WorldWideScience

Sample records for neural tissue simulation

  1. ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.

    Science.gov (United States)

    Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

    2017-07-20

    Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

  2. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  3. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Electrospun Nanofibrous Materials for Neural Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Yee-Shuan Lee

    2011-02-01

    Full Text Available The use of biomaterials processed by the electrospinning technique has gained considerable interest for neural tissue engineering applications. The tissue engineering strategy is to facilitate the regrowth of nerves by combining an appropriate cell type with the electrospun scaffold. Electrospinning can generate fibrous meshes having fiber diameter dimensions at the nanoscale and these fibers can be nonwoven or oriented to facilitate neurite extension via contact guidance. This article reviews studies evaluating the effect of the scaffold’s architectural features such as fiber diameter and orientation on neural cell function and neurite extension. Electrospun meshes made of natural polymers, proteins and compositions having electrical activity in order to enhance neural cell function are also discussed.

  5. New Computer Simulations of Macular Neural Functioning

    Science.gov (United States)

    Ross, Muriel D.; Doshay, D.; Linton, S.; Parnas, B.; Montgomery, K.; Chimento, T.

    1994-01-01

    We use high performance graphics workstations and supercomputers to study the functional significance of the three-dimensional (3-D) organization of gravity sensors. These sensors have a prototypic architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scaled-up, 3-D versions run on a Cray Y-MP supercomputer. A semi-automated method of reconstruction of neural tissue from serial sections studied in a transmission electron microscope has been developed to eliminate tedious conventional photography. The reconstructions use a mesh as a step in generating a neural surface for visualization. Two meshes are required to model calyx surfaces. The meshes are connected and the resulting prisms represent the cytoplasm and the bounding membranes. A finite volume analysis method is employed to simulate voltage changes along the calyx in response to synapse activation on the calyx or on calyceal processes. The finite volume method insures that charge is conserved at the calyx-process junction. These and other models indicate that efferent processes act as voltage followers, and that the morphology of some afferent processes affects their functioning. In a final application, morphological information is symbolically represented in three dimensions in a computer. The possible functioning of the connectivities is tested using mathematical interpretations of physiological parameters taken from the literature. Symbolic, 3-D simulations are in progress to probe the functional significance of the connectivities. This research is expected to advance computer-based studies of macular functioning and of synaptic plasticity.

  6. Modeling of light absorption in tissue during infrared neural stimulation

    Science.gov (United States)

    Thompson, Alexander C.; Wade, Scott A.; Brown, William G. A.; Stoddart, Paul R.

    2012-07-01

    A Monte Carlo model has been developed to simulate light transport and absorption in neural tissue during infrared neural stimulation (INS). A range of fiber core sizes and numerical apertures are compared illustrating the advantages of using simulations when designing a light delivery system. A range of wavelengths, commonly used for INS, are also compared for stimulation of nerves in the cochlea, in terms of both the energy absorbed and the change in temperature due to a laser pulse. Modeling suggests that a fiber with core diameter of 200 μm and NA=0.22 is optimal for optical stimulation in the geometry used and that temperature rises in the spiral ganglion neurons are as low as 0.1°C. The results show a need for more careful experimentation to allow different proposed mechanisms of INS to be distinguished.

  7. Signal Processing and Neural Network Simulator

    Science.gov (United States)

    Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

    1995-04-01

    The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

  8. Program Helps Simulate Neural Networks

    Science.gov (United States)

    Villarreal, James; Mcintire, Gary

    1993-01-01

    Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

  9. Engineering Human Neural Tissue by 3D Bioprinting.

    Science.gov (United States)

    Gu, Qi; Tomaskovic-Crook, Eva; Wallace, Gordon G; Crook, Jeremy M

    2018-01-01

    Bioprinting provides an opportunity to produce three-dimensional (3D) tissues for biomedical research and translational drug discovery, toxicology, and tissue replacement. Here we describe a method for fabricating human neural tissue by 3D printing human neural stem cells with a bioink, and subsequent gelation of the bioink for cell encapsulation, support, and differentiation to functional neurons and supporting neuroglia. The bioink uniquely comprises the polysaccharides alginate, water-soluble carboxymethyl-chitosan, and agarose. Importantly, the method could be adapted to fabricate neural and nonneural tissues from other cell types, with the potential to be applied for both research and clinical product development.

  10. Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue.

    Directory of Open Access Journals (Sweden)

    Geir Halnes

    2016-11-01

    Full Text Available Recorded potentials in the extracellular space (ECS of the brain is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the ECS potential and its underlying neurophysiological processes are commonly used in the interpretation of such measurements. Standard methods, such as volume-conductor theory and current-source density theory, assume that diffusion has a negligible effect on the ECS potential, at least in the range of frequencies picked up by most recording systems. This assumption remains to be verified. We here present a hybrid simulation framework that accounts for diffusive effects on the ECS potential. The framework uses (1 the NEURON simulator to compute the activity and ionic output currents from multicompartmental neuron models, and (2 the electrodiffusive Kirchhoff-Nernst-Planck framework to simulate the resulting dynamics of the potential and ion concentrations in the ECS, accounting for the effect of electrical migration as well as diffusion. Using this framework, we explore the effect that ECS diffusion has on the electrical potential surrounding a small population of 10 pyramidal neurons. The neural model was tuned so that simulations over ∼100 seconds of biological time led to shifts in ECS concentrations by a few millimolars, similar to what has been seen in experiments. By comparing simulations where ECS diffusion was absent with simulations where ECS diffusion was included, we made the following key findings: (i ECS diffusion shifted the local potential by up to ∼0.2 mV. (ii The power spectral density (PSD of the diffusion-evoked potential shifts followed a 1/f2 power law. (iii Diffusion effects dominated the PSD of the ECS potential for frequencies up to several hertz. In scenarios with large, but physiologically realistic ECS concentration gradients, diffusion was thus found to affect the ECS potential well within the frequency range picked up in

  11. An electromechanical based deformable model for soft tissue simulation.

    Science.gov (United States)

    Zhong, Yongmin; Shirinzadeh, Bijan; Smith, Julian; Gu, Chengfan

    2009-11-01

    Soft tissue deformation is of great importance to surgery simulation. Although a significant amount of research efforts have been dedicated to simulating the behaviours of soft tissues, modelling of soft tissue deformation is still a challenging problem. This paper presents a new deformable model for simulation of soft tissue deformation from the electromechanical viewpoint of soft tissues. Soft tissue deformation is formulated as a reaction-diffusion process coupled with a mechanical load. The mechanical load applied to a soft tissue to cause a deformation is incorporated into the reaction-diffusion system, and consequently distributed among mass points of the soft tissue. Reaction-diffusion of mechanical load and non-rigid mechanics of motion are combined to govern the simulation dynamics of soft tissue deformation. An improved reaction-diffusion model is developed to describe the distribution of the mechanical load in soft tissues. A three-layer artificial cellular neural network is constructed to solve the reaction-diffusion model for real-time simulation of soft tissue deformation. A gradient based method is established to derive internal forces from the distribution of the mechanical load. Integration with a haptic device has also been achieved to simulate soft tissue deformation with haptic feedback. The proposed methodology does not only predict the typical behaviours of living tissues, but it also accepts both local and large-range deformations. It also accommodates isotropic, anisotropic and inhomogeneous deformations by simple modification of diffusion coefficients.

  12. Learning from large scale neural simulations

    DEFF Research Database (Denmark)

    Serban, Maria

    2017-01-01

    Large-scale neural simulations have the marks of a distinct methodology which can be fruitfully deployed to advance scientific understanding of the human brain. Computer simulation studies can be used to produce surrogate observational data for better conceptual models and new how...

  13. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

    Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.

    2012-12-01

    Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

  14. 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.

  15. Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics.

    Science.gov (United States)

    Zhang, Jinao; Zhong, Yongmin; Gu, Chengfan

    2018-05-30

    Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reaction-diffusion mechanics via neural dynamics. The potential energy stored in soft tissues due to a mechanical load to deform tissues away from their rest state is treated as the equivalent transmembrane potential energy, and it is distributed in the tissue masses in the manner of reaction-diffusion propagation of nonlinear electrical waves. The reaction-diffusion propagation of mechanical potential energy and nonrigid mechanics of motion are combined to model soft tissue deformation and its dynamics, both of which are further formulated as the dynamics of cellular neural networks to achieve real-time computational performance. The proposed methodology is implemented with a haptic device for interactive soft tissue deformation with force feedback. Experimental results demonstrate that the proposed methodology exhibits nonlinear force-displacement relationship for nonlinear soft tissue deformation. Homogeneous, anisotropic and heterogeneous soft tissue material properties can be modelled through the inherent physical properties of mass points. Graphical abstract Soft tissue deformation modelling with haptic feedback via neural dynamics-based reaction-diffusion mechanics.

  16. Electric field effects in hyperexcitable neural tissue: A review

    International Nuclear Information System (INIS)

    Durand, D.M.

    2003-01-01

    Uniform electric fields applied to neural tissue can modulate neuronal excitability with a threshold value of about 1mV mm -1 in normal physiological conditions. However, electric fields could have a lower threshold in conditions where field sensitivity is enhanced, such as those simulating epilepsy. Uniform electrical fields were applied to hippocampal brain slices exposed to picrotoxin, high potassium or low calcium solutions. The results in the low calcium medium show that neuronal activity can be completely blocked in 10% of the 30 slices tested with a field amplitude of 1mV mm -1 . These results suggest that the threshold for this effect is clearly smaller than 1mV mm -1 . The hypothesis that the extracellular resistance could affect the sensitivity to the electrical fields was tested by measuring the effect of the osmolarity of the extracellular solution on the efficacy of the field. A 10% decrease on osmolarity resulted in a 56% decrease ( n =4) in the minimum field required for full suppression. A 14% in osmolarity produced an 81% increase in the minimum field required for full suppression. These results show that the extracellular volume can modulate the efficacy of the field and could lower the threshold field amplitudes to values lower than ∼1mmV mm -. (author)

  17. Neural Network Emulation of Reionization Simulations

    Science.gov (United States)

    Schmit, Claude J.; Pritchard, Jonathan R.

    2018-05-01

    Next generation radio experiments such as LOFAR, HERA and SKA are expected to probe the Epoch of Reionization and claim a first direct detection of the cosmic 21cm signal within the next decade. One of the major challenges for these experiments will be dealing with enormous incoming data volumes. Machine learning is key to increasing our data analysis efficiency. We consider the use of an artificial neural network to emulate 21cmFAST simulations and use it in a Bayesian parameter inference study. We then compare the network predictions to a direct evaluation of the EoR simulations and analyse the dependence of the results on the training set size. We find that the use of a training set of size 100 samples can recover the error contours of a full scale MCMC analysis which evaluates the model at each step.

  18. 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...

  19. Extraintestinal heterotopic gastric tissue simulating acute appendicitis

    Institute of Scientific and Technical Information of China (English)

    Elizabeth Bender; Steven P Schmidt

    2008-01-01

    We describe the case of a 68-year-old otherwise healthy male who presented to our emergency room with signs and symptoms of acute appendicitis. Exploratory surgery revealed a normal appendix. Further examination revealed an enlarged lymph node-like mass of tissue near the appendix, in the ileocecal mesentery. This mass was removed and was found to be inflamed heterotopic gastric tissue. Although reports of heterotopic gastric tissue in the literature are common, we believe that this case represents the first report of inflamed heterotopic gastric tissue simulating appendicitis.

  20. Optimization of blanking process using neural network simulation

    International Nuclear Information System (INIS)

    Hambli, R.

    2005-01-01

    The present work describes a methodology using the finite element method and neural network simulation in order to predict the optimum punch-die clearance during sheet metal blanking processes. A damage model is used in order to describe crack initiation and propagation into the sheet. The proposed approach combines predictive finite element and neural network modeling of the leading blanking parameters. Numerical results obtained by finite element computation including damage and fracture modeling were utilized to train the developed simulation environment based on back propagation neural network modeling. The comparative study between the numerical results and the experimental ones shows the good agreement. (author)

  1. A neural network based approach for determination of optical scattering and absorption coefficients of biological tissue

    International Nuclear Information System (INIS)

    Warncke, D; Lewis, E; Leahy, M; Lochmann, S

    2009-01-01

    The propagation of light in biological tissue depends on the absorption and reduced scattering coefficient. The aim of this project is the determination of these two optical properties using spatially resolved reflectance measurements. The sensor system consists of five laser sources at different wavelengths, an optical fibre probe and five photodiodes. For these kinds of measurements it has been shown that an often used solution of the diffusion equation can not be applied. Therefore a neural network is being developed to extract the needed optical properties out of the reflectance data. Data sets for the training, validation and testing process are provided by Monte Carlo Simulations.

  2. Computer simulation system of neural PID control on nuclear reactor

    International Nuclear Information System (INIS)

    Chen Yuzhong; Yang Kaijun; Shen Yongping

    2001-01-01

    Neural network proportional integral differential (PID) controller on nuclear reactor is designed, and the control process is simulated by computer. The simulation result show that neutral network PID controller can automatically adjust its parameter to ideal state, and good control result can be gotten in reactor control process

  3. Neural tissue engineering options for peripheral nerve regeneration.

    Science.gov (United States)

    Gu, Xiaosong; Ding, Fei; Williams, David F

    2014-08-01

    Tissue engineered nerve grafts (TENGs) have emerged as a potential alternative to autologous nerve grafts, the gold standard for peripheral nerve repair. Typically, TENGs are composed of a biomaterial-based template that incorporates biochemical cues. A number of TENGs have been used experimentally to bridge long peripheral nerve gaps in various animal models, where the desired outcome is nerve tissue regeneration and functional recovery. So far, the translation of TENGs to the clinic for use in humans has met with a certain degree of success. In order to optimize the TENG design and further approach the matching of TENGs with autologous nerve grafts, many new cues, beyond the traditional ones, will have to be integrated into TENGs. Furthermore, there is a strong requirement for monitoring the real-time dynamic information related to the construction of TENGs. The aim of this opinion paper is to specifically and critically describe the latest advances in the field of neural tissue engineering for peripheral nerve regeneration. Here we delineate new attempts in the design of template (or scaffold) materials, especially in the context of biocompatibility, the choice and handling of support cells, and growth factor release systems. We further discuss the significance of RNAi for peripheral nerve regeneration, anticipate the potential application of RNAi reagents for TENGs, and speculate on the possible contributions of additional elements, including angiogenesis, electrical stimulation, molecular inflammatory mediators, bioactive peptides, antioxidant reagents, and cultured biological constructs, to TENGs. Finally, we consider that a diverse array of physicochemical and biological cues must be orchestrated within a TENG to create a self-consistent coordinated system with a close proximity to the regenerative microenvironment of the peripheral nervous system. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Simulation of nonlinear random vibrations using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Paez, T.L.; Tucker, S.; O`Gorman, C.

    1997-02-01

    The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.

  5. 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.

  6. PWR system simulation and parameter estimation with neural networks

    International Nuclear Information System (INIS)

    Akkurt, Hatice; Colak, Uener

    2002-01-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

  7. PWR system simulation and parameter estimation with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

    2002-11-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

  8. Hybrid neural network bushing model for vehicle dynamics simulation

    International Nuclear Information System (INIS)

    Sohn, Jeong Hyun; Lee, Seung Kyu; Yoo, Wan Suk

    2008-01-01

    Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers

  9. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  10. Artificial neural network simulation of battery performance

    Energy Technology Data Exchange (ETDEWEB)

    O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G.; Paez, T.L.

    1998-12-31

    Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.

  11. Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks.

    Science.gov (United States)

    Shen, Lin; Wu, Jingheng; Yang, Weitao

    2016-10-11

    Molecular dynamics simulation with multiscale quantum mechanics/molecular mechanics (QM/MM) methods is a very powerful tool for understanding the mechanism of chemical and biological processes in solution or enzymes. However, its computational cost can be too high for many biochemical systems because of the large number of ab initio QM calculations. Semiempirical QM/MM simulations have much higher efficiency. Its accuracy can be improved with a correction to reach the ab initio QM/MM level. The computational cost on the ab initio calculation for the correction determines the efficiency. In this paper we developed a neural network method for QM/MM calculation as an extension of the neural-network representation reported by Behler and Parrinello. With this approach, the potential energy of any configuration along the reaction path for a given QM/MM system can be predicted at the ab initio QM/MM level based on the semiempirical QM/MM simulations. We further applied this method to three reactions in water to calculate the free energy changes. The free-energy profile obtained from the semiempirical QM/MM simulation is corrected to the ab initio QM/MM level with the potential energies predicted with the constructed neural network. The results are in excellent accordance with the reference data that are obtained from the ab initio QM/MM molecular dynamics simulation or corrected with direct ab initio QM/MM potential energies. Compared with the correction using direct ab initio QM/MM potential energies, our method shows a speed-up of 1 or 2 orders of magnitude. It demonstrates that the neural network method combined with the semiempirical QM/MM calculation can be an efficient and reliable strategy for chemical reaction simulations.

  12. Identification and target prediction of miRNAs specifically expressed in rat neural tissue

    Directory of Open Access Journals (Sweden)

    Tu Kang

    2009-05-01

    Full Text Available Abstract Background MicroRNAs (miRNAs are a large group of RNAs that play important roles in regulating gene expression and protein translation. Several studies have indicated that some miRNAs are specifically expressed in human, mouse and zebrafish tissues. For example, miR-1 and miR-133 are specifically expressed in muscles. Tissue-specific miRNAs may have particular functions. Although previous studies have reported the presence of human, mouse and zebrafish tissue-specific miRNAs, there have been no detailed reports of rat tissue-specific miRNAs. In this study, Home-made rat miRNA microarrays which established in our previous study were used to investigate rat neural tissue-specific miRNAs, and mapped their target genes in rat tissues. This study will provide information for the functional analysis of these miRNAs. Results In order to obtain as complete a picture of specific miRNA expression in rat neural tissues as possible, customized miRNA microarrays with 152 selected miRNAs from miRBase were used to detect miRNA expression in 14 rat tissues. After a general clustering analysis, 14 rat tissues could be clearly classified into neural and non-neural tissues based on the obtained expression profiles with p values Conclusion Our work provides a global view of rat neural tissue-specific miRNA profiles and a target map of miRNAs, which is expected to contribute to future investigations of miRNA regulatory mechanisms in neural systems.

  13. Glucocorticoid control of gene transcription in neural tissue

    NARCIS (Netherlands)

    Morsink, Maarten Christian

    2007-01-01

    Glucocorticoid hormones exert modulatory effects on neural function in a delayed genomic fashion. The two receptor types that can bind glucocorticoids, the mineralocorticoid receptor (MR) and the glucocorticoid receptor (GR), are ligand-inducible transcription factors. Therefore, changes in gene

  14. Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

    Directory of Open Access Journals (Sweden)

    Ayman Hamdy Kassem

    2011-01-01

    Full Text Available This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.

  15. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

    Directory of Open Access Journals (Sweden)

    Christopher Bergmeir

    2012-01-01

    Full Text Available Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b accessibility of all of the SNNSalgorithmic functionality from R using a low-level interface, and (c a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNSfile formats.

  16. An artifical neural network for detection of simulated dental caries

    Energy Technology Data Exchange (ETDEWEB)

    Kositbowornchai, S. [Khon Kaen Univ. (Thailand). Dept. of Oral Diagnosis; Siriteptawee, S.; Plermkamon, S.; Bureerat, S. [Khon Kaen Univ. (Thailand). Dept. of Mechanical Engineering; Chetchotsak, D. [Khon Kaen Univ. (Thailand). Dept. of Industrial Engineering

    2006-08-15

    Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

  17. An artifical neural network for detection of simulated dental caries

    International Nuclear Information System (INIS)

    Kositbowornchai, S.; Siriteptawee, S.; Plermkamon, S.; Bureerat, S.; Chetchotsak, D.

    2006-01-01

    Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

  18. Dynamic simulation of a steam generator by neural networks

    International Nuclear Information System (INIS)

    Masini, R.; Padovani, E.; Ricotti, M.E.; Zio, E.

    1999-01-01

    Numerical simulation by computers of the dynamic evolution of complex systems and components is a fundamental phase of any modern engineering design activity. This is of particular importance for risk-based design projects which require that the system behavior be analyzed under several and often extreme conditions. The traditional methods of simulation typically entail long, iterative, processes which lead to large simulation times, often exceeding the transients real time. Artificial neural networks (ANNs) may be exploited in this context, their advantages residing mainly in the speed of computation, in the capability of generalizing from few examples, in the robustness to noisy and partially incomplete data and in the capability of performing empirical input-output mapping without complete knowledge of the underlying physics. In this paper we present a novel approach to dynamic simulation by ANNs based on a superposition scheme in which a set of networks are individually trained, each one to respond to a different input forcing function. The dynamic simulation of a steam generator is considered as an example to show the potentialities of this tool and to point out the difficulties and crucial issues which typically arise when attempting to establish an efficient neural network simulator. The structure of the networks system is such to feedback, at each time step, a portion of the past evolution of the transient and this allows a good reproduction of also non-linear dynamic behaviors. A nice characteristic of the approach is that the modularization of the training reduces substantially its burden and gives this neural simulation tool a nice feature of transportability. (orig.)

  19. Image reconstruction using Monte Carlo simulation and artificial neural networks

    International Nuclear Information System (INIS)

    Emert, F.; Missimner, J.; Blass, W.; Rodriguez, A.

    1997-01-01

    PET data sets are subject to two types of distortions during acquisition: the imperfect response of the scanner and attenuation and scattering in the active distribution. In addition, the reconstruction of voxel images from the line projections composing a data set can introduce artifacts. Monte Carlo simulation provides a means for modeling the distortions and artificial neural networks a method for correcting for them as well as minimizing artifacts. (author) figs., tab., refs

  20. [Simulation of lung motions using an artificial neural network].

    Science.gov (United States)

    Laurent, R; Henriet, J; Salomon, M; Sauget, M; Nguyen, F; Gschwind, R; Makovicka, L

    2011-04-01

    A way to improve the accuracy of lung radiotherapy for a patient is to get a better understanding of its lung motion. Indeed, thanks to this knowledge it becomes possible to follow the displacements of the clinical target volume (CTV) induced by the lung breathing. This paper presents a feasibility study of an original method to simulate the positions of points in patient's lung at all breathing phases. This method, based on an artificial neural network, allowed learning the lung motion on real cases and then to simulate it for new patients for which only the beginning and the end breathing data are known. The neural network learning set is made up of more than 600 points. These points, shared out on three patients and gathered on a specific lung area, were plotted by a MD. The first results are promising: an average accuracy of 1mm is obtained for a spatial resolution of 1 × 1 × 2.5mm(3). We have demonstrated that it is possible to simulate lung motion with accuracy using an artificial neural network. As future work we plan to improve the accuracy of our method with the addition of new patient data and a coverage of the whole lungs. Copyright © 2010 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.

  1. Simulation of lung motions using an artificial neural network

    International Nuclear Information System (INIS)

    Laurent, R.; Henriet, J.; Sauget, M.; Gschwind, R.; Makovicka, L.; Salomon, M.; Nguyen, F.

    2011-01-01

    Purpose. A way to improve the accuracy of lung radiotherapy for a patient is to get a better understanding of its lung motion. Indeed, thanks to this knowledge it becomes possible to follow the displacements of the clinical target volume (CTV) induced by the lung breathing. This paper presents a feasibility study of an original method to simulate the positions of points in patient's lung at all breathing phases. Patients and methods. This method, based on an artificial neural network, allowed learning the lung motion on real cases and then to simulate it for new patients for which only the beginning and the end breathing data are known. The neural network learning set is made up of more than 600 points. These points, shared out on three patients and gathered on a specific lung area, were plotted by a MD. Results. - The first results are promising: an average accuracy of 1 mm is obtained for a spatial resolution of 1 x 1 x 2.5 mm 3 . Conclusion. We have demonstrated that it is possible to simulate lung motion with accuracy using an artificial neural network. As future work we plan to improve the accuracy of our method with the addition of new patient data and a coverage of the whole lungs. (authors)

  2. Event-driven simulation of neural population synchronization facilitated by electrical coupling.

    Science.gov (United States)

    Carrillo, Richard R; Ros, Eduardo; Barbour, Boris; Boucheny, Christian; Coenen, Olivier

    2007-02-01

    Most neural communication and processing tasks are driven by spikes. This has enabled the application of the event-driven simulation schemes. However the simulation of spiking neural networks based on complex models that cannot be simplified to analytical expressions (requiring numerical calculation) is very time consuming. Here we describe briefly an event-driven simulation scheme that uses pre-calculated table-based neuron characterizations to avoid numerical calculations during a network simulation, allowing the simulation of large-scale neural systems. More concretely we explain how electrical coupling can be simulated efficiently within this computation scheme, reproducing synchronization processes observed in detailed simulations of neural populations.

  3. Wnt/Yes-Associated Protein Interactions During Neural Tissue Patterning of Human Induced Pluripotent Stem Cells.

    Science.gov (United States)

    Bejoy, Julie; Song, Liqing; Zhou, Yi; Li, Yan

    2018-04-01

    Human induced pluripotent stem cells (hiPSCs) have special ability to self-assemble into neural spheroids or mini-brain-like structures. During the self-assembly process, Wnt signaling plays an important role in regional patterning and establishing positional identity of hiPSC-derived neural progenitors. Recently, the role of Wnt signaling in regulating Yes-associated protein (YAP) expression (nuclear or cytoplasmic), the pivotal regulator during organ growth and tissue generation, has attracted increasing interests. However, the interactions between Wnt and YAP expression for neural lineage commitment of hiPSCs remain poorly explored. The objective of this study is to investigate the effects of Wnt signaling and YAP expression on the cellular population in three-dimensional (3D) neural spheroids derived from hiPSCs. In this study, Wnt signaling was activated using CHIR99021 for 3D neural spheroids derived from human iPSK3 cells through embryoid body formation. Our results indicate that Wnt activation induces nuclear localization of YAP and upregulates the expression of HOXB4, the marker for hindbrain/spinal cord. By contrast, the cells exhibit more rostral forebrain neural identity (expression of TBR1) without Wnt activation. Cytochalasin D was then used to induce cytoplasmic YAP and the results showed the decreased HOXB4 expression. In addition, the incorporation of microparticles in the neural spheroids was investigated for the perturbation of neural patterning. This study may indicate the bidirectional interactions of Wnt signaling and YAP expression during neural tissue patterning, which have the significance in neurological disease modeling, drug screening, and neural tissue regeneration.

  4. Accelerator and feedback control simulation using neural networks

    International Nuclear Information System (INIS)

    Nguyen, D.; Lee, M.; Sass, R.; Shoaee, H.

    1991-05-01

    Unlike present constant model feedback system, neural networks can adapt as the dynamics of the process changes with time. Using a process model, the ''Accelerator'' network is first trained to simulate the dynamics of the beam for a given beam line. This ''Accelerator'' network is then used to train a second ''Controller'' network which performs the control function. In simulation, the networks are used to adjust corrector magnetics to control the launch angle and position of the beam to keep it on the desired trajectory when the incoming beam is perturbed. 4 refs., 3 figs

  5. The neural basis of event simulation: an FMRI study.

    Directory of Open Access Journals (Sweden)

    Yukihito Yomogida

    Full Text Available Event simulation (ES is the situational inference process in which perceived event features such as objects, agents, and actions are associated in the brain to represent the whole situation. ES provides a common basis for various cognitive processes, such as perceptual prediction, situational understanding/prediction, and social cognition (such as mentalizing/trait inference. Here, functional magnetic resonance imaging was used to elucidate the neural substrates underlying important subdivisions within ES. First, the study investigated whether ES depends on different neural substrates when it is conducted explicitly and implicitly. Second, the existence of neural substrates specific to the future-prediction component of ES was assessed. Subjects were shown contextually related object pictures implying a situation and performed several picture-word-matching tasks. By varying task goals, subjects were made to infer the implied situation implicitly/explicitly or predict the future consequence of that situation. The results indicate that, whereas implicit ES activated the lateral prefrontal cortex and medial/lateral parietal cortex, explicit ES activated the medial prefrontal cortex, posterior cingulate cortex, and medial/lateral temporal cortex. Additionally, the left temporoparietal junction plays an important role in the future-prediction component of ES. These findings enrich our understanding of the neural substrates of the implicit/explicit/predictive aspects of ES-related cognitive processes.

  6. Development of biomaterial scaffold for nerve tissue engineering: Biomaterial mediated neural regeneration

    Directory of Open Access Journals (Sweden)

    Sethuraman Swaminathan

    2009-11-01

    Full Text Available Abstract Neural tissue repair and regeneration strategies have received a great deal of attention because it directly affects the quality of the patient's life. There are many scientific challenges to regenerate nerve while using conventional autologous nerve grafts and from the newly developed therapeutic strategies for the reconstruction of damaged nerves. Recent advancements in nerve regeneration have involved the application of tissue engineering principles and this has evolved a new perspective to neural therapy. The success of neural tissue engineering is mainly based on the regulation of cell behavior and tissue progression through the development of a synthetic scaffold that is analogous to the natural extracellular matrix and can support three-dimensional cell cultures. As the natural extracellular matrix provides an ideal environment for topographical, electrical and chemical cues to the adhesion and proliferation of neural cells, there exists a need to develop a synthetic scaffold that would be biocompatible, immunologically inert, conducting, biodegradable, and infection-resistant biomaterial to support neurite outgrowth. This review outlines the rationale for effective neural tissue engineering through the use of suitable biomaterials and scaffolding techniques for fabrication of a construct that would allow the neurons to adhere, proliferate and eventually form nerves.

  7. Development of biomaterial scaffold for nerve tissue engineering: Biomaterial mediated neural regeneration

    Science.gov (United States)

    2009-01-01

    Neural tissue repair and regeneration strategies have received a great deal of attention because it directly affects the quality of the patient's life. There are many scientific challenges to regenerate nerve while using conventional autologous nerve grafts and from the newly developed therapeutic strategies for the reconstruction of damaged nerves. Recent advancements in nerve regeneration have involved the application of tissue engineering principles and this has evolved a new perspective to neural therapy. The success of neural tissue engineering is mainly based on the regulation of cell behavior and tissue progression through the development of a synthetic scaffold that is analogous to the natural extracellular matrix and can support three-dimensional cell cultures. As the natural extracellular matrix provides an ideal environment for topographical, electrical and chemical cues to the adhesion and proliferation of neural cells, there exists a need to develop a synthetic scaffold that would be biocompatible, immunologically inert, conducting, biodegradable, and infection-resistant biomaterial to support neurite outgrowth. This review outlines the rationale for effective neural tissue engineering through the use of suitable biomaterials and scaffolding techniques for fabrication of a construct that would allow the neurons to adhere, proliferate and eventually form nerves. PMID:19939265

  8. Neural Control of Hemorrhage-Induced Tissue Cytokine Production

    National Research Council Canada - National Science Library

    Molina, Patrica E

    2007-01-01

    .... Opiate pathway activation favors hemodynamic instability and a pro-inflammatory tissue response while sympathetic nervous system activation counteracts the inflammatory response and contributes...

  9. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Dissipative particle dynamics simulations for biological tissues: rheology and competition

    International Nuclear Information System (INIS)

    Basan, Markus; Prost, Jacques; Joanny, Jean-François; Elgeti, Jens

    2011-01-01

    In this work, we model biological tissues using a simple, mechanistic simulation based on dissipative particle dynamics. We investigate the continuum behavior of the simulated tissue and determine its dependence on the properties of the individual cell. Cells in our simulation adhere to each other, expand in volume, divide after reaching a specific size checkpoint and undergo apoptosis at a constant rate, leading to a steady-state homeostatic pressure in the tissue. We measure the dependence of the homeostatic state on the microscopic parameters of our model and show that homeostatic pressure, rather than the unconfined rate of cell division, determines the outcome of tissue competitions. Simulated cell aggregates are cohesive and round up due to the effect of tissue surface tension, which we measure for different tissues. Furthermore, mixtures of different cells unmix according to their adhesive properties. Using a variety of shear and creep simulations, we study tissue rheology by measuring yield stresses, shear viscosities, complex viscosities as well as the loss tangents as a function of model parameters. We find that cell division and apoptosis lead to a vanishing yield stress and fluid-like tissues. The effects of different adhesion strengths and levels of noise on the rheology of the tissue are also measured. In addition, we find that the level of cell division and apoptosis drives the diffusion of cells in the tissue. Finally, we present a method for measuring the compressibility of the tissue and its response to external stress via cell division and apoptosis

  11. Assessing the quality of force feedback in soft tissue simulation.

    Science.gov (United States)

    Basafa, Ehsan; Sefati, Shahin; Okamura, Allison M

    2011-01-01

    Many types of deformable models have been proposed for simulation of soft tissue in surgical simulators, but their realism in comparison to actual tissue is rarely assessed. In this paper, a nonlinear mass-spring model is used for realtime simulation of deformable soft tissues and providing force feedback to a human operator. Force-deformation curves of real soft tissue samples were obtained experimentally, and the model was tuned accordingly. To test the realism of the model, we conducted two human-user experiments involving palpation with a rigid probe. First, in a discrimination test, users identified the correct category of real and virtual tissue better than chance, and tended to identify the tissues as real more often than virtual. Second, users identified real and virtual tissues by name, after training on only real tissues. The sorting accuracy was the same for both real and virtual tissues. These results indicate that, despite model limitations, the simulation could convey the feel of touching real tissues. This evaluation approach could be used to compare and validate various soft-tissue simulators.

  12. Brian: a simulator for spiking neural networks in Python

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2008-11-01

    Full Text Available Brian is a new simulator for spiking neural networks, written in Python (http://brian.di.ens.fr. It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

  13. Brian: a simulator for spiking neural networks in python.

    Science.gov (United States)

    Goodman, Dan; Brette, Romain

    2008-01-01

    "Brian" is a new simulator for spiking neural networks, written in Python (http://brian. di.ens.fr). It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

  14. Neural network stochastic simulation applied for quantifying uncertainties

    Directory of Open Access Journals (Sweden)

    N Foudil-Bey

    2016-09-01

    Full Text Available Generally the geostatistical simulation methods are used to generate several realizations of physical properties in the sub-surface, these methods are based on the variogram analysis and limited to measures correlation between variables at two locations only. In this paper, we propose a simulation of properties based on supervised Neural network training at the existing drilling data set. The major advantage is that this method does not require a preliminary geostatistical study and takes into account several points. As a result, the geological information and the diverse geophysical data can be combined easily. To do this, we used a neural network with multi-layer perceptron architecture like feed-forward, then we used the back-propagation algorithm with conjugate gradient technique to minimize the error of the network output. The learning process can create links between different variables, this relationship can be used for interpolation of the properties on the one hand, or to generate several possible distribution of physical properties on the other hand, changing at each time and a random value of the input neurons, which was kept constant until the period of learning. This method was tested on real data to simulate multiple realizations of the density and the magnetic susceptibility in three-dimensions at the mining camp of Val d'Or, Québec (Canada.

  15. Characterization of the central neural projections to brown, white, and beige adipose tissue.

    Science.gov (United States)

    Wiedmann, Nicole M; Stefanidis, Aneta; Oldfield, Brian J

    2017-11-01

    The functional recruitment of classic brown adipose tissue (BAT) and inducible brown-like or beige fat is, to a large extent, dependent on intact sympathetic neural input. Whereas the central neural circuits directed specifically to BAT or white adipose tissue (WAT) are well established, there is only a developing insight into the nature of neural inputs common to both fat types. Moreover, there is no clear view of the specific central and peripheral innervation of the browned component of WAT: beige fat. The objective of the present study is to examine the neural input to both BAT and WAT in the same animal and, by exposing different cohorts of rats to either thermoneutral or cold conditions, define changes in central neural organization that will ensure that beige fat is appropriately recruited and modulated after browning of inguinal WAT (iWAT). At thermoneutrality, injection of the neurotropic (pseudorabies) viruses into BAT and WAT demonstrates that there are dedicated axonal projections, as well as collateral axonal branches of command neurons projecting to both types of fat. After cold exposure, central neural circuits directed to iWAT showed evidence of reorganization with a greater representation of command neurons projecting to both brown and beiged WAT in hypothalamic (paraventricular nucleus and lateral hypothalamus) and brainstem (raphe pallidus and locus coeruleus) sites. This shift was driven by a greater number of supraspinal neurons projecting to iWAT under cold conditions. These data provide evidence for a reorganization of the nervous system at the level of neural connectivity following browning of WAT.-Wiedmann, N. M., Stefanidis, A., Oldfield, B. J. Characterization of the central neural projections to brown, white, and beige adipose tissue. © FASEB.

  16. 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.

  17. The Use of Endothelial Progenitor Cells for the Regeneration of Musculoskeletal and Neural Tissues

    Directory of Open Access Journals (Sweden)

    Naosuke Kamei

    2017-01-01

    Full Text Available Endothelial progenitor cells (EPCs derived from bone marrow and blood can differentiate into endothelial cells and promote neovascularization. In addition, EPCs are a promising cell source for the repair of various types of vascularized tissues and have been used in animal experiments and clinical trials for tissue repair. In this review, we focused on the kinetics of endogenous EPCs during tissue repair and the application of EPCs or stem cell populations containing EPCs for tissue regeneration in musculoskeletal and neural tissues including the bone, skeletal muscle, ligaments, spinal cord, and peripheral nerves. EPCs can be mobilized from bone marrow and recruited to injured tissue to contribute to neovascularization and tissue repair. In addition, EPCs or stem cell populations containing EPCs promote neovascularization and tissue repair through their differentiation to endothelial cells or tissue-specific cells, the upregulation of growth factors, and the induction and activation of endogenous stem cells. Human peripheral blood CD34(+ cells containing EPCs have been used in clinical trials of bone repair. Thus, EPCs are a promising cell source for the treatment of musculoskeletal and neural tissue injury.

  18. ANNarchy: a code generation approach to neural simulations on parallel hardware

    Science.gov (United States)

    Vitay, Julien; Dinkelbach, Helge Ü.; Hamker, Fred H.

    2015-01-01

    Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions. PMID:26283957

  19. Establishment of Human Neural Progenitor Cells from Human Induced Pluripotent Stem Cells with Diverse Tissue Origins

    Directory of Open Access Journals (Sweden)

    Hayato Fukusumi

    2016-01-01

    Full Text Available Human neural progenitor cells (hNPCs have previously been generated from limited numbers of human induced pluripotent stem cell (hiPSC clones. Here, 21 hiPSC clones derived from human dermal fibroblasts, cord blood cells, and peripheral blood mononuclear cells were differentiated using two neural induction methods, an embryoid body (EB formation-based method and an EB formation method using dual SMAD inhibitors (dSMADi. Our results showed that expandable hNPCs could be generated from hiPSC clones with diverse somatic tissue origins. The established hNPCs exhibited a mid/hindbrain-type neural identity and uniform expression of neural progenitor genes.

  20. Neural Networks Simulation of the Transport of Contaminants in Groundwater

    Directory of Open Access Journals (Sweden)

    Enrico Zio

    2009-12-01

    Full Text Available The performance assessment of an engineered solution for the disposal of radioactive wastes is based on mathematical models of the disposal system response to predefined accidental scenarios, within a probabilistic approach to account for the involved uncertainties. As the most significant potential pathway for the return of radionuclides to the biosphere is groundwater flow, intensive computational efforts are devoted to simulating the behaviour of the groundwater system surrounding the waste deposit, for different values of its hydrogeological parameters and for different evolution scenarios. In this paper, multilayered neural networks are trained to simulate the transport of contaminants in monodimensional and bidimensional aquifers. The results obtained in two case studies indicate that the approximation errors are within the uncertainties which characterize the input data.

  1. 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.

