations between stimuli based upon prior experiences. Results of investigations on transitive inference (TI highlight a prominent role for the medial temporal lobe in maintaining associative relations among sequentially arranged stimuli (A > B > C > D > E. In this investigation, medial temporal lobe activity was assessed while subjects completed "Stimulus Equivalence" (SE tests that required deriving conditional relations among stimuli within a class (A ≡ B ≡ C. Methods Stimuli consisted of six consonant-vowel-consonant triads divided into two classes (A1, B1, C1; A2, B2, C2. A simultaneous matching-to-sample task and differential reinforcement were employed during pretraining to establish the conditional relations A1:B1 and B1:C1 in class 1 and A2:B2 and B2:C2 in class 2. During functional neuroimaging, recombined stimulus pairs were presented and subjects judged (yes/no whether stimuli were related. SE tests involved presenting three different types of within-class pairs: Symmetrical (B1 A1; C1 B1; B2 A2; C2 B2, and Transitive (A1 C1; A2 C2 and Equivalence (C1 A1; C2 A2 relations separated by a nodal stimulus. Cross-class 'Foils' consisting of unrelated stimuli (e.g., A1 C2 were also presented. Results Relative to cross-class Foils, Transitive and Equivalence relations requiring inferential judgments elicited bilateral activation in the anterior hippocampus while Symmetrical relations elicited activation in the parahippocampus. Relative to each derived relation, Foils generally elicited bilateral activation in the parahippocampus, as well as in frontal and parietal lobe regions. Conclusion Activation observed in the hippocampus to nodal-dependent derived conditional relations (Transitive and Equivalence relations highlights its involvement in maintaining relational structure and flexible memory expression among stimuli within a

  3. Web based educational tool for neural network robot control

    Directory of Open Access Journals (Sweden)

    Jure Čas

    2007-05-01

    Full Text Available Abstract— This paper describes the application for teleoperations of the SCARA robot via the internet. The SCARA robot is used by students of mehatronics at the University of Maribor as a remote educational tool. The developed software consists of two parts i.e. the continuous neural network sliding mode controller (CNNSMC and the graphical user interface (GUI. Application is based on two well-known commercially available software packages i.e. MATLAB/Simulink and LabVIEW. Matlab/Simulink and the DSP2 Library for Simulink are used for control algorithm development, simulation and executable code generation. While this code is executing on the DSP-2 Roby controller and through the analog and digital I/O lines drives the real process, LabVIEW virtual instrument (VI, running on the PC, is used as a user front end. LabVIEW VI provides the ability for on-line parameter tuning, signal monitoring, on-line analysis and via Remote Panels technology also teleoperation. The main advantage of a CNNSMC is the exploitation of its self-learning capability. When friction or an unexpected impediment occurs for example, the user of a remote application has no information about any changed robot dynamic and thus is unable to dispatch it manually. This is not a control problem anymore because, when a CNNSMC is used, any approximation of changed robot dynamic is estimated independently of the remote’s user. Index Terms—LabVIEW; Matlab/Simulink; Neural network control; remote educational tool; robotics

  4. FOXOs modulate proteasome activity in human-induced pluripotent stem cells of Huntington's disease and their derived neural cells.

    Science.gov (United States)

    Liu, Yanying; Qiao, Fangfang; Leiferman, Patricia C; Ross, Alan; Schlenker, Evelyn H; Wang, Hongmin

    2017-11-15

    Although it has been speculated that proteasome dysfunction may contribute to the pathogenesis of Huntington's disease (HD), a devastating neurodegenerative disorder, how proteasome activity is regulated in HD affected stem cells and somatic cells remains largely unclear. To better understand the pathogenesis of HD, we analyzed proteasome activity and the expression of FOXO transcription factors in three wild-type (WT) and three HD induced-pluripotent stem cell (iPSC) lines. HD iPSCs exhibited elevated proteasome activity and higher levels of FOXO1 and FOXO4 proteins. Knockdown of FOXO4 but not FOXO1 expression decreased proteasome activity. Following neural differentiation, the HD-iPSC-derived neural progenitor cells (NPCs) demonstrated lower levels of proteasome activity and FOXO expressions than their WT counterparts. More importantly, overexpression of FOXO4 but not FOXO1 in HD NPCs dramatically enhanced proteasome activity. When HD NPCs were further differentiated into DARPP32-positive neurons, these HD neurons were more susceptible to death than WT neurons and formed Htt aggregates under the condition of oxidative stress. Similar to HD NPCs, HD-iPSC-derived neurons showed reduced proteasome activity and diminished FOXO4 expression compared to WT-iPSC-derived neurons. Furthermore, HD iPSCs had lower AKT activities than WT iPSCs, whereas the neurons derived from HD iPSC had higher AKT activities than their WT counterparts. Inhibiting AKT activity increased both FOXO4 level and proteasome activity, indicating a potential role of AKT in regulating FOXO levels. These data suggest that FOXOs modulate proteasome activity, and thus represents a potentially valuable therapeutic target for HD. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  5. A Neural Network Based Dutch Part of Speech Tagger

    NARCIS (Netherlands)

    Boschman, E.; op den Akker, Hendrikus J.A.; Nijholt, A.; Nijholt, Antinus; Pantic, Maja; Pantic, M.; Poel, M.; Poel, Mannes; Hondorp, G.H.W.

    2008-01-01

    In this paper a Neural Network is designed for Part-of-Speech Tagging of Dutch text. Our approach uses the Corpus Gesproken Nederlands (CGN) consisting of almost 9 million transcribed words of spoken Dutch, divided into 15 different categories. The outcome of the design is a Neural Network with an

  6. An industrial robot singular trajectories planning based on graphs and neural networks

    Science.gov (United States)

    Łęgowski, Adrian; Niezabitowski, Michał

    2016-06-01

    Singular trajectories are rarely used because of issues during realization. A method of planning trajectories for given set of points in task space with use of graphs and neural networks is presented. In every desired point the inverse kinematics problem is solved in order to derive all possible solutions. A graph of solutions is made. The shortest path is determined to define required nodes in joint space. Neural networks are used to define the path between these nodes.

  7. Thermoelastic steam turbine rotor control based on neural network

    Science.gov (United States)

    Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

    2015-12-01

    Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

  8. Template measurement for plutonium pit based on neural networks

    International Nuclear Information System (INIS)

    Zhang Changfan; Gong Jian; Liu Suping; Hu Guangchun; Xiang Yongchun

    2012-01-01

    Template measurement for plutonium pit extracts characteristic data from-ray spectrum and the neutron counts emitted by plutonium. The characteristic data of the suspicious object are compared with data of the declared plutonium pit to verify if they are of the same type. In this paper, neural networks are enhanced as the comparison algorithm for template measurement of plutonium pit. Two kinds of neural networks are created, i.e. the BP and LVQ neural networks. They are applied in different aspects for the template measurement and identification. BP neural network is used for classification for different types of plutonium pits, which is often used for management of nuclear materials. LVQ neural network is used for comparison of inspected objects to the declared one, which is usually applied in the field of nuclear disarmament and verification. (authors)

  9. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    Joorabian, M.

    1999-05-01

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  10. Structural reliability calculation method based on the dual neural network and direct integration method.

    Science.gov (United States)

    Li, Haibin; He, Yun; Nie, Xiaobo

    2018-01-01

    Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

  11. Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

    Science.gov (United States)

    Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

    2010-09-01

    This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

  12. Village Building Identification Based on Ensemble Convolutional Neural Networks

    Science.gov (United States)

    Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

    2017-01-01

    In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

  13. Biologically based neural network for mobile robot navigation

    Science.gov (United States)

    Torres Muniz, Raul E.

    1999-01-01

    The new tendency in mobile robots is to crete non-Cartesian system based on reactions to their environment. This emerging technology is known as Evolutionary Robotics, which is combined with the Biorobotic field. This new approach brings cost-effective solutions, flexibility, robustness, and dynamism into the design of mobile robots. It also provides fast reactions to the sensory inputs, and new interpretation of the environment or surroundings of the mobile robot. The Subsumption Architecture (SA) and the action selection dynamics developed by Brooks and Maes, respectively, have successfully obtained autonomous mobile robots initiating this new trend of the Evolutionary Robotics. Their design keeps the mobile robot control simple. This work present a biologically inspired modification of these schemes. The hippocampal-CA3-based neural network developed by Williams Levy is used to implement the SA, while the action selection dynamics emerge from iterations of the levels of competence implemented with the HCA3. This replacement by the HCA3 results in a closer biological model than the SA, combining the Behavior-based intelligence theory with neuroscience. The design is kept simple, and it is implemented in the Khepera Miniature Mobile Robot. The used control scheme obtains an autonomous mobile robot that can be used to execute a mail delivery system and surveillance task inside a building floor.

  14. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  15. In vitro generation of three-dimensional substrate-adherent embryonic stem cell-derived neural aggregates for application in animal models of neurological disorders.

    Science.gov (United States)

    Hargus, Gunnar; Cui, Yi-Fang; Dihné, Marcel; Bernreuther, Christian; Schachner, Melitta

    2012-05-01

    In vitro-differentiated embryonic stem (ES) cells comprise a useful source for cell replacement therapy, but the efficiency and safety of a translational approach are highly dependent on optimized protocols for directed differentiation of ES cells into the desired cell types in vitro. Furthermore, the transplantation of three-dimensional ES cell-derived structures instead of a single-cell suspension may improve graft survival and function by providing a beneficial microenvironment for implanted cells. To this end, we have developed a new method to efficiently differentiate mouse ES cells into neural aggregates that consist predominantly (>90%) of postmitotic neurons, neural progenitor cells, and radial glia-like cells. When transplanted into the excitotoxically lesioned striatum of adult mice, these substrate-adherent embryonic stem cell-derived neural aggregates (SENAs) showed significant advantages over transplanted single-cell suspensions of ES cell-derived neural cells, including improved survival of GABAergic neurons, increased cell migration, and significantly decreased risk of teratoma formation. Furthermore, SENAs mediated functional improvement after transplantation into animal models of Parkinson's disease and spinal cord injury. This unit describes in detail how SENAs are efficiently derived from mouse ES cells in vitro and how SENAs are isolated for transplantation. Furthermore, methods are presented for successful implantation of SENAs into animal models of Huntington's disease, Parkinson's disease, and spinal cord injury to study the effects of stem cell-derived neural aggregates in a disease context in vivo.

  16. MR-based imaging of neural stem cells

    Energy Technology Data Exchange (ETDEWEB)

    Politi, Letterio S. [San Raffaele Scientific Institute, Neuroradiology Department, Milano (Italy)

    2007-06-15

    The efficacy of therapies based on neural stem cells (NSC) has been demonstrated in preclinical models of several central nervous system (CNS) diseases. Before any potential human application of such promising therapies can be envisaged, there are some important issues that need to be solved. The most relevant one is the requirement for a noninvasive technique capable of monitoring NSC delivery, homing to target sites and trafficking. Knowledge of the location and temporospatial migration of either transplanted or genetically modified NSC is of the utmost importance in analyzing mechanisms of correction and cell distribution. Further, such a technique may represent a crucial step toward clinical application of NSC-based approaches in humans, for both designing successful protocols and monitoring their outcome. Among the diverse imaging approaches available for noninvasive cell tracking, such as nuclear medicine techniques, fluorescence and bioluminescence, magnetic resonance imaging (MRI) has unique advantages. Its high temporospatial resolution, high sensitivity and specificity render MRI one of the most promising imaging modalities available, since it allows dynamic visualization of migration of transplanted cells in animal models and patients during clinically useful time periods. Different cellular and molecular labeling approaches for MRI depiction of NSC are described and discussed in this review, as well as the most relevant issues to be considered in optimizing molecular imaging techniques for clinical application. (orig.)

  17. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

    Santosh; Vinod, Gopika; Saraf, R.K.

    2003-01-01

    Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

  18. Variance decomposition-based sensitivity analysis via neural networks

    International Nuclear Information System (INIS)

    Marseguerra, Marzio; Masini, Riccardo; Zio, Enrico; Cojazzi, Giacomo

    2003-01-01

    This paper illustrates a method for efficiently performing multiparametric sensitivity analyses of the reliability model of a given system. These analyses are of great importance for the identification of critical components in highly hazardous plants, such as the nuclear or chemical ones, thus providing significant insights for their risk-based design and management. The technique used to quantify the importance of a component parameter with respect to the system model is based on a classical decomposition of the variance. When the model of the system is realistically complicated (e.g. by aging, stand-by, maintenance, etc.), its analytical evaluation soon becomes impractical and one is better off resorting to Monte Carlo simulation techniques which, however, could be computationally burdensome. Therefore, since the variance decomposition method requires a large number of system evaluations, each one to be performed by Monte Carlo, the need arises for possibly substituting the Monte Carlo simulation model with a fast, approximated, algorithm. Here we investigate an approach which makes use of neural networks appropriately trained on the results of a Monte Carlo system reliability/availability evaluation to quickly provide with reasonable approximation, the values of the quantities of interest for the sensitivity analyses. The work was a joint effort between the Department of Nuclear Engineering of the Polytechnic of Milan, Italy, and the Institute for Systems, Informatics and Safety, Nuclear Safety Unit of the Joint Research Centre in Ispra, Italy which sponsored the project

  19. MR-based imaging of neural stem cells

    International Nuclear Information System (INIS)

    Politi, Letterio S.

    2007-01-01

    The efficacy of therapies based on neural stem cells (NSC) has been demonstrated in preclinical models of several central nervous system (CNS) diseases. Before any potential human application of such promising therapies can be envisaged, there are some important issues that need to be solved. The most relevant one is the requirement for a noninvasive technique capable of monitoring NSC delivery, homing to target sites and trafficking. Knowledge of the location and temporospatial migration of either transplanted or genetically modified NSC is of the utmost importance in analyzing mechanisms of correction and cell distribution. Further, such a technique may represent a crucial step toward clinical application of NSC-based approaches in humans, for both designing successful protocols and monitoring their outcome. Among the diverse imaging approaches available for noninvasive cell tracking, such as nuclear medicine techniques, fluorescence and bioluminescence, magnetic resonance imaging (MRI) has unique advantages. Its high temporospatial resolution, high sensitivity and specificity render MRI one of the most promising imaging modalities available, since it allows dynamic visualization of migration of transplanted cells in animal models and patients during clinically useful time periods. Different cellular and molecular labeling approaches for MRI depiction of NSC are described and discussed in this review, as well as the most relevant issues to be considered in optimizing molecular imaging techniques for clinical application. (orig.)

  20. Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

    Directory of Open Access Journals (Sweden)

    Farahnaz SADOUGHI

    2014-03-01

    Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

  1. Role of motoneuron-derived neurotrophin 3 in survival and axonal projection of sensory neurons during neural circuit formation.

    Science.gov (United States)

    Usui, Noriyoshi; Watanabe, Keisuke; Ono, Katsuhiko; Tomita, Koichi; Tamamaki, Nobuaki; Ikenaka, Kazuhiro; Takebayashi, Hirohide

    2012-03-01

    Sensory neurons possess the central and peripheral branches and they form unique spinal neural circuits with motoneurons during development. Peripheral branches of sensory axons fasciculate with the motor axons that extend toward the peripheral muscles from the central nervous system (CNS), whereas the central branches of proprioceptive sensory neurons directly innervate motoneurons. Although anatomically well documented, the molecular mechanism underlying sensory-motor interaction during neural circuit formation is not fully understood. To investigate the role of motoneuron on sensory neuron development, we analyzed sensory neuron phenotypes in the dorsal root ganglia (DRG) of Olig2 knockout (KO) mouse embryos, which lack motoneurons. We found an increased number of apoptotic cells in the DRG of Olig2 KO embryos at embryonic day (E) 10.5. Furthermore, abnormal axonal projections of sensory neurons were observed in both the peripheral branches at E10.5 and central branches at E15.5. To understand the motoneuron-derived factor that regulates sensory neuron development, we focused on neurotrophin 3 (Ntf3; NT-3), because Ntf3 and its receptors (Trk) are strongly expressed in motoneurons and sensory neurons, respectively. The significance of motoneuron-derived Ntf3 was analyzed using Ntf3 conditional knockout (cKO) embryos, in which we observed increased apoptosis and abnormal projection of the central branch innervating motoneuron, the phenotypes being apparently comparable with that of Olig2 KO embryos. Taken together, we show that the motoneuron is a functional source of Ntf3 and motoneuron-derived Ntf3 is an essential pre-target neurotrophin for survival and axonal projection of sensory neurons.