  2. Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling

    Directory of Open Access Journals (Sweden)

    Ferris Daniel P

    2010-12-01

    Full Text Available Abstract Background When humans perform rhythmic upper and lower limb locomotor-like movements, there is an excitatory effect of upper limb exertion on lower limb muscle recruitment. To investigate potential neural mechanisms for this behavioral observation, we developed computer simulations modeling interlimb neural pathways among central pattern generators. We hypothesized that enhancement of muscle recruitment from interlimb spinal mechanisms was not sufficient to explain muscle enhancement levels observed in experimental data. Methods We used Matsuoka oscillators for the central pattern generators (CPG and determined parameters that enhanced amplitudes of rhythmic steady state bursts. Potential mechanisms for output enhancement were excitatory and inhibitory sensory feedback gains, excitatory and inhibitory interlimb coupling gains, and coupling geometry. We first simulated the simplest case, a single CPG, and then expanded the model to have two CPGs and lastly four CPGs. In the two and four CPG models, the lower limb CPGs did not receive supraspinal input such that the only mechanisms available for enhancing output were interlimb coupling gains and sensory feedback gains. Results In a two-CPG model with inhibitory sensory feedback gains, only excitatory gains of ipsilateral flexor-extensor/extensor-flexor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 26%. In a two-CPG model with excitatory sensory feedback gains, excitatory gains of contralateral flexor-flexor/extensor-extensor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 100%. However, within a given excitatory sensory feedback gain, enhancement due to excitatory interlimb gains could only reach levels up to 20%. Interconnecting four CPGs to have ipsilateral flexor-extensor/extensor-flexor coupling, contralateral flexor-flexor/extensor-extensor coupling, and bilateral flexor-extensor/extensor-flexor coupling could enhance

  3. Neural tube closure depends on expression of Grainyhead-like 3 in multiple tissues.

    Science.gov (United States)

    De Castro, Sandra C P; Hirst, Caroline S; Savery, Dawn; Rolo, Ana; Lickert, Heiko; Andersen, Bogi; Copp, Andrew J; Greene, Nicholas D E

    2018-03-15

    Failure of neural tube closure leads to neural tube defects (NTDs), common congenital abnormalities in humans. Among the genes whose loss of function causes NTDs in mice, Grainyhead-like3 (Grhl3) is essential for spinal neural tube closure, with null mutants exhibiting fully penetrant spina bifida. During spinal neurulation Grhl3 is initially expressed in the surface (non-neural) ectoderm, subsequently in the neuroepithelial component of the neural folds and at the node-streak border, and finally in the hindgut endoderm. Here, we show that endoderm-specific knockout of Grhl3 causes late-arising spinal NTDs, preceded by increased ventral curvature of the caudal region which was shown previously to suppress closure of the spinal neural folds. This finding supports the hypothesis that diminished Grhl3 expression in the hindgut is the cause of spinal NTDs in the curly tail, carrying a hypomorphic Grhl3 allele. Complete loss of Grhl3 function produces a more severe phenotype in which closure fails earlier in neurulation, before the stage of onset of expression in the hindgut of wild-type embryos. This implicates additional tissues and NTD mechanisms in Grhl3 null embryos. Conditional knockout of Grhl3 in the neural plate and node-streak border has minimal effect on closure, suggesting that abnormal function of surface ectoderm, where Grhl3 transcripts are first detected, is primarily responsible for early failure of spinal neurulation in Grhl3 null embryos. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    M La Noce

    2014-10-01

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

  5. Simulation of ultrasound interaction with tissue

    International Nuclear Information System (INIS)

    Edee, M.K.A.; Ogulu, A.

    1995-08-01

    We model the effect of an ultrasound beam on a water phantom by considering water as an incompressible Newtonian viscous fluid. The two-dimensional flow velocities (u,v) induced in the water phantom, mimic displacement in living tissues for a phantom of unit width. The displacements depend on the ultrasound signal which is emitted and the model also predicts the nature of the signal received. 13 refs, 3 figs

  6. Artificial neural net system for interactive tissue classification with MR imaging and image segmentation

    International Nuclear Information System (INIS)

    Clarke, L.P.; Silbiger, M.; Naylor, C.; Brown, K.

    1990-01-01

    This paper reports on the development of interactive methods for MR tissue classification that permit mathematically rigorous methods for three-dimensional image segmentation and automatic organ/tumor contouring, as required for surgical and RTP planning. The authors investigate a number of image-intensity based tissue- classification methods that make no implicit assumptions on the MR parameters and hence are not limited by image data set. Similarly, we have trained artificial neural net (ANN) systems for both supervised and unsupervised tissue classification

  7. Stochastic sensitivity analysis and Langevin simulation for neural network learning

    International Nuclear Information System (INIS)

    Koda, Masato

    1997-01-01

    A comprehensive theoretical framework is proposed for the learning of a class of gradient-type neural networks with an additive Gaussian white noise process. The study is based on stochastic sensitivity analysis techniques, and formal expressions are obtained for stochastic learning laws in terms of functional derivative sensitivity coefficients. The present method, based on Langevin simulation techniques, uses only the internal states of the network and ubiquitous noise to compute the learning information inherent in the stochastic correlation between noise signals and the performance functional. In particular, the method does not require the solution of adjoint equations of the back-propagation type. Thus, the present algorithm has the potential for efficiently learning network weights with significantly fewer computations. Application to an unfolded multi-layered network is described, and the results are compared with those obtained by using a back-propagation method

  8. Neural stem cell proliferation and differentiation in the conductive PEDOT-HA/Cs/Gel scaffold for neural tissue engineering.

    Science.gov (United States)

    Wang, Shuping; Guan, Shui; Xu, Jianqiang; Li, Wenfang; Ge, Dan; Sun, Changkai; Liu, Tianqing; Ma, Xuehu

    2017-09-26

    Engineering scaffolds with excellent electro-activity is increasingly important in tissue engineering and regenerative medicine. Herein, conductive poly(3,4-ethylenedioxythiophene) doped with hyaluronic acid (PEDOT-HA) nanoparticles were firstly synthesized via chemical oxidant polymerization. A three-dimensional (3D) PEDOT-HA/Cs/Gel scaffold was then developed by introducing PEDOT-HA nanoparticles into a chitosan/gelatin (Cs/Gel) matrix. HA, as a bridge, not only was used as a dopant, but also combined PEDOT into the Cs/Gel via chemical crosslinking. The PEDOT-HA/Cs/Gel scaffold was used as a conductive substrate for neural stem cell (NSC) culture in vitro. The results demonstrated that the PEDOT-HA/Cs/Gel scaffold had excellent biocompatibility for NSC proliferation and differentiation. 3D confocal fluorescence images showed cells attached on the channel surface of Cs/Gel and PEDOT-HA/Cs/Gel scaffolds with a normal neuronal morphology. Compared to the Cs/Gel scaffold, the PEDOT-HA/Cs/Gel scaffold not only promoted NSC proliferation with up-regulated expression of Ki67, but also enhanced NSC differentiation into neurons and astrocytes with up-regulated expression of β tubulin-III and GFAP, respectively. It is expected that this electro-active and bio-active PEDOT-HA/Cs/Gel scaffold will be used as a conductive platform to regulate NSC behavior for neural tissue engineering.

  9. Development of a neural net paradigm that predicts simulator sickness

    Energy Technology Data Exchange (ETDEWEB)

    Allgood, G.O.

    1993-03-01

    A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.

  10. Electrically polarized PLLA nanofibers as neural tissue engineering scaffolds with improved neuritogenesis.

    Science.gov (United States)

    Barroca, Nathalie; Marote, Ana; Vieira, Sandra I; Almeida, Abílio; Fernandes, Maria H V; Vilarinho, Paula M; da Cruz E Silva, Odete A B

    2018-07-01

    Tissue engineering is evolving towards the production of smart platforms exhibiting stimulatory cues to guide tissue regeneration. This work explores the benefits of electrical polarization to produce more efficient neural tissue engineering platforms. Poly (l-lactic) acid (PLLA)-based scaffolds were prepared as solvent cast films and electrospun aligned nanofibers, and electrically polarized by an in-lab built corona poling device. The characterization of the platforms by thermally stimulated depolarization currents reveals a polarization of 60 × 10 -10 C cm -2 that is stable on poled electrospun nanofibers for up to 6 months. Further in vitro studies using neuroblastoma cells reveals that platforms' polarization potentiates Retinoic Acid-induced neuronal differentiation. Additionally, in differentiating embryonic cortical neurons, poled aligned nanofibers further increased neurite outgrowth by 30% (+70 μm) over non-poled aligned nanofibers, and by 50% (+100 μm) over control conditions. Therefore, the synergy of topographical cues and electrical polarization of poled aligned nanofibers places them as promising biocompatible and bioactive platforms for neural tissue regeneration. Given their long lasting induced polarization, these PLLA poled nanofibrous scaffolds can be envisaged as therapeutic devices of long shelf life for neural repair applications. Copyright © 2018 Elsevier B.V. All rights reserved.

  11. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  12. The neural basis for simulated drawing and the semantic implications.

    Science.gov (United States)

    Harrington, Greg S; Farias, Dana; Davis, Christine H

    2009-03-01

    This functional magnetic resonance imaging (fMRI) study of the mental simulation of drawing (1) investigated the neural substrates of drawing and (2) delineated the semantic aspects of drawing. The goal was to advance our understanding of how drawing a familiar object is linked to lexical semantics and therefore a viable method to use to rehabilitate aphasia. We hypothesized that the semantic aspects of drawing familiar objects compared to drawing non-objects would yield greater activation in the inferior temporal cortex and the inferior frontal cortex of the left hemisphere. To test this hypothesis, eight right-handed subjects performed an fMRI experiment that directly contrasted drawing familiar objects to non-objects using mental imagery. Simulated drawing recruited a large, distributed network of frontal, parietal, and temporal structures. In the contrast comparing drawing familiar objects to non-objects there was stronger activation in the left hemisphere within the inferior temporal, anterior inferior frontal, inferior parietal and superior frontal cortices. The activation within the inferior temporal cortex was associated with visual semantic processing and semantic mediated naming. We suggest that the anterior inferior frontal activation is linked to the inferior temporal cortex and is involved in the selection of specific semantic features of the object as well as retrieval of information regarding the perceptual aspects of the object.

  13. 3-D Bioprinting of Neural Tissue for Applications in Cell Therapy and Drug Screening

    Directory of Open Access Journals (Sweden)

    Michaela Thomas

    2017-11-01

    Full Text Available Neurodegenerative diseases affect millions of individuals in North America and cost the health-care industry billions of dollars for treatment. Current treatment options for degenerative diseases focus on physical rehabilitation or drug therapies, which temporarily mask the effects of cell damage, but quickly lose their efficacy. Cell therapies for the central nervous system remain an untapped market due to the complexity involved in growing neural tissues, controlling their differentiation, and protecting them from the hostile environment they meet upon implantation. Designing tissue constructs for the discovery of better drug treatments are also limited due to the resolution needed for an accurate cellular representation of the brain, in addition to being expensive and difficult to translate to biocompatible materials. 3-D printing offers a streamlined solution for engineering brain tissue for drug discovery or, in the future, for implantation. New microfluidic and bioplotting devices offer increased resolution, little impact on cell viability and have been tested with several bioink materials including fibrin, collagen, hyaluronic acid, poly(caprolactone, and poly(ethylene glycol. This review details current efforts at bioprinting neural tissue and highlights promising avenues for future work.

  14. 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

  15. Tissue classification and segmentation of pressure injuries using convolutional neural networks.

    Science.gov (United States)

    Zahia, Sofia; Sierra-Sosa, Daniel; Garcia-Zapirain, Begonya; Elmaghraby, Adel

    2018-06-01

    This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems for segmentation and tissue type identification are needed in order to achieve better treatment results. Our proposed system is based on a Convolutional Neural Network (CNN) devoted to performing optimized segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues). A preprocessing step removes the flash light and creates a set of 5x5 sub-images which are used as input for the CNN network. The network output will classify every sub-image of the validation set into one of the three classes studied. The metrics used to evaluate our approach show an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%, and an average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue. Our system has been proven to make recognition of complicated structures in biomedical images feasible. Copyright © 2018 Elsevier B.V. All rights reserved.

  16. 3D bioprinting: A new insight into the therapeutic strategy of neural tissue regeneration.

    Science.gov (United States)

    Hsieh, Fu-Yu; Hsu, Shan-hui

    2015-01-01

    Acute traumatic injuries and chronic degenerative diseases represent the world's largest unmet medical need. There are over 50 million people worldwide suffering from neurodegenerative diseases. However, there are only a few treatment options available for acute traumatic injuries and neurodegenerative diseases. Recently, 3D bioprinting is being applied to regenerative medicine to address the need for tissues and organs suitable for transplantation. In this commentary, the newly developed 3D bioprinting technique involving neural stem cells (NSCs) embedded in the thermoresponsive biodegradable polyurethane (PU) bioink is reviewed. The thermoresponsive and biodegradable PU dispersion can form gel near 37 °C without any crosslinker. NSCs embedded within the water-based PU hydrogel with appropriate stiffness showed comparable viability and differentiation after printing. Moreover, in the zebrafish embryo neural deficit model, injection of the NSC-laden PU hydrogels promoted the repair of damaged CNS. In addition, the function of adult zebrafish with traumatic brain injury was rescued after implantation of the 3D-printed NSC-laden constructs. Therefore, the newly developed 3D bioprinting technique may offer new possibilities for future therapeutic strategy of neural tissue regeneration.

  17. Improved Selectivity From a Wavelength Addressable Device for Wireless Stimulation of Neural Tissue

    Directory of Open Access Journals (Sweden)

    Elif Ç. Seymour

    2014-02-01

    Full Text Available Electrical neural stimulation with micro electrodes is a promising technique for restoring lost functions in the central nervous system as a result of injury or disease. One of the problems related to current neural stimulators is the tissue response due to the connecting wires and the presence of a rigid electrode inside soft neural tissue. We have developed a novel, optically activated, microscale photovoltaic neurostimulator based on a custom layered compound semiconductor heterostructure that is both wireless and has a comparatively small volume. Optical activation provides a wireless means of energy transfer to the neurostimulator, eliminating wires and the associated complications. This neurostimulator was shown to evoke action potentials and a functional motor response in the rat spinal cord. In this work, we extend our design to include wavelength selectivity and thus allowing independent activation of devices. As a proof of concept, we fabricated two different microscale devices with different spectral responsivities in the near-infrared region. We assessed the improved addressability of individual devices via wavelength selectivity as compared to spatial selectivity alone through on-bench optical measurements of the devices in combination with an in vivo light intensity profile in the rat cortex obtained in a previous study. We show that wavelength selectivity improves the individual addressability of the floating stimulators, thus increasing the number of devices that can be implanted in close proximity to each other.

  18. PANP is a novel O-glycosylated PILR{alpha} ligand expressed in neural tissues

    Energy Technology Data Exchange (ETDEWEB)

    Kogure, Amane [Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871 (Japan); Laboratory of Immunochemistry, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871 (Japan); Shiratori, Ikuo [Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871 (Japan); Wang, Jing [Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871 (Japan); Laboratory of Immunochemistry, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871 (Japan); Lanier, Lewis L. [Department of Microbiology and Immunology and the Cancer Research Institute, University of California San Francisco, San Francisco, CA 94143 (United States); Arase, Hisashi, E-mail: arase@biken.osaka-u.ac.jp [Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871 (Japan); Laboratory of Immunochemistry, WPI Immunology Frontier Research Center, Osaka University, Osaka 565-0871 (Japan); JST CREST, Saitama 332-0012 (Japan)

    2011-02-18

    Research highlights: {yields} A Novel molecule, PANP, was identified to be a PILR{alpha} ligand. {yields} Sialylated O-glycan structures on PANP were required for PILR{alpha} recognition. {yields} Transcription of PANP was mainly observed in neural tissues. {yields} PANP seems to be involved in immune regulation as a ligand for PILR{alpha}. -- Abstract: PILR{alpha} is an immune inhibitory receptor possessing an immunoreceptor tyrosine-based inhibitory motif (ITIM) in its cytoplasmic domain enabling it to deliver inhibitory signals. Binding of PILR{alpha} to its ligand CD99 is involved in immune regulation; however, whether there are other PILR{alpha} ligands in addition to CD99 is not known. Here, we report that a novel molecule, PILR-associating neural protein (PANP), acts as an additional ligand for PILR{alpha}. Transcription of PANP was mainly observed in neural tissues. PILR{alpha}-Ig fusion protein bound cells transfected with PANP and the transfectants stimulated PILR{alpha} reporter cells. Specific O-glycan structures on PANP were found to be required for PILR recognition of this ligand. These results suggest that PANP is involved in immune regulation as a ligand of the PILR{alpha}.

  19. Neural stem cells encapsulated in a functionalized self-assembling peptide hydrogel for brain tissue engineering.

    Science.gov (United States)

    Cheng, Tzu-Yun; Chen, Ming-Hong; Chang, Wen-Han; Huang, Ming-Yuan; Wang, Tzu-Wei

    2013-03-01

    Brain injury is almost irreparable due to the poor regenerative capability of neural tissue. Nowadays, new therapeutic strategies have been focused on stem cell therapy and supplying an appropriate three dimensional (3D) matrix for the repair of injured brain tissue. In this study, we specifically linked laminin-derived IKVAV motif on the C-terminal to enrich self-assembling peptide RADA(16) as a functional peptide-based scaffold. Our purpose is providing a functional self-assembling peptide 3D hydrogel with encapsulated neural stem cells to enhance the reconstruction of the injured brain. The physiochemical properties reported that RADA(16)-IKVAV can self-assemble into nanofibrous morphology with bilayer β-sheet structure and become gelationed hydrogel with mechanical stiffness similar to brain tissue. The in vitro results showed that the extended IKVAV sequence can serve as a signal or guiding cue to direct the encapsulated neural stem cells (NSCs) adhesion and then towards neuronal differentiation. Animal study was conducted in a rat brain surgery model to demonstrate the damage in cerebral neocortex/neopallium loss. The results showed that the injected peptide solution immediately in situ formed the 3D hydrogel filling up the cavity and bridging the gaps. The histological analyses revealed the RADA(16)-IKVAV self-assembling peptide hydrogel not only enhanced survival of encapsulated NSCs but also reduced the formation of glial astrocytes. The peptide hydrogel with IKVAV extended motifs also showed the support of encapsulated NSCs in neuronal differentiation and the improvement in brain tissue regeneration after 6 weeks post-transplantation. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Osteotomy simulation and soft tissue prediction using computer tomography scans

    International Nuclear Information System (INIS)

    Teschner, M.; Girod, S.; Girod, B.

    1999-01-01

    In this paper, a system is presented that can be used to simulate osteotomies of the skull and to estimate the resulting of tissue changes. Thus, the three-dimensional, photorealistic, postoperative appearance of a patient can be assessed. The system is based on a computer tomography scan and a photorealistic laser scan of the patient's face. In order to predict the postoperative appearance of a patient the soft tissue must follow the movement of the underlying bone. In this paper, a multi-layer soft tissue model is proposed that is based on springs. It incorporates features like skin turgor, gravity and sliding bone contact. The prediction of soft tissue changes due to bone realignments is computed using a very efficient and robust optimization method. The system can handle individual patient data sets and has been tested with several clinical cases. (author)

  1. Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature

    Directory of Open Access Journals (Sweden)

    Ding Fang

    2013-06-01

    Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.

  2. 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.

  3. BWR-plant simulator and its neural network companion with programming under mat lab environment

    International Nuclear Information System (INIS)

    Ghenniwa, Fatma Suleiman

    2008-01-01

    Stand alone nuclear power plant simulators, as well as building blocks based nuclear power simulator are available from different companies throughout the world. In this work, a review of such simulators has been explored for both types. Also a survey of the possible authoring tools for such simulators development has been performed. It is decided, in this research, to develop prototype simulator based on components building blocks. Further more, the authoring tool (Mat lab software) has been selected for programming. It has all the basic tools required for the simulator development similar to that developed by specialized companies for simulator like MMS, APROS and others. Components simulations, as well as integrated components for power plant simulation have been demonstrated. Preliminary neural network reactor model as part of a prepared neural network modules library has been used to demonstrate module order shuffling during simulation. The developed components library can be refined and extended for further development. (author)

  4. Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Au, Whitlow; Larsen, Jan

    1999-01-01

    This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness...... difference experiment and demonstrates high accuracy for small wall thickness differences. Results from the experiment are compared with results obtained by a false killer whale (pseudorca crassidens)....

  5. Growing tissues in real and simulated microgravity: new methods for tissue engineering.

    Science.gov (United States)

    Grimm, Daniela; Wehland, Markus; Pietsch, Jessica; Aleshcheva, Ganna; Wise, Petra; van Loon, Jack; Ulbrich, Claudia; Magnusson, Nils E; Infanger, Manfred; Bauer, Johann

    2014-12-01

    Tissue engineering in simulated (s-) and real microgravity (r-μg) is currently a topic in Space medicine contributing to biomedical sciences and their applications on Earth. The principal aim of this review is to highlight the advances and accomplishments in the field of tissue engineering that could be achieved by culturing cells in Space or by devices created to simulate microgravity on Earth. Understanding the biology of three-dimensional (3D) multicellular structures is very important for a more complete appreciation of in vivo tissue function and advancing in vitro tissue engineering efforts. Various cells exposed to r-μg in Space or to s-μg created by a random positioning machine, a 2D-clinostat, or a rotating wall vessel bioreactor grew in the form of 3D tissues. Hence, these methods represent a new strategy for tissue engineering of a variety of tissues, such as regenerated cartilage, artificial vessel constructs, and other organ tissues as well as multicellular cancer spheroids. These aggregates are used to study molecular mechanisms involved in angiogenesis, cancer development, and biology and for pharmacological testing of, for example, chemotherapeutic drugs or inhibitors of neoangiogenesis. Moreover, they are useful for studying multicellular responses in toxicology and radiation biology, or for performing coculture experiments. The future will show whether these tissue-engineered constructs can be used for medical transplantations. Unveiling the mechanisms of microgravity-dependent molecular and cellular changes is an up-to-date requirement for improving Space medicine and developing new treatment strategies that can be translated to in vivo models while reducing the use of laboratory animals.

  6. Development of neural network simulating power distribution of a BWR fuel bundle

    International Nuclear Information System (INIS)

    Tanabe, A.; Yamamoto, T.; Shinfuku, K.; Nakamae, T.

    1992-01-01

    A neural network model is developed to simulate the precise nuclear physics analysis program code for quick scoping survey calculations. The relation between enrichment and local power distribution of BWR fuel bundles was learned using two layers neural network (ENET). A new model is to introduce burnable neutron absorber (Gadolinia), added to several fuel rods to decrease initial reactivity of fresh bundle. The 2nd stages three layers neural network (GNET) is added on the 1st stage network ENET. GNET studies the local distribution difference caused by Gadolinia. Using this method, it becomes possible to survey of the gradients of sigmoid functions and back propagation constants with reasonable time. Using 99 learning patterns of zero burnup, good error convergence curve is obtained after many trials. This neural network model is able to simulate no learned cases fairly as well as the learned cases. Computer time of this neural network model is about 100 times faster than a precise analysis model. (author)

  7. Application of neural network technology to setpoint control of a simulated reactor experiment loop

    International Nuclear Information System (INIS)

    Cordes, G.A.; Bryan, S.R.; Powell, R.H.; Chick, D.R.

    1991-01-01

    This paper describes the design, implementation, and application of artificial neural networks to achieve temperature and flow rate control for a simulation of a typical experiment loop in the Advanced Test Reactor (ATR) located at the Idaho National Engineering Laboratory (INEL). The goal of the project was to research multivariate, nonlinear control using neural networks. A loop simulation code was adapted for the project and used to create a training set and test the neural network controller for comparison with the existing loop controllers. The results for the best neural network design are documented and compared with existing loop controller action. The neural network was shown to be as accurate at loop control as the classical controllers in the operating region represented by the training set. 5 refs., 8 figs., 3 tabs

  8. Three-dimensional hydrogel cell culture systems for modeling neural tissue

    Science.gov (United States)

    Frampton, John

    Two-dimensional (2-D) neural cell culture systems have served as physiological models for understanding the cellular and molecular events that underlie responses to physical and chemical stimuli, control sensory and motor function, and lead to the development of neurological diseases. However, the development of three-dimensional (3-D) cell culture systems will be essential for the advancement of experimental research in a variety of fields including tissue engineering, chemical transport and delivery, cell growth, and cell-cell communication. In 3-D cell culture, cells are provided with an environment similar to tissue, in which they are surrounded on all sides by other cells, structural molecules and adhesion ligands. Cells grown in 3-D culture systems display morphologies and functions more similar to those observed in vivo, and can be cultured in such a way as to recapitulate the structural organization and biological properties of tissue. This thesis describes a hydrogel-based culture system, capable of supporting the growth and function of several neural cell types in 3-D. Alginate hydrogels were characterized in terms of their biomechanical and biochemical properties and were functionalized by covalent attachment of whole proteins and peptide epitopes. Methods were developed for rapid cross-linking of alginate hydrogels, thus permitting the incorporation of cells into 3-D scaffolds without adversely affecting cell viability or function. A variety of neural cell types were tested including astrocytes, microglia, and neurons. Cells remained viable and functional for longer than two weeks in culture and displayed process outgrowth in 3-D. Cell constructs were created that varied in cell density, type and organization, providing experimental flexibility for studying cell interactions and behavior. In one set of experiments, 3-D glial-endothelial cell co-cultures were used to model blood-brain barrier (BBB) structure and function. This co-culture system was

  9. Bioanalytical and chemical sensors using living taste, olfactory, and neural cells and tissues: a short review.

    Science.gov (United States)

    Wu, Chunsheng; Lillehoj, Peter B; Wang, Ping

    2015-11-07

    Biosensors utilizing living tissues and cells have recently gained significant attention as functional devices for chemical sensing and biochemical analysis. These devices integrate biological components (i.e. single cells, cell networks, tissues) with micro-electro-mechanical systems (MEMS)-based sensors and transducers. Various types of cells and tissues derived from natural and bioengineered sources have been used as recognition and sensing elements, which are generally characterized by high sensitivity and specificity. This review summarizes the state of the art in tissue- and cell-based biosensing platforms with an emphasis on those using taste, olfactory, and neural cells and tissues. Many of these devices employ unique integration strategies and sensing schemes based on sensitive transducers including microelectrode arrays (MEAs), field effect transistors (FETs), and light-addressable potentiometric sensors (LAPSs). Several groups have coupled these hybrid biosensors with microfluidics which offers added benefits of small sample volumes and enhanced automation. While this technology is currently limited to lab settings due to the limited stability of living biological components, further research to enhance their robustness will enable these devices to be employed in field and clinical settings.

  10. CUDA accelerated simulation of needle insertions in deformable tissue

    International Nuclear Information System (INIS)

    Patriciu, Alexandru

    2012-01-01

    This paper presents a stiff needle-deformable tissue interaction model. The model uses a mesh-less discretization of continuum; avoiding thus the expensive remeshing required by the finite element models. The proposed model can accommodate both linear and nonlinear material characteristics. The needle-deformable tissue interaction is modeled through fundamental boundaries. The forces applied by the needle on the tissue are divided in tangent forces and constraint forces. The constraint forces are adaptively computed such that the material is properly constrained by the needle. The implementation is accelerated using NVidia CUDA. We present detailed analysis of the execution timing in both serial and parallel case. The proposed needle insertion model was integrated in a custom software that loads DICOM images, generate the deformable model, and can simulate different insertion strategies.

  11. Realistic soft tissue deformation strategies for real time surgery simulation.

    Science.gov (United States)

    Shen, Yunhe; Zhou, Xiangmin; Zhang, Nan; Tamma, Kumar; Sweet, Robert

    2008-01-01

    A volume-preserving deformation method (VPDM) is developed in complement with the mass-spring method (MSM) to improve the deformation quality of the MSM to model soft tissue in surgical simulation. This method can also be implemented as a stand-alone model. The proposed VPDM satisfies the Newton's laws of motion by obtaining the resultant vectors form an equilibrium condition. The proposed method has been tested in virtual surgery systems with haptic rendering demands.

  12. EEG-fMRI Bayesian framework for neural activity estimation: a simulation study

    Science.gov (United States)

    Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Del Gratta, Cosimo

    2016-12-01

    Objective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

  13. Mammogram synthesis using a 3D simulation. I. Breast tissue model and image acquisition simulation

    International Nuclear Information System (INIS)

    Bakic, Predrag R.; Albert, Michael; Brzakovic, Dragana; Maidment, Andrew D. A.

    2002-01-01

    A method is proposed for generating synthetic mammograms based upon simulations of breast tissue and the mammographic imaging process. A computer breast model has been designed with a realistic distribution of large and medium scale tissue structures. Parameters controlling the size and placement of simulated structures (adipose compartments and ducts) provide a method for consistently modeling images of the same simulated breast with modified position or acquisition parameters. The mammographic imaging process is simulated using a compression model and a model of the x-ray image acquisition process. The compression model estimates breast deformation using tissue elasticity parameters found in the literature and clinical force values. The synthetic mammograms were generated by a mammogram acquisition model using a monoenergetic parallel beam approximation applied to the synthetically compressed breast phantom

  14. Effects of collagen microstructure and material properties on the deformation of the neural tissues of the lamina cribrosa.

    Science.gov (United States)

    Voorhees, A P; Jan, N-J; Sigal, I A

    2017-08-01

    It is widely considered that intraocular pressure (IOP)-induced deformation within the neural tissue pores of the lamina cribrosa (LC) contributes to neurodegeneration and glaucoma. Our goal was to study how the LC microstructure and mechanical properties determine the mechanical insult to the neural tissues within the pores of the LC. Polarized light microscopy was used to measure the collagen density and orientation in histology sections of three sheep optic nerve heads (ONH) at both mesoscale (4.4μm) and microscale (0.73μm) resolutions. Mesoscale fiber-aware FE models were first used to calculate ONH deformations at an IOP of 30mmHg. The results were then used as boundary conditions for microscale models of LC regions. Models predicted large insult to the LC neural tissues, with 95th percentile 1st principal strains ranging from 7 to 12%. Pores near the scleral boundary suffered significantly higher stretch compared to pores in more central regions (10.0±1.4% vs. 7.2±0.4%; p=0.014; mean±SD). Variations in material properties altered the minimum, median, and maximum levels of neural tissue insult but largely did not alter the patterns of pore-to-pore variation, suggesting these patterns are determined by the underlying structure and geometry of the LC beams and pores. To the best of our knowledge, this is the first computational model that reproduces the highly heterogeneous neural tissue strain fields observed experimentally. The loss of visual function associated with glaucoma has been attributed to sustained mechanical insult to the neural tissues of the lamina cribrosa due to elevated intraocular pressure. Our study is the first computational model built from specimen-specific tissue microstructure to consider the mechanics of the neural tissues of the lamina separately from the connective tissue. We found that the deformation of the neural tissue was much larger than that predicted by any recent microstructure-aware models of the lamina. These results

  15. Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware.

    Science.gov (United States)

    Rast, Alexander; Galluppi, Francesco; Davies, Sergio; Plana, Luis; Patterson, Cameron; Sharp, Thomas; Lester, David; Furber, Steve

    2011-11-01

    Dedicated hardware is becoming increasingly essential to simulate emerging very-large-scale neural models. Equally, however, it needs to be able to support multiple models of the neural dynamics, possibly operating simultaneously within the same system. This may be necessary either to simulate large models with heterogeneous neural types, or to simplify simulation and analysis of detailed, complex models in a large simulation by isolating the new model to a small subpopulation of a larger overall network. The SpiNNaker neuromimetic chip is a dedicated neural processor able to support such heterogeneous simulations. Implementing these models on-chip uses an integrated library-based tool chain incorporating the emerging PyNN interface that allows a modeller to input a high-level description and use an automated process to generate an on-chip simulation. Simulations using both LIF and Izhikevich models demonstrate the ability of the SpiNNaker system to generate and simulate heterogeneous networks on-chip, while illustrating, through the network-scale effects of wavefront synchronisation and burst gating, methods that can provide effective behavioural abstractions for large-scale hardware modelling. SpiNNaker's asynchronous virtual architecture permits greater scope for model exploration, with scalable levels of functional and temporal abstraction, than conventional (or neuromorphic) computing platforms. The complete system illustrates a potential path to understanding the neural model of computation, by building (and breaking) neural models at various scales, connecting the blocks, then comparing them against the biology: computational cognitive neuroscience. Copyright © 2011 Elsevier Ltd. All rights reserved.

  16. A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    A.J.G. da Cruz

    1997-12-01

    Full Text Available The production of penicillin G by Penicillium chrysogenum IFO 8644 was simulated employing a feedforward neural network with three layers. The neural network training procedure used an algorithm combining two procedures: random search and backpropagation. The results of this approach were very promising, and it was observed that the neural network was able to accurately describe the nonlinear behavior of the process. Besides, the results showed that this technique can be successfully applied to control process algorithms due to its long processing time and its flexibility in the incorporation of new data

  17. Development of Fast-Running Simulation Methodology Using Neural Networks for Load Follow Operation

    International Nuclear Information System (INIS)

    Seong, Seung-Hwan; Park, Heui-Youn; Kim, Dong-Hoon; Suh, Yong-Suk; Hur, Seop; Koo, In-Soo; Lee, Un-Chul; Jang, Jin-Wook; Shin, Yong-Chul

    2002-01-01

    A new fast-running analytic model has been developed for analyzing the load follow operation. The new model was based on the neural network theory, which has the capability of modeling the input/output relationships of a nonlinear system. The new model is made up of two error back-propagation neural networks and procedures to calculate core parameters, such as the distributions and density of xenon in a quasi-steady-state core like load follow operation. One neural network is designed to retrieve the axial offset of power distribution, and the other is for reactivity corresponding to a given core condition. The training data sets for learning the neural networks in the new model are generated with a three-dimensional nodal code and, also, the measured data of the first-day test of load follow operation. Using the new model, the simulation results of the 5-day load follow test in a pressurized water reactor show a good agreement between the simulation data and the actual measured data. Required computing time for simulating a load follow operation is comparable to that of a fast-running lumped model. Moreover, the new model does not require additional engineering factors to compensate for the difference between the actual measurements and analysis results because the neural network has the inherent learning capability of neural networks to new situations

  18. A facile route to the synthesis of anilinic electroactive colloidal hydrogels for neural tissue engineering applications.

    Science.gov (United States)

    Zarrintaj, Payam; Urbanska, Aleksandra M; Gholizadeh, Saman Seyed; Goodarzi, Vahabodin; Saeb, Mohammad Reza; Mozafari, Masoud

    2018-04-15

    An innovative drug-loaded colloidal hydrogel was synthesized for applications in neural interfaces in tissue engineering by reacting carboxyl capped aniline dimer and gelatin molecules. Dexamethasone was loaded into the gelatin-aniline dimer solution as a model drug to form an in situ drug-loaded colloidal hydrogel. The conductivity of the hydrogel samples fluctuated around 10 -5  S/cm which appeared suitable for cellular activities. Cyclic voltammetry was used for electroactivity determination, in which 2 redox states were observed, suggesting that the short chain length and steric hindrance prevented the gel from achieving a fully oxidized state. Rheological data depicted the modulus decreasing with aniline dimer increment due to limited hydrogen bonds accessibility. Though the swelling ratio of pristine gelatin (600%) decreased by the introduction and increasing the concentration of aniline dimer because of its hydrophobic nature, it took the value of 300% at worst, which still seems promising for drug delivery uses. Degradation rate of hydrogel was similarly decreased by adding aniline dimer. Drug release was evaluated in passive and stimulated patterns demonstrating tendency of aniline dimer to form a vesicle that controls the drug release behavior. The optimal cell viability, proper cell attachment and neurite extension was achieved in the case of hydrogel containing 10 wt% aniline dimer. Based on tissue/organ behavior, it was promisingly possible to adjust the characteristics of the hydrogels for an optimal drug release. The outcome of this simple and effective approach can potentially offer additional tunable characteristics for recording and stimulating purposes in neural interfaces. Copyright © 2018 Elsevier Inc. All rights reserved.

  19. Dynamic changes in connexin expression following engraftment of neural stem cells to striatal tissue

    International Nuclear Information System (INIS)

    Jaederstad, Johan; Jaederstad, Linda Maria; Herlenius, Eric

    2011-01-01

    Gap-junctional intercellular communication between grafted neural stem cells (NSCs) and host cells seem to be essential for many of the beneficial effects associated with NSC engraftment. Utilizing murine NSCs (mNSCs) grafted into an organotypic ex vivo model system for striatal tissue we examined the prerequisites for formation of gap-junctional couplings between graft and host cells at different time points following implantation. We utilized flow cytometry (to quantify the proportion of connexin (Cx) 26 and 43 expressing cells), immunohistochemistry (for localization of the gap-junctional proteins in graft and host cells), dye-transfer studies with and without pharmacological gap-junctional blockers (assaying the functionality of the formed gap-junctional couplings), and proliferation assays (to estimate the role of gap junctions for NSC well-being) to this end. Immunohistochemical staining and dye-transfer studies revealed that the NSCs already form functional gap junctions prior to engraftment, thereby creating a substrate for subsequent graft and host communication. The expression of Cx43 by grafted NSCs was decreased by neurotrophin-3 overexpression in NSCs and culturing of grafted tissue in serum-free Neurobasal B27 medium. Cx43 expression in NSC-derived cells also changed significantly following engraftment. In host cells the expression of Cx43 peaked following traumatic stimulation and then declined within two weeks, suggesting a window of opportunity for successful host cell rescue by NSC engraftment. Further investigation of the dynamic changes in gap junction expression in graft and host cells and the associated variations in intercellular communication between implanted and endogenous cells might help to understand and control the early positive and negative effects evident following neural stem cell transplantation and thereby optimize the outcome of future clinical NSC transplantation therapies.

  20. Soluble Neural-cadherin as a novel biomarker for malignant bone and soft tissue tumors

    International Nuclear Information System (INIS)

    Niimi, Rui; Matsumine, Akihiko; Iino, Takahiro; Nakazora, Shigeto; Nakamura, Tomoki; Uchida, Atsumasa; Sudo, Akihiro

    2013-01-01

    Neural-cadherin (N-cadherin) is one of the most important molecules involved in tissue morphogenesis, wound healing, and the maintenance of tissue integrity. Recently, the cleavage of N-cadherin has become a focus of attention in the field of cancer biology. Cadherin and their ectodomain proteolytic shedding play important roles during cancer progression. The aims of this study are to investigate the serum soluble N-cadherin (sN-CAD) levels in patients with malignant bone and soft tissue tumors, and to evaluate the prognostic significance of the sN-CAD levels. We examined the level of serum sN-CAD using an ELISA in 80 malignant bone and soft tissue tumors (bone sarcoma, n = 23; soft tissue sarcoma, n = 50; metastatic cancer, n = 7) and 87 normal controls. The mean age of the patients was 51 years (range, 10–85 years) and the mean follow-up period was 43 months (range, 1–115 months). The median serum sN-CAD level was 1,267 ng/ml (range, 135–2,860 ng/ml) in all patients. The mean serum sN-CAD level was 1,269 ng/ml (range, 360–2,860 ng/ml) in sarcoma patients, otherwise 1,246 ng/ml (range, 135–2,140 ng/ml) in cancer patients. The sN-CAD levels in patient were higher than those found in the controls, who had a median serum level of 108 ng/ml (range, 0–540 ng/ml). The patients with tumors larger than 5 cm had higher serum sN-CAD levels than the patients with tumors smaller than 5 cm. The histological grade in the patients with higher serum sN-CAD levels was higher than that in the patients with lower serum sN-CAD levels. A univariate analysis demonstrated that the patients with higher serum sN-CAD levels showed a worse disease-free survival rate, local recurrence-free survival rate, metastasis-free survival rate, and overall survival rate compared to those with lower serum sN-CAD levels. In the multivariate analysis, sN-CAD was an independent factor predicting disease-free survival. sN-CAD is a biomarker for malignant bone and soft tissue tumors, and a

  1. Tissue motion in blood velocity estimation and its simulation

    DEFF Research Database (Denmark)

    Schlaikjer, Malene; Torp-Pedersen, Søren; Jensen, Jørgen Arendt

    1998-01-01

    to the improvement of color flow imaging. Optimization based on in-vivo data is difficult since the blood and tissue signals cannot be accurately distinguished and the correct extend of the vessel under investigation is often unknown. This study introduces a model for the simulation of blood velocity data in which...... tissue motion is included. Tissue motion from breathing, heart beat, and vessel pulsation were determined based on in-vivo RF-data obtained from 10 healthy volunteers. The measurements were taken at the carotid artery at one condition and in the liver at three conditions. Each measurement was repeated 10....... The motion due to the heart, when the volunteer was asked to hold his breath, gave a peak velocity of 4.2±1.7 mm/s. The movement of the carotid artery wall due to changing blood pressure had a peak velocity of 8.9±3.7 mm/s over the cardiac cycle. The variations are due to differences in heart rhythm...