  2. Neural Signature of Value-Based Sensorimotor Prioritization in Humans.

    Science.gov (United States)

    Blangero, Annabelle; Kelly, Simon P

    2017-11-01

    value biases in sensorimotor decision making have been widely studied, little is known about the neural processes that set these biases in place beforehand. Here, we report the discovery of a transient, spatially selective neural signal in humans that encodes the relative value of competing decision alternatives and strongly predicts behavioral value biases in decisions made ∼500 ms later. Follow-up manipulations of value differential, reward valence, response modality, sensory features, and time constraints establish that the signal reflects an active, feature- and effector-general preparatory mechanism for value-based prioritization. Copyright © 2017 the authors 0270-6474/17/3710725-13$15.00/0.

  3. In vivo impact of Dlx3 conditional inactivation in Neural Crest-Derived Craniofacial Bones

    Science.gov (United States)

    Duverger, Olivier; Isaac, Juliane; Zah, Angela; Hwang, Joonsung; Berdal, Ariane; Lian, Jane B.; Morasso, Maria I.

    2012-01-01

    Mutations in DLX3 in humans lead to defects in craniofacial and appendicular bones, yet the in vivo activity related to Dlx3 function during normal skeletal development have not been fully elucidated. Here we used a conditional knockout approach to analyze the effects of neural crest deletion of Dlx3 on craniofacial bones development. At birth, mutant mice exhibit a normal overall positioning of the skull bones, but a change in the shape of the calvaria was observed. Molecular analysis of the genes affected in the frontal bones and mandibles from these mice identified several bone markers known to affect bone development, with a strong prediction for increased bone formation and mineralization in vivo. Interestingly, while a subset of these genes were similarly affected in frontal bones and mandibles (Sost, Mepe, Bglap, Alp, Ibsp, Agt), several genes, including Lect1 and Calca, were specifically affected in frontal bones. Consistent with these molecular alterations, cells isolated from the frontal bone of mutant mice exhibited increased differentiation and mineralization capacities ex vivo, supporting cell autonomous defects in neural crest cells. However, adult mutant animals exhibited decreased bone mineral density in both mandibles and calvaria, as well as a significant increase in bone porosity. Together, these observations suggest that mature osteoblasts in the adult respond to signals that regulate adult bone mass and remodeling. This study provides new downstream targets for Dlx3 in craniofacial bone, and gives additional evidence of the complex regulation of bone formation and homeostasis in the adult skeleton. PMID:22886599

  4. Chaos Control and Synchronization of Cellular Neural Network with Delays Based on OPNCL Control

    International Nuclear Information System (INIS)

    Qian, Tang; Xing-Yuan, Wang

    2010-01-01

    The problem of chaos control and complete synchronization of cellular neural network with delays is studied. Based on the open plus nonlinear closed loop (OPNCL) method, the control scheme and synchronization scheme are designed. Both the schemes can achieve the chaos control and complete synchronization of chaotic neural network respectively, and their validity is further verified by numerical simulation experiments. (general)

  5. Simultaneous surface and depth neural activity recording with graphene transistor-based dual-modality probes.

    Science.gov (United States)

    Du, Mingde; Xu, Xianchen; Yang, Long; Guo, Yichuan; Guan, Shouliang; Shi, Jidong; Wang, Jinfen; Fang, Ying

    2018-05-15

    Subdural surface and penetrating depth probes are widely applied to record neural activities from the cortical surface and intracortical locations of the brain, respectively. Simultaneous surface and depth neural activity recording is essential to understand the linkage between the two modalities. Here, we develop flexible dual-modality neural probes based on graphene transistors. The neural probes exhibit stable electrical performance even under 90° bending because of the excellent mechanical properties of graphene, and thus allow multi-site recording from the subdural surface of rat cortex. In addition, finite element analysis was carried out to investigate the mechanical interactions between probe and cortex tissue during intracortical implantation. Based on the simulation results, a sharp tip angle of π/6 was chosen to facilitate tissue penetration of the neural probes. Accordingly, the graphene transistor-based dual-modality neural probes have been successfully applied for simultaneous surface and depth recording of epileptiform activity of rat brain in vivo. Our results show that graphene transistor-based dual-modality neural probes can serve as a facile and versatile tool to study tempo-spatial patterns of neural activities. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. Memristor-based neural networks: Synaptic versus neuronal stochasticity

    KAUST Repository

    Naous, Rawan; Alshedivat, Maruan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

    2016-01-01

    In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors

  7. Prediction of Shanghai Index based on Additive Legendre Neural Network

    Directory of Open Access Journals (Sweden)

    Yang Bin

    2017-01-01

    Full Text Available In this paper, a novel Legendre neural network model is proposed, namely additive Legendre neural network (ALNN. A new hybrid evolutionary method besed on binary particle swarm optimization (BPSO algorithm and firefly algorithm is proposed to optimize the structure and parameters of ALNN model. Shanghai stock exchange composite index is used to evaluate the performance of ALNN. Results reveal that ALNN performs better than LNN model.

  8. Gear Fault Diagnosis Based on BP Neural Network

    Science.gov (United States)

    Huang, Yongsheng; Huang, Ruoshi

    2018-03-01

    Gear transmission is more complex, widely used in machinery fields, which form of fault has some nonlinear characteristics. This paper uses BP neural network to train the gear of four typical failure modes, and achieves satisfactory results. Tested by using test data, test results have an agreement with the actual results. The results show that the BP neural network can effectively solve the complex state of gear fault in the gear fault diagnosis.

  9. Migratory capabilities of human umbilical cord blood-derived neural stem cells (HUCB-NSC) in vitro.

    Science.gov (United States)

    Janowski, Miroslaw; Lukomska, Barbara; Domanska-Janik, Krystyna

    2011-01-01

    Many types of neural progenitors from various sources have been evaluated for therapy of CNS disorders. Prerequisite for success in cell therapy is the ability for transplanted cells to reach appropriate target such as stroke lesion. We have established neural stem cell line from human umbilical cord blood neural stem (HUCB-NSC). In the present study we evaluated migratory capabilities of cells (HUCB-NSC) and the presence of various migration-related receptors. Immunocytochemical analysis revealed abundant expression of CXCR4, PDGFR-alpha, PDGFR-beta, c-Met, VEGFR, IGF-1R and PSA-NCAM receptors in non-adherent population of HUCB-NSC cultured in serum free (SF) conditions (SF cells). Biological activity of selected receptors was confirmed by HUCB-NSC in vitro migration towards SDF-1 and IGF-1 ligands. Additionally, rat brain-derived homogenates have been assessed for their chemoattractive activity of HUCB-NSC. Our experiments unveiled that brain tissue was more attracted for HUCB-NSC than single ligands with higher potency of injured than intact brain. Moreover, adherent HUCB-NSC cultured in low serum (LS) conditions (LS cells) were employed to investigate an impact of different extracellular matrix (ECM) proteins on cell motility. It turned out that laminin provided most permissive microenvironment for cell migration, followed by fibronectin and gelatin. Unexpected nuclear localization of CXCR4 in SF cells prompted us to characterize intracellular pattern of this expression in relation to developmental stage of cells cultured in different conditions. Continuous culture of LS cells revealed cytoplasmatic pattern of CXCR4 expression while HUCB-NSC cultured in high serum conditions (HS cells) resulted in gradual translocation of CXCR4 from nucleus to cytoplasm and then to arising processes. Terminal differentiation of HUCB-NSC was followed by CXCR4 expression decline.

  10. Linear programming based on neural networks for radiotherapy treatment planning

    International Nuclear Information System (INIS)

    Xingen Wu; Limin Luo

    2000-01-01

    In this paper, we propose a neural network model for linear programming that is designed to optimize radiotherapy treatment planning (RTP). This kind of neural network can be easily implemented by using a kind of 'neural' electronic system in order to obtain an optimization solution in real time. We first give an introduction to the RTP problem and construct a non-constraint objective function for the neural network model. We adopt a gradient algorithm to minimize the objective function and design the structure of the neural network for RTP. Compared to traditional linear programming methods, this neural network model can reduce the time needed for convergence, the size of problems (i.e., the number of variables to be searched) and the number of extra slack and surplus variables needed. We obtained a set of optimized beam weights that result in a better dose distribution as compared to that obtained using the simplex algorithm under the same initial condition. The example presented in this paper shows that this model is feasible in three-dimensional RTP. (author)

  11. Depression in chronic ketamine users: Sex differences and neural bases.

    Science.gov (United States)

    Li, Chiang-Shan R; Zhang, Sheng; Hung, Chia-Chun; Chen, Chun-Ming; Duann, Jeng-Ren; Lin, Ching-Po; Lee, Tony Szu-Hsien

    2017-11-30

    Chronic ketamine use leads to cognitive and affective deficits including depression. Here, we examined sex differences and neural bases of depression in chronic ketamine users. Compared to non-drug using healthy controls (HC), ketamine-using females but not males showed increased depression score as assessed by the Center of Epidemiological Studies Depression Scale (CES-D). We evaluated resting state functional connectivity (rsFC) of the subgenual anterior cingulate cortex (sgACC), a prefrontal structure consistently implicated in the pathogenesis of depression. Compared to HC, ketamine users (KU) did not demonstrate significant changes in sgACC connectivities at a corrected threshold. However, in KU, a linear regression against CES-D score showed less sgACC connectivity to the orbitofrontal cortex (OFC) with increasing depression severity. Examined separately, male and female KU showed higher sgACC connectivity to bilateral superior temporal gyrus and dorsomedial prefrontal cortex (dmPFC), respectively, in correlation with depression. The linear correlation of sgACC-OFC and sgACC-dmPFC connectivity with depression was significantly different in slope between KU and HC. These findings highlighted changes in rsFC of the sgACC as associated with depression and sex differences in these changes in chronic ketamine users. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  13. Convolution neural-network-based detection of lung structures

    Science.gov (United States)

    Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

    1994-05-01

    Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

  14. Neural Online Filtering Based on Preprocessed Calorimeter Data

    CERN Document Server

    Torres, R C; The ATLAS collaboration; Simas Filho, E F; De Seixas, J M

    2009-01-01

    Among LHC detectors, ATLAS aims at coping with such high event rate by designing a three-level online triggering system. The first level trigger output will be ~75 kHz. This level will mark the regions where relevant events were found. The second level will validate LVL1 decision by looking only at the approved data using full granularity. At the level two output, the event rate will be reduced to ~2 kHz. Finally, the third level will look at full event information and a rate of ~200 Hz events is expected to be approved, and stored in persistent media for further offline analysis. Many interesting events decay into electrons, which have to be identified from the huge background noise (jets). This work proposes a high-efficient LVL2 electron / jet discrimination system based on neural networks fed from preprocessed calorimeter information. The feature extraction part of the proposed system performs a ring structure of data description. A set of concentric rings centered at the highest energy cell is generated ...

  15. Noisy Ocular Recognition Based on Three Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Min Beom Lee

    2017-12-01

    Full Text Available In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera, specular reflection (SR and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs. Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II training dataset (selected from the university of Beira iris (UBIRIS.v2 database, mobile iris challenge evaluation (MICHE database, and institute of automation of Chinese academy of sciences (CASIA-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  16. Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

    Science.gov (United States)

    Mostafa, Hesham

    2017-08-01

    Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

  17. Route Selection Problem Based on Hopfield Neural Network

    Directory of Open Access Journals (Sweden)

    N. Kojic

    2013-12-01

    Full Text Available Transport network is a key factor of economic, social and every other form of development in the region and the state itself. One of the main conditions for transport network development is the construction of new routes. Often, the construction of regional roads is dominant, since the design and construction in urban areas is quite limited. The process of analysis and planning the new roads is a complex process that depends on many factors (the physical characteristics of the terrain, the economic situation, political decisions, environmental impact, etc. and can take several months. These factors directly or indirectly affect the final solution, and in combination with project limitations and requirements, sometimes can be mutually opposed. In this paper, we present one software solution that aims to find Pareto optimal path for preliminary design of the new roadway. The proposed algorithm is based on many different factors (physical and social with the ability of their increase. This solution is implemented using Hopfield's neural network, as a kind of artificial intelligence, which has shown very good results for solving complex optimization problems.

  18. Convolutional neural network features based change detection in satellite images

    Science.gov (United States)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  19. Noisy Ocular Recognition Based on Three Convolutional Neural Networks.

    Science.gov (United States)

    Lee, Min Beom; Hong, Hyung Gil; Park, Kang Ryoung

    2017-12-17

    In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user's eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  20. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  1. Neural network based adaptive control for nonlinear dynamic regimes

    Science.gov (United States)

    Shin, Yoonghyun

    Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

  2. Delivery of Brain-Derived Neurotrophic Factor by 3D Biocompatible Polymeric Scaffolds for Neural Tissue Engineering and Neuronal Regeneration

    KAUST Repository

    Limongi, Tania; Rocchi, A.; Cesca, F.; Tan, H.; Miele, E.; Giugni, Andrea; Orlando, M.; Perrone Donnorso, M.; Perozziello, G.; Benfenati, Fabio; Di Fabrizio, Enzo M.

    2018-01-01

    Biopolymers are increasingly employed for neuroscience applications as scaffolds to drive and promote neural regrowth, thanks to their ability to mediate the upload and subsequent release of active molecules and drugs. Synthetic degradable polymers are characterized by different responses ranging from tunable distension or shrinkage to total dissolution, depending on the function they are designed for. In this paper we present a biocompatible microfabricated poly-ε-caprolactone (PCL) scaffold for primary neuron growth and maturation that has been optimized for the in vitro controlled release of brain-derived neurotrophic factor (BDNF). We demonstrate that the designed morphology confers to these devices an enhanced drug delivery capability with respect to monolithic unstructured supports. After incubation with BDNF, micropillared PCL devices progressively release the neurotrophin over 21 days in vitro. Moreover, the bioactivity of released BDNF is confirmed using primary neuronal cultures, where it mediates a consistent activation of BDNF signaling cascades, increased synaptic density, and neuronal survival. These results provide the proof-of-principle on the fabrication process of micropatterned PCL devices, which represent a promising therapeutic option to enhance neuronal regeneration after lesion and for neural tissue engineering and prosthetics.

  3. Transcriptional profiling of MEF2-regulated genes in human neural progenitor cells derived from embryonic stem cells

    Directory of Open Access Journals (Sweden)

    Shing Fai Chan

    2015-03-01

    Full Text Available The myocyte enhancer factor 2 (MEF2 family of transcription factors is highly expressed in the brain and constitutes a key determinant of neuronal survival, differentiation, and synaptic plasticity. However, genome-wide transcriptional profiling of MEF2-regulated genes has not yet been fully elucidated, particularly at the neural stem cell stage. Here we report the results of microarray analysis comparing mRNAs isolated from human neural progenitor/stem cells (hNPCs derived from embryonic stem cells expressing a control vector versus progenitors expressing a constitutively-active form of MEF2 (MEF2CA, which increases MEF2 activity. Microarray experiments were performed using the Illumina Human HT-12 V4.0 expression beadchip (GEO#: GSE57184. By comparing vector-control cells to MEF2CA cells, microarray analysis identified 1880 unique genes that were differentially expressed. Among these genes, 1121 genes were up-regulated and 759 genes were down-regulated. Our results provide a valuable resource for identifying transcriptional targets of MEF2 in hNPCs.

  4. Distinct functional and temporal requirements for zebrafish Hdac1 during neural crest-derived craniofacial and peripheral neuron development.

    Science.gov (United States)

    Ignatius, Myron S; Unal Eroglu, Arife; Malireddy, Smitha; Gallagher, Glen; Nambiar, Roopa M; Henion, Paul D

    2013-01-01

    The regulation of gene expression is accomplished by both genetic and epigenetic means and is required for the precise control of the development of the neural crest. In hdac1(b382) mutants, craniofacial cartilage development is defective in two distinct ways. First, fewer hoxb3a, dlx2 and dlx3-expressing posterior branchial arch precursors are specified and many of those that are consequently undergo apoptosis. Second, in contrast, normal numbers of progenitors are present in the anterior mandibular and hyoid arches, but chondrocyte precursors fail to terminally differentiate. In the peripheral nervous system, there is a disruption of enteric, DRG and sympathetic neuron differentiation in hdac1(b382) mutants compared to wildtype embryos. Specifically, enteric and DRG-precursors differentiate into neurons in the anterior gut and trunk respectively, while enteric and DRG neurons are rarely present in the posterior gut and tail. Sympathetic neuron precursors are specified in hdac1(b382) mutants and they undergo generic neuronal differentiation but fail to undergo noradrenergic differentiation. Using the HDAC inhibitor TSA, we isolated enzyme activity and temporal requirements for HDAC function that reproduce hdac1(b382) defects in craniofacial and sympathetic neuron development. Our study reveals distinct functional and temporal requirements for zebrafish hdac1 during neural crest-derived craniofacial and peripheral neuron development.

  5. Distinct functional and temporal requirements for zebrafish Hdac1 during neural crest-derived craniofacial and peripheral neuron development.

    Directory of Open Access Journals (Sweden)

    Myron S Ignatius

    Full Text Available The regulation of gene expression is accomplished by both genetic and epigenetic means and is required for the precise control of the development of the neural crest. In hdac1(b382 mutants, craniofacial cartilage development is defective in two distinct ways. First, fewer hoxb3a, dlx2 and dlx3-expressing posterior branchial arch precursors are specified and many of those that are consequently undergo apoptosis. Second, in contrast, normal numbers of progenitors are present in the anterior mandibular and hyoid arches, but chondrocyte precursors fail to terminally differentiate. In the peripheral nervous system, there is a disruption of enteric, DRG and sympathetic neuron differentiation in hdac1(b382 mutants compared to wildtype embryos. Specifically, enteric and DRG-precursors differentiate into neurons in the anterior gut and trunk respectively, while enteric and DRG neurons are rarely present in the posterior gut and tail. Sympathetic neuron precursors are specified in hdac1(b382 mutants and they undergo generic neuronal differentiation but fail to undergo noradrenergic differentiation. Using the HDAC inhibitor TSA, we isolated enzyme activity and temporal requirements for HDAC function that reproduce hdac1(b382 defects in craniofacial and sympathetic neuron development. Our study reveals distinct functional and temporal requirements for zebrafish hdac1 during neural crest-derived craniofacial and peripheral neuron development.

  6. Delivery of Brain-Derived Neurotrophic Factor by 3D Biocompatible Polymeric Scaffolds for Neural Tissue Engineering and Neuronal Regeneration

    KAUST Repository

    Limongi, Tania

    2018-04-04

    Biopolymers are increasingly employed for neuroscience applications as scaffolds to drive and promote neural regrowth, thanks to their ability to mediate the upload and subsequent release of active molecules and drugs. Synthetic degradable polymers are characterized by different responses ranging from tunable distension or shrinkage to total dissolution, depending on the function they are designed for. In this paper we present a biocompatible microfabricated poly-ε-caprolactone (PCL) scaffold for primary neuron growth and maturation that has been optimized for the in vitro controlled release of brain-derived neurotrophic factor (BDNF). We demonstrate that the designed morphology confers to these devices an enhanced drug delivery capability with respect to monolithic unstructured supports. After incubation with BDNF, micropillared PCL devices progressively release the neurotrophin over 21 days in vitro. Moreover, the bioactivity of released BDNF is confirmed using primary neuronal cultures, where it mediates a consistent activation of BDNF signaling cascades, increased synaptic density, and neuronal survival. These results provide the proof-of-principle on the fabrication process of micropatterned PCL devices, which represent a promising therapeutic option to enhance neuronal regeneration after lesion and for neural tissue engineering and prosthetics.

  7. Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2015-07-01

    This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. A neural cell adhesion molecule-derived peptide reduces neuropathological signs and cognitive impairment induced by Abeta25-35

    DEFF Research Database (Denmark)

    Klementiev, B; Novikova, T; Novitskaya, V

    2007-01-01

    death and brain atrophy in response to Abeta25-35. Finally, the Abeta25-35-administration led to a reduced short-term memory as determined by the social recognition test. A synthetic peptide termed FGL derived from the neural cell adhesion molecule (NCAM) was able to prevent or, if already manifest......, strongly reduce all investigated signs of Abeta25-35-induced neuropathology and cognitive impairment. The FGL peptide was recently demonstrated to be able to cross the blood-brain-barrier. Accordingly, we found that the beneficial effects of FGL were achieved not only by intracisternal, but also...... and cognitive impairment involves the modulation of intracellular signal-transduction mediated through GSK3beta....

  9. Neurotrophin-3 promotes proliferation and cholinergic neuronal differentiation of bone marrow- derived neural stem cells via notch signaling pathway.

    Science.gov (United States)

    Yan, Yu-Hui; Li, Shao-Heng; Gao, Zhong; Zou, Sa-Feng; Li, Hong-Yan; Tao, Zhen-Yu; Song, Jie; Yang, Jing-Xian

    2016-12-01

    Recently, the potential for neural stem cells (NSCs) to be used in the treatment of Alzheimer's disease (AD) has been reported; however, the therapeutic effects are modest by virtue of the low neural differentiation rate. In our study, we transfected bone marrow-derived NSCs (BM-NSCs) with Neurotrophin-3 (NT-3), a superactive neurotrophic factor that promotes neuronal survival, differentiation, and migration of neuronal cells, to investigate the effects of NT-3 gene overexpression on the proliferation and differentiation into cholinergic neuron of BM-NSCs in vitro and its possible molecular mechanism. BM-NSCs were generated from BM mesenchymal cells of adult C57BL/6 mice and cultured in vitro. After transfected with NT-3 gene, immunofluorescence and RT-PCR method were used to determine the ability of BM-NSCs on proliferation and differentiation into cholinergic neuron; Acetylcholine Assay Kit was used for acetylcholine (Ach). RT-PCR and WB analysis were used to characterize mRNA and protein level related to the Notch signaling pathway. We found that NT-3 can promote the proliferation and differentiation of BM-NSCs into cholinergic neurons and elevate the levels of acetylcholine (ACh) in the supernatant. Furthermore, NT-3 gene overexpression increase the expression of Hes1, decreased the expression of Mash1 and Ngn1 during proliferation of BM-NSCs. Whereas, the expression of Hes1 was down-regulated, and Mash1 and Ngn1 expression were up-regulated during differentiation of BM-NSCs. Our findings support the prospect of using NT-3-transduced BM-NSCs in developing therapies for AD due to their equivalent therapeutic potential as subventricular zone-derived NSCs (SVZ-NSCs), greater accessibility, and autogenous attributes. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Intraocular Injection of ES Cell-Derived Neural Progenitors Improve Visual Function in Retinal Ganglion Cell-Depleted Mouse Models

    Directory of Open Access Journals (Sweden)

    Mundackal S. Divya

    2017-09-01

    Full Text Available Retinal ganglion cell (RGC transplantation is a promising strategy to restore visual function resulting from irreversible RGC degeneration occurring in glaucoma or inherited optic neuropathies. We previously demonstrated FGF2 induced differentiation of mouse embryonic stem cells (ESC to RGC lineage, capable of retinal ganglion cell layer (GCL integration upon transplantation. Here, we evaluated possible improvement of visual function by transplantation of ES cell derived neural progenitors in RGC depleted glaucoma mice models. ESC derived neural progenitors (ES-NP were transplanted into N-Methyl-D-Aspartate (NMDA injected, RGC-ablated mouse models and a pre-clinical glaucoma mouse model (DBA/2J having sustained higher intra ocular pressure (IOP. Visual acuity and functional integration was evaluated by behavioral experiments and immunohistochemistry, respectively. GFP-expressing ES-NPs transplanted in NMDA-injected RGC-depleted mice differentiated into RGC lineage and possibly integrating into GCL. An improvement in visual acuity was observed after 2 months of transplantation, when compared to the pre-transplantation values. Expression of c-Fos in the transplanted cells, upon light induction, further suggests functional integration into the host retinal circuitry. However, the transplanted cells did not send axonal projections into optic nerve. Transplantation experiments in DBA/2J mouse showed no significant improvement in visual functions, possibly due to both host and transplanted retinal cell death which could be due to an inherent high IOP. We showed that, ES NPs transplanted into the retina of RGC-ablated mouse models could survive, differentiate to RGC lineage, and possibly integrate into GCL to improve visual function. However, for the survival of transplanted cells in glaucoma, strategies to control the IOP are warranted.

  11. Photosensitive-polyimide based method for fabricating various neural electrode architectures

    Directory of Open Access Journals (Sweden)

    Yasuhiro X Kato

    2012-06-01

    Full Text Available An extensive photosensitive polyimide (PSPI-based method for designing and fabricating various neural electrode architectures was developed. The method aims to broaden the design flexibility and expand the fabrication capability for neural electrodes to improve the quality of recorded signals and integrate other functions. After characterizing PSPI’s properties for micromachining processes, we successfully designed and fabricated various neural electrodes even on a non-flat substrate using only one PSPI as an insulation material and without the time-consuming dry etching processes. The fabricated neural electrodes were an electrocorticogram electrode, a mesh intracortical electrode with a unique lattice-like mesh structure to fixate neural tissue, and a guide cannula electrode with recording microelectrodes placed on the curved surface of a guide cannula as a microdialysis probe. In vivo neural recordings using anesthetized rats demonstrated that these electrodes can be used to record neural activities repeatedly without any breakage and mechanical failures, which potentially promises stable recordings for long periods of time. These successes make us believe that this PSPI-based fabrication is a powerful method, permitting flexible design and easy optimization of electrode architectures for a variety of electrophysiological experimental research with improved neural recording performance.

  12. Neural network based method for conversion of solar radiation data

    International Nuclear Information System (INIS)

    Celik, Ali N.; Muneer, Tariq

    2013-01-01

    Highlights: ► Generalized regression neural network is used to predict the solar radiation on tilted surfaces. ► The above network, amongst many such as multilayer perceptron, is the most successful one. ► The present neural network returns a relative mean absolute error value of 9.1%. ► The present model leads to a mean absolute error value of estimate of 14.9 Wh/m 2 . - Abstract: The receiving ends of the solar energy conversion systems that generate heat or electricity from radiation is usually tilted at an optimum angle to increase the solar incident on the surface. Solar irradiation data measured on horizontal surfaces is readily available for many locations where such solar energy conversion systems are installed. Various equations have been developed to convert solar irradiation data measured on horizontal surface to that on tilted one. These equations constitute the conventional approach. In this article, an alternative approach, generalized regression type of neural network, is used to predict the solar irradiation on tilted surfaces, using the minimum number of variables involved in the physical process, namely the global solar irradiation on horizontal surface, declination and hour angles. Artificial neural networks have been successfully used in recent years for optimization, prediction and modeling in energy systems as alternative to conventional modeling approaches. To show the merit of the presently developed neural network, the solar irradiation data predicted from the novel model was compared to that from the conventional approach (isotropic and anisotropic models), with strict reference to the irradiation data measured in the same location. The present neural network model was found to provide closer solar irradiation values to the measured than the conventional approach, with a mean absolute error value of 14.9 Wh/m 2 . The other statistical values of coefficient of determination and relative mean absolute error also indicate the

  13. Classification of urine sediment based on convolution neural network

    Science.gov (United States)

    Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

    2018-04-01

    By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

  14. Planning music-based amelioration and training in infancy and childhood based on neural evidence.

    Science.gov (United States)

    Huotilainen, Minna; Tervaniemi, Mari

    2018-05-04

    Music-based amelioration and training of the developing auditory system has a long tradition, and recent neuroscientific evidence supports using music in this manner. Here, we present the available evidence showing that various music-related activities result in positive changes in brain structure and function, becoming helpful for auditory cognitive processes in everyday life situations for individuals with typical neural development and especially for individuals with hearing, learning, attention, or other deficits that may compromise auditory processing. We also compare different types of music-based training and show how their effects have been investigated with neural methods. Finally, we take a critical position on the multitude of error sources found in amelioration and training studies and on publication bias in the field. We discuss some future improvements of these issues in the field of music-based training and their potential results at the neural and behavioral levels in infants and children for the advancement of the field and for a more complete understanding of the possibilities and significance of the training. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

  15. Template-based procedures for neural network interpretation.

    Science.gov (United States)

    Alexander, J A.; Mozer, M C.

    1999-04-01

    Although neural networks often achieve impressive learning and generalization performance, their internal workings are typically all but impossible to decipher. This characteristic of the networks, their opacity, is one of the disadvantages of connectionism compared to more traditional, rule-oriented approaches to artificial intelligence. Without a thorough understanding of the network behavior, confidence in a system's results is lowered, and the transfer of learned knowledge to other processing systems - including humans - is precluded. Methods that address the opacity problem by casting network weights in symbolic terms are commonly referred to as rule extraction techniques. This work describes a principled approach to symbolic rule extraction from standard multilayer feedforward networks based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressions. With an appropriate choice of representation, we show how template parameters may be efficiently identified and instantiated to yield the optimal match to the actual weights of a unit. Depending on the requirements of the application domain, the approach can accommodate n-ary disjunctions and conjunctions with O(k) complexity, simple n-of-m expressions with O(k(2)) complexity, or more general classes of recursive n-of-m expressions with O(k(L+2)) complexity, where k is the number of inputs to an unit and L the recursion level of the expression class. Compared to other approaches in the literature, our method of rule extraction offers benefits in simplicity, computational performance, and overall flexibility. Simulation results on a variety of problems demonstrate the application of our procedures as well as the strengths and the weaknesses of our general approach.

  16. PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller

    Directory of Open Access Journals (Sweden)

    MARABA, V. A.

    2011-11-01

    Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.

  17. A case study to estimate costs using Neural Networks and regression based models

    Directory of Open Access Journals (Sweden)

    Nadia Bhuiyan

    2012-07-01

    Full Text Available Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be considered in order to determine the most accurate method to predict the cost of a main landing gear. Several trials are presented for the design and use of the neural network model. The analysis for the case under study shows the flexibility in the design of the neural network model. Furthermore, the performance of the neural network model is deemed superior to the parametric models for this case study.

  18. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  19. Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors

    Directory of Open Access Journals (Sweden)

    Alma Y. Alanis

    2013-01-01

    Full Text Available This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high-order neural network (RHONN trained with a novel algorithm based on extended Kalman filter (EKF and particle swarm optimization (PSO, using an online series-parallel con…figuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.