  2. Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

    Directory of Open Access Journals (Sweden)

    Chunqing Li

    2012-01-01

    Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.

  3. DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation.

    Science.gov (United States)

    Sherfey, Jason S; Soplata, Austin E; Ardid, Salva; Roberts, Erik A; Stanley, David A; Pittman-Polletta, Benjamin R; Kopell, Nancy J

    2018-01-01

    DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. DynaSim is freely available at http://dynasimtoolbox.org. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.

  4. Effect of Tissue Heterogeneity on the Transmembrane Potential of Type-1 Spiral Ganglion Neurons: A Simulation Study.

    Science.gov (United States)

    Sriperumbudur, Kiran Kumar; Pau, Hans Wilhelm; van Rienen, Ursula

    2018-03-01

    Electric stimulation of the auditory nerve by cochlear implants has been a successful clinical intervention to treat the sensory neural deafness. In this pathological condition of the cochlea, type-1 spiral ganglion neurons in Rosenthal's canal play a vital role in the action potential initiation. Various morphological studies of the human temporal bones suggest that the spiral ganglion neurons are surrounded by heterogeneous structures formed by a variety of cells and tissues. However, the existing simulation models have not considered the tissue heterogeneity in the Rosenthal's canal while studying the electric field interaction with spiral ganglion neurons. Unlike the existing models, we have implemented the tissue heterogeneity in the Rosenthal's canal using a computationally inexpensive image based method in a two-dimensional finite element model. Our simulation results suggest that the spatial heterogeneity of surrounding tissues influences the electric field distribution in the Rosenthal's canal, and thereby alters the transmembrane potential of the spiral ganglion neurons. In addition to the academic interest, these results are especially useful to understand how the latest tissue regeneration methods such as gene therapy and drug-induced resprouting of peripheral axons, which probably modify the density of the tissues in the Rosenthal's canal, affect the cochlear implant functionality.

  5. Exploring the mechanical behavior of degrading swine neural tissue at low strain rates via the fractional Zener constitutive model.

    Science.gov (United States)

    Bentil, Sarah A; Dupaix, Rebecca B

    2014-02-01

    The ability of the fractional Zener constitutive model to predict the behavior of postmortem swine brain tissue was examined in this work. Understanding tissue behavior attributed to degradation is invaluable in many fields such as the forensic sciences or cases where only cadaveric tissue is available. To understand how material properties change with postmortem age, the fractional Zener model was considered as it includes parameters to describe brain stiffness and also the parameter α, which quantifies the viscoelasticity of a material. The relationship between the viscoelasticity described by α and tissue degradation was examined by fitting the model to data collected in a previous study (Bentil, 2013). This previous study subjected swine neural tissue to in vitro unconfined compression tests using four postmortem age groups (week). All samples were compressed to a strain level of 10% using two compressive rates: 1mm/min and 5mm/min. Statistical analysis was used as a tool to study the influence of the fractional Zener constants on factors such as tissue degradation and compressive rate. Application of the fractional Zener constitutive model to the experimental data showed that swine neural tissue becomes less stiff with increased postmortem age. The fractional Zener model was also able to capture the nonlinear viscoelastic features of the brain tissue at low strain rates. The results showed that the parameter α was better correlated with compressive rate than with postmortem age. © 2013 Published by Elsevier Ltd.

  6. Butyrate reduces appetite and activates brown adipose tissue via the gut-brain neural circuit.

    Science.gov (United States)

    Li, Zhuang; Yi, Chun-Xia; Katiraei, Saeed; Kooijman, Sander; Zhou, Enchen; Chung, Chih Kit; Gao, Yuanqing; van den Heuvel, José K; Meijer, Onno C; Berbée, Jimmy F P; Heijink, Marieke; Giera, Martin; Willems van Dijk, Ko; Groen, Albert K; Rensen, Patrick C N; Wang, Yanan

    2017-11-03

    Butyrate exerts metabolic benefits in mice and humans, the underlying mechanisms being still unclear. We aimed to investigate the effect of butyrate on appetite and energy expenditure, and to what extent these two components contribute to the beneficial metabolic effects of butyrate. Acute effects of butyrate on appetite and its method of action were investigated in mice following an intragastric gavage or intravenous injection of butyrate. To study the contribution of satiety to the metabolic benefits of butyrate, mice were fed a high-fat diet with butyrate, and an additional pair-fed group was included. Mechanistic involvement of the gut-brain neural circuit was investigated in vagotomised mice. Acute oral, but not intravenous, butyrate administration decreased food intake, suppressed the activity of orexigenic neurons that express neuropeptide Y in the hypothalamus, and decreased neuronal activity within the nucleus tractus solitarius and dorsal vagal complex in the brainstem. Chronic butyrate supplementation prevented diet-induced obesity, hyperinsulinaemia, hypertriglyceridaemia and hepatic steatosis, largely attributed to a reduction in food intake. Butyrate also modestly promoted fat oxidation and activated brown adipose tissue (BAT), evident from increased utilisation of plasma triglyceride-derived fatty acids. This effect was not due to the reduced food intake, but explained by an increased sympathetic outflow to BAT. Subdiaphragmatic vagotomy abolished the effects of butyrate on food intake as well as the stimulation of metabolic activity in BAT. Butyrate acts on the gut-brain neural circuit to improve energy metabolism via reducing energy intake and enhancing fat oxidation by activating BAT. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  7. Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity

    International Nuclear Information System (INIS)

    Entekhabi, Elahe; Haghbin Nazarpak, Masoumeh; Moztarzadeh, Fathollah; Sadeghi, Ali

    2016-01-01

    Given the large differences in nervous tissue and other tissues of the human body and its unique features, such as poor and/or lack of repair, there are many challenges in the repair process of this tissue. Tissue engineering is one of the most effective approaches to repair neural damages. Scaffolds made from electrospun fibers have special potential in cell adhesion, function and cell proliferation. This research attempted to design a high porous nanofibrous scaffold using hyaluronic acid and polycaprolactone to provide ideal conditions for nerve regeneration by applying proper physicochemical and mechanical signals. Chemical and mechanical properties of pure PCL and PCL/HA nanofibrous scaffolds were measured by FTIR and tensile test. Morphology, swelling behavior, and biodegradability of the scaffolds were evaluated too. Porosity of various layers of scaffolds was measured by image analysis method. To assess the cell–scaffold interaction, SH-SY5Y human neuroblastoma cell line were cultured on the electrospun scaffolds. Taken together, these results suggest that the blended nanofibrous scaffolds PCL/HA 95:5 exhibit the most balanced properties to meet all of the required specifications for neural cells and have potential application in neural tissue engineering. - Highlights: • This paper focuses on design a high porous nanofibrous scaffold. • Hyaluronic acid and polycaprolactone were used as materials to provide ideal conditions for nerve regeneration. • Proper physicochemical and mechanical signals applied for improving cell attachment

  8. Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity.

    Science.gov (United States)

    Entekhabi, Elahe; Haghbin Nazarpak, Masoumeh; Moztarzadeh, Fathollah; Sadeghi, Ali

    2016-12-01

    Given the large differences in nervous tissue and other tissues of the human body and its unique features, such as poor and/or lack of repair, there are many challenges in the repair process of this tissue. Tissue engineering is one of the most effective approaches to repair neural damages. Scaffolds made from electrospun fibers have special potential in cell adhesion, function and cell proliferation. This research attempted to design a high porous nanofibrous scaffold using hyaluronic acid and polycaprolactone to provide ideal conditions for nerve regeneration by applying proper physicochemical and mechanical signals. Chemical and mechanical properties of pure PCL and PCL/HA nanofibrous scaffolds were measured by FTIR and tensile test. Morphology, swelling behavior, and biodegradability of the scaffolds were evaluated too. Porosity of various layers of scaffolds was measured by image analysis method. To assess the cell-scaffold interaction, SH-SY5Y human neuroblastoma cell line were cultured on the electrospun scaffolds. Taken together, these results suggest that the blended nanofibrous scaffolds PCL/HA 95:5 exhibit the most balanced properties to meet all of the required specifications for neural cells and have potential application in neural tissue engineering. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Design and manufacture of neural tissue engineering scaffolds using hyaluronic acid and polycaprolactone nanofibers with controlled porosity

    Energy Technology Data Exchange (ETDEWEB)

    Entekhabi, Elahe [Department of Biomedical Engineering, Amirkabir University of Technology, P.O. Box: 15875/4413, Tehran 159163/4311 (Iran, Islamic Republic of); Haghbin Nazarpak, Masoumeh, E-mail: mhaghbinn@gmail.com [New Technologies Research Center (NTRC), Amirkabir University of Technology, Tehran 15875-4413 (Iran, Islamic Republic of); Moztarzadeh, Fathollah; Sadeghi, Ali [Department of Biomedical Engineering, Amirkabir University of Technology, P.O. Box: 15875/4413, Tehran 159163/4311 (Iran, Islamic Republic of)

    2016-12-01

    Given the large differences in nervous tissue and other tissues of the human body and its unique features, such as poor and/or lack of repair, there are many challenges in the repair process of this tissue. Tissue engineering is one of the most effective approaches to repair neural damages. Scaffolds made from electrospun fibers have special potential in cell adhesion, function and cell proliferation. This research attempted to design a high porous nanofibrous scaffold using hyaluronic acid and polycaprolactone to provide ideal conditions for nerve regeneration by applying proper physicochemical and mechanical signals. Chemical and mechanical properties of pure PCL and PCL/HA nanofibrous scaffolds were measured by FTIR and tensile test. Morphology, swelling behavior, and biodegradability of the scaffolds were evaluated too. Porosity of various layers of scaffolds was measured by image analysis method. To assess the cell–scaffold interaction, SH-SY5Y human neuroblastoma cell line were cultured on the electrospun scaffolds. Taken together, these results suggest that the blended nanofibrous scaffolds PCL/HA 95:5 exhibit the most balanced properties to meet all of the required specifications for neural cells and have potential application in neural tissue engineering. - Highlights: • This paper focuses on design a high porous nanofibrous scaffold. • Hyaluronic acid and polycaprolactone were used as materials to provide ideal conditions for nerve regeneration. • Proper physicochemical and mechanical signals applied for improving cell attachment.

  10. Tissue heterogeneity as a mechanism for localized neural stimulation by applied electric fields

    International Nuclear Information System (INIS)

    Miranda, P C; Correia, L; Salvador, R; Basser, P J

    2007-01-01

    We investigate the heterogeneity of electrical conductivity as a new mechanism to stimulate excitable tissues via applied electric fields. In particular, we show that stimulation of axons crossing internal boundaries can occur at boundaries where the electric conductivity of the volume conductor changes abruptly. The effectiveness of this and other stimulation mechanisms was compared by means of models and computer simulations in the context of transcranial magnetic stimulation. While, for a given stimulation intensity, the largest membrane depolarization occurred where an axon terminates or bends sharply in a high electric field region, a slightly smaller membrane depolarization, still sufficient to generate action potentials, also occurred at an internal boundary where the conductivity jumped from 0.143 S m -1 to 0.333 S m -1 , simulating a white-matter-grey-matter interface. Tissue heterogeneity can also give rise to local electric field gradients that are considerably stronger and more focal than those impressed by the stimulation coil and that can affect the membrane potential, albeit to a lesser extent than the two mechanisms mentioned above. Tissue heterogeneity may play an important role in electric and magnetic 'far-field' stimulation

  11. Tissue heterogeneity as a mechanism for localized neural stimulation by applied electric fields

    Energy Technology Data Exchange (ETDEWEB)

    Miranda, P C [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon (Portugal); Correia, L [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon (Portugal); Salvador, R [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon (Portugal); Basser, P J [Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD 20892-1428 (United States)

    2007-09-21

    We investigate the heterogeneity of electrical conductivity as a new mechanism to stimulate excitable tissues via applied electric fields. In particular, we show that stimulation of axons crossing internal boundaries can occur at boundaries where the electric conductivity of the volume conductor changes abruptly. The effectiveness of this and other stimulation mechanisms was compared by means of models and computer simulations in the context of transcranial magnetic stimulation. While, for a given stimulation intensity, the largest membrane depolarization occurred where an axon terminates or bends sharply in a high electric field region, a slightly smaller membrane depolarization, still sufficient to generate action potentials, also occurred at an internal boundary where the conductivity jumped from 0.143 S m{sup -1} to 0.333 S m{sup -1}, simulating a white-matter-grey-matter interface. Tissue heterogeneity can also give rise to local electric field gradients that are considerably stronger and more focal than those impressed by the stimulation coil and that can affect the membrane potential, albeit to a lesser extent than the two mechanisms mentioned above. Tissue heterogeneity may play an important role in electric and magnetic 'far-field' stimulation.

  12. Visual NNet: An Educational ANN's Simulation Environment Reusing Matlab Neural Networks Toolbox

    Science.gov (United States)

    Garcia-Roselló, Emilio; González-Dacosta, Jacinto; Lado, Maria J.; Méndez, Arturo J.; Garcia Pérez-Schofield, Baltasar; Ferrer, Fátima

    2011-01-01

    Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because…

  13. Network bursts in cortical neuronal cultures: 'noise - versus pacemaker'- driven neural network simulations

    NARCIS (Netherlands)

    Gritsun, T.; Stegenga, J.; le Feber, Jakob; Rutten, Wim

    2009-01-01

    In this paper we address the issue of spontaneous bursting activity in cortical neuronal cultures and explain what might cause this collective behavior using computer simulations of two different neural network models. While the common approach to acivate a passive network is done by introducing

  14. Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation

    CSIR Research Space (South Africa)

    Ngwangwa, HM

    2010-04-01

    Full Text Available -1 Journal of Terramechanics Volume 47, Issue 2, April 2010, Pages 97-111 Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation H.M. Ngwangwaa, P.S. Heynsa, , , F...

  15. Thymidine Kinase-Negative Herpes Simplex Virus 1 Can Efficiently Establish Persistent Infection in Neural Tissues of Nude Mice.

    Science.gov (United States)

    Huang, Chih-Yu; Yao, Hui-Wen; Wang, Li-Chiu; Shen, Fang-Hsiu; Hsu, Sheng-Min; Chen, Shun-Hua

    2017-02-15

    Herpes simplex virus 1 (HSV-1) establishes latency in neural tissues of immunocompetent mice but persists in both peripheral and neural tissues of lymphocyte-deficient mice. Thymidine kinase (TK) is believed to be essential for HSV-1 to persist in neural tissues of immunocompromised mice, because infectious virus of a mutant with defects in both TK and UL24 is detected only in peripheral tissues, but not in neural tissues, of severe combined immunodeficiency mice (T. Valyi-Nagy, R. M. Gesser, B. Raengsakulrach, S. L. Deshmane, B. P. Randazzo, A. J. Dillner, and N. W. Fraser, Virology 199:484-490, 1994, https://doi.org/10.1006/viro.1994.1150). Here we find infiltration of CD4 and CD8 T cells in peripheral and neural tissues of mice infected with a TK-negative mutant. We therefore investigated the significance of viral TK and host T cells for HSV-1 to persist in neural tissues using three genetically engineered mutants with defects in only TK or in both TK and UL24 and two strains of nude mice. Surprisingly, all three mutants establish persistent infection in up to 100% of brain stems and 93% of trigeminal ganglia of adult nude mice at 28 days postinfection, as measured by the recovery of infectious virus. Thus, in mouse neural tissues, host T cells block persistent HSV-1 infection, and viral TK is dispensable for the virus to establish persistent infection. Furthermore, we found 30- to 200-fold more virus in neural tissues than in the eye and detected glycoprotein C, a true late viral antigen, in brainstem neurons of nude mice persistently infected with the TK-negative mutant, suggesting that adult mouse neurons can support the replication of TK-negative HSV-1. Acyclovir is used to treat herpes simplex virus 1 (HSV-1)-infected immunocompromised patients, but treatment is hindered by the emergence of drug-resistant viruses, mostly those with mutations in viral thymidine kinase (TK), which activates acyclovir. TK mutants are detected in brains of immunocompromised

  16. NEVESIM: event-driven neural simulation framework with a Python interface.

    Science.gov (United States)

    Pecevski, Dejan; Kappel, David; Jonke, Zeno

    2014-01-01

    NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.

  17. Large-scale simulations of plastic neural networks on neuromorphic hardware

    Directory of Open Access Journals (Sweden)

    James Courtney Knight

    2016-04-01

    Full Text Available SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 20000 neurons and 51200000 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.

  18. 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.

  19. Artificial neural networks for dynamic monitoring of simulated-operating parameters of high temperature gas cooled engineering test reactor (HTTR)

    International Nuclear Information System (INIS)

    Seker, Serhat; Tuerkcan, Erdinc; Ayaz, Emine; Barutcu, Burak

    2003-01-01

    This paper addresses to the problem of utilisation of the artificial neural networks (ANNs) for detecting anomalies as well as physical parameters of a nuclear power plant during power operation in real time. Three different types of neural network algorithms were used namely, feed-forward neural network (back-propagation, BP) and two types of recurrent neural networks (RNN). The data used in this paper were gathered from the simulation of the power operation of the Japan's High Temperature Engineering Testing Reactor (HTTR). For the wide range of power operation, 56 signals were generated by the reactor dynamic simulation code for several hours of normal power operation at different power ramps between 30 and 100% nominal power. Paper will compare the outcomes of different neural networks and presents the neural network system and the determination of physical parameters from the simulated operating data

  20. Simulation of activation and propagation delay during tripolar neural stimulation

    NARCIS (Netherlands)

    Goodall, E.V.; Goodall, Eleanor V.; Kosterman, L. Martin; Struijk, Johannes J.; Struijk, J.J.; Holsheimer, J.

    1993-01-01

    Computer simulations were perfonned to investigate the influence of stimulus amplitude on cathodal activation delay, propagation delay and blocking during stimulation with a bipolar cuff electrode. Activation and propagation delays were combined in a total delay term which was minimized between the

  1. Limits to high-speed simulations of spiking neural networks using general-purpose computers.

    Science.gov (United States)

    Zenke, Friedemann; Gerstner, Wulfram

    2014-01-01

    To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed toward synaptic plasticity. In particular spike-timing-dependent plasticity (STDP) creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall, these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite.

  2. Maternal Antiviral Immunoglobulin Accumulates in Neural Tissue of Neonates To Prevent HSV Neurological Disease

    Directory of Open Access Journals (Sweden)

    Yike Jiang

    2017-07-01

    Full Text Available While antibody responses to neurovirulent pathogens are critical for clearance, the extent to which antibodies access the nervous system to ameliorate infection is poorly understood. In this study on herpes simplex virus 1 (HSV-1, we demonstrate that HSV-specific antibodies are present during HSV-1 latency in the nervous systems of both mice and humans. We show that antibody-secreting cells entered the trigeminal ganglion (TG, a key site of HSV infection, and persisted long after the establishment of latent infection. We also demonstrate the ability of passively administered IgG to enter the TG independently of infection, showing that the naive TG is accessible to antibodies. The translational implication of this finding is that human fetal neural tissue could contain HSV-specific maternally derived antibodies. Exploring this possibility, we observed HSV-specific IgG in HSV DNA-negative human fetal TG, suggesting passive transfer of maternal immunity into the prenatal nervous system. To further investigate the role of maternal antibodies in the neonatal nervous system, we established a murine model to demonstrate that maternal IgG can access and persist in neonatal TG. This maternal antibody not only prevented disseminated infection but also completely protected the neonate from neurological disease and death following HSV challenge. Maternal antibodies therefore have a potent protective role in the neonatal nervous system against HSV infection. These findings strongly support the concept that prevention of prenatal and neonatal neurotropic infections can be achieved through maternal immunization.

  3. Neural tissue engineering scaffold with sustained RAPA release relieves neuropathic pain in rats.

    Science.gov (United States)

    Ding, Tan; Zhu, Chao; Kou, Zhen-Zhen; Yin, Jun-Bin; Zhang, Ting; Lu, Ya-Cheng; Wang, Li-Ying; Luo, Zhuo-Jing; Li, Yun-Qing

    2014-09-01

    To investigate the effect of locally slow-released rapamycin (RAPA) from bionic peripheral nerve stent to reduce the incidence of neuropathic pain or mitigate the degree of pain after nerve injury. We constructed a neural tissue engineering scaffold with sustained release of RAPA to repair 20mm defects in rat sciatic nerves. Four presurgical and postsurgical time windows were selected to monitor the changes in the expression of pain-related dorsal root ganglion (DRG) voltage-gated sodium channels 1.3 (Nav1.3), 1.7 (Nav1.7), and 1.8 (Nav1.8) through immunohistochemistry (IHC) and Western Blot, along with the observation of postsurgical pathological pain in rats by pain-related behavior approaches. Relatively small upregulation of DRG sodium channels was observed in the experimental group (RAPA+poly(lactic-co-glycolic acid) (PLGA)+stent) after surgery, along with low degrees of neuropathic pain and anxiety, which were similar to those in the Autologous nerve graft group. Autoimmune inflammatory response plays a leading role in the occurrence of post-traumatic neuropathic pain, and that RAPA significantly inhibits the abnormal upregulation of sodium channels to reduce pain by alleviating inflammatory response. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Artificial neural network prediction of ischemic tissue fate in acute stroke imaging

    Science.gov (United States)

    Huang, Shiliang; Shen, Qiang; Duong, Timothy Q

    2010-01-01

    Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin–spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBF+ADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis. PMID:20424631

  5. Adaptive complementary fuzzy self-recurrent wavelet neural network controller for the electric load simulator system

    Directory of Open Access Journals (Sweden)

    Wang Chao

    2016-03-01

    Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.

  6. NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.

    Science.gov (United States)

    Cheung, Kit; Schultz, Simon R; Luk, Wayne

    2015-01-01

    NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

  7. SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method.

    Science.gov (United States)

    Bernal, Javier; Torres-Jimenez, Jose

    2015-01-01

    SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller's algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.

  8. Personalizes lung motion simulation fore external radiotherapy using an artificial neural network

    International Nuclear Information System (INIS)

    Laurent, R.

    2011-01-01

    The development of new techniques in the field of external radiotherapy opens new ways of gaining accuracy in dose distribution, in particular through the knowledge of individual lung motion. The numeric simulation NEMOSIS (Neural Network Motion Simulation System) we describe is based on artificial neural networks (ANN) and allows, in addition to determining motion in a personalized way, to reduce the necessary initial doses to determine it. In the first part, we will present current treatment options, lung motion as well as existing simulation or estimation methods. The second part describes the artificial neural network used and the steps for defining its parameters. An accurate evaluation of our approach was carried out on original patient data. The obtained results are compared with an existing motion estimated method. The extremely short computing time, in the range of milliseconds for the generation of one respiratory phase, would allow its use in clinical routine. Modifications to NEMOSIS in order to meet the requirements for its use in external radiotherapy are described, and a study of the motion of tumor outlines is carried out. This work lays the basis for lung motion simulation with ANNs and validates our approach. Its real time implementation coupled to its predication accuracy makes NEMOSIS promising tool for the simulation of motion synchronized with breathing. (author)

  9. Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

    Directory of Open Access Journals (Sweden)

    Jan Hahne

    2017-05-01

    Full Text Available Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

  10. Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.

    Science.gov (United States)

    Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

    2017-01-01

    Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

  11. A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

    Directory of Open Access Journals (Sweden)

    Roger V Hoang

    2013-10-01

    Full Text Available Computational Neuroscience is an emerging field that provides unique opportunities to studycomplex brain structures through realistic neural simulations. However, as biological details are added tomodels, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they haveshown significant improvement in execution time compared to Central Processing Units (CPUs. Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks,the NeoCortical Simulator version 6 (NCS6. NCS6 is a free, open-source, parallelizable, and scalable simula-tor, designed to run on clusters of multiple machines, potentially with high performance computing devicesin each of them. It has built-in leaky-integrate-and-fire (LIF and Izhikevich (IZH neuron models, but usersalso have the capability to design their own plug-in interface for different neuron types as desired. NCS6is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing dataacross these heterogeneous clusters of CPUs and GPUs.

  12. Neural control of muscle force: indications from a simulation model

    Science.gov (United States)

    Luca, Carlo J. De

    2013-01-01

    We developed a model to investigate the influence of the muscle force twitch on the simulated firing behavior of motoneurons and muscle force production during voluntary isometric contractions. The input consists of an excitatory signal common to all the motor units in the pool of a muscle, consistent with the “common drive” property. Motor units respond with a hierarchically structured firing behavior wherein at any time and force, firing rates are inversely proportional to recruitment threshold, as described by the “onion skin” property. Time- and force-dependent changes in muscle force production are introduced by varying the motor unit force twitches as a function of time or by varying the number of active motor units. A force feedback adjusts the input excitation, maintaining the simulated force at a target level. The simulations replicate motor unit behavior characteristics similar to those reported in previous empirical studies of sustained contractions: 1) the initial decrease and subsequent increase of firing rates, 2) the derecruitment and recruitment of motor units throughout sustained contractions, and 3) the continual increase in the force fluctuation caused by the progressive recruitment of larger motor units. The model cautions the use of motor unit behavior at recruitment and derecruitment without consideration of changes in the muscle force generation capacity. It describes an alternative mechanism for the reserve capacity of motor units to generate extraordinary force. It supports the hypothesis that the control of motoneurons remains invariant during force-varying and sustained isometric contractions. PMID:23236008

  13. Equation-oriented specification of neural models for simulations

    Directory of Open Access Journals (Sweden)

    Marcel eStimberg

    2014-02-01

    Full Text Available Simulating biological neuronal networks is a core method of research in computational neuroscience. A full specification of such a network model includes a description of the dynamics and state changes of neurons and synapses, as well as the synaptic connectivity patterns and the initial values of all parameters. A standard approach in neuronal modelling software is to build models based on a library of pre-defined models and mechanisms; if a model component does not yet exist, it has to be defined in a special-purpose or general low-level language and potentially be compiled and linked with the simulator. Here we propose an alternative approach that allows flexible definition of models by writing textual descriptions based on mathematical notation. We demonstrate that this approach allows the definition of a wide range of models with minimal syntax. Furthermore, such explicit model descriptions allow the generation of executable code for various target languages and devices, since the description is not tied to an implementation. Finally, this approach also has advantages for readability and reproducibility, because the model description is fully explicit, and because it can be automatically parsed and transformed into formatted descriptions.The presented approach has been implemented in the Brian2 simulator.

  14. Advanced biomaterial strategies to transplant preformed micro-tissue engineered neural networks into the brain

    Science.gov (United States)

    Harris, J. P.; Struzyna, L. A.; Murphy, P. L.; Adewole, D. O.; Kuo, E.; Cullen, D. K.

    2016-02-01

    Objective. Connectome disruption is a hallmark of many neurological diseases and trauma with no current strategies to restore lost long-distance axonal pathways in the brain. We are creating transplantable micro-tissue engineered neural networks (micro-TENNs), which are preformed constructs consisting of embedded neurons and long axonal tracts to integrate with the nervous system to physically reconstitute lost axonal pathways. Approach. We advanced micro-tissue engineering techniques to generate micro-TENNs consisting of discrete populations of mature primary cerebral cortical neurons spanned by long axonal fascicles encased in miniature hydrogel micro-columns. Further, we improved the biomaterial encasement scheme by adding a thin layer of low viscosity carboxymethylcellulose (CMC) to enable needle-less insertion and rapid softening for mechanical similarity with brain tissue. Main results. The engineered architecture of cortical micro-TENNs facilitated robust neuronal viability and axonal cytoarchitecture to at least 22 days in vitro. Micro-TENNs displayed discrete neuronal populations spanned by long axonal fasciculation throughout the core, thus mimicking the general systems-level anatomy of gray matter—white matter in the brain. Additionally, micro-columns with thin CMC-coating upon mild dehydration were able to withstand a force of 893 ± 457 mN before buckling, whereas a solid agarose cylinder of similar dimensions was predicted to withstand less than 150 μN of force. This thin CMC coating increased the stiffness by three orders of magnitude, enabling needle-less insertion into brain while significantly reducing the footprint of previous needle-based delivery methods to minimize insertion trauma. Significance. Our novel micro-TENNs are the first strategy designed for minimally invasive implantation to facilitate nervous system repair by simultaneously providing neuronal replacement and physical reconstruction of long-distance axon pathways in the brain

  15. New bioactive motifs and their use in functionalized self-assembling peptides for NSC differentiation and neural tissue engineering

    Science.gov (United States)

    Gelain, F.; Cigognini, D.; Caprini, A.; Silva, D.; Colleoni, B.; Donegá, M.; Antonini, S.; Cohen, B. E.; Vescovi, A.

    2012-04-01

    Developing functionalized biomaterials for enhancing transplanted cell engraftment in vivo and stimulating the regeneration of injured tissues requires a multi-disciplinary approach customized for the tissue to be regenerated. In particular, nervous tissue engineering may take a great advantage from the discovery of novel functional motifs fostering transplanted stem cell engraftment and nervous fiber regeneration. Using phage display technology we have discovered new peptide sequences that bind to murine neural stem cell (NSC)-derived neural precursor cells (NPCs), and promote their viability and differentiation in vitro when linked to LDLK12 self-assembling peptide (SAPeptide). We characterized the newly functionalized LDLK12 SAPeptides via atomic force microscopy, circular dichroism and rheology, obtaining nanostructured hydrogels that support human and murine NSC proliferation and differentiation in vitro. One functionalized SAPeptide (Ac-FAQ), showing the highest stem cell viability and neural differentiation in vitro, was finally tested in acute contusive spinal cord injury in rats, where it fostered nervous tissue regrowth and improved locomotor recovery. Interestingly, animals treated with the non-functionalized LDLK12 had an axon sprouting/regeneration intermediate between Ac-FAQ-treated animals and controls. These results suggest that hydrogels functionalized with phage-derived peptides may constitute promising biomimetic scaffolds for in vitro NSC differentiation, as well as regenerative therapy of the injured nervous system. Moreover, this multi-disciplinary approach can be used to customize SAPeptides for other specific tissue engineering applications.Developing functionalized biomaterials for enhancing transplanted cell engraftment in vivo and stimulating the regeneration of injured tissues requires a multi-disciplinary approach customized for the tissue to be regenerated. In particular, nervous tissue engineering may take a great advantage from the

  16. Fabrication of Chitosan/Poly (vinyl alcohol/Carbon Nanotube/Bioactive Glass Nanocomposite Scaffolds for Neural Tissue Engineering

    Directory of Open Access Journals (Sweden)

    S. Nikbakht Katouli

    2016-06-01

    5 and 10 wt% incorporated electrospun chitosan (CS/polyvinyl alcohol (PVA nanofibers for potential neural tissue engineering applications.The morphology, structure, and mechanical properties of the formed electrospun fibrous mats were characterized using scanning electron microscopy (SEM and mechanical testing, respectively. In vitro cell culture of embryonal carcinoma stem cells (P19 were seeded onto the electrospun scaffolds. The results showed that the incorporation of CNTs and BG nanoparticles did not appreciably affect the morphology of the CS/PVA nanofibers. The maximum tensile strength (7.9 MPa was observed in the composite sample with 5 %wt bioactive glass nanoparticles. The results suggest that BG and CNT-incorporated CS/PVA nanofibrous scaffolds with small diameters, high porosity, and promoted mechanical properties can potentially provide many possibilities for applications in the fields of neural tissue engineering and regenerative medicine.

  17. Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS

    Directory of Open Access Journals (Sweden)

    Philipp Weidel

    2016-08-01

    Full Text Available In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS to the Multi-Simulator Coordinator (MUSIC. This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning.

  18. A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

    Science.gov (United States)

    Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

    2016-09-01

    Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

  19. Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs.

    Science.gov (United States)

    Mishchenko, Yuriy

    2009-01-30

    We describe an approach for automation of the process of reconstruction of neural tissue from serial section transmission electron micrographs. Such reconstructions require 3D segmentation of individual neuronal processes (axons and dendrites) performed in densely packed neuropil. We first detect neuronal cell profiles in each image in a stack of serial micrographs with multi-scale ridge detector. Short breaks in detected boundaries are interpolated using anisotropic contour completion formulated in fuzzy-logic framework. Detected profiles from adjacent sections are linked together based on cues such as shape similarity and image texture. Thus obtained 3D segmentation is validated by human operators in computer-guided proofreading process. Our approach makes possible reconstructions of neural tissue at final rate of about 5 microm3/manh, as determined primarily by the speed of proofreading. To date we have applied this approach to reconstruct few blocks of neural tissue from different regions of rat brain totaling over 1000microm3, and used these to evaluate reconstruction speed, quality, error rates, and presence of ambiguous locations in neuropil ssTEM imaging data.

  20. Probing neural tissue with airy light-sheet microscopy: investigation of imaging performance at depth within turbid media

    Science.gov (United States)

    Nylk, Jonathan; McCluskey, Kaley; Aggarwal, Sanya; Tello, Javier A.; Dholakia, Kishan

    2017-02-01

    Light-sheet microscopy (LSM) has received great interest for fluorescent imaging applications in biomedicine as it facilitates three-dimensional visualisation of large sample volumes with high spatiotemporal resolution whilst minimising irradiation of, and photo-damage to the specimen. Despite these advantages, LSM can only visualize superficial layers of turbid tissues, such as mammalian neural tissue. Propagation-invariant light modes have played a key role in the development of high-resolution LSM techniques as they overcome the natural divergence of a Gaussian beam, enabling uniform and thin light-sheets over large distances. Most notably, Bessel and Airy beam-based light-sheet imaging modalities have been demonstrated. In the single-photon excitation regime and in lightly scattering specimens, Airy-LSM has given competitive performance with advanced Bessel-LSM techniques. Airy and Bessel beams share the property of self-healing, the ability of the beam to regenerate its transverse beam profile after propagation around an obstacle. Bessel-LSM techniques have been shown to increase the penetration-depth of the illumination into turbid specimens but this effect has been understudied in biologically relevant tissues, particularly for Airy beams. It is expected that Airy-LSM will give a similar enhancement over Gaussian-LSM. In this paper, we report on the comparison of Airy-LSM and Gaussian-LSM imaging modalities within cleared and non-cleared mouse brain tissue. In particular, we examine image quality versus tissue depth by quantitative spatial Fourier analysis of neural structures in virally transduced fluorescent tissue sections, showing a three-fold enhancement at 50 μm depth into non-cleared tissue with Airy-LSM. Complimentary analysis is performed by resolution measurements in bead-injected tissue sections.

  1. Simbrain 3.0: A flexible, visually-oriented neural network simulator.

    Science.gov (United States)

    Tosi, Zachary; Yoshimi, Jeffrey

    2016-11-01

    Simbrain 3.0 is a software package for neural network design and analysis, which emphasizes flexibility (arbitrarily complex networks can be built using a suite of basic components) and a visually rich, intuitive interface. These features support both students and professionals. Students can study all of the major classes of neural networks in a familiar graphical setting, and can easily modify simulations, experimenting with networks and immediately seeing the results of their interventions. With the 3.0 release, Simbrain supports models on the order of thousands of neurons and a million synapses. This allows the same features that support education to support research professionals, who can now use the tool to quickly design, run, and analyze the behavior of large, highly customizable simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.

    Science.gov (United States)

    Kooi, Thijs; van Ginneken, Bram; Karssemeijer, Nico; den Heeten, Ard

    2017-03-01

    It is estimated that 7% of women in the western world will develop palpable breast cysts in their lifetime. Even though cysts have been correlated with risk of developing breast cancer, many of them are benign and do not require follow-up. We develop a method to discriminate benign solitary cysts from malignant masses in digital mammography. We think a system like this can have merit in the clinic as a decision aid or complementary to specialized modalities. We employ a deep convolutional neural network (CNN) to classify cyst and mass patches. Deep CNNs have been shown to be powerful classifiers, but need a large amount of training data for which medical problems are often difficult to come by. The key contribution of this paper is that we show good performance can be obtained on a small dataset by pretraining the network on a large dataset of a related task. We subsequently investigate the following: (a) when a mammographic exam is performed, two different views of the same breast are recorded. We investigate the merit of combining the output of the classifier from these two views. (b) We evaluate the importance of the resolution of the patches fed to the network. (c) A method dubbed tissue augmentation is subsequently employed, where we extract normal tissue from normal patches and superimpose this onto the actual samples aiming for a classifier invariant to occluding tissue. (d) We combine the representation extracted using the deep CNN with our previously developed features. We show that using the proposed deep learning method, an area under the ROC curve (AUC) value of 0.80 can be obtained on a set of benign solitary cysts and malignant mass findings recalled in screening. We find that it works significantly better than our previously developed approach by comparing the AUC of the ROC using bootstrapping. By combining views, the results can be further improved, though this difference was not found to be significant. We find no significant difference between

  3. Brain tissue aspiration neural tube defect Aspiração de tecido cerebral em casos de defeitos de fechamento do tubo neural

    Directory of Open Access Journals (Sweden)

    Luiz Cesar Peres

    2005-09-01

    Full Text Available The study aimed to find out how frequent is brain tissue aspiration and if brain tissue heterotopia could be found in the lung of human neural tube defect cases. Histological sections of each lobe of both lungs of 22 fetuses and newborn with neural tube defect were immunostained for glial fibrillary acidic protein (GFAP. There were 15 (68.2% females and 7 (31.8% males. Age ranged from 18 to 40 weeks of gestation (mean= 31.8. Ten (45.5% were stillborn, the same newborn, and 2 (9.1% were abortuses. Diagnosis were: craniorrhachischisis (9 cases, 40.9%, anencephaly (8 cases, 36,4%, ruptured occipital encephalocele and rachischisis (2 cases, 9.1% each, and early amniotic band disruption sequence (1 case, 4.5%. Only one case (4.5% exhibited GFAP positive cells inside bronchioles and alveoli admixed to epithelial amniotic squames. No heterotopic tissue was observed in the lung interstitium. We concluded that aspiration of brain tissue from the amniotic fluid in neural tube defect cases may happen but it is infrequent and heterotopia was not observed.O objetivo do estudo foi identificar qual a freqüência de aspiração de tecido cerebral e a existência de heterotopia nos pulmões de casos humanos de defeito de fechamento do tubo neural através da reação imuno-histoquímica para proteína fibrilar glial ácida (GFAP em cortes histológicos de todos os lobos de ambos os pulmões de 22 casos de fetos e neonatos com defeito de fechamento do tubo neural. Havia 15 casos femininos (68,2% e 7 masculinos (31,8%, com idade gestacional variando de 18 a 40 semanas (média= 31,8, sendo natimortos e neomortos 10 (45,5% cada e 2 (9,1% abortos. Os diagnósticos foram: Craniorraquisquise (9 casos, 40,9%, anencefalia (8 casos, 36,4%, encefalocele occipital rota e raquisquise (2 casos, 9,1% e 1 (4,5%caso de seqüência de disruptura amniótica precoce. Somente 1 caso (4,5% apresentou células positivas dentro de bronquíolos e alvéolos em meio a células epiteliais

  4. Ethanol production from steam exploded rapeseed straw and the process simulation using artificial neural networks

    DEFF Research Database (Denmark)

    Talebnia, Farid; Mighani, Moein; Rahimnejad, Mostafa

    2015-01-01

    and 67% of maximum theoretical value. Next, data of the experimental runs were exploited for modeling the processes by artificial neural networks (ANNs) and performance of the developed models was evaluated. The ANN-based models showed a great potential for time-course prediction of the studied processes....... Efficiency of the joint network for simulating the whole process was also determined and promising results were obtained....