  20. In vitro evaluation of biocompatibility of uncoated thermally reduced graphene and carbon nanotube-loaded PVDF membranes with adult neural stem cell-derived neurons and glia

    Directory of Open Access Journals (Sweden)

    Çagla Defterali

    2016-12-01

    Full Text Available Graphene, graphene-based nanomaterials (GBNs and carbon nanotubes (CNTs are being investigated as potential substrates for the growth of neural cells. However, in most in vitro studies the cells were seeded on these materials coated with various proteins implying that the observed effects on the cells could not solely be attributed to the GBN and CNT properties. Here we studied the biocompatibility of uncoated thermally reduced graphene (TRG and poly-vinylidene fluoride (PVDF membranes loaded with multi walled CNTs (MWCNTs using neural stem cells (NSCs isolated from the adult mouse olfactory bulb (termed aOBSCs. When aOBSCs were induced to differentiate on coverslips treated with TRG or control materials (polyethyleneimine-PEI and polyornithine plus fibronectin-PLO/F in a serum-free medium, neurons, astrocytes, and oligodendrocytes were generated in all conditions, indicating that TRG permits the multi-lineage differentiation of aOBSCs. However, the total number of cells was reduced on both PEI and TRG. In a serum-containing medium, aOBSC-derived neurons and oligodendrocytes grown on TRG were more numerous than in controls; the neurons developed synaptic boutons and oligodendrocytes were more branched. In contrast, neurons growing on PVDF membranes had reduced neurite branching and on MWCNTs-loaded membranes, oligodendrocytes were lower in numbers than in controls. Overall, these findings indicate that uncoated TRG may be biocompatible with the generation, differentiation, and maturation of aOBSC-derived neurons and glial cells, implying a potential use for TRG to study functional neuronal networks.

  1. Evolving Neural Turing Machines for Reward-based Learning

    DEFF Research Database (Denmark)

    Greve, Rasmus Boll; Jacobsen, Emil Juul; Risi, Sebastian

    2016-01-01

    An unsolved problem in neuroevolution (NE) is to evolve artificial neural networks (ANN) that can store and use information to change their behavior online. While plastic neural networks have shown promise in this context, they have difficulties retaining information over longer periods of time...... version of the double T-Maze, a complex reinforcement-like learning problem. In the T-Maze learning task the agent uses the memory bank to display adaptive behavior that normally requires a plastic ANN, thereby suggesting a complementary and effective mechanism for adaptive behavior in NE....

  2. Neural network based photovoltaic electrical forecasting in south Algeria

    International Nuclear Information System (INIS)

    Hamid Oudjana, S.; Hellal, A.; Hadj Mahammed, I

    2014-01-01

    Photovoltaic electrical forecasting is significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants, and it is important task in renewable energy electrical system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic electrical forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) for one year of 2013 using a data acquisition system. Simulations were run and the results are discussed showing that neural networks Technique is capable to decrease the photovoltaic electrical forecasting error. (author)

  3. The method in γ spectrum analysis with artificial neural network based on MATLAB

    International Nuclear Information System (INIS)

    Bai Lixin; Zhang Yiyun; Xu Jiayun; Wu Liping

    2003-01-01

    Analyzing γ spectrum with artificial neural network have the advantage of using the information of whole spectrum and having high analyzing precision. A convenient realization based on MATLAB was present in this

  4. Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks

    International Nuclear Information System (INIS)

    Zhou Liming; Zhang Yingyue; Chen Tianlun

    2005-01-01

    Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse, the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.

  5. Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor.

    Science.gov (United States)

    Dai, Chenyun; Zheng, Yang; Hu, Xiaogang

    2018-01-01

    Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.

  6. An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

    Science.gov (United States)

    Cabessa, Jérémie; Villa, Alessandro E. P.

    2014-01-01

    We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

  7. Radioactivity nuclide identification based on BP and LM algorithm neural network

    International Nuclear Information System (INIS)

    Wang Jihong; Sun Jian; Wang Lianghou

    2012-01-01

    The paper provides the method which can identify radioactive nuclide based on the BP and LM algorithm neural network. Then, this paper compares the above-mentioned method with FR algorithm. Through the result of the Matlab simulation, the method of radioactivity nuclide identification based on the BP and LM algorithm neural network is superior to the FR algorithm. With the better effect and the higher accuracy, it will be the best choice. (authors)

  8. High Speed PAM -8 Optical Interconnects with Digital Equalization based on Neural Network

    DEFF Research Database (Denmark)

    Gaiarin, Simone; Pang, Xiaodan; Ozolins, Oskars

    2016-01-01

    We experimentally evaluate a high-speed optical interconnection link with neural network equalization. Enhanced equalization performances are shown comparing to standard linear FFE for an EML-based 32 GBd PAM-8 signal after 4-km SMF transmission.......We experimentally evaluate a high-speed optical interconnection link with neural network equalization. Enhanced equalization performances are shown comparing to standard linear FFE for an EML-based 32 GBd PAM-8 signal after 4-km SMF transmission....

  9. A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

    OpenAIRE

    Tao, Yong; Zheng, Jiaqi; Lin, Yuanchang

    2016-01-01

    A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

  10. Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices

    OpenAIRE

    Lim, Suhwan; Bae, Jong-Ho; Eum, Jai-Ho; Lee, Sungtae; Kim, Chul-Heung; Kwon, Dongseok; Park, Byung-Gook; Lee, Jong-Ho

    2017-01-01

    In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron net...

  11. ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

    Science.gov (United States)

    Cao, Renzhi; Freitas, Colton; Chan, Leong; Sun, Miao; Jiang, Haiqing; Chen, Zhangxin

    2017-10-17

    With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.

  12. Differentiated human midbrain-derived neural progenitor cells express excitatory strychnine-sensitive glycine receptors containing α2β subunits.

    Directory of Open Access Journals (Sweden)

    Florian Wegner

    Full Text Available BACKGROUND: Human fetal midbrain-derived neural progenitor cells (NPCs may deliver a tissue source for drug screening and regenerative cell therapy to treat Parkinson's disease. While glutamate and GABA(A receptors play an important role in neurogenesis, the involvement of glycine receptors during human neurogenesis and dopaminergic differentiation as well as their molecular and functional characteristics in NPCs are largely unknown. METHODOLOGY/PRINCIPAL FINDINGS: Here we investigated NPCs in respect to their glycine receptor function and subunit expression using electrophysiology, calcium imaging, immunocytochemistry, and quantitative real-time PCR. Whole-cell recordings demonstrate the ability of NPCs to express functional strychnine-sensitive glycine receptors after differentiation for 3 weeks in vitro. Pharmacological and molecular analyses indicate a predominance of glycine receptor heteromers containing α2β subunits. Intracellular calcium measurements of differentiated NPCs suggest that glycine evokes depolarisations mediated by strychnine-sensitive glycine receptors and not by D-serine-sensitive excitatory glycine receptors. Culturing NPCs with additional glycine, the glycine-receptor antagonist strychnine, or the Na(+-K(+-Cl(- co-transporter 1 (NKCC1-inhibitor bumetanide did not significantly influence cell proliferation and differentiation in vitro. CONCLUSIONS/SIGNIFICANCE: These data indicate that NPCs derived from human fetal midbrain tissue acquire essential glycine receptor properties during neuronal maturation. However, glycine receptors seem to have a limited functional impact on neurogenesis and dopaminergic differentiation of NPCs in vitro.

  13. A neural network based seafloor classification using acoustic backscatter

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.

    This paper presents a study results of the Artificial Neural Network (ANN) architectures [Self-Organizing Map (SOM) and Multi-Layer Perceptron (MLP)] using single beam echosounding data. The single beam echosounder, operable at 12 kHz, has been used...

  14. Artificial Neural Networks for SCADA Data based Load Reconstruction (poster)

    NARCIS (Netherlands)

    Hofemann, C.; Van Bussel, G.J.W.; Veldkamp, H.

    2011-01-01

    If at least one reference wind turbine is available, which provides sufficient information about the wind turbine loads, the loads acting on the neighbouring wind turbines can be predicted via an artificial neural network (ANN). This research explores the possibilities to apply such a network not

  15. MODELLING OF CONCENTRATION LIMITS BASED ON NEURAL NETWORKS.

    Directory of Open Access Journals (Sweden)

    A. L. Osipov

    2017-02-01

    Full Text Available We study the forecasting model with the concentration limits is-the use of neural network technology. The software for the implementation of these models. It is shown that the efficiency of the system in the experimental material.

  16. Active Control of Sound based on Diagonal Recurrent Neural Network

    NARCIS (Netherlands)

    Jayawardhana, Bayu; Xie, Lihua; Yuan, Shuqing

    2002-01-01

    Recurrent neural network has been known for its dynamic mapping and better suited for nonlinear dynamical system. Nonlinear controller may be needed in cases where the actuators exhibit the nonlinear characteristics, or in cases when the structure to be controlled exhibits nonlinear behavior. The

  17. A fuzzy art neural network based color image processing and ...

    African Journals Online (AJOL)

    To improve the learning process from the input data, a new learning rule was suggested. In this paper, a new method is proposed to deal with the RGB color image pixels, which enables a Fuzzy ART neural network to process the RGB color images. The application of the algorithm was implemented and tested on a set of ...

  18. Image objects detection based on boosting neural network

    NARCIS (Netherlands)

    Liang, N.; Hegt, J.A.; Mladenov, V.M.

    2010-01-01

    This paper discusses the problem of object area detection of video frames. The goal is to design a pixel accurate detector for grass, which could be used for object adaptive video enhancement. A boosting neural network is used for creating such a detector. The resulted detector uses both textural

  19. Neural network based satellite tracking for deep space applications

    Science.gov (United States)

    Amoozegar, F.; Ruggier, C.

    2003-01-01

    The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions and examine the trade-off between tracing accuracy and communication link performance.

  20. Artificial-neural-network-based failure detection and isolation

    Science.gov (United States)

    Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

    1998-03-01

    This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

  1. Leukemia inhibitory factor (LIF) enhances MAP2 + and HUC/D + neurons and influences neurite extension during differentiation of neural progenitors derived from human embryonic stem cells.

    Science.gov (United States)

    Leukemia Inhibitory Factor (L1F), a member of the Interleukin 6 cytokine family, has a role in differentiation of Human Neural Progenitor (hNP) cells in vitro. hNP cells, derived from Human Embryonic Stem (hES) cells, have an unlimited capacity for self-renewal in monolayer cultu...

  2. A recurrent neural network approach to quantitatively studying solar wind effects on TEC derived from GPS; preliminary results

    Directory of Open Access Journals (Sweden)

    J. B. Habarulema

    2009-05-01

    Full Text Available This paper attempts to describe the search for the parameter(s to represent solar wind effects in Global Positioning System total electron content (GPS TEC modelling using the technique of neural networks (NNs. A study is carried out by including solar wind velocity (Vsw, proton number density (Np and the Bz component of the interplanetary magnetic field (IMF Bz obtained from the Advanced Composition Explorer (ACE satellite as separate inputs to the NN each along with day number of the year (DN, hour (HR, a 4-month running mean of the daily sunspot number (R4 and the running mean of the previous eight 3-hourly magnetic A index values (A8. Hourly GPS TEC values derived from a dual frequency receiver located at Sutherland (32.38° S, 20.81° E, South Africa for 8 years (2000–2007 have been used to train the Elman neural network (ENN and the result has been used to predict TEC variations for a GPS station located at Cape Town (33.95° S, 18.47° E. Quantitative results indicate that each of the parameters considered may have some degree of influence on GPS TEC at certain periods although a decrease in prediction accuracy is also observed for some parameters for different days and seasons. It is also evident that there is still a difficulty in predicting TEC values during disturbed conditions. The improvements and degradation in prediction accuracies are both close to the benchmark values which lends weight to the belief that diurnal, seasonal, solar and magnetic variabilities may be the major determinants of TEC variability.

  3. Automated implementation of rule-based expert systems with neural networks for time-critical applications

    Science.gov (United States)

    Ramamoorthy, P. A.; Huang, Song; Govind, Girish

    1991-01-01

    In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

  4. Image object recognition based on the Zernike moment and neural networks

    Science.gov (United States)

    Wan, Jianwei; Wang, Ling; Huang, Fukan; Zhou, Liangzhu

    1998-03-01

    This paper first give a comprehensive discussion about the concept of artificial neural network its research methods and the relations with information processing. On the basis of such a discussion, we expound the mathematical similarity of artificial neural network and information processing. Then, the paper presents a new method of image recognition based on invariant features and neural network by using image Zernike transform. The method not only has the invariant properties for rotation, shift and scale of image object, but also has good fault tolerance and robustness. Meanwhile, it is also compared with statistical classifier and invariant moments recognition method.

  5. Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

    Science.gov (United States)

    Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

    2018-01-01

    In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

  6. Error Concealment using Neural Networks for Block-Based Image Coding

    Directory of Open Access Journals (Sweden)

    M. Mokos

    2006-06-01

    Full Text Available In this paper, a novel adaptive error concealment (EC algorithm, which lowers the requirements for channel coding, is proposed. It conceals errors in block-based image coding systems by using neural network. In this proposed algorithm, only the intra-frame information is used for reconstruction of the image with separated damaged blocks. The information of pixels surrounding a damaged block is used to recover the errors using the neural network models. Computer simulation results show that the visual quality and the MSE evaluation of a reconstructed image are significantly improved using the proposed EC algorithm. We propose also a simple non-neural approach for comparison.

  7. An Application to the Prediction of LOD Change Based on General Regression Neural Network

    Science.gov (United States)

    Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

    2011-07-01

    Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

  8. Optical implementation of a feature-based neural network with application to automatic target recognition

    Science.gov (United States)

    Chao, Tien-Hsin; Stoner, William W.

    1993-01-01

    An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

  9. Automatic target recognition using a feature-based optical neural network

    Science.gov (United States)

    Chao, Tien-Hsin

    1992-01-01

    An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

  10. Prediction of welding shrinkage deformation of bridge steel box girder based on wavelet neural network

    Science.gov (United States)

    Tao, Yulong; Miao, Yunshui; Han, Jiaqi; Yan, Feiyun

    2018-05-01

    Aiming at the low accuracy of traditional forecasting methods such as linear regression method, this paper presents a prediction method for predicting the relationship between bridge steel box girder and its displacement with wavelet neural network. Compared with traditional forecasting methods, this scheme has better local characteristics and learning ability, which greatly improves the prediction ability of deformation. Through analysis of the instance and found that after compared with the traditional prediction method based on wavelet neural network, the rigid beam deformation prediction accuracy is higher, and is superior to the BP neural network prediction results, conform to the actual demand of engineering design.

  11. High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

    Science.gov (United States)

    Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

    2015-09-01

    In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Neural Network-Based Resistance Spot Welding Control and Quality Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

    1999-07-10

    This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

  13. MATLAB Simulation of Gradient-Based Neural Network for Online Matrix Inversion

    Science.gov (United States)

    Zhang, Yunong; Chen, Ke; Ma, Weimu; Li, Xiao-Dong

    This paper investigates the simulation of a gradient-based recurrent neural network for online solution of the matrix-inverse problem. Several important techniques are employed as follows to simulate such a neural system. 1) Kronecker product of matrices is introduced to transform a matrix-differential-equation (MDE) to a vector-differential-equation (VDE); i.e., finally, a standard ordinary-differential-equation (ODE) is obtained. 2) MATLAB routine "ode45" is introduced to solve the transformed initial-value ODE problem. 3) In addition to various implementation errors, different kinds of activation functions are simulated to show the characteristics of such a neural network. Simulation results substantiate the theoretical analysis and efficacy of the gradient-based neural network for online constant matrix inversion.

  14. Results from a MA16-based neural trigger in an experiment looking for beauty

    International Nuclear Information System (INIS)

    Baldanza, C.; Beichter, J.; Bisi, F.; Bruels, N.; Bruschini, C.; Cotta-Ramusino, A.; D'Antone, I.; Malferrari, L.; Mazzanti, P.; Musico, P.; Novelli, P.; Odorici, F.; Odorico, R.; Passaseo, M.; Zuffa, M.