  5. Novel High-Viscosity Polyacrylamidated Chitosan for Neural Tissue Engineering: Fabrication of Anisotropic Neurodurable Scaffold via Molecular Disposition of Persulfate-Mediated Polymer Slicing and Complexation

    Directory of Open Access Journals (Sweden)

    Viness Pillay

    2012-10-01

    Full Text Available Macroporous polyacrylamide-grafted-chitosan scaffolds for neural tissue engineering were fabricated with varied synthetic and viscosity profiles. A novel approach and mechanism was utilized for polyacrylamide grafting onto chitosan using potassium persulfate (KPS mediated degradation of both polymers under a thermally controlled environment. Commercially available high molecular mass polyacrylamide was used instead of the acrylamide monomer for graft copolymerization. This grafting strategy yielded an enhanced grafting efficiency (GE = 92%, grafting ratio (GR = 263%, intrinsic viscosity (IV = 5.231 dL/g and viscometric average molecular mass (MW = 1.63 × 106 Da compared with known acrylamide that has a GE = 83%, GR = 178%, IV = 3.901 dL/g and MW = 1.22 × 106 Da. Image processing analysis of SEM images of the newly grafted neurodurable scaffold was undertaken based on the polymer-pore threshold. Attenuated Total Reflectance-FTIR spectral analyses in conjugation with DSC were used for the characterization and comparison of the newly grafted copolymers. Static Lattice Atomistic Simulations were employed to investigate and elucidate the copolymeric assembly and reaction mechanism by exploring the spatial disposition of chitosan and polyacrylamide with respect to the reactional profile of potassium persulfate. Interestingly, potassium persulfate, a peroxide, was found to play a dual role initially degrading the polymers—“polymer slicing”—thereby initiating the formation of free radicals and subsequently leading to synthesis of the high molecular mass polyacrylamide-grafted-chitosan (PAAm-g-CHT—“polymer complexation”. Furthermore, the applicability of the uniquely grafted scaffold for neural tissue engineering was evaluated via PC12 neuronal cell seeding. The novel PAAm-g-CHT exhibited superior neurocompatibility in terms of cell infiltration owing to the anisotropic porous architecture, high molecular mass mediated robustness

  6. Evaluation and characterization of X-ray scattering in tissues and mammographic simulators using Monte Carlo simulation

    International Nuclear Information System (INIS)

    Oliveira, Monica G. Nunes; Braz, Delson; Silva, Regina Cely B. da S.

    2005-01-01

    The computer simulation has been widely used in physical researches by both the viability of the codes and the growth of the power of computers in the last decades. The Monte Carlo simulation program, EGS4 code is a simulation program used in the area of radiation transport. The simulators, surrogate tissues, phantoms are objects used to perform studies on dosimetric quantities and quality testing of images. The simulators have characteristics of scattering and absorption of radiation similar to tissues that make up the body. The aim of this work is to translate the effects of radiation interactions in a real healthy breast tissues, sick and on simulators using the EGS4 Monte Carlo simulation code

  7. Expression of neural cell adhesion molecules and neurofilament protein isoforms in Ewing's sarcoma of bone and soft tissue sarcomas of other than rhabdomyosarcoma

    NARCIS (Netherlands)

    Molenaar, W.M.; Muntinghe, F.L.H.

    1999-01-01

    In a previous study, it was shown that rhabdomyosarcomas widely express "neural" markers, such as neural cell adhesion molecules (N-CAM) and neurofilament protein isoforms, In the current study, a series of Ewing's sarcomas of bone and soft tissue sarcomas other than rhabdomyosarcoma was probed for

  8. MCSLTT, Monte Carlo Simulation of Light Transport in Tissue

    International Nuclear Information System (INIS)

    2008-01-01

    Description of program or function: Understanding light-tissue interaction is fundamental in the field of Biomedical Optics. It has important implications for both therapeutic and diagnostic technologies. In this program, light transport in scattering tissue is modeled by absorption and scattering events as each photon travels through the tissue. The path of each photon is determined statistically by calculating probabilities of scattering and absorption. Other measured quantities are total reflected light, total transmitted light, and total heat absorbed

  9. Fluorescence-Activated Cell Sorting of EGFP-Labeled Neural Crest Cells From Murine Embryonic Craniofacial Tissue

    Directory of Open Access Journals (Sweden)

    Saurabh Singh

    2005-01-01

    Full Text Available During the early stages of embryogenesis, pluripotent neural crest cells (NCC are known to migrate from the neural folds to populate multiple target sites in the embryo where they differentiate into various derivatives, including cartilage, bone, connective tissue, melanocytes, glia, and neurons of the peripheral nervous system. The ability to obtain pure NCC populations is essential to enable molecular analyses of neural crest induction, migration, and/or differentiation. Crossing Wnt1-Cre and Z/EG transgenic mouse lines resulted in offspring in which the Wnt1-Cre transgene activated permanent EGFP expression only in NCC. The present report demonstrates a flow cytometric method to sort and isolate populations of EGFP-labeled NCC. The identity of the sorted neural crest cells was confirmed by assaying expression of known marker genes by TaqMan Quantitative Real-Time Polymerase Chain Reaction (QRT-PCR. The molecular strategy described in this report provides a means to extract intact RNA from a pure population of NCC thus enabling analysis of gene expression in a defined population of embryonic precursor cells critical to development.

  10. DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Marília Gonçalves Dutra da Silva

    2016-04-01

    Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

  11. Ultrasound Shear Wave Simulation of Breast Tumor Using Nonlinear Tissue Elasticity

    Directory of Open Access Journals (Sweden)

    Dae Woo Park

    2016-01-01

    Full Text Available Shear wave elasticity imaging (SWEI can assess the elasticity of tissues, but the shear modulus estimated in SWEI is often less sensitive to a subtle change of the stiffness that produces only small mechanical contrast to the background tissues. Because most soft tissues exhibit mechanical nonlinearity that differs in tissue types, mechanical contrast can be enhanced if the tissues are compressed. In this study, a finite element- (FE- based simulation was performed for a breast tissue model, which consists of a circular (D: 10 mm, hard tumor and surrounding tissue (soft. The SWEI was performed with 0% to 30% compression of the breast tissue model. The shear modulus of the tumor exhibited noticeably high nonlinearity compared to soft background tissue above 10% overall applied compression. As a result, the elastic modulus contrast of the tumor to the surrounding tissue was increased from 0.46 at 0% compression to 1.45 at 30% compression.

  12. Ultrasound Shear Wave Simulation of Breast Tumor Using Nonlinear Tissue Elasticity.

    Science.gov (United States)

    Park, Dae Woo

    2015-01-01

    Shear wave elasticity imaging (SWEI) can assess the elasticity of tissues, but the shear modulus estimated in SWEI is often less sensitive to a subtle change of the stiffness that produces only small mechanical contrast to the background tissues. Because most soft tissues exhibit mechanical nonlinearity that differs in tissue types, mechanical contrast can be enhanced if the tissues are compressed. In this study, a finite element- (FE-) based simulation was performed for a breast tissue model, which consists of a circular (D: 10 mm, hard) tumor and surrounding tissue (soft). The SWEI was performed with 0% to 30% compression of the breast tissue model. The shear modulus of the tumor exhibited noticeably high nonlinearity compared to soft background tissue above 10% overall applied compression. As a result, the elastic modulus contrast of the tumor to the surrounding tissue was increased from 0.46 at 0% compression to 1.45 at 30% compression.

  13. Training Knowledge Bots for Physics-Based Simulations Using Artificial Neural Networks

    Science.gov (United States)

    Samareh, Jamshid A.; Wong, Jay Ming

    2014-01-01

    Millions of complex physics-based simulations are required for design of an aerospace vehicle. These simulations are usually performed by highly trained and skilled analysts, who execute, monitor, and steer each simulation. Analysts rely heavily on their broad experience that may have taken 20-30 years to accumulate. In addition, the simulation software is complex in nature, requiring significant computational resources. Simulations of system of systems become even more complex and are beyond human capacity to effectively learn their behavior. IBM has developed machines that can learn and compete successfully with a chess grandmaster and most successful jeopardy contestants. These machines are capable of learning some complex problems much faster than humans can learn. In this paper, we propose using artificial neural network to train knowledge bots to identify the idiosyncrasies of simulation software and recognize patterns that can lead to successful simulations. We examine the use of knowledge bots for applications of computational fluid dynamics (CFD), trajectory analysis, commercial finite-element analysis software, and slosh propellant dynamics. We will show that machine learning algorithms can be used to learn the idiosyncrasies of computational simulations and identify regions of instability without including any additional information about their mathematical form or applied discretization approaches.

  14. Decoherence and Entanglement Simulation in a Model of Quantum Neural Network Based on Quantum Dots

    Directory of Open Access Journals (Sweden)

    Altaisky Mikhail V.

    2016-01-01

    Full Text Available We present the results of the simulation of a quantum neural network based on quantum dots using numerical method of path integral calculation. In the proposed implementation of the quantum neural network using an array of single-electron quantum dots with dipole-dipole interaction, the coherence is shown to survive up to 0.1 nanosecond in time and up to the liquid nitrogen temperature of 77K.We study the quantum correlations between the quantum dots by means of calculation of the entanglement of formation in a pair of quantum dots on the GaAs based substrate with dot size of 100 ÷ 101 nanometer and interdot distance of 101 ÷ 102 nanometers order.

  15. Combining neural networks and signed particles to simulate quantum systems more efficiently

    Science.gov (United States)

    Sellier, Jean Michel

    2018-04-01

    Recently a new formulation of quantum mechanics has been suggested which describes systems by means of ensembles of classical particles provided with a sign. This novel approach mainly consists of two steps: the computation of the Wigner kernel, a multi-dimensional function describing the effects of the potential over the system, and the field-less evolution of the particles which eventually create new signed particles in the process. Although this method has proved to be extremely advantageous in terms of computational resources - as a matter of fact it is able to simulate in a time-dependent fashion many-body systems on relatively small machines - the Wigner kernel can represent the bottleneck of simulations of certain systems. Moreover, storing the kernel can be another issue as the amount of memory needed is cursed by the dimensionality of the system. In this work, we introduce a new technique which drastically reduces the computation time and memory requirement to simulate time-dependent quantum systems which is based on the use of an appropriately tailored neural network combined with the signed particle formalism. In particular, the suggested neural network is able to compute efficiently and reliably the Wigner kernel without any training as its entire set of weights and biases is specified by analytical formulas. As a consequence, the amount of memory for quantum simulations radically drops since the kernel does not need to be stored anymore as it is now computed by the neural network itself, only on the cells of the (discretized) phase-space which are occupied by particles. As its is clearly shown in the final part of this paper, not only this novel approach drastically reduces the computational time, it also remains accurate. The author believes this work opens the way towards effective design of quantum devices, with incredible practical implications.

  16. Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

    International Nuclear Information System (INIS)

    Sahiner, B.; Chan, H.P.; Petrick, N.; Helvie, M.A.; Adler, D.D.; Goodsitt, M.M.; Wei, D.

    1996-01-01

    The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROI's containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms

  17. Effects of detergent on calcium-activated neutral proteinase (CANP) of neural and non-neural tissues in rat. A comparative study

    International Nuclear Information System (INIS)

    Banik, N.L.; Chakrabarti, A.K.; Hogan, E.L.

    1987-01-01

    Homogenates of brain, liver, kidney, heart and skeletal muscle of rat were prepared in 0.32 M-sucrose containing 2 mM EDTA. The CANP activity was assayed using 14 C-azocasein as substrate in 50 mM Tris acetate buffer, pH 7.4, 0.1% Triton X-100 and 5 mM-β-mercaptoethanol, with and without CaCl 2 . Addition to CNS membranes of other non-ionic detergents including sodium deoxycholate, β-D-thiogluco-pyranoside, and cetyltrimethyl-ammonium bromide activated the enzyme to varying extent depending on the detergent concentration. The ionic detergent sodium dodecyl sulfate abolished CANP activity completely in all preparations and this effect could not be reversed by non-ionic detergents. The most interesting feature of the Triton X-100 effect was a ten-fold increase of CNS CANP activity whereas non-neural CANP was not at all induced by Triton. CNS CANP was found mainly in the particulate fraction and only 30% in cytosol. In contrast, non-neural CANP was present mainly in cytosol. These results suggest that the bulk of CANP is membrane bound in CNS and differs from other tissue where it remains cytosolic

  18. Application of artificial neural networks to identify equilibration in computer simulations

    Science.gov (United States)

    Leibowitz, Mitchell H.; Miller, Evan D.; Henry, Michael M.; Jankowski, Eric

    2017-11-01

    Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are “equilibrated” and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.

  19. Design and simulation of a nanoelectronic DG MOSFET current source using artificial neural networks

    International Nuclear Information System (INIS)

    Djeffal, F.; Dibi, Z.; Hafiane, M.L.; Arar, D.

    2007-01-01

    The double gate (DG) MOSFET has received great attention in recent years owing to the inherent suppression of short channel effects (SCEs), excellent subthreshold slope (S), improved drive current (I ds ) and transconductance (gm), volume inversion for symmetric devices and excellent scalability. Therefore, simulation tools which can be applied to design nanoscale transistors in the future require new theory and modeling techniques that capture the physics of quantum transport accurately and efficiently. In this sense, this work presents the applicability of the artificial neural networks (ANN) for the design and simulation of a nanoelectronic DG MOSFET current source. The latter is based on the 2D numerical Non-Equilibrium Green's Function (NEGF) simulation of the current-voltage characteristics of an undoped symmetric DG MOSFET. Our results are discussed in order to obtain some new and useful information about the ULSI technology

  20. The Use of Endothelial Progenitor Cells for the Regeneration of Musculoskeletal and Neural Tissues

    OpenAIRE

    Kamei, Naosuke; Atesok, Kivanc; Ochi, Mitsuo

    2017-01-01

    Endothelial progenitor cells (EPCs) derived from bone marrow and blood can differentiate into endothelial cells and promote neovascularization. In addition, EPCs are a promising cell source for the repair of various types of vascularized tissues and have been used in animal experiments and clinical trials for tissue repair. In this review, we focused on the kinetics of endogenous EPCs during tissue repair and the application of EPCs or stem cell populations containing EPCs for tissue regenera...

  1. Establishment of Human Neural Progenitor Cells from Human Induced Pluripotent Stem Cells with Diverse Tissue Origins

    OpenAIRE

    Hayato Fukusumi; Tomoko Shofuda; Yohei Bamba; Atsuyo Yamamoto; Daisuke Kanematsu; Yukako Handa; Keisuke Okita; Masaya Nakamura; Shinya Yamanaka; Hideyuki Okano; Yonehiro Kanemura

    2016-01-01

    Human neural progenitor cells (hNPCs) have previously been generated from limited numbers of human induced pluripotent stem cell (hiPSC) clones. Here, 21 hiPSC clones derived from human dermal fibroblasts, cord blood cells, and peripheral blood mononuclear cells were differentiated using two neural induction methods, an embryoid body (EB) formation-based method and an EB formation method using dual SMAD inhibitors (dSMADi). Our results showed that expandable hNPCs could be generated from hiPS...

  2. Effects of epidermal growth factor on neural crest cells in tissue culture

    International Nuclear Information System (INIS)

    Erickson, C.A.; Turley, E.A.

    1987-01-01

    Epidermal growth factor (EGF) stimulates the release of hyaluronic acid (HA) and chondroitin sulfate proteoglycan (CSPG) from quail trunk neural crest cultures in a dose-dependent fashion. It also promotes the expression of cell-associated heparan sulfate proteoglycan (HSPG) as detected by immunofluorescence and immunoprecipitation of the 3 H-labeled proteoglycan. Furthermore, EGF stimulates [ 3 H]thymidine incorporation into total cell DNA. These results raise the possibility that EGF or an analogous growth factor is involved in regulation of neural crest cell morphogenesis

  3. Introducing ab initio based neural networks for transition-rate prediction in kinetic Monte Carlo simulations

    Science.gov (United States)

    Messina, Luca; Castin, Nicolas; Domain, Christophe; Olsson, Pär

    2017-02-01

    The quality of kinetic Monte Carlo (KMC) simulations of microstructure evolution in alloys relies on the parametrization of point-defect migration rates, which are complex functions of the local chemical composition and can be calculated accurately with ab initio methods. However, constructing reliable models that ensure the best possible transfer of physical information from ab initio to KMC is a challenging task. This work presents an innovative approach, where the transition rates are predicted by artificial neural networks trained on a database of 2000 migration barriers, obtained with density functional theory (DFT) in place of interatomic potentials. The method is tested on copper precipitation in thermally aged iron alloys, by means of a hybrid atomistic-object KMC model. For the object part of the model, the stability and mobility properties of copper-vacancy clusters are analyzed by means of independent atomistic KMC simulations, driven by the same neural networks. The cluster diffusion coefficients and mean free paths are found to increase with size, confirming the dominant role of coarsening of medium- and large-sized clusters in the precipitation kinetics. The evolution under thermal aging is in better agreement with experiments with respect to a previous interatomic-potential model, especially concerning the experiment time scales. However, the model underestimates the solubility of copper in iron due to the excessively high solution energy predicted by the chosen DFT method. Nevertheless, this work proves the capability of neural networks to transfer complex ab initio physical properties to higher-scale models, and facilitates the extension to systems with increasing chemical complexity, setting the ground for reliable microstructure evolution simulations in a wide range of alloys and applications.

  4. Binary Factorization in Hopfield-Like Neural Networks: Single-Step Approximation and Computer Simulations

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Sirota, A.M.; Húsek, Dušan; Muraviev, I. P.

    2004-01-01

    Roč. 14, č. 2 (2004), s. 139-152 ISSN 1210-0552 R&D Projects: GA ČR GA201/01/1192 Grant - others:BARRANDE(EU) 99010-2/99053; Intellectual computer Systems(EU) Grant 2.45 Institutional research plan: CEZ:AV0Z1030915 Keywords : nonlinear binary factor analysis * feature extraction * recurrent neural network * Single-Step approximation * neurodynamics simulation * attraction basins * Hebbian learning * unsupervised learning * neuroscience * brain function modeling Subject RIV: BA - General Mathematics

  5. Use of neural networks in process engineering. Thermodynamics, diffusion, and process control and simulation applications

    International Nuclear Information System (INIS)

    Otero, F

    1998-01-01

    This article presents the current status of the use of Artificial Neural Networks (ANNs) in process engineering applications where common mathematical methods do not completely represent the behavior shown by experimental observations, results, and plant operating data. Three examples of the use of ANNs in typical process engineering applications such as prediction of activity in solvent-polymer binary systems, prediction of a surfactant self-diffusion coefficient of micellar systems, and process control and simulation are shown. These examples are important for polymerization applications, enhanced-oil recovery, and automatic process control

  6. Travelling waves in models of neural tissue: from localised structures to periodic waves

    NARCIS (Netherlands)

    Meijer, Hil Gaétan Ellart; Coombes, Stephen

    2014-01-01

    We consider travelling waves (fronts, pulses and periodics) in spatially extended one dimensional neural field models. We demonstrate for an excitatory field with linear adaptation that, in addition to an expected stable pulse solution, a stable anti-pulse can exist. Varying the adaptation strength

  7. Neural stem cells improve neuronal survival in cultured postmortem brain tissue from aged and Alzheimer patients

    NARCIS (Netherlands)

    Wu, L.; Sluiter, A.A.; Guo, Ho Fu; Balesar, R. A.; Swaab, D. F.; Zhou, Jiang Ning; Verwer, R. W H

    Neurodegenerative diseases are progressive and incurable and are becoming ever more prevalent. To study whether neural stem cell can reactivate or rescue functions of impaired neurons in the human aging and neurodegenerating brain, we co-cultured postmortem slices from Alzheimer patients and control

  8. A fully-automated neural network analysis of AFM force-distance curves for cancer tissue diagnosis

    Science.gov (United States)

    Minelli, Eleonora; Ciasca, Gabriele; Sassun, Tanya Enny; Antonelli, Manila; Palmieri, Valentina; Papi, Massimiliano; Maulucci, Giuseppe; Santoro, Antonio; Giangaspero, Felice; Delfini, Roberto; Campi, Gaetano; De Spirito, Marco

    2017-10-01

    Atomic Force Microscopy (AFM) has the unique capability of probing the nanoscale mechanical properties of biological systems that affect and are affected by the occurrence of many pathologies, including cancer. This capability has triggered growing interest in the translational process of AFM from physics laboratories to clinical practice. A factor still hindering the current use of AFM in diagnostics is related to the complexity of AFM data analysis, which is time-consuming and needs highly specialized personnel with a strong physical and mathematical background. In this work, we demonstrate an operator-independent neural-network approach for the analysis of surgically removed brain cancer tissues. This approach allowed us to distinguish—in a fully automated fashion—cancer from healthy tissues with high accuracy, also highlighting the presence and the location of infiltrating tumor cells.

  9. Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: new emphasis on the biological interface

    Science.gov (United States)

    Michelson, Nicholas J.; Vazquez, Alberto L.; Eles, James R.; Salatino, Joseph W.; Purcell, Erin K.; Williams, Jordan J.; Cui, X. Tracy; Kozai, Takashi D. Y.

    2018-06-01

    Objective. Implantable neural electrode devices are important tools for neuroscience research and have an increasing range of clinical applications. However, the intricacies of the biological response after implantation, and their ultimate impact on recording performance, remain challenging to elucidate. Establishing a relationship between the neurobiology and chronic recording performance is confounded by technical challenges related to traditional electrophysiological, material, and histological limitations. This can greatly impact the interpretations of results pertaining to device performance and tissue health surrounding the implant. Approach. In this work, electrophysiological activity and immunohistological analysis are compared after controlling for motion artifacts, quiescent neuronal activity, and material failure of devices in order to better understand the relationship between histology and electrophysiological outcomes. Main results. Even after carefully accounting for these factors, the presence of viable neurons and lack of glial scarring does not convey single unit recording performance. Significance. To better understand the biological factors influencing neural activity, detailed cellular and molecular tissue responses were examined. Decreases in neural activity and blood oxygenation in the tissue surrounding the implant, shift in expression levels of vesicular transporter proteins and ion channels, axon and myelin injury, and interrupted blood flow in nearby capillaries can impact neural activity around implanted neural interfaces. Combined, these tissue changes highlight the need for more comprehensive, basic science research to elucidate the relationship between biology and chronic electrophysiology performance in order to advance neural technologies.

  10. An electronic system for simulation of neural networks with a micro-second real time constraint

    International Nuclear Information System (INIS)

    Chorti, Arsenia; Granado, Bertrand; Denby, Bruce; Garda, Patrick

    2001-01-01

    Neural networks implemented in hardware can perform pattern recognition very quickly, and as such have been used to advantage in the triggering systems of certain high energy physics experiments. Typically, time constants of the order of a few microseconds are required. In this paper, we present a new system. MAHARADJA, for evaluating MLP and RBF neural network paradigms in real time. The system is tested on a possible ATLAS muon triggering application suggested by the Tel Aviv ATLAS group, consisting of a 4-8-8-4 MLP which must be evaluated in 10 microseconds. The inputs to the net are dx/dz, x(z=0), dy/dz, and y(z=0), whereas the outputs give pt, tan(phi), sin(theta), and q, the charge. With a 10 MHz clock, MAHARADJA calculates the result in 6.8 microseconds; at 20 MHz, which is readily attainable, this would be reduced to only 3.4 microseconds. The system can also handle RBF networks with 3 different distance metrics (Euclidean, Manhattan and Mahalanobis), and can simulate any MLP of 10 hidden layers or less. The electronic implementation is with FPGA's, which can be optimized for a specific neural network because the number of processing elements can be modified

  11. Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Krzysztof Gajowniczek

    2018-04-01

    Full Text Available Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine the q -generalized function based on Tsallis statistics as an alternative error measure in neural networks. In order to validate different performance aspects of the proposed function and to enable identification of its strengths and weaknesses the extensive simulation was prepared based on the artificial benchmarking dataset. The results indicate that Tsallis entropy error function can be successfully introduced in the neural networks yielding satisfactory results and handling with class imbalance, noise in data or use of non-informative predictors.

  12. Toward high-speed 3D nonlinear soft tissue deformation simulations using Abaqus software.

    Science.gov (United States)

    Idkaidek, Ashraf; Jasiuk, Iwona

    2015-12-01

    We aim to achieve a fast and accurate three-dimensional (3D) simulation of a porcine liver deformation under a surgical tool pressure using the commercial finite element software Abaqus. The liver geometry is obtained using magnetic resonance imaging, and a nonlinear constitutive law is employed to capture large deformations of the tissue. Effects of implicit versus explicit analysis schemes, element type, and mesh density on computation time are studied. We find that Abaqus explicit and implicit solvers are capable of simulating nonlinear soft tissue deformations accurately using first-order tetrahedral elements in a relatively short time by optimizing the element size. This study provides new insights and guidance on accurate and relatively fast nonlinear soft tissue simulations. Such simulations can provide force feedback during robotic surgery and allow visualization of tissue deformations for surgery planning and training of surgical residents.

  13. Relaxivity of blood pool contrast agent depends on the host tissue as suggested by semianalytical simulations

    DEFF Research Database (Denmark)

    Jensen, Birgitte Fuglsang; Østergaard, Leif; Kiselev, Valerij G

    Concentration of MRI contrast agents (CA) is commonly determined indirectly using their relaxation effect. In quantitative perfusion studies, the change in the relaxation following a bolus passage is converted into concentrations assuming identical relaxivities for tissue and blood. Simulations...

  14. Simulation of electrochemical processes in cardiac tissue based on cellular automaton

    International Nuclear Information System (INIS)

    Avdeev, S A; Bogatov, N M

    2014-01-01

    A new class of cellular automata using special accumulative function for nonuniformity distribution is presented. Usage of this automata type for simulation of excitable media applied to electrochemical processes in human cardiac tissue is shown

  15. Biomechanics of cells and tissues experiments, models and simulations

    CERN Document Server

    2013-01-01

    The application of methodological approaches and mathematical formalisms proper to Physics and Engineering to investigate and describe biological processes and design biological structures has led to the development of many disciplines in the context of computational biology and biotechnology. The best known applicative domain is tissue engineering and its branches. Recent domains of interest are in the field of biophysics, e.g.: multiscale mechanics of biological membranes and films and filaments; multiscale mechanics of adhesion; biomolecular motors and force generation.   Modern hypotheses, models, and tools are currently emerging and resulting from the convergence of the methods and philosophical approaches of the different research areas and disciplines. All these emerging approaches share the purpose of disentangling the complexity of organisms, tissues, and cells and mimicking the function of living systems. The contributions presented in this book are current research highlights of six challenging an...

  16. Modeling and Characterization of Capacitive Elements With Tissue as Dielectric Material for Wireless Powering of Neural Implants.

    Science.gov (United States)

    Erfani, Reza; Marefat, Fatemeh; Sodagar, Amir M; Mohseni, Pedram

    2018-05-01

    This paper reports on the modeling and characterization of capacitive elements with tissue as the dielectric material, representing the core building block of a capacitive link for wireless power transfer to neural implants. Each capacitive element consists of two parallel plates that are aligned around the tissue layer and incorporate a grounded, guarded, capacitive pad to mitigate the adverse effect of stray capacitances and shield the plates from external interfering electric fields. The plates are also coated with a biocompatible, insulating, coating layer on the inner side of each plate in contact with the tissue. A comprehensive circuit model is presented that accounts for the effect of the coating layers and is validated by measurements of the equivalent capacitance as well as impedance magnitude/phase of the parallel plates over a wide frequency range of 1 kHz-10 MHz. Using insulating coating layers of Parylene-C at a thickness of and Parylene-N at a thickness of deposited on two sets of parallel plates with different sizes and shapes of the guarded pad, our modeling and characterization results accurately capture the effect of the thickness and electrical properties of the coating layers on the behavior of the capacitive elements over frequency and with different tissues.

  17. THERMODYNAMIC ANALYSIS AND SIMULATION OF A NEW COMBINED POWER AND REFRIGERATION CYCLE USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Hossein Rezvantalab

    2011-01-01

    Full Text Available In this study, a new combined power and refrigeration cycle is proposed, which combines the Rankine and absorption refrigeration cycles. Using a binary ammonia-water mixture as the working fluid, this combined cycle produces both power and refrigeration output simultaneously by employing only one external heat source. In order to achieve the highest possible exergy efficiency, a secondary turbine is inserted to expand the hot weak solution leaving the boiler. Moreover, an artificial neural network (ANN is used to simulate the thermodynamic properties and the relationship between the input thermodynamic variables on the cycle performance. It is shown that turbine inlet pressure, as well as heat source and refrigeration temperatures have significant effects on the net power output, refrigeration output and exergy efficiency of the combined cycle. In addition, the results of ANN are in excellent agreement with the mathematical simulation and cover a wider range for evaluation of cycle performance.

  18. Repetition-related reductions in neural activity reveal component processes of mental simulation.

    Science.gov (United States)

    Szpunar, Karl K; St Jacques, Peggy L; Robbins, Clifford A; Wig, Gagan S; Schacter, Daniel L

    2014-05-01

    In everyday life, people adaptively prepare for the future by simulating dynamic events about impending interactions with people, objects and locations. Previous research has consistently demonstrated that a distributed network of frontal-parietal-temporal brain regions supports this ubiquitous mental activity. Nonetheless, little is known about the manner in which specific regions of this network contribute to component features of future simulation. In two experiments, we used a functional magnetic resonance (fMR)-repetition suppression paradigm to demonstrate that distinct frontal-parietal-temporal regions are sensitive to processing the scenarios or what participants imagined was happening in an event (e.g., medial prefrontal, posterior cingulate, temporal-parietal and middle temporal cortices are sensitive to the scenarios associated with future social events), people (medial prefrontal cortex), objects (inferior frontal and premotor cortices) and locations (posterior cingulate/retrosplenial, parahippocampal and posterior parietal cortices) that typically constitute simulations of personal future events. This pattern of results demonstrates that the neural substrates of these component features of event simulations can be reliably identified in the context of a task that requires participants to simulate complex, everyday future experiences.

  19. Can surgical simulation be used to train detection and classification of neural networks?

    Science.gov (United States)

    Zisimopoulos, Odysseas; Flouty, Evangello; Stacey, Mark; Muscroft, Sam; Giataganas, Petros; Nehme, Jean; Chow, Andre; Stoyanov, Danail

    2017-10-01

    Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.

  20. Proteomic Profiling of Neuroblastoma Cells Adhesion on Hyaluronic Acid-Based Surface for Neural Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Ming-Hui Yang

    2016-01-01

    Full Text Available The microenvironment of neuron cells plays a crucial role in regulating neural development and regeneration. Hyaluronic acid (HA biomaterial has been applied in a wide range of medical and biological fields and plays important roles in neural regeneration. PC12 cells have been reported to be capable of endogenous NGF synthesis and secretion. The purpose of this research was to assess the effect of HA biomaterial combining with PC12 cells conditioned media (PC12 CM in neural regeneration. Using SH-SY5Y cells as an experimental model, we found that supporting with PC12 CM enhanced HA function in SH-SY5Y cell proliferation and adhesion. Through RP-nano-UPLC-ESI-MS/MS analyses, we identified increased expression of HSP60 and RanBP2 in SH-SY5Y cells grown on HA-modified surface with cotreatment of PC12 CM. Moreover, we also identified factors that were secreted from PC12 cells and may promote SH-SY5Y cell proliferation and adhesion. Here, we proposed a biomaterial surface enriched with neurotrophic factors for nerve regeneration application.

  1. Alteration of putative amino acid levels and morphological findings in neural tissues of methylmercury-intoxicated mice

    Energy Technology Data Exchange (ETDEWEB)

    Hirayama, K.; Inouye, M.; Fujisaki, T.

    1985-04-01

    Methylmercury chloride was administered PO to male Kud:ddY mice at a dose of 5 mg/kg/day for 20 days. The contents of taurine, aspartate, glutamate, glycine, and ..gamma..-aminobutyric acid were determined in tissue and crude synaptosomal (P/sub 2/) fraction of cerebellum, cerebral cortex, and spinal cord of methylmercury-treated mice with or without ataxia. In the cerebellum of ataxic mice, increased levels of taurine and glycine were found in the tissue and P/sub 2/ fraction, and increased levels of glutamate were found in the P/sub 2/ fraction. In the cerebral cortex, the levels of ..gamma..-aminobutylic acid decreased in the tissue and in the P/sub 2/ fraction of ataxic mice, but increased levels were found in the tissue of non-ataxic mice. A decreased asparate level in the cerebral cortex of ataxic mice and an increased taurine level in the cerebral cortex of non-ataxic mice were also found. In the spinal cord of ataxic mice, taurine increased in the tissue and in the P/sub 2/ fraction. Glutamate level decreased in the spinal cord of ataxic mice, but increased in the P/sub 2/ fraction of non-ataxic mice. Increased glycine levels in the P/sub 2/ fraction of the spinal cord were also found in non-axtaxic mice. Histologically, some degenerative changes were demonstrated in the cerebral and cerebellar cortices of ataxic mice. Such changes were also present to a mild degree in non-ataxic mice. In conclusion, methylmercury treatment altered the levels of putative neurotransmitter amino acids in neutral tissue of mice. These alterations might be caused by specific neural cell dysfunction and could be related to the appearance of ataxia.

  2. Absorbed radiation by various tissues during simulated endodontic radiography

    International Nuclear Information System (INIS)

    Torabinejad, M.; Danforth, R.; Andrews, K.; Chan, C.

    1989-01-01

    The amount of absorbed radiation by various organs was determined by placing lithium fluoride thermoluminescent chip dosimeters at selected anatomical sites in and on a human-like X-ray phantom and exposing them to radiation at 70- and 90-kV X-ray peaks during simulated endodontic radiography. The mean exposure dose was determined for each anatomical site. The results show that endodontic X-ray doses received by patients are low when compared with other radiographic procedures

  3. Reliability of in vivo measurements of the dielectric properties of anisotropic tissue: a simulative study

    International Nuclear Information System (INIS)

    Huo Xuyang; Shi Xuetao; You Fusheng; Fu Feng; Liu Ruigang; Tang Chi; Dong Xiuzhen; Lu Qiang

    2013-01-01

    A simulative study was performed to measure the dielectric properties of anisotropic tissue using several in vivo and in vitro probes. COMSOL Multiphysics was selected to carry out the simulation. Five traditional probes and a newly designed probe were used in this study. One of these probes was an in vitro measurement probe and the other five were in vivo. The simulations were performed in terms of the minimal tissue volume for in vivo measurements, the calibration of a probe constant, the measurement performed on isotropic tissue and the measurement performed on anisotropic tissue. Results showed that the in vitro probe can be used to measure the in-cell dielectric properties of isotropic and anisotropic tissues. When measured with the five in vivo probes, the dielectric properties of isotropic tissue were all measured accurately. For the measurements performed on anisotropic tissue, large errors were observed when the four traditional in vivo probes were used, but only a small error was observed when the new in vivo probe was used. This newly designed five-electrode in vivo probe may indicate the dielectric properties of anisotropic tissue more accurately than these four traditional in vivo probes. (paper)

  4. Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.

    Science.gov (United States)

    Shen, Lin; Yang, Weitao

    2018-03-13

    Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in a complex environment but also very time-consuming. The computational cost of QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive idea. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler [ Behler Int. J. Quantum Chem. 2015 , 115 , 1032 ; Behler Angew. Chem., Int. Ed. 2017 , 56 , 12828 ] was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of

  5. Input and output for surgical simulation: devices to measure tissue properties in vivo and a haptic interface for laparoscopy simulators.

    Science.gov (United States)

    Ottensmeyer, M P; Ben-Ur, E; Salisbury, J K

    2000-01-01

    Current efforts in surgical simulation very often focus on creating realistic graphical feedback, but neglect some or all tactile and force (haptic) feedback that a surgeon would normally receive. Simulations that do include haptic feedback do not typically use real tissue compliance properties, favoring estimates and user feedback to determine realism. When tissue compliance data are used, there are virtually no in vivo property measurements to draw upon. Together with the Center for Innovative Minimally Invasive Therapy at the Massachusetts General Hospital, the Haptics Group is developing tools to introduce more comprehensive haptic feedback in laparoscopy simulators and to provide biological tissue material property data for our software simulation. The platform for providing haptic feedback is a PHANToM Haptic Interface, produced by SensAble Technologies, Inc. Our devices supplement the PHANToM to provide for grasping and optionally, for the roll axis of the tool. Together with feedback from the PHANToM, which provides the pitch, yaw and thrust axes of a typical laparoscopy tool, we can recreate all of the haptic sensations experienced during laparoscopy. The devices integrate real laparoscopy toolhandles and a compliant torso model to complete the set of visual and tactile sensations. Biological tissues are known to exhibit non-linear mechanical properties, and change their properties dramatically when removed from a living organism. To measure the properties in vivo, two devices are being developed. The first is a small displacement, 1-D indenter. It will measure the linear tissue compliance (stiffness and damping) over a wide range of frequencies. These data will be used as inputs to a finite element or other model. The second device will be able to deflect tissues in 3-D over a larger range, so that the non-linearities due to changes in the tissue geometry will be measured. This will allow us to validate the performance of the model on large tissue

  6. Bee waxes: a model of characterization for using as base simulator tissue in teletherapy with photons

    International Nuclear Information System (INIS)

    Silva, Rogerio Matias Vidal da; Souza, Divanizia do Nascimento

    2011-01-01

    This paper presents a model of characterization and selection of bee waxes which makes possible to certify the usage viability of that base simulator tissue in the manufacture of appropriated objects for external radiotherapy with mega volt photon beams. The work was divide into three stages, where was evaluated physical and chemical properties besides the aspects related to the capacity of beam attenuation. All the process was carefully accompanied related to the wax origin such as the bee specimen and the flora surrounding the beehives. The chemical composition of the waxes is similar to others simulators usually used in radiotherapy. The behavior of mass attenuation coefficient in the radiotherapeutic energy range is comparable to other simulators, and consequently to the soft tissue. The proposed model is efficient and allows the affirmative that the usage of determined bee wax as base simulator tissue is convenient

  7. 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.

  8. 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.

  9. Polyploidization of glia in neural development links tissue growth to blood-brain barrier integrity.

    Science.gov (United States)

    Unhavaithaya, Yingdee; Orr-Weaver, Terry L

    2012-01-01

    Proper development requires coordination in growth of the cell types composing an organ. Many plant and animal cells are polyploid, but how these polyploid tissues contribute to organ growth is not well understood. We found the Drosophila melanogaster subperineurial glia (SPG) to be polyploid, and ploidy is coordinated with brain mass. Inhibition of SPG polyploidy caused rupture of the septate junctions necessary for the blood-brain barrier. Thus, the increased SPG cell size resulting from polyploidization is required to maintain the SPG envelope surrounding the growing brain. Polyploidization likely is a conserved strategy to coordinate tissue growth during organogenesis, with potential vertebrate examples.

  10. Outsourcing neural active control to passive composite mechanics: a tissue engineered cyborg ray

    Science.gov (United States)

    Gazzola, Mattia; Park, Sung Jin; Park, Kyung Soo; Park, Shirley; di Santo, Valentina; Deisseroth, Karl; Lauder, George V.; Mahadevan, L.; Parker, Kevin Kit

    2016-11-01

    Translating the blueprint that stingrays and skates provide, we create a cyborg swimming ray capable of orchestrating adaptive maneuvering and phototactic navigation. The impossibility of replicating the neural system of batoids fish is bypassed by outsourcing algorithmic functionalities to the body composite mechanics, hence casting the active control problem into a design, passive one. We present a first step in engineering multilevel "brain-body-flow" systems that couple sensory information to motor coordination and movement, leading to behavior. This work paves the way for the development of autonomous and adaptive artificial creatures able to process multiple sensory inputs and produce complex behaviors in distributed systems and may represent a path toward soft-robotic "embodied cognition".

  11. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

    Science.gov (United States)

    Liu, Fang; Zhou, Zhaoye; Jang, Hyungseok; Samsonov, Alexey; Zhao, Gengyan; Kijowski, Richard

    2018-04-01

    To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379-2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  12. A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

    International Nuclear Information System (INIS)

    Azadeh, A.; Ghaderi, S.F.; Sohrabkhani, S.

    2008-01-01

    This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures. First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The chosen model, therefore, can be compared to that of estimated by time series model. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The simulated-based ANN model is then developed. Therefore, there are four treatments to be considered in analysis of variance (ANOVA), which are actual data, time series, ANN and simulated-based ANN. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being statistically equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan method (DMRT) of paired comparison is used to select the optimum model which could be time series, ANN or simulated-based ANN. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted ANN model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to

  13. Raman Monte Carlo simulation for light propagation for tissue with embedded objects

    Science.gov (United States)

    Periyasamy, Vijitha; Jaafar, Humaira Bte; Pramanik, Manojit

    2018-02-01

    Monte Carlo (MC) stimulation is one of the prominent simulation technique and is rapidly becoming the model of choice to study light-tissue interaction. Monte Carlo simulation for light transport in multi-layered tissue (MCML) is adapted and modelled with different geometry by integrating embedded objects of various shapes (i.e., sphere, cylinder, cuboid and ellipsoid) into the multi-layered structure. These geometries would be useful in providing a realistic tissue structure such as modelling for lymph nodes, tumors, blood vessels, head and other simulation medium. MC simulations were performed on various geometric medium. Simulation of MCML with embedded object (MCML-EO) was improvised for propagation of the photon in the defined medium with Raman scattering. The location of Raman photon generation is recorded. Simulations were experimented on a modelled breast tissue with tumor (spherical and ellipsoidal) and blood vessels (cylindrical). Results were presented in both A-line and B-line scans for embedded objects to determine spatial location where Raman photons were generated. Studies were done for different Raman probabilities.

  14. Spatio-temporal regulation of ADAR editing during development in porcine neural tissues

    DEFF Research Database (Denmark)

    Venø, Morten Trillingsgaard; Bramsen, Jesper Bertram; Bendixen, Christian

    2012-01-01

    Editing by ADAR enzymes is essential for mammalian life. Still, knowledge of the spatio-temporal editing patterns in mammals is limited. By use of 454 amplicon sequencing we examined the editing status of 12 regionally extracted mRNAs from porcine developing brain encompassing a total of 64...... putative ADAR editing sites. In total 24 brain tissues, dissected from up to five regions from embryonic gestation day 23, 42, 60, 80, 100 and 115, were examined for editing....

  15. Cavitation Induced Structural and Neural Damage in Live Brain Tissue Slices: Relevance to TBI

    Science.gov (United States)

    2014-09-29

    objective of this project is to determine the conditions conducive for cavitation in cerebrospinal fluid (CSF) and corresponding tissue injury in 2-D brain...the radius of an isolated spherical bubble in an infinite, incompressible liquid is given by Where, R is the instantaneous bubble radius, which can...by the pressure transducer placed in the test chamber, and PR is the pressure in the liquid at the boundary of the bubble. The measurable bubble

  16. Validation of a power-law noise model for simulating small-scale breast tissue

    International Nuclear Information System (INIS)

    Reiser, I; Edwards, A; Nishikawa, R M

    2013-01-01

    We have validated a small-scale breast tissue model based on power-law noise. A set of 110 patient images served as truth. The statistical model parameters were determined by matching the radially averaged power-spectrum of the projected simulated tissue with that of the central tomosynthesis patient breast projections. Observer performance in a signal-known exactly detection task in simulated and actual breast backgrounds was compared. Observers included human readers, a pre-whitening observer model and a channelized Hotelling observer model. For all observers, good agreement between performance in the simulated and actual backgrounds was found, both in the tomosynthesis central projections and the reconstructed images. This tissue model can be used for breast x-ray imaging system optimization. The complete statistical description of the model is provided. (paper)

  17. Flank wears Simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling

    Science.gov (United States)

    Hazza, Muataz Hazza F. Al; Adesta, Erry Y. T.; Riza, Muhammad

    2013-12-01

    High speed milling has many advantages such as higher removal rate and high productivity. However, higher cutting speed increase the flank wear rate and thus reducing the cutting tool life. Therefore estimating and predicting the flank wear length in early stages reduces the risk of unaccepted tooling cost. This research presents a neural network model for predicting and simulating the flank wear in the CNC end milling process. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the flank wear length. Then the measured data have been used to train the developed neural network model. Artificial neural network (ANN) was applied to predict the flank wear length. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed flank wear which indicates the validity of the models.

  18. Flank wears Simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling

    International Nuclear Information System (INIS)

    Al Hazza, Muataz Hazza F; Adesta, Erry Y T; Riza, Muhammad

    2013-01-01

    High speed milling has many advantages such as higher removal rate and high productivity. However, higher cutting speed increase the flank wear rate and thus reducing the cutting tool life. Therefore estimating and predicting the flank wear length in early stages reduces the risk of unaccepted tooling cost. This research presents a neural network model for predicting and simulating the flank wear in the CNC end milling process. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the flank wear length. Then the measured data have been used to train the developed neural network model. Artificial neural network (ANN) was applied to predict the flank wear length. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed flank wear which indicates the validity of the models

  19. 3D printing of microtube in solid phantom to simulate tissue oxygenation and perfusion (Conference Presentation)

    Science.gov (United States)

    Lv, Xiang; Xue, Yue; Wang, Haili; Shen, Shu Wei; Zhou, Ximing; Liu, Guangli; Dong, Erbao; Xu, Ronald X.

    2017-03-01

    Tissue-simulating phantoms with interior vascular network may facilitate traceable calibration and quantitative validation of many medical optical devices. However, a solid phantom that reliably simulates tissue oxygenation and blood perfusion is still not available. This paper presents a new method to fabricate hollow microtubes for blood vessel simulation in solid phantoms. The fabrication process combines ultraviolet (UV) rapid prototyping technique with fluid mechanics of a coaxial jet flow. Polydimethylsiloxane (PDMS) and a UV-curable polymer are mixed at the designated ratio and extruded through a coaxial needle device to produce a coaxial jet flow. The extruded jet flow is quickly photo-polymerized by ultraviolet (UV) light to form vessel-simulating solid structures at different sizes ranging from 700 μm to 1000 μm. Microtube structures with adequate mechanical properties can be fabricated by adjusting material compositions and illumination intensity. Curved, straight and stretched microtubes can be formed by adjusting the extrusion speed of the materials and the speed of the 3D printing platform. To simulate vascular structures in biologic tissue, we embed vessel-simulating microtubes in a gel wax phantom of 10 cm x10 cm x 5 cm at the depth from 1 to 2 mm. Bloods at different oxygenation and hemoglobin concentration levels are circulated through the microtubes at different flow rates in order to simulate different oxygenation and perfusion conditions. The simulated physiologic parameters are detected by a tissue oximeter and a laser speckle blood flow meter respectively and compared with the actual values. Our experiments demonstrate that the proposed 3D printing process is able to produce solid phantoms with simulated vascular networks for potential applications in medical device calibration and drug delivery studies.

  20. 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.

  1. Expandable and Rapidly Differentiating Human Induced Neural Stem Cell Lines for Multiple Tissue Engineering Applications

    Directory of Open Access Journals (Sweden)

    Dana M. Cairns

    2016-09-01

    Full Text Available Limited availability of human neurons poses a significant barrier to progress in biological and preclinical studies of the human nervous system. Current stem cell-based approaches of neuron generation are still hindered by prolonged culture requirements, protocol complexity, and variability in neuronal differentiation. Here we establish stable human induced neural stem cell (hiNSC lines through the direct reprogramming of neonatal fibroblasts and adult adipose-derived stem cells. These hiNSCs can be passaged indefinitely and cryopreserved as colonies. Independently of media composition, hiNSCs robustly differentiate into TUJ1-positive neurons within 4 days, making them ideal for innervated co-cultures. In vivo, hiNSCs migrate, engraft, and contribute to both central and peripheral nervous systems. Lastly, we demonstrate utility of hiNSCs in a 3D human brain model. This method provides a valuable interdisciplinary tool that could be used to develop drug screening applications as well as patient-specific disease models related to disorders of innervation and the brain.

  2. [Research progress on real-time deformable models of soft tissues for surgery simulation].

    Science.gov (United States)

    Xu, Shaoping; Liu, Xiaoping; Zhang, Hua; Luo, Jie

    2010-04-01

    Biological tissues generally exhibit nonlinearity, anisotropy, quasi-incompressibility and viscoelasticity about material properties. Simulating the behaviour of elastic objects in real time is one of the current objectives of virtual surgery simulation which is still a challenge for researchers to accurately depict the behaviour of human tissues. In this paper, we present a classification of the different deformable models that have been developed. We present the advantages and disadvantages of each one. Finally, we make a comparison of deformable models and perform an evaluation of the state of the art and the future of deformable models.

  3. Hydrolytic Degradation and Mechanical Stability of Poly(ε-Caprolactone)/Reduced Graphene Oxide Membranes as Scaffolds for In Vitro Neural Tissue Regeneration.

    Science.gov (United States)

    Sánchez-González, Sandra; Diban, Nazely; Urtiaga, Ane

    2018-03-05

    The present work studies the functional behavior of novel poly(ε-caprolactone) (PCL) membranes functionalized with reduced graphene oxide (rGO) nanoplatelets under simulated in vitro culture conditions (phosphate buffer solution (PBS) at 37 °C) during 1 year, in order to elucidate their applicability as scaffolds for in vitro neural regeneration. The morphological, chemical, and DSC results demonstrated that high internal porosity of the membranes facilitated water permeation and procured an accelerated hydrolytic degradation throughout the bulk pathway. Therefore, similar molecular weight reduction, from 80 kDa to 33 kDa for the control PCL, and to 27 kDa for PCL/rGO membranes, at the end of the study, was observed. After 1 year of hydrolytic degradation, though monomers coming from the hydrolytic cleavage of PCL diffused towards the PBS medium, the pH was barely affected, and the rGO nanoplatelets mainly remained in the membranes which envisaged low cytotoxic effect. On the other hand, the presence of rGO nanomaterials accelerated the loss of mechanical stability of the membranes. However, it is envisioned that the gradual degradation of the PCL/rGO membranes could facilitate cells infiltration, interconnectivity, and tissue formation.

  4. Propagation based phase retrieval of simulated intensity measurements using artificial neural networks

    Science.gov (United States)

    Kemp, Z. D. C.

    2018-04-01

    Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.

  5. Pressure-induced phase transitions in silicon studied by neural network-based metadynamics simulations

    Energy Technology Data Exchange (ETDEWEB)

    Behler, Joerg [Department of Chemistry and Applied Biosciences, ETH Zurich, USI-Campus, Lugano (Switzerland); Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum, 44780 Bochum (Germany); Martonak, Roman [Department of Chemistry and Applied Biosciences, ETH Zurich, USI-Campus, Lugano (Switzerland); Department of Experimental Physics, Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynska dolina F2, 84248 Bratislava (Slovakia); Donadio, Davide [Department of Chemistry and Applied Biosciences, ETH Zurich, USI-Campus, Lugano (Switzerland); Department of Chemistry, UC Davis, One Shields Ave., Davis, CA 95616 (United States); Parrinello, Michele [Department of Chemistry and Applied Biosciences, ETH Zurich, USI-Campus, Lugano (Switzerland)

    2008-12-15

    We present a combination of the metadynamics method for the investigation of pressure-induced phase transitions in solids with a neural network representation of high-dimensional density-functional theory (DFT) potential-energy surfaces. In a recent illustration of the method for the complex high-pressure phase diagram of silicon[Behler et al., Phys. Rev. Lett. 100, 185501 (2008)] we have shown that the full sequence of phases can be reconstructed by a series of subsequent simulations. In the present paper we give a detailed account of the underlying methodology and discuss the scope and limitations of the approach, which promises to be a valuable tool for the investigation of a variety of inorganic materials. The method is several orders of magnitude faster than a direct coupling of metadynamics with electronic structure calculations, while the accuracy is essentially maintained, thus providing access to extended simulations of large systems. (copyright 2008 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  6. A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG.

    Science.gov (United States)

    Cona, F; Zavaglia, M; Massimini, M; Rosanova, M; Ursino, M

    2011-08-01

    Knowledge of cortical rhythms represents an important aspect of modern neuroscience, to understand how the brain realizes its functions. Recent data suggest that different regions in the brain may exhibit distinct electroencephalogram (EEG) rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) and that these rhythms can change due to the connectivity among regions. In this context, in silico simulations may help the validation of these hypotheses that would be difficult to be verified in vivo. Neural mass models can be very useful to simulate specific aspects of electrical brain activity and, above all, to analyze and identify the overall frequency content of EEG in a cortical region of interest (ROI). In this work we implemented a model of connectivity among cortical regions to fit the impulse responses in three ROIs recorded during a series of TMS/EEG experiments performed in five subjects and using three different impulse intensities. In particular we investigated Brodmann Area (BA) 19 (occipital lobe), BA 7 (parietal lobe) and BA 6 (frontal lobe). Results show that the model can reproduce the natural rhythms of the three regions quite well, acting on a few internal parameters. Moreover, the model can explain most rhythm changes induced by stimulation of another region, and inter-subject variability, by estimating just a few long-range connectivity parameters among ROIs. Copyright © 2011 Elsevier Inc. All rights reserved.

  7. Comparison of Artificial Neural Networks and ARIMA statistical models in simulations of target wind time series

    Science.gov (United States)

    Kolokythas, Kostantinos; Vasileios, Salamalikis; Athanassios, Argiriou; Kazantzidis, Andreas

    2015-04-01

    The wind is a result of complex interactions of numerous mechanisms taking place in small or large scales, so, the better knowledge of its behavior is essential in a variety of applications, especially in the field of power production coming from wind turbines. In the literature there is a considerable number of models, either physical or statistical ones, dealing with the problem of simulation and prediction of wind speed. Among others, Artificial Neural Networks (ANNs) are widely used for the purpose of wind forecasting and, in the great majority of cases, outperform other conventional statistical models. In this study, a number of ANNs with different architectures, which have been created and applied in a dataset of wind time series, are compared to Auto Regressive Integrated Moving Average (ARIMA) statistical models. The data consist of mean hourly wind speeds coming from a wind farm on a hilly Greek region and cover a period of one year (2013). The main goal is to evaluate the models ability to simulate successfully the wind speed at a significant point (target). Goodness-of-fit statistics are performed for the comparison of the different methods. In general, the ANN showed the best performance in the estimation of wind speed prevailing over the ARIMA models.

  8. Real-time simulation of the nonlinear visco-elastic deformations of soft tissues.

    Science.gov (United States)

    Basafa, Ehsan; Farahmand, Farzam

    2011-05-01

    Mass-spring-damper (MSD) models are often used for real-time surgery simulation due to their fast response and fairly realistic deformation replication. An improved real time simulation model of soft tissue deformation due to a laparoscopic surgical indenter was developed and tested. The mechanical realization of conventional MSD models was improved using nonlinear springs and nodal dampers, while their high computational efficiency was maintained using an adapted implicit integration algorithm. New practical algorithms for model parameter tuning, collision detection, and simulation were incorporated. The model was able to replicate complex biological soft tissue mechanical properties under large deformations, i.e., the nonlinear and viscoelastic behaviors. The simulated response of the model after tuning of its parameters to the experimental data of a deer liver sample, closely tracked the reference data with high correlation and maximum relative differences of less than 5 and 10%, for the tuning and testing data sets respectively. Finally, implementation of the proposed model and algorithms in a graphical environment resulted in a real-time simulation with update rates of 150 Hz for interactive deformation and haptic manipulation, and 30 Hz for visual rendering. The proposed real time simulation model of soft tissue deformation due to a laparoscopic surgical indenter was efficient, realistic, and accurate in ex vivo testing. This model is a suitable candidate for testing in vivo during laparoscopic surgery.

  9. Targeted neural differentiation of murine mesenchymal stem cells by a protocol simulating the inflammatory site of neural injury

    Czech Academy of Sciences Publication Activity Database

    Chudíčková, Milada; Brůža, M.; Zajícová, Alena; Trošan, Peter; Svobodová, Lucie; Javorková, Eliška; Kubinová, Šárka; Holáň, Vladimír

    2017-01-01

    Roč. 11, č. 5 (2017), s. 1588-1597 ISSN 1932-6254 R&D Projects: GA ČR(CZ) GAP304/11/0653; GA ČR(CZ) GA14-12580S; GA ČR(CZ) GAP301/11/1568; GA MŠk(CZ) ED1.1.00/02.0109; GA MŠk(CZ) LO1309 Institutional support: RVO:68378041 Keywords : mouse * adipose-derived mesenchymal stem cells * neural differentiation Subject RIV: EB - Genetics ; Molecular Biology OBOR OECD: Cell biology Impact factor: 3.989, year: 2016

  10. Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

    Science.gov (United States)

    Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

    2015-12-01

    Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

  11. Studies of stimulus parameters for seizure disruption using neural network simulations.

    Science.gov (United States)

    Anderson, William S; Kudela, Pawel; Cho, Jounhong; Bergey, Gregory K; Franaszczuk, Piotr J

    2007-08-01

    A large scale neural network simulation with realistic cortical architecture has been undertaken to investigate the effects of external electrical stimulation on the propagation and evolution of ongoing seizure activity. This is an effort to explore the parameter space of stimulation variables to uncover promising avenues of research for this therapeutic modality. The model consists of an approximately 800 mum x 800 mum region of simulated cortex, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. The cell dynamics are governed by a modified version of the Hodgkin-Huxley equations in single compartment format. Axonal connections are patterned after histological data and published models of local cortical wiring. Stimulation induced action potentials take place at the axon initial segments, according to threshold requirements on the applied electric field distribution. Stimulation induced action potentials in horizontal axonal branches are also separately simulated. The calculations are performed on a 16 node distributed 32-bit processor system. Clear differences in seizure evolution are presented for stimulated versus the undisturbed rhythmic activity. Data is provided for frequency dependent stimulation effects demonstrating a plateau effect of stimulation efficacy as the applied frequency is increased from 60 to 200 Hz. Timing of the stimulation with respect to the underlying rhythmic activity demonstrates a phase dependent sensitivity. Electrode height and position effects are also presented. Using a dipole stimulation electrode arrangement, clear orientation effects of the dipole with respect to the model connectivity is also demonstrated. A sensitivity analysis of these results as a function of the stimulation threshold is also provided.

  12. Elemental imbalance studies by INAA on extra neural tissues from amyotrophic lateral sclerosis patients

    International Nuclear Information System (INIS)

    Tandon, L.; Ehmann, W.D.

    1995-01-01

    Human kidney and liver tissues were studied for generalized elemental imbalances in amyotrophic lateral sclerosis (ALS) by instrumental neutron activation analysis (INAA). Iron was significantly increased (p<0.05) in ALS kidneys and Co and Fe (marginal, p<0.10) were increased in ALS liver compared with their respective controls. Mercury values were almost two-fold higher for ALS kidney and 17% higher for ALS liver as compared with their respective controls, However, the Hg data exhibited large variations and ALS-control differences were not significant. Data from the present study are discussed with reference to the role of metallothioneins (MT) in ALS, and a possible linkage between a free radical mediated mechanism and degeneration of cells in ALS is also explored. (author). 43 refs., 2 tabs

  13. Virtual Plant Tissue: Building Blocks for Next-Generation Plant Growth Simulation

    Directory of Open Access Journals (Sweden)

    Dirk De Vos

    2017-05-01

    Full Text Available Motivation: Computational modeling of plant developmental processes is becoming increasingly important. Cellular resolution plant tissue simulators have been developed, yet they are typically describing physiological processes in an isolated way, strongly delimited in space and time.Results: With plant systems biology moving toward an integrative perspective on development we have built the Virtual Plant Tissue (VPTissue package to couple functional modules or models in the same framework and across different frameworks. Multiple levels of model integration and coordination enable combining existing and new models from different sources, with diverse options in terms of input/output. Besides the core simulator the toolset also comprises a tissue editor for manipulating tissue geometry and cell, wall, and node attributes in an interactive manner. A parameter exploration tool is available to study parameter dependence of simulation results by distributing calculations over multiple systems.Availability: Virtual Plant Tissue is available as open source (EUPL license on Bitbucket (https://bitbucket.org/vptissue/vptissue. The project has a website https://vptissue.bitbucket.io.

  14. Spatial dose and microdose distribution in tissues. Ionization, nuclear reactions, multiple scattering simulation of beam transport

    International Nuclear Information System (INIS)

    Jacquot, C.

    1976-01-01

    Computer simulation and nuclear emulsion and gelatin techniques enabled to give the total elastic and inelastic cross sections and to forecast the spatial microdose distributions in cells, nuclei and molecules. For this purpose, the transport of a beam into tissues having a given composition is calculated, the nuclear reactions are generated and the energy depositions in standard planes perpendicular to the beam are recorded

  15. Tissue simulant response at projectile impact on flexible fabric armour systems

    NARCIS (Netherlands)

    Bree, J.L.M.J. van; Volker, A.; Heiden, N. van der

    2006-01-01

    Behind Armour Blunt Trauma is a phenomenon which has been studied extensively for rigid personal protective armour systems. These systems used in e.g. bullet proof vests manage to defeat high velocity small arms projectiles. Tissue simulants are used to study behind armour effects. At high velocity

  16. Numerical simulation of ultrasound-thermotherapy combining nonlinear wave propagation with broadband soft-tissue absorption.

    Science.gov (United States)

    Ginter, S

    2000-07-01

    Ultrasound (US) thermotherapy is used to treat tumours, located deep in human tissue, by heat. It features by the application of high intensity focused ultrasound (HIFU), high local temperatures of about 90 degrees C and short treating time of a few seconds. Dosage of the therapy remains a problem. To get it under control, one has to know the heat source, i.e. the amount of absorbed US power, which shows nonlinear influences. Therefore, accurate simulations are essential. In this paper, an improved simulation model is introduced which enables accurate investigations of US thermotherapy. It combines nonlinear US propagation effects, which lead to generation of higher harmonics, with a broadband frequency-power law absorption typical for soft tissue. Only the combination of both provides a reliable calculation of the generated heat. Simulations show the influence of nonlinearities and broadband damping for different source signals on the absorbed US power density distribution.

  17. Hybrid neural network model for simulating sorbitol synthesis by glucose-fructose oxidoreductase in Zymomonas mobilis CP4

    Directory of Open Access Journals (Sweden)

    Bravo S.

    2004-01-01

    Full Text Available A hybrid neural network model for simulating the process of enzymatic reduction of fructose to sorbitol process catalyzed by glucose-fructose oxidoreductase in Zymomonas mobilis CP4 is presented. Data used to derive and validate the model was obtained from experiments carried out under different conditions of pH, temperature and concentrations of both substrates (glucose and fructose involved in the reaction. Sonicated and lyophilized cells were used as source of the enzyme. The optimal pH for sorbitol synthesis at 30º C is 6.5. For a value of pH of 6, the optimal temperature is 35º C. The neural network in the model computes the value of the kinetic relationship. The hybrid neural network model is able to simulate changes in the substrates and product concentrations during sorbitol synthesis under pH and temperature conditions ranging between 5 and 7.5 and 25 and 40º C, respectively. Under these conditions the rate of sorbitol synthesis shows important differences. Values computed using the hybrid neural network model have an average error of 1.7·10-3 mole.

  18. Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks

    Science.gov (United States)

    Schmit, C. J.; Pritchard, J. R.

    2018-03-01

    Next generation radio experiments such as LOFAR, HERA, and SKA are expected to probe the Epoch of Reionization (EoR) and claim a first direct detection of the cosmic 21cm signal within the next decade. Data volumes will be enormous and can thus potentially revolutionize our understanding of the early Universe and galaxy formation. However, numerical modelling of the EoR can be prohibitively expensive for Bayesian parameter inference and how to optimally extract information from incoming data is currently unclear. Emulation techniques for fast model evaluations have recently been proposed as a way to bypass costly simulations. We consider the use of artificial neural networks as a blind emulation technique. We study the impact of training duration and training set size on the quality of the network prediction and the resulting best-fitting values of a parameter search. A direct comparison is drawn between our emulation technique and an equivalent analysis using 21CMMC. We find good predictive capabilities of our network using training sets of as low as 100 model evaluations, which is within the capabilities of fully numerical radiative transfer codes.

  19. Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator.

    Science.gov (United States)

    Drewes, Rich; Zou, Quan; Goodman, Philip H

    2009-01-01

    Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading "glue" tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS.

  20. Brainlab: a Python toolkit to aid in the design, simulation, and analysis of spiking neural networks with the NeoCortical Simulator

    Directory of Open Access Journals (Sweden)

    Richard P Drewes

    2009-05-01

    Full Text Available Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading ``glue'' tool for managing all sorts of complex programmatictasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS environment in particular. Brainlab is an integrated model building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS (the NeoCortical Simulator.

  1. Solid tissue simulating phantoms having absorption at 970 nm for diffuse optics

    Science.gov (United States)

    Kennedy, Gordon T.; Lentsch, Griffin R.; Trieu, Brandon; Ponticorvo, Adrien; Saager, Rolf B.; Durkin, Anthony J.

    2017-07-01

    Tissue simulating phantoms can provide a valuable platform for quantitative evaluation of the performance of diffuse optical devices. While solid phantoms have been developed for applications related to characterizing exogenous fluorescence and intrinsic chromophores such as hemoglobin and melanin, we report the development of a poly(dimethylsiloxane) (PDMS) tissue phantom that mimics the spectral characteristics of tissue water. We have developed these phantoms to mimic different water fractions in tissue, with the purpose of testing new devices within the context of clinical applications such as burn wound triage. Compared to liquid phantoms, cured PDMS phantoms are easier to transport and use and have a longer usable life than gelatin-based phantoms. As silicone is hydrophobic, 9606 dye was used to mimic the optical absorption feature of water in the vicinity of 970 nm. Scattering properties are determined by adding titanium dioxide, which yields a wavelength-dependent scattering coefficient similar to that observed in tissue in the near-infrared. Phantom properties were characterized and validated using the techniques of inverse adding-doubling and spatial frequency domain imaging. Results presented here demonstrate that we can fabricate solid phantoms that can be used to simulate different water fractions.

  2. Simulation on scattering features of biological tissue based on generated refractive-index model

    International Nuclear Information System (INIS)

    Wang Baoyong; Ding Zhihua

    2011-01-01

    Important information on morphology of biological tissue can be deduced from elastic scattering spectra, and their analyses are based on the known refractive-index model of tissue. In this paper, a new numerical refractive-index model is put forward, and its scattering properties are intensively studied. Spectral decomposition [1] is a widely used method to generate random medium in geology, but it is never used in biology. Biological tissue is different from geology in the sense of random medium. Autocorrelation function describe almost all of features in geology, but biological tissue is not as random as geology, its structure is regular in the sense of fractal geometry [2] , and fractal dimension can be used to describe its regularity under random. Firstly scattering theories of this fractal media are reviewed. Secondly the detailed generation process of refractive-index is presented. Finally the scattering features are simulated in FDTD (Finite Difference Time Domain) Solutions software. From the simulation results, we find that autocorrelation length and fractal dimension controls scattering feature of biological tissue.

  3. Laser treatment of female stress urinary incontinence: optical, thermal, and tissue damage simulations

    Science.gov (United States)

    Hardy, Luke A.; Chang, Chun-Hung; Myers, Erinn M.; Kennelly, Michael J.; Fried, Nathaniel M.

    2016-02-01

    Treatment of female stress urinary incontinence (SUI) by laser thermal remodeling of subsurface tissues is studied. Light transport, heat transfer, and thermal damage simulations were performed for transvaginal and transurethral methods. Monte Carlo (MC) provided absorbed photon distributions in tissue layers (vaginal wall, endopelvic fascia, urethral wall). Optical properties (n,μa,μs,g) were assigned to each tissue at λ=1064 nm. A 5-mm-diameter laser beam and power of 5 W for 15 s was used, based on previous experiments. MC output was converted into absorbed energy, serving as input for ANSYS finite element heat transfer simulations of tissue temperatures over time. Convective heat transfer was simulated with contact cooling probe set at 0 °C. Thermal properties (κ,c,ρ) were assigned to each tissue layer. MATLAB code was used for Arrhenius integral thermal damage calculations. A temperature matrix was constructed from ANSYS output, and finite sum was incorporated to approximate Arrhenius integral calculations. Tissue damage properties (Ea,A) were used to compute Arrhenius sums. For the transvaginal approach, 37% of energy was absorbed in endopelvic fascia layer with 0.8% deposited beyond it. Peak temperature was 71°C, treatment zone was 0.8-mm-diameter, and almost all of 2.7-mm-thick vaginal wall was preserved. For transurethral approach, 18% energy was absorbed in endopelvic fascia with 0.3% deposited beyond it. Peak temperature was 80°C, treatment zone was 2.0-mm-diameter, and only 0.6 mm of 2.4-mm-thick urethral wall was preserved. A transvaginal approach is more feasible than transurethral approach for laser treatment of SUI.

  4. Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

    Science.gov (United States)

    Buch, A. M.; Narain, A.; Pandey, P. C.

    1994-01-01

    The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

  5. Integration of soft tissue model and open haptic device for medical training simulator

    Science.gov (United States)

    Akasum, G. F.; Ramdhania, L. N.; Suprijanto; Widyotriatmo, A.

    2016-03-01

    Minimally Invasive Surgery (MIS) has been widely used to perform any surgical procedures nowadays. Currently, MIS has been applied in some cases in Indonesia. Needle insertion is one of simple MIS procedure that can be used for some purposes. Before the needle insertion technique used in the real situation, it essential to train this type of medical student skills. The research has developed an open platform of needle insertion simulator with haptic feedback that providing the medical student a realistic feel encountered during the actual procedures. There are three main steps in build the training simulator, which are configure hardware system, develop a program to create soft tissue model and the integration of hardware and software. For evaluating its performance, haptic simulator was tested by 24 volunteers on a scenario of soft tissue model. Each volunteer must insert the needle on simulator until rearch the target point with visual feedback that visualized on the monitor. From the result it can concluded that the soft tissue model can bring the sensation of touch through the perceived force feedback on haptic actuator by looking at the different force in accordance with different stiffness in each layer.

  6. Tissue distribution of enrofloxacin after intramammary or simulated systemic administration in isolated perfused sheep udders.

    Science.gov (United States)

    López Cadenas, Cristina; Fernández Martínez, Nélida; Sierra Vega, Matilde; Diez Liébana, Maria J; Gonzalo Orden, Jose M; Sahagún Prieto, Ana M; García Vieitez, Juan J

    2012-11-01

    To determine the tissue distribution of enrofloxacin after intramammary or simulated systemic administration in isolated perfused sheep udders by measuring its concentration at various sample collection sites. 26 udders (obtained following euthanasia) from 26 healthy lactating sheep. For each isolated udder, 1 mammary gland was perfused with warmed, gassed Tyrode solution. Enrofloxacin (1 g of enrofloxacin/5 g of ointment) was administered into the perfused gland via the intramammary route or systemically via the perfusion fluid (equivalent to a dose of 5 mg/kg). Samples of the perfusate were obtained every 30 minutes for 180 minutes; glandular tissue samples were obtained at 2, 4, 6, and 8 cm from the teat base after 180 minutes. The enrofloxacin content of the perfusate and tissue samples was analyzed via high-performance liquid chromatography with UV detection. After intramammary administration, maximun perfusate enrofloxacin concentration was detected at 180 minutes and, at this time, mean tissue enrofloxacin concentration was detected and mean tissue enrofloxacin concentration was 123.80, 54.48, 36.72, and 26.42 μg/g of tissue at 2, 4, 6, and 8 cm from the teat base, respectively. Following systemic administration, perfusate enrofloxacin concentration decreased with time and, at 180 minutes, tissue enrofloxacin concentrations ranged from 40.38 to 35.58 μg/g of tissue. By 180 minutes after administration via the intramammary or systemic route in isolated perfused sheep mammary glands, mean tissue concentration of enrofloxacin was greater than the minimum inhibitory concentration required to inhibit growth of 90% of many common mastitis pathogens in sheep. Use of either route of administration (or in combination) appears suitable for the treatment of acute mastitis in sheep.

  7. In-Vivo Characterization of Glassy Carbon Micro-Electrode Arrays for Neural Applications and Histological Analysis of the Brain Tissue

    Science.gov (United States)

    Vomero, Maria

    The aim of this work is to fabricate and characterize glassy carbon Microelectrode Arrays (MEAs) for sensing and stimulating neural activity, and conduct histological analysis of the brain tissue after the implant to determine long-term performance. Neural applications often require robust electrical and electrochemical response over a long period of time, and for those applications we propose to replace the commonly used noble metals like platinum, gold and iridium with glassy carbon. We submit that such material has the potential to improve the performances of traditional neural prostheses, thanks to better charge transfer capabilities and higher electrochemical stability. Great interest and attention is given in this work, in particular, to the investigation of tissue response after several weeks of implants in rodents' brain motor cortex and the associated materials degradation. As part of this work, a new set of devices for Electrocorticography (ECoG) has been designed and fabricated to improve durability and quality of the previous generation of devices, designed and manufactured by the same research group in 2014. In-vivo long-term impedance measurements and brain activity recordings were performed to test the functionality of the neural devices. In-vitro electrical characterization of the carbon electrodes, as well as the study of the adhesion mechanisms between glassy carbon and different substrates is also part of the research described in this book.

  8. A new ChainMail approach for real-time soft tissue simulation.

    Science.gov (United States)

    Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

    2016-07-03

    This paper presents a new ChainMail method for real-time soft tissue simulation. This method enables the use of different material properties for chain elements to accommodate various materials. Based on the ChainMail bounding region, a new time-saving scheme is developed to improve computational efficiency for isotropic materials. The proposed method also conserves volume and strain energy. Experimental results demonstrate that the proposed ChainMail method can not only accommodate isotropic, anisotropic and heterogeneous materials but also model incompressibility and relaxation behaviors of soft tissues. Further, the proposed method can achieve real-time computational performance.

  9. Face-based smoothed finite element method for real-time simulation of soft tissue

    Science.gov (United States)

    Mendizabal, Andrea; Bessard Duparc, Rémi; Bui, Huu Phuoc; Paulus, Christoph J.; Peterlik, Igor; Cotin, Stéphane

    2017-03-01

    In soft tissue surgery, a tumor and other anatomical structures are usually located using the preoperative CT or MR images. However, due to the deformation of the concerned tissues, this information suffers from inaccuracy when employed directly during the surgery. In order to account for these deformations in the planning process, the use of a bio-mechanical model of the tissues is needed. Such models are often designed using the finite element method (FEM), which is, however, computationally expensive, in particular when a high accuracy of the simulation is required. In our work, we propose to use a smoothed finite element method (S-FEM) in the context of modeling of the soft tissue deformation. This numerical technique has been introduced recently to overcome the overly stiff behavior of the standard FEM and to improve the solution accuracy and the convergence rate in solid mechanics problems. In this paper, a face-based smoothed finite element method (FS-FEM) using 4-node tetrahedral elements is presented. We show that in some cases, the method allows for reducing the number of degrees of freedom, while preserving the accuracy of the discretization. The method is evaluated on a simulation of a cantilever beam loaded at the free end and on a simulation of a 3D cube under traction and compression forces. Further, it is applied to the simulation of the brain shift and of the kidney's deformation. The results demonstrate that the method outperforms the standard FEM in a bending scenario and that has similar accuracy as the standard FEM in the simulations of the brain-shift and of the kidney's deformation.

  10. A novel culture method reveals unique neural stem/progenitors in mature porcine iris tissues that differentiate into neuronal and rod photoreceptor-like cells.

    Science.gov (United States)

    Royall, Lars N; Lea, Daniel; Matsushita, Tamami; Takeda, Taka-Aki; Taketani, Shigeru; Araki, Masasuke

    2017-11-15

    Iris neural stem/progenitor cells from mature porcine eyes were investigated using a new protocol for tissue culture, which consists of dispase treatment and Matrigel embedding. We used a number of culture conditions and found an intense differentiation of neuronal cells from both the iris pigmented epithelial (IPE) cells and the stroma tissue cells. Rod photoreceptor-like cells were also observed but mostly in a later stage of culture. Neuronal differentiation does not require any additives such as fetal bovine serum or FGF2, although FGF2 and IGF2 appeared to promote neural differentiation in the IPE cultures. Furthermore, the stroma-derived cells were able to be maintained in vitro indefinitely. The evolutionary similarity between humans and domestic pigs highlight the potential for this methodology in the modeling of human diseases and characterizing human ocular stem cells. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Simulation of lung motions using an artificial neural network; Utilisation d'un reseau de neurones artificiels pour la simulation des mouvements pulmonaires

    Energy Technology Data Exchange (ETDEWEB)

    Laurent, R.; Henriet, J.; Sauget, M.; Gschwind, R.; Makovicka, L. [IRMA/ENISYS/FEMTO-ST, UMR 6174 CNRS, pole universitaire des Portes du Jura, BP 71427, 25211 Montbeliard cedex (France); Salomon, M. [AND/LIFC, universite de Franche-Comte, BP 527, rue Engel-Gros, 90016 Belfort cedex (France); Nguyen, F. [Service de radiotherapie, CHU Jean-Minjoz, 3, boulevard Fleming, 25030 Besancon cedex (France)

    2011-04-15

    Purpose. A way to improve the accuracy of lung radiotherapy for a patient is to get a better understanding of its lung motion. Indeed, thanks to this knowledge it becomes possible to follow the displacements of the clinical target volume (CTV) induced by the lung breathing. This paper presents a feasibility study of an original method to simulate the positions of points in patient's lung at all breathing phases. Patients and methods. This method, based on an artificial neural network, allowed learning the lung motion on real cases and then to simulate it for new patients for which only the beginning and the end breathing data are known. The neural network learning set is made up of more than 600 points. These points, shared out on three patients and gathered on a specific lung area, were plotted by a MD. Results. - The first results are promising: an average accuracy of 1 mm is obtained for a spatial resolution of 1 x 1 x 2.5 mm{sup 3}. Conclusion. We have demonstrated that it is possible to simulate lung motion with accuracy using an artificial neural network. As future work we plan to improve the accuracy of our method with the addition of new patient data and a coverage of the whole lungs. (authors)

  12. Effectiveness of a Treatment Involving Soft Tissue Techniques and/or Neural Mobilization Techniques in the Management of Tension-Type Headache: A Randomized Controlled Trial.

    Science.gov (United States)

    Ferragut-Garcías, Alejandro; Plaza-Manzano, Gustavo; Rodríguez-Blanco, Cleofás; Velasco-Roldán, Olga; Pecos-Martín, Daniel; Oliva-Pascual-Vaca, Jesús; Llabrés-Bennasar, Bartomeu; Oliva-Pascual-Vaca, Ángel

    2017-02-01

    To evaluate the effects of a protocol involving soft tissue techniques and/or neural mobilization techniques in the management of patients with frequent episodic tension-type headache (FETTH) and those with chronic tension-type headache (CTTH). Randomized, double-blind, placebo-controlled before and after trial. Rehabilitation area of the local hospital and a private physiotherapy center. Patients (N=97; 78 women, 19 men) diagnosed with FETTH or CTTH were randomly assigned to groups A, B, C, or D. (A) Placebo superficial massage; (B) soft tissue techniques; (C) neural mobilization techniques; (D) a combination of soft tissue and neural mobilization techniques. The pressure pain threshold (PPT) in the temporal muscles (points 1 and 2) and supraorbital region (point 3), the frequency and maximal intensity of pain crisis, and the score in the Headache Impact Test-6 (HIT-6) were evaluated. All variables were assessed before the intervention, at the end of the intervention, and 15 and 30 days after the intervention. Groups B, C, and D had an increase in PPT and a reduction in frequency, maximal intensity, and HIT-6 values in all time points after the intervention as compared with baseline and group A (P<.001 for all cases). Group D had the highest PPT values and the lowest frequency and HIT-6 values after the intervention. The application of soft tissue and neural mobilization techniques to patients with FETTH or CTTH induces significant changes in PPT, the characteristics of pain crisis, and its effect on activities of daily living as compared with the application of these techniques as isolated interventions. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  13. Histological characterization and quantification of cellular events following neural and fibroblast(-like) stem cell grafting in healty and demyelinated CNS tissue

    OpenAIRE

    Praet, J.; SANTERMANS, Eva; Reekmans, K.; de Vocht, N.; Le Blon, D.; Hoornaert, C.; Daans, J.; Goossens, H.; Berneman, Z.; HENS, Niel; Van der Linden, A.; Ponsaerts, P.

    2014-01-01

    Preclinical animal studies involving intracerebral (stem) cell grafting are gaining popularity in many laboratories due to the reported beneficial effects of cell grafting on various diseases or traumata of the central nervous system (CNS). In this chapter, we describe a histological workflow to characterize and quantify cellular events following neural and fibroblast(-like) stem cell grafting in healthy and demyelinated CNS tissue. First, we provide standardized protocols to isolate and cult...