    1996-01-01

    Results from a neural-network trigger based on the digital MA16 chip of Siemens are reported. The neural trigger has been applied to data from the WA92 experiment, looking for beauty particles, which have been collected during a run in which a neural trigger module based on Intel's analog neural chip ETANN operated, as already reported. The MA16 board hosting the chip has a 16-bit I/O precision and a 53-bit precision for internal calculations. It operated at 50 MHz, yielding a response time for a 16 input-variable net of 3 μs for a Fisher discriminant (1-layer net) and of 6 μs for a 2-layer net. Results are compared with those previously obtained with the ETANN trigger. (orig.)

  15. Results from a MA16-based neural trigger in an experiment looking for beauty

    Energy Technology Data Exchange (ETDEWEB)

    Baldanza, C. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Beichter, J. [Siemens AG, ZFE T ME2, 81730 Munich (Germany); Bisi, F. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Bruels, N. [Siemens AG, ZFE T ME2, 81730 Munich (Germany); Bruschini, C. [INFN/Genoa, Via Dodecaneso 33, 16146 Genoa (Italy); Cotta-Ramusino, A. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); D`Antone, I. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Malferrari, L. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Mazzanti, P. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Musico, P. [INFN/Genoa, Via Dodecaneso 33, 16146 Genoa (Italy); Novelli, P. [INFN/Genoa, Via Dodecaneso 33, 16146 Genoa (Italy); Odorici, F. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Odorico, R. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy); Passaseo, M. [CERN, 1211 Geneva 23 (Switzerland); Zuffa, M. [Istituto Nazionale di Fisica Nucleare, Bologna (Italy)

    1996-07-11

    Results from a neural-network trigger based on the digital MA16 chip of Siemens are reported. The neural trigger has been applied to data from the WA92 experiment, looking for beauty particles, which have been collected during a run in which a neural trigger module based on Intel`s analog neural chip ETANN operated, as already reported. The MA16 board hosting the chip has a 16-bit I/O precision and a 53-bit precision for internal calculations. It operated at 50 MHz, yielding a response time for a 16 input-variable net of 3 {mu}s for a Fisher discriminant (1-layer net) and of 6 {mu}s for a 2-layer net. Results are compared with those previously obtained with the ETANN trigger. (orig.).

  16. Computational neural network regression model for Host based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Gautam

    2016-09-01

    Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

  17. Memristor-based neural networks: Synaptic versus neuronal stochasticity

    KAUST Repository

    Naous, Rawan

    2016-11-02

    In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is incorporated into abstract models of stochastic neurons and synapses. Two approaches of stochastic neural networks are investigated. Aside from the size and area perspective, the impact on the system performance, in terms of accuracy, recognition rates, and learning, among these two approaches and where the memristor would fall into place are the main comparison points to be considered.

  18. Neural network based PWM AC chopper fed induction motor drive

    Directory of Open Access Journals (Sweden)

    Venkatesan Jamuna

    2009-01-01

    Full Text Available In this paper, a new Simulink model for a neural network controlled PWM AC chopper fed single phase induction motor is proposed. Closed loop speed control is achieved using a neural network controller. To maintain a constant fluid flow with a variation in pressure head, drives like fan and pump are operated with closed loop speed control. The need to improve the quality and reliability of the drive circuit has increased because of the growing demand for improving the performance of motor drives. With the increased availability of MOSFET's and IGBT's, PWM converters can be used efficiently in low and medium power applications. From the simulation studies, it is seen that the PWM AC chopper has a better harmonic spectrum and lesser copper loss than the Phase controlled AC chopper. It is observed that the drive system with the proposed model produces better dynamic performance, reduced overshoot and fast transient response. .

  19. Stock prices forecasting based on wavelet neural networks with PSO

    OpenAIRE

    Wang Kai-Cheng; Yang Chi-I; Chang Kuei-Fang

    2017-01-01

    This research examines the forecasting performance of wavelet neural network (WNN) model using published stock data obtained from Financial Times Stock Exchange (FTSE) Taiwan Stock Exchange (TWSE) 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO) to choose the appropriate initial network values for different companies. The findings come with two advantages. First...

  20. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  1. Neural bases of selective attention in action video game players

    OpenAIRE

    Bavelier, D; Achtman, RL; Mani, M; Föcker, J

    2011-01-01

    Over the past few years, the very act of playing action video games has been shown to enhance several different aspects of visual selective attention. Yet little is known about the neural mechanisms that mediate such attentional benefits. A review of the aspects of attention enhanced in action game players suggests there are changes in the mechanisms that control attention allocation and its efficiency (Hubert-Wallander et al., 2010). The present study used brain imaging to test this hypothes...

  2. Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing.

    Science.gov (United States)

    van der Velde, Frank

    2016-01-01

    In situ concept-based computing is based on the notion that conceptual representations in the human brain are "in situ." In this way, they are grounded in perception and action. Examples are neuronal assemblies, whose connection structures develop over time and are distributed over different brain areas. In situ concepts representations cannot be copied or duplicated because that will disrupt their connection structure, and thus the meaning of these concepts. Higher-level cognitive processes, as found in language and reasoning, can be performed with in situ concepts by embedding them in specialized neurally inspired "blackboards." The interactions between the in situ concepts and the blackboards form the basis for in situ concept computing architectures. In these architectures, memory (concepts) and processing are interwoven, in contrast with the separation between memory and processing found in Von Neumann architectures. Because the further development of Von Neumann computing (more, faster, yet power limited) is questionable, in situ concept computing might be an alternative for concept-based computing. In situ concept computing will be illustrated with a recently developed BABI reasoning task. Neurorobotics can play an important role in the development of in situ concept computing because of the development of in situ concept representations derived in scenarios as needed for reasoning tasks. Neurorobotics would also benefit from power limited and in situ concept computing.

  3. Classification of Land Cover and Land Use Based on Convolutional Neural Networks

    Science.gov (United States)

    Yang, Chun; Rottensteiner, Franz; Heipke, Christian

    2018-04-01

    Land cover describes the physical material of the earth's surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7 % and 77.4 % can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

  4. Neural network-based expert system for severe accident management

    International Nuclear Information System (INIS)

    Klopp, G.T.; Silverman, E.B.

    1992-01-01

    This paper presents the results of the second phase of a three-phase Severe Accident Management expert system program underway at Commonwealth Edison Company (CECo). Phase I successfully demonstrated the feasibility of Artificial Neural Networks to support several of the objectives of severe accident management. Simulated accident scenarios were generated by the Modular Accident Analysis Program (MAAP) code currently in use by CECo as part of their Individual Plant Evaluations (IPE)/Accident Management Program. The primary objectives of the second phase were to develop and demonstrate four capabilities of neural networks with respect to nuclear power plant severe accident monitoring and prediction. The results of this work would form the foundation of a demonstration system which included expert system performance features. These capabilities included the ability to: (1) Predict the time available prior to support plate (and reactor vessel) failure; (2) Calculate the time remaining until recovery actions were too late to prevent core damage; (3) Predict future parameter values of each of the MAAP parameter variables; and (4) Detect simulated sensor failure and provide best-value estimates for further processing in the presence of a sensor failure. A variety of accident scenarios for the Zion and Dresden plants were used to train and test the neural network expert system. These included large and small break LOCAs as well as a range of transient events. 3 refs., 1 fig., 1 tab

  5. Three neural network based sensor systems for environmental monitoring

    International Nuclear Information System (INIS)

    Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1994-05-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field

  6. Down image recognition based on deep convolutional neural network

    Directory of Open Access Journals (Sweden)

    Wenzhu Yang

    2018-06-01

    Full Text Available Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN. To deal with the above problems, a Deep Convolutional Neural Network (DCNN for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN. Keywords: Deep convolutional neural network, Weight initialization, Sparse autoencoder, Visual saliency model, Image recognition

  7. Parallel protein secondary structure prediction based on neural networks.

    Science.gov (United States)

    Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

    2004-01-01

    Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

  8. Expert music performance: cognitive, neural, and developmental bases.

    Science.gov (United States)

    Brown, Rachel M; Zatorre, Robert J; Penhune, Virginia B

    2015-01-01

    In this chapter, we explore what happens in the brain of an expert musician during performance. Understanding expert music performance is interesting to cognitive neuroscientists not only because it tests the limits of human memory and movement, but also because studying expert musicianship can help us understand skilled human behavior in general. In this chapter, we outline important facets of our current understanding of the cognitive and neural basis for music performance, and developmental factors that may underlie musical ability. We address three main questions. (1) What is expert performance? (2) How do musicians achieve expert-level performance? (3) How does expert performance come about? We address the first question by describing musicians' ability to remember, plan, execute, and monitor their performances in order to perform music accurately and expressively. We address the second question by reviewing evidence for possible cognitive and neural mechanisms that may underlie or contribute to expert music performance, including the integration of sound and movement, feedforward and feedback motor control processes, expectancy, and imagery. We further discuss how neural circuits in auditory, motor, parietal, subcortical, and frontal cortex all contribute to different facets of musical expertise. Finally, we address the third question by reviewing evidence for the heritability of musical expertise and for how expertise develops through training and practice. We end by discussing outlooks for future work. © 2015 Elsevier B.V. All rights reserved.

  9. A Bootstrap Neural Network Based Heterogeneous Panel Unit Root Test: Application to Exchange Rates

    OpenAIRE

    Christian de Peretti; Carole Siani; Mario Cerrato

    2010-01-01

    This paper proposes a bootstrap artificial neural network based panel unit root test in a dynamic heterogeneous panel context. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided to investigate the Purchase Power Parity (PPP). The combination of neural network and bootstrapping significantly changes the findings of the economic study in favour of PPP.

  10. Breakout Prediction Based on BP Neural Network in Continuous Casting Process

    Directory of Open Access Journals (Sweden)

    Zhang Ben-guo

    2016-01-01

    Full Text Available An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that verified the feasibility of the model.

  11. Cyclone track forecasting based on satellite images using artificial neural networks

    OpenAIRE

    Kovordanyi, Rita; Roy, Chandan

    2009-01-01

    Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on sate...

  12. Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays

    OpenAIRE

    Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

    2017-01-01

    Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and di...

  13. Neural Network based Minimization of BER in Multi-User Detection in SDMA

    OpenAIRE

    VENKATA REDDY METTU; KRISHAN KUMAR,; SRIKANTH PULLABHATLA

    2011-01-01

    In this paper we investigate the use of neural network based minimization of BER in MUD. Neural networks can be used for linear design, Adaptive prediction, Amplitude detection, Character Recognition and many other applications. Adaptive prediction is used in detecting the errors caused in AWGN channel. These errors are rectified by using Widrow-Hoff algorithm by updating their weights andAdaptive prediction methods. Both Widrow-Hoff and Adaptive prediction have been used for rectifying the e...

  14. ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation

    OpenAIRE

    Visin, Francesco; Ciccone, Marco; Romero, Adriana; Kastner, Kyle; Cho, Kyunghyun; Bengio, Yoshua; Matteucci, Matteo; Courville, Aaron

    2015-01-01

    We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally ...

  15. A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

    Science.gov (United States)

    de Dumast, Priscille; Mirabel, Clément; Cevidanes, Lucia; Ruellas, Antonio; Yatabe, Marilia; Ioshida, Marcos; Ribera, Nina Tubau; Michoud, Loic; Gomes, Liliane; Huang, Chao; Zhu, Hongtu; Muniz, Luciana; Shoukri, Brandon; Paniagua, Beatriz; Styner, Martin; Pieper, Steve; Budin, Francois; Vimort, Jean-Baptiste; Pascal, Laura; Prieto, Juan Carlos

    2018-07-01

    The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. The findings of this

  16. A Human Neural Crest Stem Cell-Derived Dopaminergic Neuronal Model Recapitulates Biochemical Abnormalities in GBA1 Mutation Carriers

    Directory of Open Access Journals (Sweden)

    Shi-Yu Yang

    2017-03-01

    Full Text Available Numerically the most important risk factor for the development of Parkinson's disease (PD is the presence of mutations in the glucocerebrosidase GBA1 gene. In vitro and in vivo studies show that GBA1 mutations reduce glucocerebrosidase (GCase activity and are associated with increased α-synuclein levels, reflecting similar changes seen in idiopathic PD brain. We have developed a neural crest stem cell-derived dopaminergic neuronal model that recapitulates biochemical abnormalities in GBA1 mutation-associated PD. Cells showed reduced GCase protein and activity, impaired macroautophagy, and increased α-synuclein levels. Advantages of this approach include easy access to stem cells, no requirement to reprogram, and retention of the intact host genome. Treatment with a GCase chaperone increased GCase protein levels and activity, rescued the autophagic defects, and decreased α-synuclein levels. These results provide the basis for further investigation of GCase chaperones or similar drugs to slow the progression of PD.

  17. Effect of controlled release of brain-derived neurotrophic factor and neurotrophin-3 from collagen gel on neural stem cells.

    Science.gov (United States)

    Huang, Fei; Wu, Yunfeng; Wang, Hao; Chang, Jun; Ma, Guangwen; Yin, Zongsheng

    2016-01-20

    This study aimed to examine the effect of controlled release of brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NT-3) from collagen gel on rat neural stem cells (NSCs). With three groups of collagen gel, BDNF/collagen gel, and NT-3/collagen gel as controls, BDNF and NT-3 were tested in the BDNF-NT-3/collagen gel group at different time points. The enzyme-linked immunosorbent assay results showed that BDNF and NT-3 were steadily released from collagen gels for 10 days. The cell viability test and the bromodeoxyuridine incorporation assay showed that BDNF-NT-3/collagen gel supported the survival and proliferation of NSCs. The results also showed that the length of processes was markedly longer and differentiation percentage from NSCs into neurons was much higher in the BDNF-NT-3/collagen gel group than those in the collagen gel, BDNF/collagen gel, and NT-3/collagen gel groups. These findings suggest that BDNF-NT-3/collagen gel could significantly improve the ability of NSCs proliferation and differentiation.

  18. Derivation of a correlation for Drag coefficient in two-dimensional bounded supercavitating flows, using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Shafaghat, R.; Hosseinalipour, S.M.; Derakhshani, S.M.E. [Iran University of Science and Technology, Department of Mechanical Engineering, Tehran (Iran)

    2010-07-15

    Artificial neural networks (ANNs) are used as a new approach for the determination of the relations between drag coefficient and Cavitation Number with cavity geometry in supercavitating flows which have been most widely used in the hydrodynamics researches. Also the result of the ANNs as a cost function potentially will be used in an optimization algorithm. Instead of complex differential equations and limited experimental data, faster and simpler solutions were obtained using equations derived from the ANN model. For training of the ANN the numerical results are used that are obtained from a boundary element method (BEM). At this problem, a two-dimensional supercavitation potential inviscid flow pasts a symmetric two-dimensional cavitator, which is placed perpendicular to the flow in a channel of infinite width and immediately a cavity is formed behind the cavitator. It was found that the coefficient of multiple determination (R{sup 2}-value) between the actual and ANN predicted data is equal to about 0.9998 for the drag coefficient and Cavitation number. As seen from the obtained results, the calculated cavity geometry for all drag coefficients and Cavitation Numbers are obviously within acceptable limits. (orig.)

  19. In vitro differentiation of adipose-tissue-derived mesenchymal stem cells into neural retinal cells through expression of human PAX6 (5a) gene.

    Science.gov (United States)

    Rezanejad, Habib; Soheili, Zahra-Soheila; Haddad, Farhang; Matin, Maryam M; Samiei, Shahram; Manafi, Ali; Ahmadieh, Hamid

    2014-04-01

    The neural retina is subjected to various degenerative conditions. Regenerative stem-cell-based therapy holds great promise for treating severe retinal degeneration diseases, although many drawbacks remain to be overcome. One important problem is to gain authentically differentiated cells for replacement. Paired box 6 protein (5a) (PAX6 (5a)) is a highly conserved master control gene that has an essential role in the development of the vertebrate visual system. Human adipose-tissue-derived stem cell (hADSC) isolation was performed by using fat tissues and was confirmed by the differentiation potential of the cells into adipocytes and osteocytes and by their surface marker profile. The coding region of the human PAX6 (5a) gene isoform was cloned and lentiviral particles were propagated in HEK293T. The differentiation of hADSCs into retinal cells was characterized by morphological characteristics, quantitative real-time reverse transcription plus the polymerase chain reaction (qPCR) and immunocytochemistry (ICC) for some retinal cell-specific and retinal pigmented epithelial (RPE) cell-specific markers. hADSCs were successfully isolated. Flow cytometric analysis of surface markers indicated the high purity (~97 %) of isolated hADSCs. After 30 h of post-transduction, cells gradually showed the characteristic morphology of neuronal cells and small axon-like processes emerged. qPCR and ICC confirmed the differentiation of some neural retinal cells and RPE cells. Thus, PAX6 (5a) transcription factor expression, together with medium supplemented with fibronectin, is able to induce the differentiation of hADSCs into retinal progenitors, RPE cells and photoreceptors.

  20. MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

    Directory of Open Access Journals (Sweden)

    Yang Liu

    2015-01-01

    Full Text Available Artificial neural networks (ANNs have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

  1. MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

    Science.gov (United States)

    Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

    2015-01-01

    Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

  2. Experimental method to predict avalanches based on neural networks

    Directory of Open Access Journals (Sweden)

    V. V. Zhdanov

    2016-01-01

    Full Text Available The article presents results of experimental use of currently available statistical methods to classify the avalanche‑dangerous precipitations and snowfalls in the Kishi Almaty river basin. The avalanche service of Kazakhstan uses graphical methods for prediction of avalanches developed by I.V. Kondrashov and E.I. Kolesnikov. The main objective of this work was to develop a modern model that could be used directly at the avalanche stations. Classification of winter precipitations into dangerous snowfalls and non‑dangerous ones was performed by two following ways: the linear discriminant function (canonical analysis and artificial neural networks. Observational data on weather and avalanches in the gorge Kishi Almaty in the gorge Kishi Almaty were used as a training sample. Coefficients for the canonical variables were calculated by the software «Statistica» (Russian version 6.0, and then the necessary formula had been constructed. The accuracy of the above classification was 96%. Simulator by the authors L.N. Yasnitsky and F.М. Cherepanov was used to learn the neural networks. The trained neural network demonstrated 98% accuracy of the classification. Prepared statistical models are recommended to be tested at the snow‑avalanche stations. Results of the tests will be used for estimation of the model quality and its readiness for the operational work. In future, we plan to apply these models for classification of the avalanche danger by the five‑point international scale.

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

    Science.gov (United States)

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

    2014-05-01

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

  4. Risk assessment of logistics outsourcing based on BP neural network

    Science.gov (United States)

    Liu, Xiaofeng; Tian, Zi-you

    The purpose of this article is to evaluate the risk of the enterprises logistics outsourcing. To get this goal, the paper first analysed he main risks existing in the logistics outsourcing, and then set up a risk evaluation index system of the logistics outsourcing; second applied BP neural network into the logistics outsourcing risk evaluation and used MATLAB to the simulation. It proved that the network error is small and has strong practicability. And this method can be used by enterprises to evaluate the risks of logistics outsourcing.

  5. Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

    Science.gov (United States)

    Hasan, M. A.; Reaz, M. B. I.

    2012-01-01

    The aim of this paper is to model the algorithm for Fetal ECG (FECG) extraction from composite abdominal ECG (AECG) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) for FPGA (Field Programmable Gate Array) implementation. Artificial Neural Network that provides efficient and effective ways of separating FECG signal from composite AECG signal has been designed. The proposed method gives an accuracy of 93.7% for R-peak detection in FHR monitoring. The designed VHDL model is synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 8.0 Web Edition for FPGA implementation.

  6. neural network based model o work based model of an industrial oil

    African Journals Online (AJOL)

    eobe

    technique. g, Neural Network Model, Regression, Mean Square Error, PID controller. ... during the training processes. An additio ... used to carry out simulation studies of the mode .... A two-layer feed-forward neural network with Matlab.

  7. Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

    Science.gov (United States)

    Ren, Jinzhi; Xiang, Wei; Zhao, Lin; Wu, Jianbo; Huang, Lianzhen; Tu, Qinggang; Zhao, Heming

    2018-01-01

    In order to make the intelligent ranch management mode replace the traditional artificial one gradually, this paper proposes a pasture environment control system based on cloud server, and puts forward the PID control algorithm based on BP neural network to control temperature and humidity better in the pasture environment. First, to model the temperature and humidity (controlled object) of the pasture, we can get the transfer function. Then the traditional PID control algorithm and the PID one based on BP neural network are applied to the transfer function. The obtained step tracking curves can be seen that the PID controller based on BP neural network has obvious superiority in adjusting time and error, etc. This algorithm, calculating reasonable control parameters of the temperature and humidity to control environment, can be better used in the cloud service platform.

  8. Standard cell-based implementation of a digital optoelectronic neural-network hardware.

    Science.gov (United States)

    Maier, K D; Beckstein, C; Blickhan, R; Erhard, W

    2001-03-10

    A standard cell-based implementation of a digital optoelectronic neural-network architecture is presented. The overall structure of the multilayer perceptron network that was used, the optoelectronic interconnection system between the layers, and all components required in each layer are defined. The design process from VHDL-based modeling from synthesis and partly automatic placing and routing to the final editing of one layer of the circuit of the multilayer perceptrons are described. A suitable approach for the standard cell-based design of optoelectronic systems is presented, and shortcomings of the design tool that was used are pointed out. The layout for the microelectronic circuit of one layer in a multilayer perceptron neural network with a performance potential 1 magnitude higher than neural networks that are purely electronic based has been successfully designed.

  9. Image Finder Mobile Application Based on Neural Networks

    Directory of Open Access Journals (Sweden)

    Nabil M. Hewahi

    2017-04-01

    Full Text Available Nowadays taking photos via mobile phone has become a very important part of everyone’s life. Almost each and every person who has a smart phone also has thousands of photos in their mobile device. At times it becomes very difficult to find a particular photo from thousands of photos, and it takes time. This research was done to come up with an innovative solution that could solve this problem. The solution will allow the user to find the required photo by simply drawing a sketch on the objects in the required picture, for example a tree or car, etc. Two types of supervised Artificial Neural Networks are used for this purpose; one is trained to identify the handmade sketches and other is trained to identify the images. The proposed approach introduces a mechanism to relate the sketches with the images by matching them after training. The experimentation results for testing the trained neural networks reached 100% for the sketches, and 84% for the images of two objects as a case study.

  10. Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

    Science.gov (United States)

    Li, Zhaoyu; Xu, Rui; Cui, Pingyuan; Zhu, Shengying

    2018-04-01

    The ability to plan and react fast in dynamic space environments is central to intelligent behavior of spacecraft. For space and robotic applications, many planners have been used. But it is difficult to encode the domain knowledge and directly use existing techniques such as heuristic to improve the performance of the application systems. Therefore, regarding planning as an advanced control problem, this paper first proposes an autonomous mission planning and action selection mechanism through a multiple layer perceptron neural network approach to select actions in planning process and improve efficiency. To prove the availability and effectiveness, we use autonomous mission planning problems of the spacecraft, which is a sophisticated system with complex subsystems and constraints as an example. Simulation results have shown that artificial neural networks (ANNs) are usable for planning problems. Compared with the existing planning method in EUROPA, the mechanism using ANNs is more efficient and can guarantee stable performance. Therefore, the mechanism proposed in this paper is more suitable for planning problems of spacecraft that require real time and stability.

  11. Artificial neural network based particle size prediction of polymeric nanoparticles.

    Science.gov (United States)

    Youshia, John; Ali, Mohamed Ehab; Lamprecht, Alf

    2017-10-01

    Particle size of nanoparticles and the respective polydispersity are key factors influencing their biopharmaceutical behavior in a large variety of therapeutic applications. Predicting these attributes would skip many preliminary studies usually required to optimize formulations. The aim was to build a mathematical model capable of predicting the particle size of polymeric nanoparticles produced by a pharmaceutical polymer of choice. Polymer properties controlling the particle size were identified as molecular weight, hydrophobicity and surface activity, and were quantified by measuring polymer viscosity, contact angle and interfacial tension, respectively. A model was built using artificial neural network including these properties as input with particle size and polydispersity index as output. The established model successfully predicted particle size of nanoparticles covering a range of 70-400nm prepared from other polymers. The percentage bias for particle prediction was 2%, 4% and 6%, for the training, validation and testing data, respectively. Polymer surface activity was found to have the highest impact on the particle size followed by viscosity and finally hydrophobicity. Results of this study successfully highlighted polymer properties affecting particle size and confirmed the usefulness of artificial neural networks in predicting the particle size and polydispersity of polymeric nanoparticles. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Neural-network-based depth computation for blind navigation

    Science.gov (United States)

    Wong, Farrah; Nagarajan, Ramachandran R.; Yaacob, Sazali

    2004-12-01

    A research undertaken to help blind people to navigate autonomously or with minimum assistance is termed as "Blind Navigation". In this research, an aid that could help blind people in their navigation is proposed. Distance serves as an important clue during our navigation. A stereovision navigation aid implemented with two digital video cameras that are spaced apart and fixed on a headgear to obtain the distance information is presented. In this paper, a neural network methodology is used to obtain the required parameters of the camera which is known as camera calibration. These parameters are not known but obtained by adjusting the weights in the network. The inputs to the network consist of the matching features in the stereo pair images. A back propagation network with 16-input neurons, 3 hidden neurons and 1 output neuron, which gives depth, is created. The distance information is incorporated into the final processed image as four gray levels such as white, light gray, dark gray and black. Preliminary results have shown that the percentage errors fall below 10%. It is envisaged that the distance provided by neural network shall enable blind individuals to go near and pick up an object of interest.

  13. Raingauge-Based Rainfall Nowcasting with Artificial Neural Network

    Science.gov (United States)

    Liong, Shie-Yui; He, Shan

    2010-05-01

    Rainfall forecasting and nowcasting are of great importance, for instance, in real-time flood early warning systems. Long term rainfall forecasting demands global climate, land, and sea data, thus, large computing power and storage capacity are required. Rainfall nowcasting's computing requirement, on the other hand, is much less. Rainfall nowcasting may use data captured by radar and/or weather stations. This paper presents the application of Artificial Neural Network (ANN) on rainfall nowcasting using data observed at weather and/or rainfall stations. The study focuses on the North-East monsoon period (December, January and February) in Singapore. Rainfall and weather data from ten stations, between 2000 and 2006, were selected and divided into three groups for training, over-fitting test and validation of the ANN. Several neural network architectures were tried in the study. Two architectures, Backpropagation ANN and Group Method of Data Handling ANN, yielded better rainfall nowcasting, up to two hours, than the other architectures. The obtained rainfall nowcasts were then used by a catchment model to forecast catchment runoff. The results of runoff forecast are encouraging and promising.With ANN's high computational speed, the proposed approach may be deliverable for creating the real-time flood early warning system.

  14. Neural network based daily precipitation generator (NNGEN-P)

    Energy Technology Data Exchange (ETDEWEB)

    Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

    2007-02-15

    Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)

  15. Exponential lag function projective synchronization of memristor-based multidirectional associative memory neural networks via hybrid control

    Science.gov (United States)

    Yuan, Manman; Wang, Weiping; Luo, Xiong; Li, Lixiang; Kurths, Jürgen; Wang, Xiao

    2018-03-01

    This paper is concerned with the exponential lag function projective synchronization of memristive multidirectional associative memory neural networks (MMAMNNs). First, we propose a new model of MMAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying discrete delays and distributed time delays. Second, we design two kinds of hybrid controllers. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the controllers are carefully designed to confirm the process of different types of synchronization in the MMAMNNs. Third, sufficient criteria guaranteeing the synchronization of system are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.

  16. System and method for deriving a process-based specification

    Science.gov (United States)

    Hinchey, Michael Gerard (Inventor); Rash, James Larry (Inventor); Rouff, Christopher A. (Inventor)

    2009-01-01

    A system and method for deriving a process-based specification for a system is disclosed. The process-based specification is mathematically inferred from a trace-based specification. The trace-based specification is derived from a non-empty set of traces or natural language scenarios. The process-based specification is mathematically equivalent to the trace-based specification. Code is generated, if applicable, from the process-based specification. A process, or phases of a process, using the features disclosed can be reversed and repeated to allow for an interactive development and modification of legacy systems. The process is applicable to any class of system, including, but not limited to, biological and physical systems, electrical and electro-mechanical systems in addition to software, hardware and hybrid hardware-software systems.

  17. Vitamin E-Mediated Modulation of Glutamate Receptor Expression in an Oxidative Stress Model of Neural Cells Derived from Embryonic Stem Cell Cultures

    Directory of Open Access Journals (Sweden)

    Afifah Abd Jalil

    2017-01-01

    Full Text Available Glutamate is the primary excitatory neurotransmitter in the central nervous system. Excessive concentrations of glutamate in the brain can be excitotoxic and cause oxidative stress, which is associated with Alzheimer’s disease. In the present study, the effects of vitamin E in the form of tocotrienol-rich fraction (TRF and alpha-tocopherol (α-TCP in modulating the glutamate receptor and neuron injury markers in an in vitro model of oxidative stress in neural-derived embryonic stem (ES cell cultures were elucidated. A transgenic mouse ES cell line (46C was differentiated into a neural lineage in vitro via induction with retinoic acid. These cells were then subjected to oxidative stress with a significantly high concentration of glutamate. Measurement of reactive oxygen species (ROS was performed after inducing glutamate excitotoxicity, and recovery from this toxicity in response to vitamin E was determined. The gene expression levels of glutamate receptors and neuron-specific enolase were elucidated using real-time PCR. The results reveal that neural cells derived from 46C cells and subjected to oxidative stress exhibit downregulation of NMDA, kainate receptor, and NSE after posttreatment with different concentrations of TRF and α-TCP, a sign of neurorecovery. Treatment of either TRF or α-TCP reduced the levels of ROS in neural cells subjected to glutamate-induced oxidative stress; these results indicated that vitamin E is a potent antioxidant.

  18. Alternating optimization method based on nonnegative matrix factorizations for deep neural networks

    OpenAIRE

    Sakurai, Tetsuya; Imakura, Akira; Inoue, Yuto; Futamura, Yasunori

    2016-01-01

    The backpropagation algorithm for calculating gradients has been widely used in computation of weights for deep neural networks (DNNs). This method requires derivatives of objective functions and has some difficulties finding appropriate parameters such as learning rate. In this paper, we propose a novel approach for computing weight matrices of fully-connected DNNs by using two types of semi-nonnegative matrix factorizations (semi-NMFs). In this method, optimization processes are performed b...

  19. Chronic exposure to graphene-based nanomaterials induces behavioral deficits and neural damage in Caenorhabditis elegans.