  14. Teaching Students to Model Neural Circuits and Neural Networks Using an Electronic Spreadsheet Simulator. Microcomputing Working Paper Series.

    Science.gov (United States)

    Hewett, Thomas T.

    There are a number of areas in psychology where an electronic spreadsheet simulator can be used to study and explore functional relationships among a number of parameters. For example, when dealing with sensation, perception, and pattern recognition, it is sometimes desirable for students to understand both the basic neurophysiology and the…

  15. Unfolding neutron spectra from simulated response of thermoluminescence dosimeters inside a polyethylene sphere using GRNN neural network

    Science.gov (United States)

    Lotfalizadeh, F.; Faghihi, R.; Bahadorzadeh, B.; Sina, S.

    2017-07-01

    Neutron spectrometry using a single-sphere containing dosimeters has been developed recently, as an effective replacement for Bonner sphere spectrometry. The aim of this study is unfolding the neutron energy spectra using GRNN artificial neural network, from the response of thermoluminescence dosimeters, TLDs, located inside a polyethylene sphere. The spectrometer was simulated using MCNP5. TLD-600 and TLD-700 dosimeters were simulated at different positions in all directions. Then the GRNN was used for neutron spectra prediction, using the TLDs' readings. Comparison of spectra predicted by the network with the real spectra, show that the single-sphere dosimeter is an effective instrument in unfolding neutron spectra.

  16. Real-time simulation of contact and cutting of heterogeneous soft-tissues.

    Science.gov (United States)

    Courtecuisse, Hadrien; Allard, Jérémie; Kerfriden, Pierre; Bordas, Stéphane P A; Cotin, Stéphane; Duriez, Christian

    2014-02-01

    This paper presents a numerical method for interactive (real-time) simulations, which considerably improves the accuracy of the response of heterogeneous soft-tissue models undergoing contact, cutting and other topological changes. We provide an integrated methodology able to deal both with the ill-conditioning issues associated with material heterogeneities, contact boundary conditions which are one of the main sources of inaccuracies, and cutting which is one of the most challenging issues in interactive simulations. Our approach is based on an implicit time integration of a non-linear finite element model. To enable real-time computations, we propose a new preconditioning technique, based on an asynchronous update at low frequency. The preconditioner is not only used to improve the computation of the deformation of the tissues, but also to simulate the contact response of homogeneous and heterogeneous bodies with the same accuracy. We also address the problem of cutting the heterogeneous structures and propose a method to update the preconditioner according to the topological modifications. Finally, we apply our approach to three challenging demonstrators: (i) a simulation of cataract surgery (ii) a simulation of laparoscopic hepatectomy (iii) a brain tumor surgery. Copyright © 2013 Elsevier B.V. All rights reserved.

  17. FABRICATION OF TISSUE-SIMULATIVE PHANTOMS AND CAPILLARIES AND THEIR INVESTIGATION BY OPTICAL COHERENCE TOMOGRAPHY TECHNIQUES

    Directory of Open Access Journals (Sweden)

    A. V. Bykov

    2013-03-01

    Full Text Available Methods of tissue-simulative phantoms and capillaries fabrication from PVC-plastisol and silicone for application as test-objects in optical coherence tomography (OCT and skin and capillary emulation are considered. Comparison characteristics of these materials and recommendations for their application are given. Examples of phantoms visualization by optical coherence tomography method are given. Possibility of information using from B-scans for refractive index evaluation is shown.

  18. Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Shih, Tzu-Ching [Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, 40402, Taiwan (China); Chen, Jeon-Hor; Nie Ke; Lin Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying [Tu and Yuen Center for Functional Onco-Imaging and Radiological Sciences, University of California, Irvine, CA 92697 (United States); Liu Dongxu; Sun Lizhi, E-mail: shih@mail.cmu.edu.t [Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697 (United States)

    2010-07-21

    This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo (registered) 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc (registered) software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under

  19. Multi-model ensemble hydrological simulation using a BP Neural Network for the upper Yalongjiang River Basin, China

    Science.gov (United States)

    Li, Zhanjie; Yu, Jingshan; Xu, Xinyi; Sun, Wenchao; Pang, Bo; Yue, Jiajia

    2018-06-01

    Hydrological models are important and effective tools for detecting complex hydrological processes. Different models have different strengths when capturing the various aspects of hydrological processes. Relying on a single model usually leads to simulation uncertainties. Ensemble approaches, based on multi-model hydrological simulations, can improve application performance over single models. In this study, the upper Yalongjiang River Basin was selected for a case study. Three commonly used hydrological models (SWAT, VIC, and BTOPMC) were selected and used for independent simulations with the same input and initial values. Then, the BP neural network method was employed to combine the results from the three models. The results show that the accuracy of BP ensemble simulation is better than that of the single models.

  20. Temperature distribution analysis of tissue water vaporization during microwave ablation: experiments and simulations.

    Science.gov (United States)

    Ai, Haiming; Wu, Shuicai; Gao, Hongjian; Zhao, Lei; Yang, Chunlan; Zeng, Yi

    2012-01-01

    The temperature distribution in the region near a microwave antenna is a critical factor that affects the entire temperature field during microwave ablation of tissue. It is challenging to predict this distribution precisely, because the temperature in the near-antenna region varies greatly. The effects of water vaporisation and subsequent tissue carbonisation in an ex vivo porcine liver were therefore studied experimentally and in simulations. The enthalpy and high-temperature specific absorption rate (SAR) of liver tissues were calculated and incorporated into the simulation process. The accuracy of predictions for near-field temperatures in our simulations has reached the level where the average maximum error is less than 5°C. In addition, a modified thermal model that accounts for water vaporisation and the change in the SAR distribution pattern is proposed and validated with experiment. The results from this study may be useful in the clinical practice of microwave ablation and can be applied to predict the temperature field in surgical planning.

  1. Tissue

    Directory of Open Access Journals (Sweden)

    David Morrissey

    2012-01-01

    Full Text Available Purpose. In vivo gene therapy directed at tissues of mesenchymal origin could potentially augment healing. We aimed to assess the duration and magnitude of transene expression in vivo in mice and ex vivo in human tissues. Methods. Using bioluminescence imaging, plasmid and adenoviral vector-based transgene expression in murine quadriceps in vivo was examined. Temporal control was assessed using a doxycycline-inducible system. An ex vivo model was developed and optimised using murine tissue, and applied in ex vivo human tissue. Results. In vivo plasmid-based transgene expression did not silence in murine muscle, unlike in liver. Although maximum luciferase expression was higher in muscle with adenoviral delivery compared with plasmid, expression reduced over time. The inducible promoter cassette successfully regulated gene expression with maximum levels a factor of 11 greater than baseline. Expression was re-induced to a similar level on a temporal basis. Luciferase expression was readily detected ex vivo in human muscle and tendon. Conclusions. Plasmid constructs resulted in long-term in vivo gene expression in skeletal muscle, in a controllable fashion utilising an inducible promoter in combination with oral agents. Successful plasmid gene transfection in human ex vivo mesenchymal tissue was demonstrated for the first time.

  2. Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole

    2011-01-01

    It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is ab...... to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude....

  3. Optimization of tissue physical parameters for accurate temperature estimation from finite-element simulation of radiofrequency ablation

    International Nuclear Information System (INIS)

    Subramanian, Swetha; Mast, T Douglas

    2015-01-01

    Computational finite element models are commonly used for the simulation of radiofrequency ablation (RFA) treatments. However, the accuracy of these simulations is limited by the lack of precise knowledge of tissue parameters. In this technical note, an inverse solver based on the unscented Kalman filter (UKF) is proposed to optimize values for specific heat, thermal conductivity, and electrical conductivity resulting in accurately simulated temperature elevations. A total of 15 RFA treatments were performed on ex vivo bovine liver tissue. For each RFA treatment, 15 finite-element simulations were performed using a set of deterministically chosen tissue parameters to estimate the mean and variance of the resulting tissue ablation. The UKF was implemented as an inverse solver to recover the specific heat, thermal conductivity, and electrical conductivity corresponding to the measured area of the ablated tissue region, as determined from gross tissue histology. These tissue parameters were then employed in the finite element model to simulate the position- and time-dependent tissue temperature. Results show good agreement between simulated and measured temperature. (note)

  4. Optimization of tissue physical parameters for accurate temperature estimation from finite-element simulation of radiofrequency ablation.

    Science.gov (United States)

    Subramanian, Swetha; Mast, T Douglas

    2015-10-07

    Computational finite element models are commonly used for the simulation of radiofrequency ablation (RFA) treatments. However, the accuracy of these simulations is limited by the lack of precise knowledge of tissue parameters. In this technical note, an inverse solver based on the unscented Kalman filter (UKF) is proposed to optimize values for specific heat, thermal conductivity, and electrical conductivity resulting in accurately simulated temperature elevations. A total of 15 RFA treatments were performed on ex vivo bovine liver tissue. For each RFA treatment, 15 finite-element simulations were performed using a set of deterministically chosen tissue parameters to estimate the mean and variance of the resulting tissue ablation. The UKF was implemented as an inverse solver to recover the specific heat, thermal conductivity, and electrical conductivity corresponding to the measured area of the ablated tissue region, as determined from gross tissue histology. These tissue parameters were then employed in the finite element model to simulate the position- and time-dependent tissue temperature. Results show good agreement between simulated and measured temperature.

  5. Acquiring molecular interference functions of X-ray coherent scattering for breast tissues by combination of simulation and experimental methods

    International Nuclear Information System (INIS)

    Chaparian, A.; Oghabian, M. A.; Changizi, V.

    2009-01-01

    Recently, it has been indicated that X-ray coherent scatter from biological tissues can be used to access signature of tissue. Some scientists are interested in studying this effect to get early detection of breast cancer. Since experimental methods for optimization are time consuming and expensive, some scientists suggest using simulation. Monte Carlo codes are the best option for radiation simulation: however, one permanent defect with Monte Carlo codes has been the lack of a sufficient physical model for coherent (Rayleigh) scattering, including molecular interference effects. Materials and Methods: It was decided to obtain molecular interference functions of coherent X-ray scattering for normal breast tissues by combination of modeling and experimental methods. A Monte Carlo simulation program was written to simulate the angular distribution of scattered photons for the normal breast tissue samples. Moreover, experimental diffraction patterns of these tissues were measured by means of energy dispersive X-ray diffraction method. The simulation and experimental data were used to obtain a tabulation of molecular interference functions for breast tissues. Results: With this study a tabulation of molecular interference functions for normal breast tissues Was prepared to facilitate the simulation diffraction patterns of the tissues without any experimental. Conclusion: The method may lead to design new systems for early detection of breast cancer.

  6. Electrospun Collagen/Silk Tissue Engineering Scaffolds: Fiber Fabrication, Post-Treatment Optimization, and Application in Neural Differentiation of Stem Cells

    Science.gov (United States)

    Zhu, Bofan

    Biocompatible scaffolds mimicking the locally aligned fibrous structure of native extracellular matrix (ECM) are in high demand in tissue engineering. In this thesis research, unidirectionally aligned fibers were generated via a home-built electrospinning system. Collagen type I, as a major ECM component, was chosen in this study due to its support of cell proliferation and promotion of neuroectodermal commitment in stem cell differentiation. Synthetic dragline silk proteins, as biopolymers with remarkable tensile strength and superior elasticity, were also used as a model material. Good alignment, controllable fiber size and morphology, as well as a desirable deposition density of fibers were achieved via the optimization of solution and electrospinning parameters. The incorporation of silk proteins into collagen was found to significantly enhance mechanical properties and stability of electrospun fibers. Glutaraldehyde (GA) vapor post-treatment was demonstrated as a simple and effective way to tune the properties of collagen/silk fibers without changing their chemical composition. With 6-12 hours GA treatment, electrospun collagen/silk fibers were not only biocompatible, but could also effectively induce the polarization and neural commitment of stem cells, which were optimized on collagen rich fibers due to the unique combination of biochemical and biophysical cues imposed to cells. Taken together, electrospun collagen rich composite fibers are mechanically strong, stable and provide excellent cell adhesion. The unidirectionally aligned fibers can accelerate neural differentiation of stem cells, representing a promising therapy for neural tissue degenerative diseases and nerve injuries.

  7. Finite-element simulation of blood perfusion in muscle tissue during compression and sustained contraction.

    Science.gov (United States)

    Vankan, W J; Huyghe, J M; Slaaf, D W; van Donkelaar, C C; Drost, M R; Janssen, J D; Huson, A

    1997-09-01

    Mechanical interaction between tissue stress and blood perfusion in skeletal muscles plays an important role in blood flow impediment during sustained contraction. The exact mechanism of this interaction is not clear, and experimental investigation of this mechanism is difficult. We developed a finite-element model of the mechanical behavior of blood-perfused muscle tissue, which accounts for mechanical blood-tissue interaction in maximally vasodilated vasculature. Verification of the model was performed by comparing finite-element results of blood pressure and flow with experimental measurements in a muscle that is subject to well-controlled mechanical loading conditions. In addition, we performed simulations of blood perfusion during tetanic, isometric contraction and maximal vasodilation in a simplified, two-dimensional finite-element model of a rat calf muscle. A vascular waterfall in the venous compartment was identified as the main cause for blood flow impediment both in the experiment and in the finite-element simulations. The validated finite-element model offers possibilities for detailed analysis of blood perfusion in three-dimensional muscle models under complicated loading conditions.

  8. 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.

  9. Investigations of the metabolism of NAD in embryonic neural tissue of mice after irradiation with X-rays

    International Nuclear Information System (INIS)

    Beuningen, M. van.

    1974-01-01

    Female mice of an institutes own inbred strain were killed on the 9th-13th day of pregnancy and the embryos were removed by caesarian section. The NAD content and protein content in the embryonic neural tissue of the mice increase the most from the 10th to 11th day. There is a relationship between NAD quantity and increase in size measured by the protein content. The enzymal activity of the NMN pyrophosphorylase runs parallel to the NAD rise and fall except for on the 11th day on which the enzyme increases further. The NAD biosynthesis from nicotinamide measured by the incorporation of 14-C nicotinamide in the NAD rises from the 10th to the 13th day. If one refers the incorporation to the protein content, however, the NAD synthesis falls from the 10th day onwards. An increase of the NAD content in the embryonic brain by the addition of nicotinamide in a high dose was not possible on the 10th and 12th day, whereas a clear increase was registered in the mother animal liver. Following an X-radiation with 200 R on the 9th day, the NAD content/brain dropped on the 11th day to its lowest point and had reached its normal value again on the 13th day, contrary to the protein content which only decreases on the 11th day. If one refers the NAD content, however, to protein quantity, then this only falls on the 10th day and rises on the 11th day almost to the normal value and has reached the latter by the 12th day. The NMN pyrophosphorylase activity falls on the 10th and 11th day, has its normal value on the 12th day and exceeds it on the 13th day. If one refers the enzyme activity to protein content, then it drops on the 10th day, reaches its lowest value on the 11th day, has its normal value on the 12th day and shoots above it on the 13th day. On the 10th day, the NAD content falls only after an X-ray with 200 R given on the 9th day, whereas the protein content remains constant. The NAD content does not change in the region of 50 to 150 R. (orig./LH) [de

  10. Connective-Tissue Growth Factor (CTGF/CCN2 Induces Astrogenesis and Fibronectin Expression of Embryonic Neural Cells In Vitro.

    Directory of Open Access Journals (Sweden)

    Fabio A Mendes

    Full Text Available Connective-tissue growth factor (CTGF is a modular secreted protein implicated in multiple cellular events such as chondrogenesis, skeletogenesis, angiogenesis and wound healing. CTGF contains four different structural modules. This modular organization is characteristic of members of the CCN family. The acronym was derived from the first three members discovered, cysteine-rich 61 (CYR61, CTGF and nephroblastoma overexpressed (NOV. CTGF is implicated as a mediator of important cell processes such as adhesion, migration, proliferation and differentiation. Extensive data have shown that CTGF interacts particularly with the TGFβ, WNT and MAPK signaling pathways. The capacity of CTGF to interact with different growth factors lends it an important role during early and late development, especially in the anterior region of the embryo. ctgf knockout mice have several cranio-facial defects, and the skeletal system is also greatly affected due to an impairment of the vascular-system development during chondrogenesis. This study, for the first time, indicated that CTGF is a potent inductor of gliogenesis during development. Our results showed that in vitro addition of recombinant CTGF protein to an embryonic mouse neural precursor cell culture increased the number of GFAP- and GFAP/Nestin-positive cells. Surprisingly, CTGF also increased the number of Sox2-positive cells. Moreover, this induction seemed not to involve cell proliferation. In addition, exogenous CTGF activated p44/42 but not p38 or JNK MAPK signaling, and increased the expression and deposition of the fibronectin extracellular matrix protein. Finally, CTGF was also able to induce GFAP as well as Nestin expression in a human malignant glioma stem cell line, suggesting a possible role in the differentiation process of gliomas. These results implicate ctgf as a key gene for astrogenesis during development, and suggest that its mechanism may involve activation of p44/42 MAPK signaling

  11. A novel bioreactor to simulate urinary bladder mechanical properties and compliance for bladder functional tissue engineering.

    Science.gov (United States)

    Wei, Xin; Li, Dao-bing; Xu, Feng; Wang, Yan; Zhu, Yu-chun; Li, Hong; Wang, Kun-jie

    2011-02-01

    Bioreactors are pivotal tools for generating mechanical stimulation in functional tissue engineering study. This study aimed to create a bioreactor that can simulate urinary bladder mechanical properties, and to investigate the effects of a mechanically stimulated culture on urothelial cells and bladder smooth muscle cells. We designed a bioreactor to simulate the mechanical properties of bladder. A pressure-record system was used to evaluate the mechanical properties of the bioreactor by measuring the pressure in culture chambers. To test the biocompatibility of the bioreactor, viabilities of urothelial cells and smooth muscle cells cultured in the bioreactor under static and mechanically changed conditions were measured after 7-day culture. To evaluate the effect of mechanical stimulations on the vital cells, urethral cells and smooth muscle cells were cultured in the simulated mechanical conditions. After that, the viability and the distribution pattern of the cells were observed and compared with cells cultured in non-mechanical stimulated condition. The bioreactor system successfully generated waveforms similar to the intended programmed model while maintaining a cell-seeded elastic membrane between the chambers. There were no differences between viabilities of urothelial cells ((91.90 ± 1.22)% vs. (93.14 ± 1.78)%, P > 0.05) and bladder smooth muscle cells ((93.41 ± 1.49)% vs. (92.61 ± 1.34)%, P > 0.05). The viability of cells and tissue structure observation after cultured in simulated condition showed that mechanical stimulation was the only factor affected cells in the bioreactor and improved the arrangement of cells on silastic membrane. This bioreactor can effectively simulate the physiological and mechanical properties of the bladder. Mechanical stimulation is the only factor that affected the viability of cells cultured in the bioreactor. The bioreactor can change the growth behavior of urothelial cells and bladder smooth muscle cells, resulting in

  12. Dynamic skin deformation simulation using musculoskeletal model and soft tissue dynamics

    Institute of Scientific and Technical Information of China (English)

    Akihiko Murai; Q. Youn Hong; Katsu Yamane; Jessica K. Hodgins

    2017-01-01

    Deformation of skin and muscle is essential for bringing an animated character to life. This deformation is difficult to animate in a realistic fashion using traditional techniques because of the subtlety of the skin deformations that must move appropriately for the character design. In this paper, we present an algorithm that generates natural, dynamic, and detailed skin deformation (movement and jiggle) from joint angle data sequences. The algorithm has two steps: identification of parameters for a quasi-static muscle deformation model, and simulation of skin deformation. In the identification step, we identify the model parameters using a musculoskeletal model and a short sequence of skin deformation data captured via a dense marker set. The simulation step first uses the quasi-static muscle deformation model to obtain the quasi-static muscle shape at each frame of the given motion sequence (slow jump). Dynamic skin deformation is then computed by simulating the passive muscle and soft tissue dynamics modeled as a mass–spring–damper system. Having obtained the model parameters, we can simulate dynamic skin deformations for subjects with similar body types from new motion data. We demonstrate our method by creating skin deformations for muscle co-contraction and external impacts from four different behaviors captured as skeletal motion capture data. Experimental results show that the simulated skin deformations are quantitatively and qualitatively similar to measured actual skin deformations.

  13. Dynamic skin deformation simulation using musculoskeletal model and soft tissue dynamics

    Institute of Scientific and Technical Information of China (English)

    Akihiko Murai; Q.Youn Hong; Katsu Yamane; Jessica K.Hodgins

    2017-01-01

    Deformation of skin and muscle is essential for bringing an animated character to life. This deformation is difficult to animate in a realistic fashion using traditional techniques because of the subtlety of the skin deformations that must move appropriately for the character design. In this paper, we present an algorithm that generates natural, dynamic, and detailed skin deformation(movement and jiggle) from joint angle data sequences. The algorithm has two steps: identification of parameters for a quasi-static muscle deformation model, and simulation of skin deformation. In the identification step, we identify the model parameters using a musculoskeletal model and a short sequence of skin deformation data captured via a dense marker set. The simulation step first uses the quasi-static muscle deformation model to obtain the quasi-static muscle shape at each frame of the given motion sequence(slow jump). Dynamic skin deformation is then computed by simulating the passive muscle and soft tissue dynamics modeled as a mass–spring–damper system. Having obtained the model parameters, we can simulate dynamic skin deformations for subjects with similar body types from new motion data. We demonstrate our method by creating skin deformations for muscle co-contraction and external impacts from four different behaviors captured as skeletal motion capture data. Experimental results show that the simulated skin deformations are quantitatively and qualitatively similar to measured actual skin deformations.

  14. Unilateral hypertrophy of tensor fascia lata: a soft tissue tumor simulator

    Energy Technology Data Exchange (ETDEWEB)

    Ilaslan, H. [Department of Radiology, Mayo Clinic, 200 First Street, 55905, SW Rochester, MN (United States); Department of Radiology A21, Cleveland Clinic, Cleveland, OH (United States); Wenger, D.E. [Department of Radiology, Mayo Clinic, 200 First Street, 55905, SW Rochester, MN (United States); Shives, T.C. [Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (United States); Unni, K.K. [Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN (United States)

    2003-11-01

    To describe the imaging findings in eight cases of unilateral tensor fascia lata (TFL) hypertrophy presenting as soft tissue masses. Imaging studies and medical charts of eight patients were reviewed retrospectively. The imaging studies included five radiographs, five computed tomography (CT) and six magnetic resonance imaging (MRI) examinations. The majority of patients (seven of eight) presented with a palpable proximal anterior thigh mass. One patient was asymptomatic and incidentally diagnosed. There were six females and two males. Ages ranged from 27 to 86 years old (mean 61). MRI and CT showed unilateral enlargement of the TFL muscle in all cases. TFL muscle hypertrophy is an uncommon clinical entity, which can simulate a soft tissue tumor. The characteristic appearance on CT or MRI allows a confident diagnosis of muscle hypertrophy to be made, avoiding unnecessary biopsy or surgical intervention. (orig.)

  15. Unilateral hypertrophy of tensor fascia lata: a soft tissue tumor simulator

    International Nuclear Information System (INIS)

    Ilaslan, H.; Wenger, D.E.; Shives, T.C.; Unni, K.K.

    2003-01-01

    To describe the imaging findings in eight cases of unilateral tensor fascia lata (TFL) hypertrophy presenting as soft tissue masses. Imaging studies and medical charts of eight patients were reviewed retrospectively. The imaging studies included five radiographs, five computed tomography (CT) and six magnetic resonance imaging (MRI) examinations. The majority of patients (seven of eight) presented with a palpable proximal anterior thigh mass. One patient was asymptomatic and incidentally diagnosed. There were six females and two males. Ages ranged from 27 to 86 years old (mean 61). MRI and CT showed unilateral enlargement of the TFL muscle in all cases. TFL muscle hypertrophy is an uncommon clinical entity, which can simulate a soft tissue tumor. The characteristic appearance on CT or MRI allows a confident diagnosis of muscle hypertrophy to be made, avoiding unnecessary biopsy or surgical intervention. (orig.)

  16. Soft tissue deformation for surgical simulation: a position-based dynamics approach.

    Science.gov (United States)

    Camara, Mafalda; Mayer, Erik; Darzi, Ara; Pratt, Philip

    2016-06-01

    To assist the rehearsal and planning of robot-assisted partial nephrectomy, a real-time simulation platform is presented that allows surgeons to visualise and interact with rapidly constructed patient-specific biomechanical models of the anatomical regions of interest. Coupled to a framework for volumetric deformation, the platform furthermore simulates intracorporeal 2D ultrasound image acquisition, using preoperative imaging as the data source. This not only facilitates the planning of optimal transducer trajectories and viewpoints, but can also act as a validation context for manually operated freehand 3D acquisitions and reconstructions. The simulation platform was implemented within the GPU-accelerated NVIDIA FleX position-based dynamics framework. In order to validate the model and determine material properties and other simulation parameter values, a porcine kidney with embedded fiducial beads was CT-scanned and segmented. Acquisitions for the rest position and three different levels of probe-induced deformation were collected. Optimal values of the cluster stiffness coefficients were determined for a range of different particle radii, where the objective function comprised the mean distance error between real and simulated fiducial positions over the sequence of deformations. The mean fiducial error at each deformation stage was found to be compatible with the level of ultrasound probe calibration error typically observed in clinical practice. Furthermore, the simulation exhibited unconditional stability on account of its use of clustered shape-matching constraints. A novel position-based dynamics implementation of soft tissue deformation has been shown to facilitate several desirable simulation characteristics: real-time performance, unconditional stability, rapid model construction enabling patient-specific behaviour and accuracy with respect to reference CT images.

  17. Simulation studies of optimum energies for DXA: dependence on tissue type, patient size and dose model

    International Nuclear Information System (INIS)

    Michael, G. J.; Henderson, C. J.

    1999-01-01

    Dual-energy x-ray absorptiometry (DXA) is a well established technique for measuring bone mineral density (BMD). However, in recent years DXA is increasingly being used to measure body composition in terms of fat and fat-free mass. DXA scanners must also determine the soft tissue baseline value from soft-tissue-only regions adjacent to bone. The aim of this work is to determine, using computer simulations, the optimum x- ray energies for a number of dose models, different tissues, i.e. bone mineral, average soft tissue, lean soft tissue and fat; and a range of anatomical sites and patient sizes. Three models for patient dose were evaluated total beam energy, entrance exposure and absorbed dose calculated by Monte Carlo modelling. A range of tissue compositions and thicknesses were chosen to cover typical patient variations for the three sites femoral neck, PA spine and lateral spine. In this work, the optimisation of the energies is based on (1) the uncertainty that arises from the quantum statistical nature of the number of x-rays recorded by the detector, and (2) the radiation dose received by the patient. This study has deliberately not considered other parameters such as detector response, electronic noise, x-ray tube heat load etc, because these are technology dependent parameters, not ones that are inherent to the measuring technique. Optimisation of the energies is achieved by minimisation of the product of variance of density measurement and dose which is independent of the absolute intensities of the x-ray beams. The results obtained indicate that if solving for bone density, then E-low in the range 34 to 42 keV, E-high in the range 100 to 200 keV and incident intensity ratio (low energy/high energy) in the range 3 to 10 is a reasonable compromise for the normal range of patient sizes. The choice of energies is complicated by the fact that the DXA unit must also solve for fat and lean soft tissue in soft- tissue-only regions adjacent to the bone. In this

  18. Monte Carlo simulations support non-Cerenkov radioluminescence production in tissue

    Science.gov (United States)

    Ackerman, Nicole L.; Boschi, Federico; Spinelli, Antonello E.

    2017-08-01

    There is experimental evidence for the production of non-Cerenkov radioluminescence in a variety of materials, including tissue. We constructed a Geant4 Monte Carlo simulation of the radiation from P32 and Tc99m interacting in chicken breast and used experimental imaging data to model a scintillation-like emission. The same radioluminescence spectrum is visible from both isotopes and cannot otherwise be explained through fluorescence or filter miscalibration. We conclude that chicken breast has a near-infrared scintillation-like response with a light yield three orders of magnitude smaller than BGO.

  19. Neural Networks: Implementations and Applications

    OpenAIRE

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  20. Impact of simulated microgravity and caloric restriction on autonomic nervous system function in adipose tissue

    Science.gov (United States)

    Boschmann, Michael; Adams, Frauke; Tank, Jens; Schaller, Karin; Boese, Andrea; Heer, Martina; Klause, Susanne; Luft, Friedrich C.; Jordan, Jens

    2005-08-01

    Long term immobilization and reduced food intake is often associated with development of orthostatic intolerance. Blocking the norepinephrine transporter (NET) can also mimic symptoms of orthostatic intolerance. Therefore, we hypothesized that simulated microgravity (14 days bed rest at head down tilt, BR) can cause changes in postganglionic NET function and adrenoreceptor (AR) sensitivity and these changes can be aggravated by hypocaloric food intake. For testing, two microdialysis probes were inserted into subcutaneous adipose tissue of eight young healthy men at day 1 and 14 of BR and perfused with Ringer's solution and increasing doses of tyramine and isoproterenol in order to simulate NET blockade and stimulate AR, respectively. At day 14 of eucaloric diet and BR, isoproterenol induced lipolysis was greater, whereas at day 14 of hypocaloric diet and BR, tyramine induced lipolysis was greater when compared to day 1. Therefore, the nutritional state affects NET function and AR sensitivity differently during BR.

  1. High-fidelity meshes from tissue samples for diffusion MRI simulations.

    Science.gov (United States)

    Panagiotaki, Eleftheria; Hall, Matt G; Zhang, Hui; Siow, Bernard; Lythgoe, Mark F; Alexander, Daniel C

    2010-01-01

    This paper presents a method for constructing detailed geometric models of tissue microstructure for synthesizing realistic diffusion MRI data. We construct three-dimensional mesh models from confocal microscopy image stacks using the marching cubes algorithm. Random-walk simulations within the resulting meshes provide synthetic diffusion MRI measurements. Experiments optimise simulation parameters and complexity of the meshes to achieve accuracy and reproducibility while minimizing computation time. Finally we assess the quality of the synthesized data from the mesh models by comparison with scanner data as well as synthetic data from simple geometric models and simplified meshes that vary only in two dimensions. The results support the extra complexity of the three-dimensional mesh compared to simpler models although sensitivity to the mesh resolution is quite robust.

  2. A New Hybrid Viscoelastic Soft Tissue Model based on Meshless Method for Haptic Surgical Simulation

    Science.gov (United States)

    Bao, Yidong; Wu, Dongmei; Yan, Zhiyuan; Du, Zhijiang

    2013-01-01

    This paper proposes a hybrid soft tissue model that consists of a multilayer structure and many spheres for surgical simulation system based on meshless. To improve accuracy of the model, tension is added to the three-parameter viscoelastic structure that connects the two spheres. By using haptic device, the three-parameter viscoelastic model (TPM) produces accurate deformationand also has better stress-strain, stress relaxation and creep properties. Stress relaxation and creep formulas have been obtained by mathematical formula derivation. Comparing with the experimental results of the real pig liver which were reported by Evren et al. and Amy et al., the curve lines of stress-strain, stress relaxation and creep of TPM are close to the experimental data of the real liver. Simulated results show that TPM has better real-time, stability and accuracy. PMID:24339837

  3. Simulation of sensory integration dysfunction in autism with dynamic neural fields model

    NARCIS (Netherlands)

    Chonnaparamutt, W.; Barakova, E.I.; Rutkowski, L.; Taseusiewicz, R.

    2008-01-01

    This paper applies dynamic neural fields model [1,23,7] to multimodal interaction of sensory cues obtained from a mobile robot, and shows the impact of different temporal aspects of the integration to the precision of movements. We speculate that temporally uncoordinated sensory integration might be

  4. Comparison of Neural Network Error Measures for Simulation of Slender Marine Structures

    DEFF Research Database (Denmark)

    Christiansen, Niels H.; Voie, Per Erlend Torbergsen; Winther, Ole

    2014-01-01

    Training of an artificial neural network (ANN) adjusts the internal weights of the network in order to minimize a predefined error measure. This error measure is given by an error function. Several different error functions are suggested in the literature. However, the far most common measure...

  5. Predicting the topology of dynamic neural networks for the simulation of electronic circuits

    NARCIS (Netherlands)

    Schilders, W.H.A.

    2009-01-01

    In this paper we discuss the use of the state-space modelling MOESP algorithm to generate precise information about the number of neurons and hidden layers in dynamic neural networks developed for the behavioural modelling of electronic circuits. The Bartels–Stewart algorithm is used to transform

  6. Optimization of neural networks for time-domain simulation of mooring lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Winther, Ole

    2016-01-01

    , they also can be used to rank the importance of the various network inputs. The dynamic response of slender marine structures often depends on several external load components, and by applying the optimization procedures to a trained artificial neural network, it is possible to classify the external force...

  7. Demonstration of Self-Training Autonomous Neural Networks in Space Vehicle Docking Simulations

    Science.gov (United States)

    Patrick, M. Clinton; Thaler, Stephen L.; Stevenson-Chavis, Katherine

    2006-01-01

    Neural Networks have been under examination for decades in many areas of research, with varying degrees of success and acceptance. Key goals of computer learning, rapid problem solution, and automatic adaptation have been elusive at best. This paper summarizes efforts at NASA's Marshall Space Flight Center harnessing such technology to autonomous space vehicle docking for the purpose of evaluating applicability to future missions.

  8. A review: Functional near infrared spectroscopy evaluation in muscle tissues using Monte Carlo simulation

    Science.gov (United States)

    Halim, A. A. A.; Laili, M. H.; Salikin, M. S.; Rusop, M.

    2018-05-01

    Monte Carlo Simulation has advanced their quantification based on number of the photon counting to solve the propagation of light inside the tissues including the absorption, scattering coefficient and act as preliminary study for functional near infrared application. The goal of this paper is to identify the optical properties using Monte Carlo simulation for non-invasive functional near infrared spectroscopy (fNIRS) evaluation of penetration depth in human muscle. This paper will describe the NIRS principle and the basis for its proposed used in Monte Carlo simulation which focused on several important parameters include ATP, ADP and relate with blow flow and oxygen content at certain exercise intensity. This will cover the advantages and limitation of such application upon this simulation. This result may help us to prove that our human muscle is transparent to this near infrared region and could deliver a lot of information regarding to the oxygenation level in human muscle. Thus, this might be useful for non-invasive technique for detecting oxygen status in muscle from living people either athletes or working people and allowing a lots of investigation muscle physiology in future.

  9. Multiaxial mechanical properties and constitutive modeling of human adipose tissue: a basis for preoperative simulations in plastic and reconstructive surgery.

    Science.gov (United States)

    Sommer, Gerhard; Eder, Maximilian; Kovacs, Laszlo; Pathak, Heramb; Bonitz, Lars; Mueller, Christoph; Regitnig, Peter; Holzapfel, Gerhard A

    2013-11-01

    A preoperative simulation of soft tissue deformations during plastic and reconstructive surgery is desirable to support the surgeon's planning and to improve surgical outcomes. The current development of constitutive adipose tissue models, for the implementation in multilayer computational frameworks for the simulation of human soft tissue deformations, has proved difficult because knowledge of the required mechanical parameters of fat tissue is limited. Therefore, for the first time, human abdominal adipose tissues were mechanically investigated by biaxial tensile and triaxial shear tests. The results of this study suggest that human abdominal adipose tissues under quasi-static and dynamic multiaxial loadings can be characterized as a nonlinear, anisotropic and viscoelastic soft biological material. The nonlinear and anisotropic features are consequences of the material's collagenous microstructure. The aligned collagenous septa observed in histological investigations causes the anisotropy of the tissue. A hyperelastic model used in this study was appropriate to represent the quasi-static multiaxial mechanical behavior of fat tissue. The constitutive parameters are intended to serve as a basis for soft tissue simulations using the finite element method, which is an apparent method for obtaining promising results in the field of plastic and reconstructive surgery. Copyright © 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

  10. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  11. Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation.

    Science.gov (United States)

    Buyukada, Musa

    2016-09-01

    Co-combustion of coal and peanut hull (PH) were investigated using artificial neural networks (ANN), particle swarm optimization, and Monte Carlo simulation as a function of blend ratio, heating rate, and temperature. The best prediction was reached by ANN61 multi-layer perception model with a R(2) of 0.99994. Blend ratio of 90 to 10 (PH to coal, wt%), temperature of 305°C, and heating rate of 49°Cmin(-1) were determined as the optimum input values and yield of 87.4% was obtained under PSO optimized conditions. The validation experiments resulted in yields of 87.5%±0.2 after three replications. Monte Carlo simulations were used for the probabilistic assessments of stochastic variability and uncertainty associated with explanatory variables of co-combustion process. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Tissue-Specific Methylation of Long Interspersed Nucleotide Element-1 of Homo Sapiens (L1Hs) During Human Embryogenesis and Roles in Neural Tube Defects.

    Science.gov (United States)

    Wang, L; Chang, S; Guan, J; Shangguan, S; Lu, X; Wang, Z; Wu, L; Zou, J; Zhao, H; Bao, Y; Qiu, Z; Niu, B; Zhang, T

    2015-01-01

    Epigenetic regulation of long interspersed nucleotide element-1 (LINE-1) retrotransposition events plays crucial roles during early development. Previously we showed that LINE-1 hypomethylation in neuronal tissues is associated with pathogenesis of neural tube defect (NTD). Herein, we further evaluated LINE-1 Homo sapiens (L1Hs) methylation in tissues derived from three germ layers of stillborn NTD fetuses, to define patterns of tissue specific methylation and site-specific hypomethylation at CpG sites within an L1Hs promoter region. Stable, tissue-specific L1Hs methylation patterns throughout three germ layer lineages of the fetus, placenta, and maternal peripheral blood were observed. Samples from maternal peripheral blood exhibited the highest level of L1Hs methylation (64.95%) and that from placenta showed the lowest (26.82%). Between samples from NTDs and controls, decrease in L1Hs methylation was only significant in NTD-affected brain tissue at 7.35%, especially in females (8.98%). L1Hs hypomethylation in NTDs was also associated with a significant increase in expression level of an L1Hs-encoded transcript in females (r = -0.846, p = 0.004). This could be due to genomic DNA instability and alternation in chromatins accessibility resulted from abnormal L1Hs hypomethylation, as showed in this study with HCT-15 cells treated with methylation inhibitor 5-Aza.

  13. Multiple POU-binding motifs, recognized by tissue-specific nuclear factors, are important for Dll1 gene expression in neural stem cells

    International Nuclear Information System (INIS)

    Nakayama, Kohzo; Nagase, Kazuko; Tokutake, Yuriko; Koh, Chang-Sung; Hiratochi, Masahiro; Ohkawara, Takeshi; Nakayama, Noriko

    2004-01-01

    We cloned the 5'-flanking region of the mouse homolog of the Delta gene (Dll1) and demonstrated that the sequence between nucleotide position -514 and -484 in the 5'-flanking region of Dll1 played a critical role in the regulation of its tissue-specific expression in neural stem cells (NSCs). Further, we showed that multiple POU-binding motifs, located within this short sequence of 30 bp, were essential for transcriptional activation of Dll1 and also that multiple tissue-specific nuclear factors recognized these POU-binding motifs in various combinations through differentiation of NSCs. Thus, POU-binding factors may play an important role in Dll1 expression in developing NSCs

  14. Modeling and simulation of permanent magnet synchronous motor based on neural network control strategy

    Science.gov (United States)

    Luo, Bingyang; Chi, Shangjie; Fang, Man; Li, Mengchao

    2017-03-01

    Permanent magnet synchronous motor is used widely in industry, the performance requirements wouldn't be met by adopting traditional PID control in some of the occasions with high requirements. In this paper, a hybrid control strategy - nonlinear neural network PID and traditional PID parallel control are adopted. The high stability and reliability of traditional PID was combined with the strong adaptive ability and robustness of neural network. The permanent magnet synchronous motor will get better control performance when switch different working modes according to different controlled object conditions. As the results showed, the speed response adopting the composite control strategy in this paper was faster than the single control strategy. And in the case of sudden disturbance, the recovery time adopting the composite control strategy designed in this paper was shorter, the recovery ability and the robustness were stronger.