    Science.gov (United States)

    Li, Ping; Xu, Tiantian; Wu, Siyu; Lei, Lili; He, Defu

    2017-10-01

    Nanomaterials of graphene and its derivatives have been widely applied in recent years, but whose impacts on the environment and health are still not well understood. In the present study, the potential adverse effects of graphite (G), graphite oxide nanoplatelets (GO) and graphene quantum dots (GQDs) on the motor nervous system were investigated using nematode Caenorhabditis elegans as the assay system. After being characterized using TEM, SEM, XPS and PLE, three nanomaterials were chronically exposed to C. elegans for 6 days. In total, 50-100 mg l -1 GO caused a significant reduction in the survival rate, but G and GDDs showed low lethality on nematodes. After chronic exposure of sub-lethal dosages, three nanomaterials were observed to distribute primarily in the pharynx and intestine; but GQDs were widespread in nematode body. Three graphene-based nanomaterials resulted in significant declines in locomotor frequency of body bending, head thrashing and pharynx pumping. In addition, mean speed, bending angle-frequency and wavelength of the crawling movement were significantly reduced after exposure. Using transgenic nematodes, we found high concentrations of graphene-based nanomaterials induced down-expression of dat-1::GFP and eat-4::GFP, but no significant changes in unc-47::GFP. This indicates that graphene-based nanomaterials can lead to damages in the dopaminergic and glutamatergic neurons. The present data suggest that chronic exposure of graphene-based nanomaterials may cause neurotoxicity risks of inducing behavioral deficits and neural damage. These findings provide useful information to understand the toxicity and safe application of graphene-based nanomaterials. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  20. The synchronous neural interactions test as a functional neuromarker for post-traumatic stress disorder (PTSD): a robust classification method based on the bootstrap

    Science.gov (United States)

    Georgopoulos, A. P.; Tan, H.-R. M.; Lewis, S. M.; Leuthold, A. C.; Winskowski, A. M.; Lynch, J. K.; Engdahl, B.

    2010-02-01

    Traumatic experiences can produce post-traumatic stress disorder (PTSD) which is a debilitating condition and for which no biomarker currently exists (Institute of Medicine (US) 2006 Posttraumatic Stress Disorder: Diagnosis and Assessment (Washington, DC: National Academies)). Here we show that the synchronous neural interactions (SNI) test which assesses the functional interactions among neural populations derived from magnetoencephalographic (MEG) recordings (Georgopoulos A P et al 2007 J. Neural Eng. 4 349-55) can successfully differentiate PTSD patients from healthy control subjects. Externally cross-validated, bootstrap-based analyses yielded >90% overall accuracy of classification. In addition, all but one of 18 patients who were not receiving medications for their disease were correctly classified. Altogether, these findings document robust differences in brain function between the PTSD and control groups that can be used for differential diagnosis and which possess the potential for assessing and monitoring disease progression and effects of therapy.

  1. A plausible neural circuit for decision making and its formation based on reinforcement learning.

    Science.gov (United States)

    Wei, Hui; Dai, Dawei; Bu, Yijie

    2017-06-01

    A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control

  2. Rapid generation of sub-type, region-specific neurons and neural networks from human pluripotent stem cell-derived neurospheres

    Directory of Open Access Journals (Sweden)

    Aynun N. Begum

    2015-11-01

    Full Text Available Stem cell-based neuronal differentiation has provided a unique opportunity for disease modeling and regenerative medicine. Neurospheres are the most commonly used neuroprogenitors for neuronal differentiation, but they often clump in culture, which has always represented a challenge for neurodifferentiation. In this study, we report a novel method and defined culture conditions for generating sub-type or region-specific neurons from human embryonic and induced pluripotent stem cells derived neurosphere without any genetic manipulation. Round and bright-edged neurospheres were generated in a supplemented knockout serum replacement medium (SKSRM with 10% CO2, which doubled the expression of the NESTIN, PAX6 and FOXG1 genes compared with those cultured with 5% CO2. Furthermore, an additional step (AdSTEP was introduced to fragment the neurospheres and facilitate the formation of a neuroepithelial-type monolayer that we termed the “neurosphederm”. The large neural tube-type rosette (NTTR structure formed from the neurosphederm, and the NTTR expressed higher levels of the PAX6, SOX2 and NESTIN genes compared with the neuroectoderm-derived neuroprogenitors. Different layers of cortical, pyramidal, GABAergic, glutamatergic, cholinergic neurons appeared within 27 days using the neurosphederm, which is a shorter period than in traditional neurodifferentiation-protocols (42–60 days. With additional supplements and timeline dopaminergic and Purkinje neurons were also generated in culture too. Furthermore, our in vivo results indicated that the fragmented neurospheres facilitated significantly better neurogenesis in severe combined immunodeficiency (SCID mouse brains compared with the non-fragmented neurospheres. Therefore, this neurosphere-based neurodifferentiation protocol is a valuable tool for studies of neurodifferentiation, neuronal transplantation and high throughput screening assays.

  3. Adaptive online state-of-charge determination based on neuro-controller and neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shen Yanqing, E-mail: network_hawk@126.co [Department of Automation, Chongqing Industry Polytechnic College, Jiulongpo District, Chongqing 400050 (China)

    2010-05-15

    This paper presents a novel approach using adaptive artificial neural network based model and neuro-controller for online cell State of Charge (SOC) determination. Taking cell SOC as model's predictive control input unit, radial basis function neural network, which can adjust its structure to prediction error with recursive least square algorithm, is used to simulate battery system. Besides that, neuro-controller based on Back-Propagation Neural Network (BPNN) and modified PID controller is used to decide the control input of battery system, i.e., cell SOC. Finally this algorithm is applied for the SOC determination of lead-acid batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Results show that the ANN based battery system model adaptively simulates battery system with great accuracy, and the predicted SOC simultaneously converges to the real value quickly within the error of +-1 as time goes on.

  4. The neural correlates of gist-based true and false recognition

    Science.gov (United States)

    Gutchess, Angela H.; Schacter, Daniel L.

    2012-01-01

    When information is thematically related to previously studied information, gist-based processes contribute to false recognition. Using functional MRI, we examined the neural correlates of gist-based recognition as a function of increasing numbers of studied exemplars. Sixteen participants incidentally encoded small, medium, and large sets of pictures, and we compared the neural response at recognition using parametric modulation analyses. For hits, regions in middle occipital, middle temporal, and posterior parietal cortex linearly modulated their activity according to the number of related encoded items. For false alarms, visual, parietal, and hippocampal regions were modulated as a function of the encoded set size. The present results are consistent with prior work in that the neural regions supporting veridical memory also contribute to false memory for related information. The results also reveal that these regions respond to the degree of relatedness among similar items, and implicate perceptual and constructive processes in gist-based false memory. PMID:22155331

  5. A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Fangzhao Li

    2018-01-01

    Full Text Available Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

  6. The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

    Science.gov (United States)

    Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

    2017-05-30

    The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

  7. The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

    Directory of Open Access Journals (Sweden)

    Gaining Han

    2017-05-01

    Full Text Available The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS, the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

  8. Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller

    Science.gov (United States)

    Lopez-Franco, Carlos; Alanis, Alma Y.; Arana-Daniel, Nancy; Villaseñor, Carlos

    2017-01-01

    In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position. This integration requires a tight coordination between control algorithms, models of the system to be controlled, sensors, hardware and software platforms and well-defined interfaces, to allow the real-time implementation, as well as the design of different processing stages with their respective communication architecture. All of these issues and others provoke the idea that real-time implementations can be considered as a difficult task. For the purpose of showing the effectiveness of the sensor integration and control algorithm to address these issues on a high nonlinear system with noisy sensors as cameras, experiments were performed on the Asctec Firefly on-board computer, including both simulation and experimenta results. PMID:28805689

  9. A universal concept based on cellular neural networks for ultrafast and flexible solving of differential equations.

    Science.gov (United States)

    Chedjou, Jean Chamberlain; Kyamakya, Kyandoghere

    2015-04-01

    This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stability, convergence, and lowest-possible memory requirements are ensured by the CNN processor architecture. A significant challenge solved in this paper is that all these cited computing features are ensured in all system-states (regular or chaotic ones) and in all bifurcation conditions that may be experienced by NDEs.One particular quintessence of this paper is to develop and demonstrate a solver concept that shows and ensures that CNN processors (realized either in hardware or in software) are universal solvers of NDE models. The solving logic or algorithm of given NDEs (possible examples are: Duffing, Mathieu, Van der Pol, Jerk, Chua, Rössler, Lorenz, Burgers, and the transport equations) through a CNN processor system is provided by a set of templates that are computed by our comprehensive templates calculation technique that we call nonlinear adaptive optimization. This paper is therefore a significant contribution and represents a cutting-edge real-time computational engineering approach, especially while considering the various scientific and engineering applications of this ultrafast, energy-and-memory-efficient, and high-precise NDE solver concept. For illustration purposes, three NDE models are demonstratively solved, and related CNN templates are derived and used: the periodically excited Duffing equation, the Mathieu equation, and the transport equation.

  10. Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller.

    Science.gov (United States)

    Lopez-Franco, Carlos; Gomez-Avila, Javier; Alanis, Alma Y; Arana-Daniel, Nancy; Villaseñor, Carlos

    2017-08-12

    In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position. This integration requires a tight coordination between control algorithms, models of the system to be controlled, sensors, hardware and software platforms and well-defined interfaces, to allow the real-time implementation, as well as the design of different processing stages with their respective communication architecture. All of these issues and others provoke the idea that real-time implementations can be considered as a difficult task. For the purpose of showing the effectiveness of the sensor integration and control algorithm to address these issues on a high nonlinear system with noisy sensors as cameras, experiments were performed on the Asctec Firefly on-board computer, including both simulation and experimenta results.

  11. A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

    Science.gov (United States)

    Deng, Lei; Fan, Chao; Zeng, Zhiwen

    2017-12-28

    Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.

  12. Delay-Dependent Exponential Optimal Synchronization for Nonidentical Chaotic Systems via Neural-Network-Based Approach

    Directory of Open Access Journals (Sweden)

    Feng-Hsiag Hsiao

    2013-01-01

    Full Text Available A novel approach is presented to realize the optimal exponential synchronization of nonidentical multiple time-delay chaotic (MTDC systems via fuzzy control scheme. A neural-network (NN model is first constructed for the MTDC system. Then, a linear differential inclusion (LDI state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, a delay-dependent exponential stability criterion of the error system derived in terms of Lyapunov's direct method is proposed to guarantee that the trajectories of the slave system can approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI. According to the LMI, a fuzzy controller is synthesized not only to realize the exponential synchronization but also to achieve the optimal performance by minimizing the disturbance attenuation level at the same time. Finally, a numerical example with simulations is given to demonstrate the effectiveness of our approach.

  13. Accurate lithography simulation model based on convolutional neural networks

    Science.gov (United States)

    Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

    2017-07-01

    Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

  14. Stock prices forecasting based on wavelet neural networks with PSO

    Directory of Open Access Journals (Sweden)

    Wang Kai-Cheng

    2017-01-01

    Full Text Available This research examines the forecasting performance of wavelet neural network (WNN model using published stock data obtained from Financial Times Stock Exchange (FTSE Taiwan Stock Exchange (TWSE 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX, hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO to choose the appropriate initial network values for different companies. The findings come with two advantages. First, the network initial values are automatically selected instead of being a constant. Second, threshold and training data percentage become constant values, because PSO assists with self-adjustment. We can achieve a success rate over 73% without the necessity to manually adjust parameter or create another math model.

  15. Neural architecture design based on extreme learning machine.

    Science.gov (United States)

    Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

    2013-12-01

    Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  17. The neural bases of framing effects in social dilemmas

    DEFF Research Database (Denmark)

    Macoveanu, Julian; Ramsøy, Thomas; Skov, Martin

    intraparietal cortex, and temporopolar cortex. Our findings provide the first insight into the mechanisms underlying framing of behavior in social dilemmas, indicating increased engagement of the hippocampus and neocortical areas involved in memory, social reasoning and mentalizing when subjects make decisions......Human behavior in social dilemmas is strongly framed by the social context, but the mechanisms underlying this framing effect remains poorly understood. To identify the behavioral and neural responses mediating framing of social interactions, subjects underwent functional Magnetic Resonance Imaging...... while playing a Prisoners Dilemma game. In separate neuroimaging sessions, the game was either framed as a cooperation game or a competition game. Social decisions where subjects were affected by the frame engaged the hippocampal formation, precuneus, dorsomedial prefrontal cortex and lateral temporal...

  18. The Neural Bases of Framing Effects in Social Dilemmas

    DEFF Research Database (Denmark)

    Macoveanu, Julian; Ramsøy, Thomas; Skov, Martin

    2015-01-01

    intraparietal cortex, and temporopolar cortex. Our findings provide the first insight into the mechanisms underlying framing of behavior in social dilemmas, indicating increased engagement of the hippocampus and neocortical areas involved in memory, social reasoning and mentalizing when subjects make decisions......Human behavior in social dilemmas is strongly framed by the social context, but the mechanisms underlying this framing effect remains poorly understood. To identify the behavioral and neural responses mediating framing of social interactions, subjects underwent functional Magnetic Resonance Imaging...... while playing a Prisoners Dilemma game. In separate neuroimaging sessions, the game was either framed as a cooperation game or a competition game. Social decisions where subjects were affected by the frame engaged the hippocampal formation, precuneus, dorsomedial prefrontal cortex and lateral temporal...

  19. The neural bases of framing effects in social dilemmas

    DEFF Research Database (Denmark)

    Macoveanu, Julian; Ramsøy, Thomas Z.; Skov, Martin

    2016-01-01

    findings provide the first insight into the mechanisms underlying framing of behavior in social dilemmas, indicating increased engagement of the hippocampus and neocortical areas involved in memory, social reasoning, and mentalizing when participants make decisions that conform to the imposed social frame.......Human behavior in social dilemmas is strongly framed by the social context, but the mechanisms underlying this framing effect remain poorly understood. To identify the behavioral and neural responses mediating framing of social interactions, participants underwent functional MRI while playing...... a prisoner’s dilemma game. In separate neuroimaging sessions, the game was either framed as a cooperation game or a competition game. The framing of social decisions engaged the hippocampal formation, precuneus, dorsomedial prefrontal cortex, and lateral temporal gyrus. Among these regions, the engagement...

  20. Early Model of Traffic Sign Reminder Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Budi Rahmani

    2012-12-01

    Full Text Available Recognizing the traffic signs installed on the streets is one of the requirements of driving on the road. Laxity in driving may result in traffic accident. This paper describes a real-time reminder model, by utilizing a camera that can be installed in a car to capture image of traffic signs, and is processed and later to inform the driver. The extracting feature harnessing the morphological elements (strel is used in this paper. Artificial Neural Networks is used to train the system and to produce a final decision. The result shows that the accuracy in detecting and recognizing the ten types of traffic signs in real-time is 80%.

  1. Single image super-resolution based on convolutional neural networks

    Science.gov (United States)

    Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia

    2018-03-01

    We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

  2. Triangulating the neural, psychological, and economic bases of guilt aversion

    Science.gov (United States)

    Chang, Luke J.; Smith, Alec; Dufwenberg, Martin; Sanfey, Alan G.