  15. Simulating the dynamics of the neutron flux in a nuclear reactor by locally recurrent neural networks

    International Nuclear Information System (INIS)

    Cadini, F.; Zio, E.; Pedroni, N.

    2007-01-01

    In this paper, a locally recurrent neural network (LRNN) is employed for approximating the temporal evolution of a nonlinear dynamic system model of a simplified nuclear reactor. To this aim, an infinite impulse response multi-layer perceptron (IIR-MLP) is trained according to a recursive back-propagation (RBP) algorithm. The network nodes contain internal feedback paths and their connections are realized by means of IIR synaptic filters, which provide the LRNN with the necessary system state memory

  16. A neural-symbolic system for automated assessment in training simulators - A position paper

    NARCIS (Netherlands)

    Penning, H.L.H. de; Kappé, B.; Bosch, K. van den

    2009-01-01

    Performance assessment in training simulators is a complex task. It requires monitoring and interpreting the student’s behaviour in the simulator using knowledge of the training task, the environment and a lot of experience. Assessment in simulators is therefore generally done by human observers. To

  17. A virtual surgical training system that simulates cutting of soft tissue using a modified pre-computed elastic model.

    Science.gov (United States)

    Toe, Kyaw Kyar; Huang, Weimin; Yang, Tao; Duan, Yuping; Zhou, Jiayin; Su, Yi; Teo, Soo-Kng; Kumar, Selvaraj Senthil; Lim, Calvin Chi-Wan; Chui, Chee Kong; Chang, Stephen

    2015-08-01

    This work presents a surgical training system that incorporates cutting operation of soft tissue simulated based on a modified pre-computed linear elastic model in the Simulation Open Framework Architecture (SOFA) environment. A precomputed linear elastic model used for the simulation of soft tissue deformation involves computing the compliance matrix a priori based on the topological information of the mesh. While this process may require a few minutes to several hours, based on the number of vertices in the mesh, it needs only to be computed once and allows real-time computation of the subsequent soft tissue deformation. However, as the compliance matrix is based on the initial topology of the mesh, it does not allow any topological changes during simulation, such as cutting or tearing of the mesh. This work proposes a way to modify the pre-computed data by correcting the topological connectivity in the compliance matrix, without re-computing the compliance matrix which is computationally expensive.

  18. Equilibrium point control of a monkey arm simulator by a fast learning tree structured artificial neural network.

    Science.gov (United States)

    Dornay, M; Sanger, T D

    1993-01-01

    A planar 17 muscle model of the monkey's arm based on realistic biomechanical measurements was simulated on a Symbolics Lisp Machine. The simulator implements the equilibrium point hypothesis for the control of arm movements. Given initial and final desired positions, it generates a minimum-jerk desired trajectory of the hand and uses the backdriving algorithm to determine an appropriate sequence of motor commands to the muscles (Flash 1987; Mussa-Ivaldi et al. 1991; Dornay 1991b). These motor commands specify a temporal sequence of stable (attractive) equilibrium positions which lead to the desired hand movement. A strong disadvantage of the simulator is that it has no memory of previous computations. Determining the desired trajectory using the minimum-jerk model is instantaneous, but the laborious backdriving algorithm is slow, and can take up to one hour for some trajectories. The complexity of the required computations makes it a poor model for biological motor control. We propose a computationally simpler and more biologically plausible method for control which achieves the benefits of the backdriving algorithm. A fast learning, tree-structured network (Sanger 1991c) was trained to remember the knowledge obtained by the backdriving algorithm. The neural network learned the nonlinear mapping from a 2-dimensional cartesian planar hand position (x,y) to a 17-dimensional motor command space (u1, . . ., u17). Learning 20 training trajectories, each composed of 26 sample points [[x,y], [u1, . . ., u17] took only 20 min on a Sun-4 Sparc workstation. After the learning stage, new, untrained test trajectories as well as the original trajectories of the hand were given to the neural network as input. The network calculated the required motor commands for these movements. The resulting movements were close to the desired ones for both the training and test cases.

  19. MRI Brain Images Healthy and Pathological Tissues Classification with the Aid of Improved Particle Swarm Optimization and Neural Network

    Science.gov (United States)

    Sheejakumari, V.; Sankara Gomathi, B.

    2015-01-01

    The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods. PMID:25977706

  20. An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: a simulation study.

    Science.gov (United States)

    Padhi, Radhakant; Bhardhwaj, Jayender R

    2009-06-01

    An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

  1. Real-time simulation of biological soft tissues: a PGD approach.

    Science.gov (United States)

    Niroomandi, S; González, D; Alfaro, I; Bordeu, F; Leygue, A; Cueto, E; Chinesta, F

    2013-05-01

    We introduce here a novel approach for the numerical simulation of nonlinear, hyperelastic soft tissues at kilohertz feedback rates necessary for haptic rendering. This approach is based upon the use of proper generalized decomposition techniques, a generalization of PODs. Proper generalized decomposition techniques can be considered as a means of a priori model order reduction and provides a physics-based meta-model without the need for prior computer experiments. The suggested strategy is thus composed of an offline phase, in which a general meta-model is computed, and an online evaluation phase in which the results are obtained at real time. Results are provided that show the potential of the proposed technique, together with some benchmark test that shows the accuracy of the method. Copyright © 2013 John Wiley & Sons, Ltd.

  2. EIT forward problem parallel simulation environment with anisotropic tissue and realistic electrode models.

    Science.gov (United States)

    De Marco, Tommaso; Ries, Florian; Guermandi, Marco; Guerrieri, Roberto

    2012-05-01

    Electrical impedance tomography (EIT) is an imaging technology based on impedance measurements. To retrieve meaningful insights from these measurements, EIT relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of current flows therein. The nonhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeoff between physical accuracy and technical feasibility, which at present severely limits the capabilities of EIT. This work presents a complete algorithmic flow for an accurate EIT modeling environment featuring high anatomical fidelity with a spatial resolution equal to that provided by an MRI and a novel realistic complete electrode model implementation. At the same time, we demonstrate that current graphics processing unit (GPU)-based platforms provide enough computational power that a domain discretized with five million voxels can be numerically modeled in about 30 s.

  3. How to measure propagation velocity in cardiac tissue: a simulation study

    Directory of Open Access Journals (Sweden)

    Andre C. Linnenbank

    2014-07-01

    Full Text Available To estimate conduction velocities from activation times in myocardial tissue, the average vector method computes all the local activation directions and velocities from local activation times and estimates the fastest and slowest propagation speed from these local values. The single vector method uses areas of apparent uniform elliptical spread of activation and chooses a single vector for the estimated longitudinal velocity and one for the transversal. A simulation study was performed to estimate the influence of grid size, anisotropy, and vector angle bin size. The results indicate that the average vector method can best be used if the grid- or bin-size is large, although systematic errors occur. The single vector method performs better, but requires human intervention for the definition of fiber direction. The average vector method can be automated.

  4. Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis

    Directory of Open Access Journals (Sweden)

    Oliveira-Esquerre K.P.

    2002-01-01

    Full Text Available This work presents a way to predict the biochemical oxygen demand (BOD of the output stream of the biological wastewater treatment plant at RIPASA S/A Celulose e Papel, one of the major pulp and paper plants in Brazil. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA before they are fed to a backpropagated neural network. The influence of input variables is analyzed and satisfactory prediction results are obtained for an optimized situation.

  5. Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network.

    Science.gov (United States)

    Wang, Yearnchee Curtis; Chan, Terence Chee-Hung; Sahakian, Alan Varteres

    2018-01-04

    Radiofrequency ablation (RFA), a method of inducing thermal ablation (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the RFA device. This study aims to determine if a neural network (NN) can estimate ablation lesion depth for control of bipolar RFA using complex electrical impedance - since tissue electrical conductivity varies as a function of tissue temperature - in real time using only the RFA therapy device's electrodes. Three-dimensional, cubic models comprised of beef liver, pork loin or pork belly represented target tissue. Temperature and complex electrical impedance from 72 data generation ablations in pork loin and belly were used for training the NN (403 s on Xeon processor). NN inputs were inquiry depth, starting complex impedance and current complex impedance. Training-validation-test splits were 70%-0%-30% and 80%-10%-10% (overfit test). Once the NN-estimated lesion depth for a margin reached the target lesion depth, RFA was stopped for that margin of tissue. The NN trained to 93% accuracy and an NN-integrated control ablated tissue to within 1.0 mm of the target lesion depth on average. Full 15-mm depth maps were calculated in 0.2 s on a single-core ARMv7 processor. The results show that a NN could make lesion depth estimations in real-time using less in situ devices than current techniques. With the NN-based technique, physicians could deliver quicker and more precise ablation therapy.

  6. Warranty optimisation based on the prediction of costs to the manufacturer using neural network model and Monte Carlo simulation

    Science.gov (United States)

    Stamenkovic, Dragan D.; Popovic, Vladimir M.

    2015-02-01

    Warranty is a powerful marketing tool, but it always involves additional costs to the manufacturer. In order to reduce these costs and make use of warranty's marketing potential, the manufacturer needs to master the techniques for warranty cost prediction according to the reliability characteristics of the product. In this paper a combination free replacement and pro rata warranty policy is analysed as warranty model for one type of light bulbs. Since operating conditions have a great impact on product reliability, they need to be considered in such analysis. A neural network model is used to predict light bulb reliability characteristics based on the data from the tests of light bulbs in various operating conditions. Compared with a linear regression model used in the literature for similar tasks, the neural network model proved to be a more accurate method for such prediction. Reliability parameters obtained in this way are later used in Monte Carlo simulation for the prediction of times to failure needed for warranty cost calculation. The results of the analysis make possible for the manufacturer to choose the optimal warranty policy based on expected product operating conditions. In such a way, the manufacturer can lower the costs and increase the profit.

  7. Energy, economic and environmental performance simulation of a hybrid renewable microgeneration system with neural network predictive control

    Directory of Open Access Journals (Sweden)

    Evgueniy Entchev

    2018-03-01

    Full Text Available The use of artificial neural networks (ANNs in various applications has grown significantly over the years. This paper compares an ANN based approach with a conventional on-off control applied to the operation of a ground source heat pump/photovoltaic thermal system serving a single house located in Ottawa (Canada for heating and cooling purposes. The hybrid renewable microgeneration system was investigated using the dynamic simulation software TRNSYS. A controller for predicting the future room temperature was developed in the MATLAB environment and six ANN control logics were analyzed.The comparison was performed in terms of ability to maintain the desired indoor comfort levels, primary energy consumption, operating costs and carbon dioxide equivalent emissions during a week of the heating period and a week of the cooling period. The results showed that the ANN approach is potentially able to alleviate the intensity of thermal discomfort associated with overheating/overcooling phenomena, but it could cause an increase in unmet comfort hours. The analysis also highlighted that the ANNs based strategies could reduce the primary energy consumption (up to around 36%, the operating costs (up to around 81% as well as the carbon dioxide equivalent emissions (up to around 36%. Keywords: Hybrid microgeneration system, Ground source heat pump, Photovoltaic thermal, Artificial neural network, Predictive control, Energy saving

  8. Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models.

    Science.gov (United States)

    Pappu, J Sharon Mano; Gummadi, Sathyanarayana N

    2016-11-01

    This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Numerical simulation of shower cooling tower based on artificial neural network

    International Nuclear Information System (INIS)

    Qi Xiaoni; Liu Zhenyan; Li Dandan

    2008-01-01

    This study was prompted by the need to design towers for applications in which, due to salt deposition on the packing and subsequent blockage, the use of tower packing is not practical. The cooling tower analyzed in this study is void of fill, named shower cooling tower (SCT). However, the present study focuses mostly on experimental investigation of the SCT, and no systematic numerical method is available. In this paper, we first developed a one dimensional model and analyzed the heat and mass transfer processes of the SCT; then we used the concept of artificial neural network (ANN) to propose a computer design tool that can help the designer evaluate the outlet water temperature from a given set of experimentally obtained data. For comparison purposes and accurate evaluation of the predictions, part of the experimental data was used to train the neural network and the remainder to test the model. The results predicted by the ANN model were compared with those of the standard model and the experimental data. The ANN model predicted the outlet water temperature with a MAE (mean absolute error) of 1.31%, whereas the standard one dimensional model showed a MAE of 9.42%

  10. Simulation electromagnetic scattering on bodies through integral equation and neural networks methods

    Science.gov (United States)

    Lvovich, I. Ya; Preobrazhenskiy, A. P.; Choporov, O. N.

    2018-05-01

    The paper deals with the issue of electromagnetic scattering on a perfectly conducting diffractive body of a complex shape. Performance calculation of the body scattering is carried out through the integral equation method. Fredholm equation of the second time was used for calculating electric current density. While solving the integral equation through the moments method, the authors have properly described the core singularity. The authors determined piecewise constant functions as basic functions. The chosen equation was solved through the moments method. Within the Kirchhoff integral approach it is possible to define the scattered electromagnetic field, in some way related to obtained electrical currents. The observation angles sector belongs to the area of the front hemisphere of the diffractive body. To improve characteristics of the diffractive body, the authors used a neural network. All the neurons contained a logsigmoid activation function and weighted sums as discriminant functions. The paper presents the matrix of weighting factors of the connectionist model, as well as the results of the optimized dimensions of the diffractive body. The paper also presents some basic steps in calculation technique of the diffractive bodies, based on the combination of integral equation and neural networks methods.

  11. A Tissue Relevance and Meshing Method for Computing Patient-Specific Anatomical Models in Endoscopic Sinus Surgery Simulation

    Science.gov (United States)

    Audette, M. A.; Hertel, I.; Burgert, O.; Strauss, G.

    This paper presents on-going work on a method for determining which subvolumes of a patient-specific tissue map, extracted from CT data of the head, are relevant to simulating endoscopic sinus surgery of that individual, and for decomposing these relevant tissues into triangles and tetrahedra whose mesh size is well controlled. The overall goal is to limit the complexity of the real-time biomechanical interaction while ensuring the clinical relevance of the simulation. Relevant tissues are determined as the union of the pathology present in the patient, of critical tissues deemed to be near the intended surgical path or pathology, and of bone and soft tissue near the intended path, pathology or critical tissues. The processing of tissues, prior to meshing, is based on the Fast Marching method applied under various guises, in a conditional manner that is related to tissue classes. The meshing is based on an adaptation of a meshing method of ours, which combines the Marching Tetrahedra method and the discrete Simplex mesh surface model to produce a topologically faithful surface mesh with well controlled edge and face size as a first stage, and Almost-regular Tetrahedralization of the same prescribed mesh size as a last stage.

  12. Simulation and prediction of the thuringiensin abiotic degradation processes in aqueous solution by a radius basis function neural network model.

    Science.gov (United States)

    Zhou, Jingwen; Xu, Zhenghong; Chen, Shouwen

    2013-04-01

    The thuringiensin abiotic degradation processes in aqueous solution under different conditions, with a pH range of 5.0-9.0 and a temperature range of 10-40°C, were systematically investigated by an exponential decay model and a radius basis function (RBF) neural network model, respectively. The half-lives of thuringiensin calculated by the exponential decay model ranged from 2.72 d to 16.19 d under the different conditions mentioned above. Furthermore, an RBF model with accuracy of 0.1 and SPREAD value 5 was employed to model the degradation processes. The results showed that the model could simulate and predict the degradation processes well. Both the half-lives and the prediction data showed that thuringiensin was an easily degradable antibiotic, which could be an important factor in the evaluation of its safety. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

    Directory of Open Access Journals (Sweden)

    Li Wang

    2017-01-01

    Full Text Available Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.

  14. Tutorial at CNS 2013: Managing complex workflows in neural simulation and data analysis

    OpenAIRE

    Grün, Sonja; Davison, Andrew

    2013-01-01

    In our attempts to uncover the mechanisms that govern brain processing on the level of interacting neurons, neuroscientists have taken on the challenge of tackling the sheer complexity exhibited by neuronal networks. Neuronal simulations are nowadays performed with a high degree of detail, covering large, heterogeneous networks. Experimentally, electrophysiologists can simultaneously record from hundreds of neurons in complicated behavioral paradigms. The data streams of simulation and experi...

  15. 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.

  16. Visualization of spiral and scroll waves in simulated and experimental cardiac tissue

    Science.gov (United States)

    Cherry, E. M.; Fenton, F. H.

    2008-12-01

    The heart is a nonlinear biological system that can exhibit complex electrical dynamics, complete with period-doubling bifurcations and spiral and scroll waves that can lead to fibrillatory states that compromise the heart's ability to contract and pump blood efficiently. Despite the importance of understanding the range of cardiac dynamics, studying how spiral and scroll waves can initiate, evolve, and be terminated is challenging because of the complicated electrophysiology and anatomy of the heart. Nevertheless, over the last two decades advances in experimental techniques have improved access to experimental data and have made it possible to visualize the electrical state of the heart in more detail than ever before. During the same time, progress in mathematical modeling and computational techniques has facilitated using simulations as a tool for investigating cardiac dynamics. In this paper, we present data from experimental and simulated cardiac tissue and discuss visualization techniques that facilitate understanding of the behavior of electrical spiral and scroll waves in the context of the heart. The paper contains many interactive media, including movies and interactive two- and three-dimensional Java appletsDisclaimer: IOP Publishing was not involved in the programming of this software and does not accept any responsibility for it. You download and run the software at your own risk. If you experience any problems with the software, please contact the author directly. To the fullest extent permitted by law, IOP Publishing Ltd accepts no responsibility for any loss, damage and/or other adverse effect on your computer system caused by your downloading and running this software. IOP Publishing Ltd accepts no responsibility for consequential loss..

  17. Neural correlates of olfactory and visual memory performance in 3D-simulated mazes after intranasal insulin application.

    Science.gov (United States)

    Brünner, Yvonne F; Rodriguez-Raecke, Rea; Mutic, Smiljana; Benedict, Christian; Freiherr, Jessica

    2016-10-01

    This fMRI study intended to establish 3D-simulated mazes with olfactory and visual cues and examine the effect of intranasally applied insulin on memory performance in healthy subjects. The effect of insulin on hippocampus-dependent brain activation was explored using a double-blind and placebo-controlled design. Following intranasal administration of either insulin (40IU) or placebo, 16 male subjects participated in two experimental MRI sessions with olfactory and visual mazes. Each maze included two separate runs. The first was an encoding maze during which subjects learned eight olfactory or eight visual cues at different target locations. The second was a recall maze during which subjects were asked to remember the target cues at spatial locations. For eleven included subjects in the fMRI analysis we were able to validate brain activation for odor perception and visuospatial tasks. However, we did not observe an enhancement of declarative memory performance in our behavioral data or hippocampal activity in response to insulin application in the fMRI analysis. It is therefore possible that intranasal insulin application is sensitive to the methodological variations e.g. timing of task execution and dose of application. Findings from this study suggest that our method of 3D-simulated mazes is feasible for studying neural correlates of olfactory and visual memory performance. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Cellular neural networks (CNN) simulation for the TN approximation of the time dependent neutron transport equation in slab geometry

    International Nuclear Information System (INIS)

    Hadad, Kamal; Pirouzmand, Ahmad; Ayoobian, Navid

    2008-01-01

    This paper describes the application of a multilayer cellular neural network (CNN) to model and solve the time dependent one-speed neutron transport equation in slab geometry. We use a neutron angular flux in terms of the Chebyshev polynomials (T N ) of the first kind and then we attempt to implement the equations in an equivalent electrical circuit. We apply this equivalent circuit to analyze the T N moments equation in a uniform finite slab using Marshak type vacuum boundary condition. The validity of the CNN results is evaluated with numerical solution of the steady state T N moments equations by MATLAB. Steady state, as well as transient simulations, shows a very good comparison between the two methods. We used our CNN model to simulate space-time response of total flux and its moments for various c (where c is the mean number of secondary neutrons per collision). The complete algorithm could be implemented using very large-scale integrated circuit (VLSI) circuitry. The efficiency of the calculation method makes it useful for neutron transport calculations

  19. The validity and reliability of modelled neural and tissue properties of the ankle muscles in children with cerebral palsy

    NARCIS (Netherlands)

    Sloot, L.H.; van der Krogt, M.M.; de Gooijer-van Groep, K.; van Eesbeek, S.; de Groot, J.; Buizer, A.I.; Meskers, C.; Becher, J.G.; de Vlugt, E.; Harlaar, J.

    2015-01-01

    Spastic cerebral palsy (CP) is characterized by increased joint resistance, caused by a mix of increased tissue stiffness, as well as involuntary reflex and background muscle activity. These properties can be quantified using a neuromechanical model of the musculoskeletal complex and instrumented

  20. Simulation of Reservoir Sediment Flushing of the Three Gorges Reservoir Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Xueying Li

    2016-05-01

    Full Text Available Reservoir sedimentation and its effect on the environment are the most serious world-wide problems in water resources development and utilization today. As one of the largest water conservancy projects, the Three Gorges Reservoir (TGR has been controversial since its demonstration period, and sedimentation is the major concern. Due to the complex physical mechanisms of water and sediment transport, this study adopts the Error Back Propagation Training Artificial Neural Network (BP-ANN to analyze the relationship between the sediment flushing efficiency of the TGR and its influencing factors. The factors are determined by the analysis on 1D unsteady flow and sediment mathematical model, mainly including reservoir inflow, incoming sediment concentration, reservoir water level, and reservoir release. Considering the distinguishing features of reservoir sediment delivery in different seasons, the monthly average data from 2003, when the TGR was put into operation, to 2011 are used to train, validate, and test the BP-ANN model. The results indicate that, although the sample space is quite limited, the whole sediment delivery process can be schematized by the established BP-ANN model, which can be used to help sediment flushing and thus decrease the reservoir sedimentation.

  1. A Theory for the Neural Basis of Language Part 2: Simulation Studies of the Model

    Science.gov (United States)

    Baron, R. J.

    1974-01-01

    Computer simulation studies of the proposed model are presented. Processes demonstrated are (1) verbally directed recall of visual experience; (2) understanding of verbal information; (3) aspects of learning and forgetting; (4) the dependence of recognition and understanding in context; and (5) elementary concepts of sentence production. (Author)

  2. An integrated neural-symbolic cognitive agent architecture for training and assessment in simulators

    NARCIS (Netherlands)

    Penning, H.L.H. de; d'Avila Garcez, A.S.; Lamb, L.C.; Meyer, J.J.C.

    2010-01-01

    Training and assessment of complex tasks has always been a complex task in itself. Training simulators can be used for training and assessment of low-order skills. High-order skills (e.g. safe driving, leadership, tactical manoeuvring, etc.) are generally trained and assessed by human experts, due

  3. Calculation of dose distribution in compressible breast tissues using finite element modeling, Monte Carlo simulation and thermoluminescence dosimeters

    Science.gov (United States)

    Mohammadyari, Parvin; Faghihi, Reza; Mosleh-Shirazi, Mohammad Amin; Lotfi, Mehrzad; Rahim Hematiyan, Mohammad; Koontz, Craig; Meigooni, Ali S.

    2015-12-01

    Compression is a technique to immobilize the target or improve the dose distribution within the treatment volume during different irradiation techniques such as AccuBoost® brachytherapy. However, there is no systematic method for determination of dose distribution for uncompressed tissue after irradiation under compression. In this study, the mechanical behavior of breast tissue between compressed and uncompressed states was investigated. With that, a novel method was developed to determine the dose distribution in uncompressed tissue after irradiation of compressed breast tissue. Dosimetry was performed using two different methods, namely, Monte Carlo simulations using the MCNP5 code and measurements using thermoluminescent dosimeters (TLD). The displacement of the breast elements was simulated using a finite element model and calculated using ABAQUS software. From these results, the 3D dose distribution in uncompressed tissue was determined. The geometry of the model was constructed from magnetic resonance images of six different women volunteers. The mechanical properties were modeled by using the Mooney-Rivlin hyperelastic material model. Experimental dosimetry was performed by placing the TLD chips into the polyvinyl alcohol breast equivalent phantom. The results determined that the nodal displacements, due to the gravitational force and the 60 Newton compression forces (with 43% contraction in the loading direction and 37% expansion in the orthogonal direction) were determined. Finally, a comparison of the experimental data and the simulated data showed agreement within 11.5%  ±  5.9%.

  4. Calculation of dose distribution in compressible breast tissues using finite element modeling, Monte Carlo simulation and thermoluminescence dosimeters

    International Nuclear Information System (INIS)

    Mohammadyari, Parvin; Faghihi, Reza; Mosleh-Shirazi, Mohammad Amin; Lotfi, Mehrzad; Hematiyan, Mohammad Rahim; Koontz, Craig; Meigooni, Ali S

    2015-01-01

    Compression is a technique to immobilize the target or improve the dose distribution within the treatment volume during different irradiation techniques such as AccuBoost ® brachytherapy. However, there is no systematic method for determination of dose distribution for uncompressed tissue after irradiation under compression. In this study, the mechanical behavior of breast tissue between compressed and uncompressed states was investigated. With that, a novel method was developed to determine the dose distribution in uncompressed tissue after irradiation of compressed breast tissue. Dosimetry was performed using two different methods, namely, Monte Carlo simulations using the MCNP5 code and measurements using thermoluminescent dosimeters (TLD). The displacement of the breast elements was simulated using a finite element model and calculated using ABAQUS software. From these results, the 3D dose distribution in uncompressed tissue was determined. The geometry of the model was constructed from magnetic resonance images of six different women volunteers. The mechanical properties were modeled by using the Mooney–Rivlin hyperelastic material model. Experimental dosimetry was performed by placing the TLD chips into the polyvinyl alcohol breast equivalent phantom. The results determined that the nodal displacements, due to the gravitational force and the 60 Newton compression forces (with 43% contraction in the loading direction and 37% expansion in the orthogonal direction) were determined. Finally, a comparison of the experimental data and the simulated data showed agreement within 11.5%  ±  5.9%. (paper)

  5. Force sensor in simulated skin and neural model mimic tactile SAI afferent spiking response to ramp and hold stimuli.

    Science.gov (United States)

    Kim, Elmer K; Wellnitz, Scott A; Bourdon, Sarah M; Lumpkin, Ellen A; Gerling, Gregory J

    2012-07-23

    The next generation of prosthetic limbs will restore sensory feedback to the nervous system by mimicking how skin mechanoreceptors, innervated by afferents, produce trains of action potentials in response to compressive stimuli. Prior work has addressed building sensors within skin substitutes for robotics, modeling skin mechanics and neural dynamics of mechanotransduction, and predicting response timing of action potentials for vibration. The effort here is unique because it accounts for skin elasticity by measuring force within simulated skin, utilizes few free model parameters for parsimony, and separates parameter fitting and model validation. Additionally, the ramp-and-hold, sustained stimuli used in this work capture the essential features of the everyday task of contacting and holding an object. This systems integration effort computationally replicates the neural firing behavior for a slowly adapting type I (SAI) afferent in its temporally varying response to both intensity and rate of indentation force by combining a physical force sensor, housed in a skin-like substrate, with a mathematical model of neuronal spiking, the leaky integrate-and-fire. Comparison experiments were then conducted using ramp-and-hold stimuli on both the spiking-sensor model and mouse SAI afferents. The model parameters were iteratively fit against recorded SAI interspike intervals (ISI) before validating the model to assess its performance. Model-predicted spike firing compares favorably with that observed for single SAI afferents. As indentation magnitude increases (1.2, 1.3, to 1.4 mm), mean ISI decreases from 98.81 ± 24.73, 54.52 ± 6.94, to 41.11 ± 6.11 ms. Moreover, as rate of ramp-up increases, ISI during ramp-up decreases from 21.85 ± 5.33, 19.98 ± 3.10, to 15.42 ± 2.41 ms. Considering first spikes, the predicted latencies exhibited a decreasing trend as stimulus rate increased, as is observed in afferent recordings. Finally, the SAI afferent's characteristic response

  6. Simulation study of melanoma detection in human skin tissues by laser-generated surface acoustic waves.

    Science.gov (United States)

    Chen, Kun; Fu, Xing; Dorantes-Gonzalez, Dante J; Lu, Zimo; Li, Tingting; Li, Yanning; Wu, Sen; Hu, Xiaotang

    2014-01-01

    Air pollution has been correlated to an increasing number of cases of human skin diseases in recent years. However, the investigation of human skin tissues has received only limited attention, to the point that there are not yet satisfactory modern detection technologies to accurately, noninvasively, and rapidly diagnose human skin at epidermis and dermis levels. In order to detect and analyze severe skin diseases such as melanoma, a finite element method (FEM) simulation study of the application of the laser-generated surface acoustic wave (LSAW) technique is developed. A three-layer human skin model is built, where LSAW’s are generated and propagated, and their effects in the skin medium with melanoma are analyzed. Frequency domain analysis is used as a main tool to investigate such issues as minimum detectable size of melanoma, filtering spectra from noise and from computational irregularities, as well as on how the FEM model meshing size and computational capabilities influence the accuracy of the results. Based on the aforementioned aspects, the analysis of the signals under the scrutiny of the phase velocity dispersion curve is verified to be a reliable, a sensitive, and a promising approach for detecting and characterizing melanoma in human skin.

  7. Simulated Response of a Tissue-equivalent Proportional Counter on the Surface of Mars.

    Science.gov (United States)

    Northum, Jeremy D; Guetersloh, Stephen B; Braby, Leslie A; Ford, John R

    2015-10-01

    Uncertainties persist regarding the assessment of the carcinogenic risk associated with galactic cosmic ray (GCR) exposure during a mission to Mars. The GCR spectrum peaks in the range of 300(-1) MeV n to 700 MeV n(-1) and is comprised of elemental ions from H to Ni. While Fe ions represent only 0.03% of the GCR spectrum in terms of particle abundance, they are responsible for nearly 30% of the dose equivalent in free space. Because of this, radiation biology studies focusing on understanding the biological effects of GCR exposure generally use Fe ions. Acting as a thin shield, the Martian atmosphere alters the GCR spectrum in a manner that significantly reduces the importance of Fe ions. Additionally, albedo particles emanating from the regolith complicate the radiation environment. The present study uses the Monte Carlo code FLUKA to simulate the response of a tissue-equivalent proportional counter on the surface of Mars to produce dosimetry quantities and microdosimetry distributions. The dose equivalent rate on the surface of Mars was found to be 0.18 Sv y(-1) with an average quality factor of 2.9 and a dose mean lineal energy of 18.4 keV μm(-1). Additionally, albedo neutrons were found to account for 25% of the dose equivalent. It is anticipated that these data will provide relevant starting points for use in future risk assessment and mission planning studies.

  8. Simulation study of melanoma detection in human skin tissues by laser-generated surface acoustic waves

    Science.gov (United States)

    Chen, Kun; Fu, Xing; Dorantes-Gonzalez, Dante J.; Lu, Zimo; Li, Tingting; Li, Yanning; Wu, Sen; Hu, Xiaotang

    2014-07-01

    Air pollution has been correlated to an increasing number of cases of human skin diseases in recent years. However, the investigation of human skin tissues has received only limited attention, to the point that there are not yet satisfactory modern detection technologies to accurately, noninvasively, and rapidly diagnose human skin at epidermis and dermis levels. In order to detect and analyze severe skin diseases such as melanoma, a finite element method (FEM) simulation study of the application of the laser-generated surface acoustic wave (LSAW) technique is developed. A three-layer human skin model is built, where LSAW's are generated and propagated, and their effects in the skin medium with melanoma are analyzed. Frequency domain analysis is used as a main tool to investigate such issues as minimum detectable size of melanoma, filtering spectra from noise and from computational irregularities, as well as on how the FEM model meshing size and computational capabilities influence the accuracy of the results. Based on the aforementioned aspects, the analysis of the signals under the scrutiny of the phase velocity dispersion curve is verified to be a reliable, a sensitive, and a promising approach for detecting and characterizing melanoma in human skin.

  9. Real-time surgical simulation for deformable soft-tissue objects with a tumour using Boundary Element techniques

    Science.gov (United States)

    Wang, P.; Becker, A. A.; Jones, I. A.; Glover, A. T.; Benford, S. D.; Vloeberghs, M.

    2009-08-01

    A virtual-reality real-time simulation of surgical operations that incorporates the inclusion of a hard tumour is presented. The software is based on Boundary Element (BE) technique. A review of the BE formulation for real-time analysis of two-domain deformable objects, using the pre-solution technique, is presented. The two-domain BE software is incorporated into a surgical simulation system called VIRS to simulate the initiation of a cut on the surface of the soft tissue and extending the cut deeper until the tumour is reached.

  10. Real-time surgical simulation for deformable soft-tissue objects with a tumour using Boundary Element techniques

    International Nuclear Information System (INIS)

    Wang, P; Becker, A A; Jones, I A; Glover, A T; Benford, S D; Vloeberghs, M

    2009-01-01

    A virtual-reality real-time simulation of surgical operations that incorporates the inclusion of a hard tumour is presented. The software is based on Boundary Element (BE) technique. A review of the BE formulation for real-time analysis of two-domain deformable objects, using the pre-solution technique, is presented. The two-domain BE software is incorporated into a surgical simulation system called VIRS to simulate the initiation of a cut on the surface of the soft tissue and extending the cut deeper until the tumour is reached.

  11. Disease-associated prion protein in neural and lymphoid tissues of mink (Mustela vison) inoculated with transmissible mink encephalopathy.

    Science.gov (United States)

    Schneider, D A; Harrington, R D; Zhuang, D; Yan, H; Truscott, T C; Dassanayake, R P; O'Rourke, K I

    2012-11-01

    Transmissible spongiform encephalopathies (TSEs) are diagnosed by immunodetection of disease-associated prion protein (PrP(d)). The distribution of PrP(d) within the body varies with the time-course of infection and between species, during interspecies transmission, as well as with prion strain. Mink are susceptible to a form of TSE known as transmissible mink encephalopathy (TME), presumed to arise due to consumption of feed contaminated with a single prion strain of ruminant origin. After extended passage of TME isolates in hamsters, two strains emerge, HY and DY, each of which is associated with unique structural isoforms of PrP(TME) and of which only the HY strain is associated with accumulation of PrP(TME) in lymphoid tissues. Information on the structural nature and lymphoid accumulation of PrP(TME) in mink is limited. In this study, 13 mink were challenged by intracerebral inoculation using late passage TME inoculum, after which brain and lymphoid tissues were collected at preclinical and clinical time points. The distribution and molecular nature of PrP(TME) was investigated by techniques including blotting of paraffin wax-embedded tissue and epitope mapping by western blotting. PrP(TME) was detected readily in the brain and retropharyngeal lymph node during preclinical infection, with delayed progression of accumulation within other lymphoid tissues. For comparison, three mink were inoculated by the oral route and examined during clinical disease. Accumulation of PrP(TME) in these mink was greater and more widespread, including follicles of rectoanal mucosa-associated lymphoid tissue. Western blot analyses revealed that PrP(TME) accumulating in the brain of mink is structurally most similar to that accumulating in the brain of hamsters infected with the DY strain. Collectively, the results of extended passage in mink are consistent with the presence of only a single strain of TME, the DY strain, capable of inducing accumulation of PrP(TME) in the lymphoid

  12. Levels of PAH-DNA adducts in cord blood and cord tissue and the risk of fetal neural tube defects in a Chinese population.

    Science.gov (United States)

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

    2015-01-01

    Maternal exposure to polycyclic aromatic hydrocarbons (PAHs) has been shown to be associated with an elevated risk for neural tube defects (NTDs). In the human body, PAHs are bioactivated and the resultant reactive epoxides can covalently bind to DNA to form PAH-DNA adducts, which may, in turn, cause transcription errors, changes in gene expression or altered patterns of apoptosis. During critical developmental phases, these changes can result in abnormal morphogenesis. We aimed to examine the relationship between the levels of PAH-DNA adducts in cord blood and cord tissue and the risk of NTDs. From 2010 to 2012, 60 NTD cases and 60 healthy controls were recruited from a population-based birth defects surveillance system in five counties of Shanxi Province in Northern China, where the emission of PAHs remains one of the highest in the country and PAHs exposure is highly prevalent. PAH-DNA adducts in cord blood of 15 NTD cases and 15 control infants, and in cord tissue of 60 NTD cases and 60 control infants were measured using the (32)P-postlabeling method. PAH-DNA adduct levels in cord blood tend to be higher in the NTD group (28.5 per 10(8) nucleotides) compared with controls (19.7 per 10(8) nucleotides), although the difference was not statistically significant (P=0.377). PAH-DNA adducts in cord tissue were significantly higher in the NTD group (24.6 per 10(6) nucleotides) than in the control group (15.3 per 10(6) nucleotides), P=0.010. A positive dose-response relationship was found between levels of PAH-DNA adducts in cord tissue and the risk of NTDs (P=0.009). When the lowest tertile was used as the referent and potential confounding factors were adjusted for, a 1.03-fold (95% CI, 0.37-2.89) and 2.96-fold (95% CI, 1.16-7.58) increase in the risk of NTDs was observed for fetuses whose cord tissue PAH-DNA adduct levels were in the second and highest tertile, respectively. High levels of PAH-DNA adducts in fetal tissues were associated with increased risks of

  13. Comparison of the Incorporation of DHA in Circulatory and Neural Tissue When Provided as Triacylglycerol (TAG), Monoacylglycerol (MAG) or Phospholipids (PL) Provides New Insight into Fatty Acid Bioavailability.

    Science.gov (United States)

    Destaillats, Frédéric; Oliveira, Manuel; Bastic Schmid, Viktoria; Masserey-Elmelegy, Isabelle; Giuffrida, Francesca; Thakkar, Sagar K; Dupuis, Lénaïck; Gosoniu, Maria Laura; Cruz-Hernandez, Cristina

    2018-05-15

    Phospholipids (PL) or partial acylglycerols such as sn -1(3)-monoacylglycerol (MAG) are potent dietary carriers of long-chain polyunsaturated fatty acids (LC-PUFA) and have been reported to provide superior bioavailability when compared to conventional triacylglycerol (TAG). The main objective of the present study was to compare the incorporation of docosahexaenoic acid (DHA) in plasma, erythrocytes, retina and brain tissues in adult rats when provided as PL (PL-DHA) and MAG (MAG-DHA). Conventional dietary DHA oil containing TAG (TAG-DHA) as well as control chow diet were used to evaluate the potency of the two alternative DHA carriers over a 60-day feeding period. Fatty acid profiles were determined in erythrocytes and plasma lipids at time 0, 7, 14, 28, 35 and 49 days of the experimental period and in retina, cortex, hypothalamus, and hippocampus at 60 days. The assessment of the longitudinal evolution of DHA in erythrocyte and plasma lipids suggest that PL-DHA and MAG-DHA are efficient carriers of dietary DHA when compared to conventional DHA oil (TAG-DHA). Under these experimental conditions, both PL-DHA and MAG-DHA led to higher incorporations of DHA erythrocytes lipids compared to TAG-DHA group. After 60 days of supplementation, statistically significant increase in DHA level incorporated in neural tissues analyzed were observed in the DHA groups compared with the control. The mechanism explaining hypothetically the difference observed in circulatory lipids is discussed.

  14. Hands-on parameter search for neural simulations by a MIDI-controller.

    Science.gov (United States)

    Eichner, Hubert; Borst, Alexander

    2011-01-01

    Computational neuroscientists frequently encounter the challenge of parameter fitting--exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical systems.

  15. Hands-on parameter search for neural simulations by a MIDI-controller.

    Directory of Open Access Journals (Sweden)

    Hubert Eichner

    Full Text Available Computational neuroscientists frequently encounter the challenge of parameter fitting--exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical systems.