    2011-01-01

    Why do people often choose to cooperate when they can better serve their interests by acting selfishly? One potential mechanism is that the anticipation of guilt can motivate cooperative behavior. We utilize a formal model of this process in conjunction with fMRI to identify brain regions that mediate cooperative behavior while participants decided whether or not to honor a partner’s trust. We observed increased activation in the insula, supplementary motor area, dorsolateral prefrontal cortex (PFC), and temporal parietal junction when participants were behaving consistent with our model, and found increased activity in the ventromedial PFC, dorsomedial PFC, and nucleus accumbens when they chose to abuse trust and maximize their financial reward. This study demonstrates that a neural system previously implicated in expectation processing plays a critical role in assessing moral sentiments that in turn can sustain human cooperation in the face of temptation. PMID:21555080

  3. Theoretical study of GC+/GC base pair derivatives

    International Nuclear Information System (INIS)

    Meng Fancui; Wang Huanjie; Xu Weiren; Liu Chengbu

    2005-01-01

    The geometries of R (R=CH 3 , CH 3 O, F, NO 2 ) substituted GC base pair derivatives and their cations have been optimized at B3LYP/6-31G* level and the substituent effects on the neutral and cationic geometric structures and energies have been discussed. The inner reorganization energies of various base pair derivatives and the native GC base pair have been calculated to discuss the substituent effects on the reorganization energy. NBO (natural bond orbital) analysis has been carried out on both the neutral and the cationic systems to investigate the differences of the charge distributions and the electronic structures. The outcomes indicate that 8-CH 3 O-G:C has the greatest reorganization energy and 8-NO 2 -G:C has the least, while the other substituted base pairs have a reorganization energy close to that of G:C. The one charge is mostly localized on guanine part after ionization and as high as 0.95e. The bond distances of N1-N3'andN2-O2' in the cationic base pair derivatives shortened and that of O6-N4' elongated as compared with the corresponding bond distances of the neutral GC base pair derivatives

  4. Spatial vision in older adults: perceptual changes and neural bases.

    Science.gov (United States)

    McKendrick, Allison M; Chan, Yu Man; Nguyen, Bao N

    2018-05-17

    The number of older adults is rapidly increasing internationally, leading to a significant increase in research on how healthy ageing impacts vision. Most clinical assessments of spatial vision involve simple detection (letter acuity, grating contrast sensitivity, perimetry). However, most natural visual environments are more spatially complicated, requiring contrast discrimination, and the delineation of object boundaries and contours, which are typically present on non-uniform backgrounds. In this review we discuss recent research that reports on the effects of normal ageing on these more complex visual functions, specifically in the context of recent neurophysiological studies. Recent research has concentrated on understanding the effects of healthy ageing on neural responses within the visual pathway in animal models. Such neurophysiological research has led to numerous, subsequently tested, hypotheses regarding the likely impact of healthy human ageing on specific aspects of spatial vision. Healthy normal ageing impacts significantly on spatial visual information processing from the retina through to visual cortex. Some human data validates that obtained from studies of animal physiology, however some findings indicate that rethinking of presumed neural substrates is required. Notably, not all spatial visual processes are altered by age. Healthy normal ageing impacts significantly on some spatial visual processes (in particular centre-surround tasks), but leaves contrast discrimination, contrast adaptation, and orientation discrimination relatively intact. The study of older adult vision contributes to knowledge of the brain mechanisms altered by the ageing process, can provide practical information regarding visual environments that older adults may find challenging, and may lead to new methods of assessing visual performance in clinical environments. © 2018 The Authors Ophthalmic & Physiological Optics © 2018 The College of Optometrists.

  5. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  6. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

    International Nuclear Information System (INIS)

    Yu, Lean; Wang, Shouyang; Lai, Kin Keung

    2008-01-01

    In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

  7. A NEW RECOGNITION TECHNIQUE NAMED SOMP BASED ON PALMPRINT USING NEURAL NETWORK BASED SELF ORGANIZING MAPS

    Directory of Open Access Journals (Sweden)

    A. S. Raja

    2012-08-01

    Full Text Available The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Palmprint has become a new class of human biometrics for passive identification with uniqueness and stability. This is considered to be reliable due to the lack of expressions and the lesser effect of aging. In this manuscript a new Palmprint based biometric system based on neural networks self organizing maps (SOM is presented. The method is named as SOMP. The paper shows that the proposed SOMP method improves the performance and robustness of recognition. The proposed method is applied to a variety of datasets and the results are shown.

  8. Transplantation of spinal cord-derived neural stem cells for ALS: Analysis of phase 1 and 2 trials.

    Science.gov (United States)

    Glass, Jonathan D; Hertzberg, Vicki S; Boulis, Nicholas M; Riley, Jonathan; Federici, Thais; Polak, Meraida; Bordeau, Jane; Fournier, Christina; Johe, Karl; Hazel, Tom; Cudkowicz, Merit; Atassi, Nazem; Borges, Lawrence F; Rutkove, Seward B; Duell, Jayna; Patil, Parag G; Goutman, Stephen A; Feldman, Eva L

    2016-07-26

    To test the safety of spinal cord transplantation of human stem cells in patients with amyotrophic lateral sclerosis (ALS) with escalating doses and expansion of the trial to multiple clinical centers. This open-label trial included 15 participants at 3 academic centers divided into 5 treatment groups receiving increasing doses of stem cells by increasing numbers of cells/injection and increasing numbers of injections. All participants received bilateral injections into the cervical spinal cord (C3-C5). The final group received injections into both the lumbar (L2-L4) and cervical cord through 2 separate surgical procedures. Participants were assessed for adverse events and progression of disease, as measured by the ALS Functional Rating Scale-Revised, forced vital capacity, and quantitative measures of strength. Statistical analysis focused on the slopes of decline of these phase 2 trial participants alone or in combination with the phase 1 participants (previously reported), comparing these groups to 3 separate historical control groups. Adverse events were mostly related to transient pain associated with surgery and to side effects of immunosuppressant medications. There was one incident of acute postoperative deterioration in neurologic function and another incident of a central pain syndrome. We could not discern differences in surgical outcomes between surgeons. Comparisons of the slopes of decline with the 3 separate historical control groups showed no differences in mean rates of progression. Intraspinal transplantation of human spinal cord-derived neural stem cells can be safely accomplished at high doses, including successive lumbar and cervical procedures. The procedure can be expanded safely to multiple surgical centers. This study provides Class IV evidence that for patients with ALS, spinal cord transplantation of human stem cells can be safely accomplished and does not accelerate the progression of the disease. This study lacks the precision to

  9. Event-Based Impulsive Control of Continuous-Time Dynamic Systems and Its Application to Synchronization of Memristive Neural Networks.

    Science.gov (United States)

    Zhu, Wei; Wang, Dandan; Liu, Lu; Feng, Gang

    2017-08-18

    This paper investigates exponential stabilization of continuous-time dynamic systems (CDSs) via event-based impulsive control (EIC) approaches, where the impulsive instants are determined by certain state-dependent triggering condition. The global exponential stability criteria via EIC are derived for nonlinear and linear CDSs, respectively. It is also shown that there is no Zeno-behavior for the concerned closed loop control system. In addition, the developed event-based impulsive scheme is applied to the synchronization problem of master and slave memristive neural networks. Furthermore, a self-triggered impulsive control scheme is developed to avoid continuous communication between the master system and slave system. Finally, two numerical simulation examples are presented to illustrate the effectiveness of the proposed event-based impulsive controllers.

  10. An Artificial Neural Network Based Short-term Dynamic Prediction of Algae Bloom

    Directory of Open Access Journals (Sweden)

    Yao Junyang

    2014-06-01

    Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.

  11. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  12. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Science.gov (United States)

    Wang, L.; Zhang, Y. Y.; Ding, L.

    2006-10-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  13. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    International Nuclear Information System (INIS)

    Wang, L; Zhang, Y Y; Ding, L

    2006-01-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module

  14. Interaction of adult human neural crest-derived stem cells with a nanoporous titanium surface is sufficient to induce their osteogenic differentiation

    Directory of Open Access Journals (Sweden)

    Matthias Schürmann

    2014-07-01

    Full Text Available Osteogenic differentiation of various adult stem cell populations such as neural crest-derived stem cells is of great interest in the context of bone regeneration. Ideally, exogenous differentiation should mimic an endogenous differentiation process, which is partly mediated by topological cues. To elucidate the osteoinductive potential of porous substrates with different pore diameters (30 nm, 100 nm, human neural crest-derived stem cells isolated from the inferior nasal turbinate were cultivated on the surface of nanoporous titanium covered membranes without additional chemical or biological osteoinductive cues. As controls, flat titanium without any topological features and osteogenic medium was used. Cultivation of human neural crest-derived stem cells on 30 nm pores resulted in osteogenic differentiation as demonstrated by alkaline phosphatase activity after seven days as well as by calcium deposition after 3 weeks of cultivation. In contrast, cultivation on flat titanium and on membranes equipped with 100 nm pores was not sufficient to induce osteogenic differentiation. Moreover, we demonstrate an increase of osteogenic transcripts including Osterix, Osteocalcin and up-regulation of Integrin β1 and α2 in the 30 nm pore approach only. Thus, transplantation of stem cells pre-cultivated on nanostructured implants might improve the clinical outcome by support of the graft adherence and acceleration of the regeneration process.

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

    Science.gov (United States)

    Li, Yuhuan; Wang, Jing; Zhang, Jixian

    2006-06-01

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

  16. The positional identity of iPSC-derived neural progenitor cells along the anterior-posterior axis is controlled in a dosage-dependent manner by bFGF and EGF

    DEFF Research Database (Denmark)

    Zhou, Shuling; Ochalek, Anna; Szczesna, Karolina

    2016-01-01

    Neural rosettes derived from human induced pluripotent stem cells (iPSCs) have been claimed to be a highly robust in vitro cellular model for biomedical application. They are able to propagate in vitro in the presence of mitogens, including basic fibroblast growth factor (bFGF) and epidermal growth...... factor (EGF). However, these two mitogens are also involved in anterior-posterior patterning in a gradient dependent manner along the neural tube axis. Here, we compared the regional identity of neural rosette cells and specific neural subtypes of their progeny propagated with low and high concentrations...... of the neural rosettes, resulting in subsequent cholinergic neuron differentiation. Thus, our results indicate that different concentrations of bFGF and EGF supplemented during propagation of neural rosettes are involved in altering the identity of the resultant neural cells....

  17. Decreased proliferative, migrative and neuro-differentiative potential of postnatal rat enteric neural crest-derived cells during culture in vitro

    International Nuclear Information System (INIS)

    Yu, Hui; Pan, Wei-Kang; Zheng, Bai-Jun; Wang, Huai-Jie; Chen, Xin-Lin; Liu, Yong; Gao, Ya

    2016-01-01

    A growing body of evidence supports the potential use of enteric neural crest-derived cells (ENCCs) as a cell replacement therapy for Hirschsprung's disease. Based on previous observations of robust propagation of primary ENCCs, as opposed to their progeny, it is suggested that their therapeutic potential after in vitro expansion may be restricted. We therefore examined the growth and differentiation activities and phenotypic characteristics of continuous ENCC cultures. ENCCs were isolated from the intestines of postnatal rats and were identified using an immunocytochemical approach. During continuous ENCC culture expansion, proliferation, migration, apoptosis, and differentiation potentials were monitored. The Cell Counting Kit-8 was used for assessment of ENCC vitality, Transwell inserts for cell migration, immunocytochemistry for cell counts and identification, and flow cytometry for apoptosis. Over six continuous generations, ENCC proliferation potency was reduced and with prolonged culture, the ratio of migratory ENCCs was decreased. The percentage of apoptosis showed an upward trend with prolonged intragenerational culture, but showed a downward trend with prolonged culture of combined generations. Furthermore, the percentage of peripherin"+ cells decreased whilst the percentage of GFAP"+ cells increased with age. The results demonstrated that alterations in ENCC growth characteristics occur with increased culture time, which may partially account for the poor results of proposed cell therapies. - Highlights: • Differences were identified between primary and daughter ENCCs. • Daughter ENCCs had reduced proliferation, migration and differentiation. • Daughter ENCCs also had increased apoptosis. • These altered characteristics warrant further investigation.

  18. Renal excretion of iodine-131 labelled meta-iodobenzylguanidine and metabolites after therapeutic doses in patients suffering from different neural crest-derived tumours

    International Nuclear Information System (INIS)

    Wafelman, A.R.; Hoefnagel, C.A.; Maessen, H.J.M.; Maes, R.A.A.; Beijnen, J.H.

    1997-01-01

    Iodine-131 labelled meta-iodobenzylguanidine ([ 131 I[MIBG) is used for diagnostic scintigraphy and radionuclide therapy of neural crest-derived tumours. After administration of therapeutic doses of [ 131 I[MIBG (3.1-7.5 GBq) to 17 patients (n=32 courses), aged 2-73 years, 56%±10%, 73%±11%, 80%±10% and 83%±10% of the dose was cumulatively excreted as total radioactivity in urine at t=24 h, 48 h, 72 h and 96 h, respectively. Except for two adult patients, who showed excretion of 14%-18% of [ 131 I[meta-iodohippuric acid ([ 131 I[MIHA), the cumulatively excreted radioactivity consisted of >85% [ 131 I[MIBG, with 6% of the dose excreted as free [ 131 I[iodide, 4% as [ 131 I[MIHA and 2.5% as an unknown iodine-131 labelled metabolite. Cumulative renal excretion rates of total radioactivity and of [ 131 I[MIBG appeared to be higher in neuroblastoma and phaeochromocytoma patients than in carcinoid patients. Based on the excretion of small amounts of [ 131 I[meta-iodobenzoic acid in two patients, a possible metabolic pathway for [ 131 I[MIBG is suggested. The degree of metabolism was not related to the extent of liver uptake of radioactivity. (orig.). With 2 figs., 5 tabs

  19. Decreased proliferative, migrative and neuro-differentiative potential of postnatal rat enteric neural crest-derived cells during culture in vitro

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Hui [Department of Pediatric Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University, No 157, Xi Wu Road, Xi’an 710004, Shaanxi (China); Institute of Neurobiology, Environment and Genes Related to Diseases Key Laboratory of Chinese Ministry of Education, Xi’an Jiaotong University, No 96, Yan Ta Xi Road, Xi’an 710061, Shaanxi (China); Pan, Wei-Kang; Zheng, Bai-Jun; Wang, Huai-Jie [Department of Pediatric Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University, No 157, Xi Wu Road, Xi’an 710004, Shaanxi (China); Chen, Xin-Lin; Liu, Yong [Institute of Neurobiology, Environment and Genes Related to Diseases Key Laboratory of Chinese Ministry of Education, Xi’an Jiaotong University, No 96, Yan Ta Xi Road, Xi’an 710061, Shaanxi (China); Gao, Ya, E-mail: ygao@mail.xjtu.edu.cn [Department of Pediatric Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University, No 157, Xi Wu Road, Xi’an 710004, Shaanxi (China)

    2016-05-01

    A growing body of evidence supports the potential use of enteric neural crest-derived cells (ENCCs) as a cell replacement therapy for Hirschsprung's disease. Based on previous observations of robust propagation of primary ENCCs, as opposed to their progeny, it is suggested that their therapeutic potential after in vitro expansion may be restricted. We therefore examined the growth and differentiation activities and phenotypic characteristics of continuous ENCC cultures. ENCCs were isolated from the intestines of postnatal rats and were identified using an immunocytochemical approach. During continuous ENCC culture expansion, proliferation, migration, apoptosis, and differentiation potentials were monitored. The Cell Counting Kit-8 was used for assessment of ENCC vitality, Transwell inserts for cell migration, immunocytochemistry for cell counts and identification, and flow cytometry for apoptosis. Over six continuous generations, ENCC proliferation potency was reduced and with prolonged culture, the ratio of migratory ENCCs was decreased. The percentage of apoptosis showed an upward trend with prolonged intragenerational culture, but showed a downward trend with prolonged culture of combined generations. Furthermore, the percentage of peripherin{sup +} cells decreased whilst the percentage of GFAP{sup +} cells increased with age. The results demonstrated that alterations in ENCC growth characteristics occur with increased culture time, which may partially account for the poor results of proposed cell therapies. - Highlights: • Differences were identified between primary and daughter ENCCs. • Daughter ENCCs had reduced proliferation, migration and differentiation. • Daughter ENCCs also had increased apoptosis. • These altered characteristics warrant further investigation.

  20. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

    Science.gov (United States)

    Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

    2017-11-01

    In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.