  16. Mechanistic simulation of normal-tissue damage in radiotherapy-implications for dose-volume analyses

    International Nuclear Information System (INIS)

    Rutkowska, Eva; Baker, Colin; Nahum, Alan

    2010-01-01

    A radiobiologically based 3D model of normal tissue has been developed in which complications are generated when 'irradiated'. The aim is to provide insight into the connection between dose-distribution characteristics, different organ architectures and complication rates beyond that obtainable with simple DVH-based analytical NTCP models. In this model the organ consists of a large number of functional subunits (FSUs), populated by stem cells which are killed according to the LQ model. A complication is triggered if the density of FSUs in any 'critical functioning volume' (CFV) falls below some threshold. The (fractional) CFV determines the organ architecture and can be varied continuously from small (series-like behaviour) to large (parallel-like). A key feature of the model is its ability to account for the spatial dependence of dose distributions. Simulations were carried out to investigate correlations between dose-volume parameters and the incidence of 'complications' using different pseudo-clinical dose distributions. Correlations between dose-volume parameters and outcome depended on characteristics of the dose distributions and on organ architecture. As anticipated, the mean dose and V 20 correlated most strongly with outcome for a parallel organ, and the maximum dose for a serial organ. Interestingly better correlation was obtained between the 3D computer model and the LKB model with dose distributions typical for serial organs than with those typical for parallel organs. This work links the results of dose-volume analyses to dataset characteristics typical for serial and parallel organs and it may help investigators interpret the results from clinical studies.

  17. Dependence of light scattering profile in tissue on blood vessel diameter and distribution: a computer simulation study.

    Science.gov (United States)

    Duadi, Hamootal; Fixler, Dror; Popovtzer, Rachela

    2013-11-01

    Most methods for measuring light-tissue interactions focus on the volume reflectance while very few measure the transmission. We investigate both diffusion reflection and diffuse transmission at all exit angles to receive the full scattering profile. We also investigate the influence of blood vessel diameter on the scattering profile of a circular tissue. The photon propagation path at a wavelength of 850 nm is calculated from the absorption and scattering constants via Monte Carlo simulation. Several simulations are performed where a different vessel diameter and location were chosen but the blood volume was kept constant. The fraction of photons exiting the tissue at several central angles is presented for each vessel diameter. The main result is that there is a central angle that below which the photon transmission decreased for lower vessel diameters while above this angle the opposite occurred. We find this central angle to be 135 deg for a two-dimensional 10-mm diameter circular tissue cross-section containing blood vessels. These findings can be useful for monitoring blood perfusion and oxygen delivery in the ear lobe and pinched tissues. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

  18. 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

  19. SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.

    Science.gov (United States)

    Sripad, Athul; Sanchez, Giovanny; Zapata, Mireya; Pirrone, Vito; Dorta, Taho; Cambria, Salvatore; Marti, Albert; Krishnamourthy, Karthikeyan; Madrenas, Jordi

    2018-01-01

    Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot Study

    Directory of Open Access Journals (Sweden)

    Gavin Robertson

    2011-01-01

    Full Text Available Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs, the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days (RMSE5 day not used during ANN training. For BGL predictions of up to 1 hour a RMSE5 day of (±SD 0.15±0.04 mmol/L was observed. For BGL predictions up to 10 hours, a RMSE5  day of (±SD 0.14±0.16 mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.

  1. [Application of simulated annealing method and neural network on optimizing soil sampling schemes based on road distribution].

    Science.gov (United States)

    Han, Zong-wei; Huang, Wei; Luo, Yun; Zhang, Chun-di; Qi, Da-cheng

    2015-03-01

    Taking the soil organic matter in eastern Zhongxiang County, Hubei Province, as a research object, thirteen sample sets from different regions were arranged surrounding the road network, the spatial configuration of which was optimized by the simulated annealing approach. The topographic factors of these thirteen sample sets, including slope, plane curvature, profile curvature, topographic wetness index, stream power index and sediment transport index, were extracted by the terrain analysis. Based on the results of optimization, a multiple linear regression model with topographic factors as independent variables was built. At the same time, a multilayer perception model on the basis of neural network approach was implemented. The comparison between these two models was carried out then. The results revealed that the proposed approach was practicable in optimizing soil sampling scheme. The optimal configuration was capable of gaining soil-landscape knowledge exactly, and the accuracy of optimal configuration was better than that of original samples. This study designed a sampling configuration to study the soil attribute distribution by referring to the spatial layout of road network, historical samples, and digital elevation data, which provided an effective means as well as a theoretical basis for determining the sampling configuration and displaying spatial distribution of soil organic matter with low cost and high efficiency.

  2. Monte Carlo and discrete-ordinate simulations of spectral radiances in a coupled air-tissue system.

    Science.gov (United States)

    Hestenes, Kjersti; Nielsen, Kristian P; Zhao, Lu; Stamnes, Jakob J; Stamnes, Knut

    2007-04-20

    We perform a detailed comparison study of Monte Carlo (MC) simulations and discrete-ordinate radiative-transfer (DISORT) calculations of spectral radiances in a 1D coupled air-tissue (CAT) system consisting of horizontal plane-parallel layers. The MC and DISORT models have the same physical basis, including coupling between the air and the tissue, and we use the same air and tissue input parameters for both codes. We find excellent agreement between radiances obtained with the two codes, both above and in the tissue. Our tests cover typical optical properties of skin tissue at the 280, 540, and 650 nm wavelengths. The normalized volume scattering function for internal structures in the skin is represented by the one-parameter Henyey-Greenstein function for large particles and the Rayleigh scattering function for small particles. The CAT-DISORT code is found to be approximately 1000 times faster than the CAT-MC code. We also show that the spectral radiance field is strongly dependent on the inherent optical properties of the skin tissue.

  3. Induced current magnetic resonance electrical impedance tomography of brain tissues based on the J-substitution algorithm: a simulation study

    International Nuclear Information System (INIS)

    Liu Yang; Zhu Shanan; He Bin

    2009-01-01

    We have investigated induced current magnetic resonance electrical impedance tomography (IC-MREIT) by means of computer simulations. The J-substitution algorithm was implemented to solve the IC-MREIT reconstruction problem. By providing physical insight into the charge accumulating on the interfaces, the convergence characteristics of the reconstruction algorithm were analyzed. The simulation results conducted on different objects were well correlated with the proposed theoretical analysis. The feasibility of IC-MREIT to reconstruct the conductivity distribution of head-brain tissues was also examined in computer simulations using a multi-compartment realistic head model. The present simulation results suggest that IC-MREIT may have the potential to become a useful conductivity imaging technique.

  4. A neural mass model of basal ganglia nuclei simulates pathological beta rhythm in Parkinson's disease

    Science.gov (United States)

    Liu, Fei; Wang, Jiang; Liu, Chen; Li, Huiyan; Deng, Bin; Fietkiewicz, Chris; Loparo, Kenneth A.

    2016-12-01

    An increase in beta oscillations within the basal ganglia nuclei has been shown to be associated with movement disorder, such as Parkinson's disease. The motor cortex and an excitatory-inhibitory neuronal network composed of the subthalamic nucleus (STN) and the external globus pallidus (GPe) are thought to play an important role in the generation of these oscillations. In this paper, we propose a neuron mass model of the basal ganglia on the population level that reproduces the Parkinsonian oscillations in a reciprocal excitatory-inhibitory network. Moreover, it is shown that the generation and frequency of these pathological beta oscillations are varied by the coupling strength and the intrinsic characteristics of the basal ganglia. Simulation results reveal that increase of the coupling strength induces the generation of the beta oscillation, as well as enhances the oscillation frequency. However, for the intrinsic properties of each nucleus in the excitatory-inhibitory network, the STN primarily influences the generation of the beta oscillation while the GPe mainly determines its frequency. Interestingly, describing function analysis applied on this model theoretically explains the mechanism of pathological beta oscillations.

  5. Integrating viscoelastic mass spring dampers into position-based dynamics to simulate soft tissue deformation in real time.

    Science.gov (United States)

    Xu, Lang; Lu, Yuhua; Liu, Qian

    2018-02-01

    We propose a novel method to simulate soft tissue deformation for virtual surgery applications. The method considers the mechanical properties of soft tissue, such as its viscoelasticity, nonlinearity and incompressibility; its speed, stability and accuracy also meet the requirements for a surgery simulator. Modifying the traditional equation for mass spring dampers (MSD) introduces nonlinearity and viscoelasticity into the calculation of elastic force. Then, the elastic force is used in the constraint projection step for naturally reducing constraint potential. The node position is enforced by the combined spring force and constraint conservative force through Newton's second law. We conduct a comparison study of conventional MSD and position-based dynamics for our new integrating method. Our approach enables stable, fast and large step simulation by freely controlling visual effects based on nonlinearity, viscoelasticity and incompressibility. We implement a laparoscopic cholecystectomy simulator to demonstrate the practicality of our method, in which liver and gallbladder deformation can be simulated in real time. Our method is an appropriate choice for the development of real-time virtual surgery applications.

  6. Spatial distribution patterns of energy deposition and cellular radiation effects in lung tissue following simulated exposure to alpha particles

    International Nuclear Information System (INIS)

    Hofmann, W.; Crawford-Brown, D.J.

    1990-01-01

    Randomly oriented sections of rat tissue have been digitised to provide the contours of tissue-air interfaces and the locations of individual cell nuclei in the alveolated region of the lung. Sources of alpha particles with varying irradiation geometries and densities are simulated to compute the resulting random pattern of cellular irradiation, i.e. spatial coordinates, frequency, track length, and energy of traversals by the emitted alpha particles. Probabilities per unit track lengths, derived from experimental data on in vitro cellular inactivation and transformation, are then applied to the results of the alpha exposure simulations to yield an estimate of the number of both dead and viable transformed cells and their spatial distributions. If lung cancer risk is linearly related to the number of transformed cells, the carcinogenic risk for hot particles is always smaller than that for a uniform nuclide distribution of the same activity. (author)

  7. Experimental Toxoplasmosis in Rats Induced Orally with Eleven Strains of Toxoplasma gondii of Seven Genotypes: Tissue Tropism, Tissue Cyst Size, Neural Lesions, Tissue Cyst Rupture without Reactivation, and Ocular Lesions.

    Directory of Open Access Journals (Sweden)

    Jitender P Dubey

    Full Text Available The protozoan parasite Toxoplasma gondii is one of the most widely distributed and successful parasites. Toxoplasma gondii alters rodent behavior such that infected rodents reverse their fear of cat odor, and indeed are attracted rather than repelled by feline urine. The location of the parasite encysted in the brain may influence this behavior. However, most studies are based on the highly susceptible rodent, the mouse.Latent toxoplasmosis was induced in rats (10 rats per T. gondii strains of the same age, strain, and sex, after oral inoculation with oocysts (natural route and natural stage of infection of 11 T. gondii strains of seven genotypes. Rats were euthanized at two months post inoculation (p.i. to investigate whether the parasite genotype affects the distribution, location, tissue cyst size, or lesions. Tissue cysts were enumerated in different regions of the brains, both in histological sections as well in saline homogenates. Tissue cysts were found in all regions of the brain. The tissue cyst density in different brain regions varied extensively between rats with many regions highly infected in some animals. Overall, the colliculus was most highly infected although there was a large amount of variability. The cerebral cortex, thalamus, and cerebellum had higher tissue cyst densities and two strains exhibited tropism for the colliculus and olfactory bulb. Histologically, lesions were confined to the brain and eyes. Tissue cyst rupture was frequent with no clear evidence for reactivation of tachyzoites. Ocular lesions were found in 23 (25% of 92 rat eyes at two months p.i. The predominant lesion was focal inflammation in the retina. Tissue cysts were seen in the sclera of one and in the optic nerve of two rats. The choroid was not affected. Only tissue cysts, not active tachyzoite infections, were detected. Tissue cysts were seen in histological sections of tongue of 20 rats but not in myocardium and leg muscle.This study reevaluated

  8. Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues

    KAUST Repository

    Cali, Corrado

    2015-07-14

    Advances for application of electron microscopy to serial imaging are opening doors to new ways of analyzing cellular structure. New and improved algorithms and workflows for manual and semiautomated segmentation allow to observe the spatial arrangement of the smallest cellular features with unprecedented detail in full three-dimensions (3D). From larger samples, higher complexity models can be generated; however, they pose new challenges to data management and analysis. Here, we review some currently available solutions and present our approach in detail. We use the fully immersive virtual reality (VR) environment CAVE (cave automatic virtual environment), a room where we are able to project a cellular reconstruction and visualize in 3D, to step into a world created with Blender, a free, fully customizable 3D modeling software with NeuroMorph plug-ins for visualization and analysis of electron microscopy (EM) preparations of brain tissue. Our workflow allows for full and fast reconstructions of volumes of brain neuropil using ilastik, a software tool for semiautomated segmentation of EM stacks. With this visualization environment, we can walk into the model containing neuronal and astrocytic processes to study the spatial distribution of glycogen granules, a major energy source that is selectively stored in astrocytes. The use of CAVE was key to observe a nonrandom distribution of glycogen, and led us to develop tools to quantitatively analyze glycogen clustering and proximity to other subcellular features. This article is protected by copyright. All rights reserved.

  9. Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal development.

    Science.gov (United States)

    Szabo, Linda; Morey, Robert; Palpant, Nathan J; Wang, Peter L; Afari, Nastaran; Jiang, Chuan; Parast, Mana M; Murry, Charles E; Laurent, Louise C; Salzman, Julia

    2015-06-16

    The pervasive expression of circular RNA is a recently discovered feature of gene expression in highly diverged eukaryotes, but the functions of most circular RNAs are still unknown. Computational methods to discover and quantify circular RNA are essential. Moreover, discovering biological contexts where circular RNAs are regulated will shed light on potential functional roles they may play. We present a new algorithm that increases the sensitivity and specificity of circular RNA detection by discovering and quantifying circular and linear RNA splicing events at both annotated and un-annotated exon boundaries, including intergenic regions of the genome, with high statistical confidence. Unlike approaches that rely on read count and exon homology to determine confidence in prediction of circular RNA expression, our algorithm uses a statistical approach. Using our algorithm, we unveiled striking induction of general and tissue-specific circular RNAs, including in the heart and lung, during human fetal development. We discover regions of the human fetal brain, such as the frontal cortex, with marked enrichment for genes where circular RNA isoforms are dominant. The vast majority of circular RNA production occurs at major spliceosome splice sites; however, we find the first examples of developmentally induced circular RNAs processed by the minor spliceosome, and an enriched propensity of minor spliceosome donors to splice into circular RNA at un-annotated, rather than annotated, exons. Together, these results suggest a potentially significant role for circular RNA in human development.

  10. Comparative Study of Various Delivery Methods for the Supply of Alpha-Ketoglutarate to the Neural Cells for Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Tanushree Vishnoi

    2013-01-01

    Full Text Available Delivery of growth factors or bioactive molecules plays an important role in tissue engineering, as the duration to which these are supplied can modulate the cell fate. Thus, the delivery method plays an important role, and the same is presented in this work wherein the exogenous supply of alpha-ketoglutarate (α-KG gave better results for fast proliferating cells as compared to delivery by microspheres or microspheres incorporated scaffolds which can be used while culturing slow growing cells. All these studies were performed in two dimensional (2D and three dimensional (3D setups in which chitosan-gelatin-polypyrrole has been used as 3-D scaffolds. Chitosan and gelatin microspheres alone as well as incorporated in the cryogels were characterized. MTT assay done using neuro-2a cell line showed approximately 42% and 70% increment in cellular proliferation when gelatin and chitosan microspheres were added in a 3-D setup, respectively, as compared to the control. Biochemical analysis of ammonia showed 6-fold reductions in ammonia level in a 3-D setup compared to the control. We also studied the synthesis of a neurotransmitter-like glutamate and found that its concentration increased up to 0.25 mg/ml when the microspheres were added exogenously in a 3-D system.

  11. Expression of the melatonin receptor Mel(1c) in neural tissues of the reef fish Siganus guttatus.

    Science.gov (United States)

    Park, Yong-Ju; Park, Ji-Gweon; Jeong, Hyung-Bok; Takeuchi, Yuki; Kim, Se-Jae; Lee, Young-Don; Takemura, Akihiro

    2007-05-01

    The golden rabbitfish, Siganus guttatus, is a reef fish exhibiting a restricted lunar-related rhythm in behavior and reproduction. Here, to understand the circadian rhythm of this lunar-synchronized spawner, a melatonin receptor subtype-Mel(1c)-was cloned. The full-length Mel(1c) melatonin receptor cDNA comprised 1747 bp with a single open reading frame (1062 bp) that encodes a 353-amino acid protein, which included 7 presumed transmembrane domains. Real-time PCR revealed high Mel(1c) mRNA expression in the retina and brain but not in the peripheral tissues. When the fish were reared under light/dark (LD 12:12) conditions, Mel(1c) mRNA in the retina and brain was expressed with daily variations and increased during nighttime. Similar variations were noted under constant conditions, suggesting that Mel(1c) mRNA expression is regulated by the circadian clock system. Daily variations of Mel(1c) mRNA expression with a peak at zeitgeber time (ZT) 12 were observed in the cultured pineal gland under LD 12:12. Exposure of the cultured pineal gland to light at ZT17 resulted in a decrease in Mel(1c) mRNA expression. When light was obstructed at ZT5, the opposite effect was obtained. These results suggest that light exerts certain effects on Mel(1c) mRNA expression directly or indirectly through melatonin actions.

  12. Immunohistochemical study of PrPSc distribution in neural and extraneural tissues of two cats with feline spongiform encephalopathy

    Directory of Open Access Journals (Sweden)

    Wunderlin Sabina S

    2009-03-01

    Full Text Available Abstract Background Two domestic shorthair cats presenting with progressive hind-limb ataxia and increased aggressiveness were necropsied and a post mortem diagnosis of Feline Spongiform Encephalopathy (FSE was made. A wide spectrum of tissue samples was collected and evaluated histologically and immunohistologically for the presence of PrPSc. Results Histopathological examination revealed a diffuse vacuolation of the grey matter neuropil with the following areas being most severely affected: corpus geniculatum medialis, thalamus, gyrus dentatus of the hippocampus, corpus striatum, and deep layers of the cerebral and cerebellar cortex as well as in the brain stem. In addition, a diffuse glial reaction involving astrocytes and microglia and intraneuronal vacuolation in a few neurons in the brain stem was present. Heavy PrPSc immunostaining was detected in brain, retina, optic nerve, pars nervosa of the pituitary gland, trigeminal ganglia and small amounts in the myenteric plexus of the small intestine (duodenum, jejunum and slightly in the medulla of the adrenal gland. Conclusion The PrPSc distribution within the brain was consistent with that described in other FSE-affected cats. The pattern of abnormal PrP in the retina corresponded to that found in a captive cheetah with FSE, in sheep with scrapie and was similar to nvCJD in humans.

  13. Temperature simulations in hyperthermia treatment planning of the head and neck region. Rigorous optimization of tissue properties

    International Nuclear Information System (INIS)

    Verhaart, Rene F.; Rijnen, Zef; Verduijn, Gerda M.; Paulides, Margarethus M.; Fortunati, Valerio; Walsum, Theo van; Veenland, Jifke F.

    2014-01-01

    Hyperthermia treatment planning (HTP) is used in the head and neck region (H and N) for pretreatment optimization, decision making, and real-time HTP-guided adaptive application of hyperthermia. In current clinical practice, HTP is based on power-absorption predictions, but thermal dose-effect relationships advocate its extension to temperature predictions. Exploitation of temperature simulations requires region- and temperature-specific thermal tissue properties due to the strong thermoregulatory response of H and N tissues. The purpose of our work was to develop a technique for patient group-specific optimization of thermal tissue properties based on invasively measured temperatures, and to evaluate the accuracy achievable. Data from 17 treated patients were used to optimize the perfusion and thermal conductivity values for the Pennes bioheat equation-based thermal model. A leave-one-out approach was applied to accurately assess the difference between measured and simulated temperature (∇T). The improvement in ∇T for optimized thermal property values was assessed by comparison with the ∇T for values from the literature, i.e., baseline and under thermal stress. The optimized perfusion and conductivity values of tumor, muscle, and fat led to an improvement in simulation accuracy (∇T: 2.1 ± 1.2 C) compared with the accuracy for baseline (∇T: 12.7 ± 11.1 C) or thermal stress (∇T: 4.4 ± 3.5 C) property values. The presented technique leads to patient group-specific temperature property values that effectively improve simulation accuracy for the challenging H and N region, thereby making simulations an elegant addition to invasive measurements. The rigorous leave-one-out assessment indicates that improvements in accuracy are required to rely only on temperature-based HTP in the clinic. (orig.) [de

  14. Simulation study of pO2 distribution in induced tumour masses and normal tissues within a microcirculation environment.

    Science.gov (United States)

    Li, Mao; Li, Yan; Wen, Peng Paul

    2014-01-01

    The biological microenvironment is interrupted when tumour masses are introduced because of the strong competition for oxygen. During the period of avascular growth of tumours, capillaries that existed play a crucial role in supplying oxygen to both tumourous and healthy cells. Due to limitations of oxygen supply from capillaries, healthy cells have to compete for oxygen with tumourous cells. In this study, an improved Krogh's cylinder model which is more realistic than the previously reported assumption that oxygen is homogeneously distributed in a microenvironment, is proposed to describe the process of the oxygen diffusion from a capillary to its surrounding environment. The capillary wall permeability is also taken into account. The simulation study is conducted and the results show that when tumour masses are implanted at the upstream part of a capillary and followed by normal tissues, the whole normal tissues suffer from hypoxia. In contrast, when normal tissues are ahead of tumour masses, their pO2 is sufficient. In both situations, the pO2 in the whole normal tissues drops significantly due to the axial diffusion at the interface of normal tissues and tumourous cells. As the existence of the axial oxygen diffusion cannot supply the whole tumour masses, only these tumourous cells that are near the interface can be partially supplied, and have a small chance to survive.

  15. Reconstruction of structure and function in tissue engineering of solid organs: Toward simulation of natural development based on decellularization.

    Science.gov (United States)

    Zheng, Chen-Xi; Sui, Bing-Dong; Hu, Cheng-Hu; Qiu, Xin-Yu; Zhao, Pan; Jin, Yan

    2018-04-27

    Failure of solid organs, such as the heart, liver, and kidney, remains a major cause of the world's mortality due to critical shortage of donor organs. Tissue engineering, which uses elements including cells, scaffolds, and growth factors to fabricate functional organs in vitro, is a promising strategy to mitigate the scarcity of transplantable organs. Within recent years, different construction strategies that guide the combination of tissue engineering elements have been applied in solid organ tissue engineering and have achieved much progress. Most attractively, construction strategy based on whole-organ decellularization has become a popular and promising approach, because the overall structure of extracellular matrix can be well preserved. However, despite the preservation of whole structure, the current constructs derived from decellularization-based strategy still perform partial functions of solid organs, due to several challenges, including preservation of functional extracellular matrix structure, implementation of functional recellularization, formation of functional vascular network, and realization of long-term functional integration. This review overviews the status quo of solid organ tissue engineering, including both advances and challenges. We have also put forward a few techniques with potential to solve the challenges, mainly focusing on decellularization-based construction strategy. We propose that the primary concept for constructing tissue-engineered solid organs is fabricating functional organs based on intact structure via simulating the natural development and regeneration processes. Copyright © 2018 John Wiley & Sons, Ltd.

  16. Coupling a Neural Network with Atmospheric Flow Simulations to Locate and Quantify CH4 Emissions at Well Pads

    Science.gov (United States)

    Travis, B. J.; Sauer, J.; Dubey, M. K.

    2017-12-01

    Methane (CH4) leaks from oil and gas production fields are a potentially significant source of atmospheric methane. US DOE's ARPA-E office is supporting research to locate methane emissions at 10 m size well pads to within 1 m. A team led by Aeris Technologies, and that includes LANL, Planetary Science Institute and Rice University has developed an autonomous leak detection system (LDS) employing a compact laser absorption methane sensor, a sonic anemometer and multiport sampling. The LDS system analyzes monitoring data using a convolutional neural network (cNN) to locate and quantify CH4 emissions. The cNN was trained using three sources: (1) ultra-high-resolution simulations of methane transport provided by LANL's coupled atmospheric transport model HIGRAD, for numerous controlled methane release scenarios and methane sampling configurations under variable atmospheric conditions, (2) Field tests at the METEC site in Ft. Collins, CO., and (3) Field data from other sites where point-source surface methane releases were monitored downwind. A cNN learning algorithm is well suited to problems in which the training and observed data are noisy, or correspond to complex sensor data as is typical of meteorological and sensor data over a well pad. Recent studies with our cNN emphasize the importance of tracking wind speeds and directions at fine resolution ( 1 second), and accounting for variations in background CH4 levels. A few cases illustrate the importance of sufficiently long monitoring; short monitoring may not provide enough information to determine accurately a leak location or strength, mainly because of short-term unfavorable wind directions and choice of sampling configuration. Length of multiport duty cycle sampling and sample line flush time as well as number and placement of monitoring sensors can significantly impact ability to locate and quantify leaks. Source location error at less than 10% requires about 30 or more training cases.

  17. WE-DE-202-02: Are Track Structure Simulations Truly Needed for Radiobiology at the Cellular and Tissue Levels?

    Energy Technology Data Exchange (ETDEWEB)

    Stewart, R. [University of Washington (United States)

    2016-06-15

    Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological

  18. WE-DE-202-02: Are Track Structure Simulations Truly Needed for Radiobiology at the Cellular and Tissue Levels?

    International Nuclear Information System (INIS)

    Stewart, R.

    2016-01-01

    Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological

  19. Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions

    Science.gov (United States)

    Schneider, Wilfried; Bortfeld, Thomas; Schlegel, Wolfgang

    2000-02-01

    We describe a new method to convert CT numbers into mass density and elemental weights of tissues required as input for dose calculations with Monte Carlo codes such as EGS4. As a first step, we calculate the CT numbers for 71 human tissues. To reduce the effort for the necessary fits of the CT numbers to mass density and elemental weights, we establish four sections on the CT number scale, each confined by selected tissues. Within each section, the mass density and elemental weights of the selected tissues are interpolated. For this purpose, functional relationships between the CT number and each of the tissue parameters, valid for media which are composed of only two components in varying proportions, are derived. Compared with conventional data fits, no loss of accuracy is accepted when using the interpolation functions. Assuming plausible values for the deviations of calculated and measured CT numbers, the mass density can be determined with an accuracy better than 0.04 g cm-3 . The weights of phosphorus and calcium can be determined with maximum uncertainties of 1 or 2.3 percentage points (pp) respectively. Similar values can be achieved for hydrogen (0.8 pp) and nitrogen (3 pp). For carbon and oxygen weights, errors up to 14 pp can occur. The influence of the elemental weights on the results of Monte Carlo dose calculations is investigated and discussed.

  20. Monte Carlo simulation of different positron emitting radionuclides incorporated in a soft tissue volume

    International Nuclear Information System (INIS)

    Olaya D, H.; Martinez O, S. A.; Sevilla M, A. C.; Vega C, H. R.

    2015-10-01

    Monte Carlo calculations were carried out where compounds with positron-emitters radionuclides, like FDG ( 18 F), Acetate ( 11 C), and Ammonium ( 13 N), were incorporated into a soft tissue volume, in the aim to estimate the type of particles produced their energies, their mean free paths, and the absorbed dose at different distances with respect to the center of the volume. The volume was modeled with a radius larger than the maximum range of positrons in order to produce 0.511 keV annihilation gamma-ray photons. With the obtained results the equivalent dose, in various organs and tissues able to metabolize different radiopharmaceutical drugs, can be estimated. (Author)

  1. Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

    Energy Technology Data Exchange (ETDEWEB)

    Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Tour 45-55/Etage 4/Case 100, UPMC, Paris Cedex 05 (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)

    2007-02-15

    Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere-Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the &apos

  2. Finite Element Methods for real-time Haptic Feedback of Soft-Tissue Models in Virtual Reality Simulators

    Science.gov (United States)

    Frank, Andreas O.; Twombly, I. Alexander; Barth, Timothy J.; Smith, Jeffrey D.; Dalton, Bonnie P. (Technical Monitor)

    2001-01-01

    We have applied the linear elastic finite element method to compute haptic force feedback and domain deformations of soft tissue models for use in virtual reality simulators. Our results show that, for virtual object models of high-resolution 3D data (>10,000 nodes), haptic real time computations (>500 Hz) are not currently possible using traditional methods. Current research efforts are focused in the following areas: 1) efficient implementation of fully adaptive multi-resolution methods and 2) multi-resolution methods with specialized basis functions to capture the singularity at the haptic interface (point loading). To achieve real time computations, we propose parallel processing of a Jacobi preconditioned conjugate gradient method applied to a reduced system of equations resulting from surface domain decomposition. This can effectively be achieved using reconfigurable computing systems such as field programmable gate arrays (FPGA), thereby providing a flexible solution that allows for new FPGA implementations as improved algorithms become available. The resulting soft tissue simulation system would meet NASA Virtual Glovebox requirements and, at the same time, provide a generalized simulation engine for any immersive environment application, such as biomedical/surgical procedures or interactive scientific applications.

  3. Freeform fabrication of tissue-simulating phantom for potential use of surgical planning in conjoined twins separation surgery.

    Science.gov (United States)

    Shen, Shuwei; Wang, Haili; Xue, Yue; Yuan, Li; Zhou, Ximing; Zhao, Zuhua; Dong, Erbao; Liu, Bin; Liu, Wendong; Cromeens, Barrett; Adler, Brent; Besner, Gail; Xu, Ronald X

    2017-09-08

    Preoperative assessment of tissue anatomy and accurate surgical planning is crucial in conjoined twin separation surgery. We developed a new method that combines three-dimensional (3D) printing, assembling, and casting to produce anatomic models of high fidelity for surgical planning. The related anatomic features of the conjoined twins were captured by computed tomography (CT), classified as five organ groups, and reconstructed as five computer models. Among these organ groups, the skeleton was produced by fused deposition modeling (FDM) using acrylonitrile-butadiene-styrene. For the other four organ groups, shell molds were prepared by FDM and cast with silica gel to simulate soft tissues, with contrast enhancement pigments added to simulate different CT and visual contrasts. The produced models were assembled, positioned firmly within a 3D printed shell mold simulating the skin boundary, and cast with transparent silica gel. The produced phantom was subject to further CT scan in comparison with that of the patient data for fidelity evaluation. Further data analysis showed that the produced model reassembled the geometric features of the original CT data with an overall mean deviation of less than 2 mm, indicating the clinical potential to use this method for surgical planning in conjoined twin separation surgery.

  4. Finite-element simulation of blood perfusion in muscle tissue during compression and sustained contraction

    NARCIS (Netherlands)

    Vankan, W.J.; Huyghe, J.M.R.J.; Slaaf, D.W.; Donkelaar, van C.C.; Drost, M.R.; Janssen, J.D.; Huson, A.

    1997-01-01

    Mechanical interaction between tissue stress and blood perfusion in skeletal muscles plays an important role in blood flow impediment during sustained contraction. The exact mechanism of this interaction is not clear, and experimental investigation of this mechanism is difficult. We developed a

  5. Simulation of RF data with tissue motion for optimizing stationary echo canceling filters

    DEFF Research Database (Denmark)

    Schlaikjer, Malene; Torp-Pedersen, S.; Jensen, Jørgen Arendt

    2003-01-01

    Blood velocity estimation is complicated by the strong echoes received from tissue surrounding the vessel under investigation. Proper blood velocity estimation necessitates use of a filter for separation of the different signal components. Development of these filters and new estimators requires ...

  6. Real-time simulation of soft tissue deformation and electrocautery procedures in laparoscopic rectal cancer radical surgery.

    Science.gov (United States)

    Sui, Yuan; Pan, Jun J; Qin, Hong; Liu, Hao; Lu, Yun

    2017-12-01

    Laparoscopic surgery (LS), also referred to as minimally invasive surgery, is a modern surgical technique which is widely applied. The fulcrum effect makes LS a non-intuitive motor skill with a steep learning curve. A hybrid model of tetrahedrons and a multi-layer triangular mesh are constructed to simulate the deformable behavior of the rectum and surrounding tissues in the Position-Based Dynamics (PBD) framework. A heat-conduction based electric-burn technique is employed to simulate the electrocautery procedure. The simulator has been applied for laparoscopic rectum cancer surgery training. From the experimental results, trainees can operate in real time with high degrees of stability and fidelity. A preliminary study was performed to evaluate the realism and usefulness. This prototype simulator has been tested and verified by colorectal surgeons through a pilot study. They believed both the visual and the haptic performance of the simulation are realistic and helpful to enhance laparoscopic skills. Copyright © 2017 John Wiley & Sons, Ltd.

  7. Dosimetric Comparison of Simulated Human Eye And Water Phantom in Investigation of Iodine Source Effects on Tumour And Healthy Tissues

    International Nuclear Information System (INIS)

    Sadi, A.S.; Masoudi, F.S. K.N.Toosi University of Technology

    2011-01-01

    For better clinical analysis in ophthalmic brachytherapy dosimetry, there is a need for the dose determination in different parts of the eye, so simulating the eye and defining the material of any parts of that, is helpful for better investigating dosimetry in human eye. However in brachytherapy dosimetry, it is common to consider the water phantom as human eye globe. In this work, a full human eye is simulated with MCNP-4C code by considering all parts of the eye like; lens, cornea, retina, choroid, sclera, anterior chamber, optic nerve, bulk of the eye comprising vitreous body and tumour. The average dose in different parts of this full model of human eye is determined and the results are compared with the dose calculated in water phantom. The central axes depth dose and the dose in whole of the tumour for these two simulated eye model are calculated too, and the results are compared. At long last, as the aim of this work is comparing the result of investigating dosimetry between two water phantom as human eye and simulated eye globe, the ratios of the absorbed dose by the healthy tissues to the absorbed dose by the tumour are calculated in these simulations and the comparison between results is done eventually.

  8. In situ measurement and modeling of biomechanical response of human cadaveric soft tissues for physics-based surgical simulation.

    Science.gov (United States)

    Lim, Yi-Je; Deo, Dhanannjay; Singh, Tejinder P; Jones, Daniel B; De, Suvranu

    2009-06-01

    Development of a laparoscopic surgery simulator that delivers high-fidelity visual and haptic (force) feedback, based on the physical models of soft tissues, requires the use of empirical data on the mechanical behavior of intra-abdominal organs under the action of external forces. As experiments on live human patients present significant risks, the use of cadavers presents an alternative. We present techniques of measuring and modeling the mechanical response of human cadaveric tissue for the purpose of developing a realistic model. The major contribution of this paper is the development of physics-based models of soft tissues that range from linear elastic models to nonlinear viscoelastic models which are efficient for application within the framework of a real-time surgery simulator. To investigate the in situ mechanical, static, and dynamic properties of intra-abdominal organs, we have developed a high-precision instrument by retrofitting a robotic device from Sensable Technologies (position resolution of 0.03 mm) with a six-axis Nano 17 force-torque sensor from ATI Industrial Automation (force resolution of 1/1,280 N along each axis), and used it to apply precise displacement stimuli and record the force response of liver and stomach of ten fresh human cadavers. The mean elastic modulus of liver and stomach is estimated as 5.9359 kPa and 1.9119 kPa, respectively over the range of indentation depths tested. We have also obtained the parameters of a quasilinear viscoelastic (QLV) model to represent the nonlinear viscoelastic behavior of the cadaver stomach and liver over a range of indentation depths and speeds. The models are found to have an excellent goodness of fit (with R (2) > 0.99). The data and models presented in this paper together with additional ones based on the principles presented in this paper would result in realistic physics-based surgical simulators.

  9. A Comparison of the Dosimetric Parameters of Cs-137 Brachytherapy Source in Different Tissues with Water Using Monte Carlo Simulation

    Directory of Open Access Journals (Sweden)

    Sedigheh Sina

    2012-03-01

    Full Text Available Introduction After the publication of Task Group number 43 dose calculation formalism by the American Association of Physicists in Medicine (AAPM, this method has been known as the most common dose calculation method in brachytherapy treatment planning. In this formalism, the water phantom is introduced as the reference dosimetry phantom, while the attenuation coefficient of the sources in the water phantom is different from that of different tissues. The purpose of this study is to investigate the effects of the phantom materials on the TG-43 dosimetery parameters of the Cs-137 brachytherapy source using MCNP4C Monte Carlo code. Materials and Methods In this research, the Cs-137 (Model Selectron brachytherapy source was simulated in different phantoms (bone, soft tissue, muscle, fat, and the inhomogeneous phantoms of water/bone of volume 27000 cm3 using MCNP4C Monte Carlo code. *F8 tally was used to obtain the dose in a fine cubical lattice. Then the TG-43 dosimetry parameters of the brachytherapy source were obtained in water phantom and compared with those of different phantoms. Results The percentage difference between the radial dose function g(r of bone and the g(r of water phantom, at a distance of 10 cm from the source center is 20%, while such differences are 1.7%, 1.6% and 1.1% for soft tissue, muscle, and fat, respectively. The largest difference of the dose rate constant of phantoms with those of water is 4.52% for the bone phantom, while the differences for soft tissue, muscle, and fat are 1.18%, 1.27%, and 0.18%, respectively. The 2D anisotropy function of the Cs-137 source for different tissues is identical to that of water. Conclusion The results of the simulations have shown that dose calculation in water phantom would introduce errors in the dose calculation around brachytherapy sources. Therefore, it is suggested that the correction factors of different tissues be applied after dose calculation in water phantoms, in order to

  10. Monte Carlo simulation of different positron emitting radionuclides incorporated in a soft tissue volume

    Energy Technology Data Exchange (ETDEWEB)

    Olaya D, H.; Martinez O, S. A. [Universidad Pedagogica y Tecnologica de Colombia, Grupo de Fisica Nuclear Aplicada y Simulacion, Av. Central del Norte 39-115, 150003 Tunja, Boyaca (Colombia); Sevilla M, A. C. [Universidad Nacional de Colombia, Departamento de Fisica, Grupo CRYOMAG, 111321 Bogota D. C. (Colombia); Vega C, H. R., E-mail: grupo.finuas@uptc.edu.co [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98060 Zacatecas, Zac. (Mexico)

    2015-10-15

    Monte Carlo calculations were carried out where compounds with positron-emitters radionuclides, like FDG ({sup 18}F), Acetate ({sup 11}C), and Ammonium ({sup 13}N), were incorporated into a soft tissue volume, in the aim to estimate the type of particles produced their energies, their mean free paths, and the absorbed dose at different distances with respect to the center of the volume. The volume was modeled with a radius larger than the maximum range of positrons in order to produce 0.511 keV annihilation gamma-ray photons. With the obtained results the equivalent dose, in various organs and tissues able to metabolize different radiopharmaceutical drugs, can be estimated. (Author)

  11. Simulation of a plane wavefront propagating in cardiac tissue using a cellular automata model

    International Nuclear Information System (INIS)

    Barbosa, Carlos R Hall

    2003-01-01

    We present a detailed description of a cellular automata model for the propagation of action potential in a planar cardiac tissue, which is very fast and easy to use. The model incorporates anisotropy in the electrical conductivity and a spatial variation of the refractory time. The transmembrane potential distribution is directly derived from the cell states, and the intracellular and extracellular potential distributions are calculated for the particular case of a plane wavefront. Once the potential distributions are known, the associated current densities are calculated by Ohm's law, and the magnetic field is determined at a plane parallel to the cardiac tissue by applying the law of Biot and Savart. The results obtained for propagation speed and for magnetic field amplitude with the cellular automata model are compared with values predicted by the bidomain formulation, for various angles between wavefront propagation and fibre direction, characterizing excellent agreement between the models

  12. Silicone-based composite materials simulate breast tissue to be used as ultrasonography training phantoms.

    Science.gov (United States)

    Ustbas, Burcin; Kilic, Deniz; Bozkurt, Ayhan; Aribal, Mustafa Erkin; Akbulut, Ozge

    2018-03-02

    A silicone-based composite breast phantom is fabricated to be used as an education model in ultrasonography training. A matrix of silicone formulations is tracked to mimic the ultrasonography and tactile response of human breast tissue. The performance of two different additives: (i) silicone oil and (ii) vinyl-terminated poly (dimethylsiloxane) (PDMS) are monitored by a home-made acoustic setup. Through the use of 75 wt% vinyl-terminated PDMS in two-component silicone elastomer mixture, a sound velocity of 1.29 ± 0.09 × 10 3  m/s and an attenuation coefficient of 12.99 ± 0.08 dB/cm-values those match closely to the human breast tissue-are measured with 5 MHz probe. This model can also be used for needle biopsy as well as for self-exam trainings. Herein, we highlight the fabrication of a realistic, durable, accessible, and cost-effective training platform that contains skin layer, inner breast tissue, and tumor masses. Copyright © 2018. Published by Elsevier B.V.

  13. Analysis of skin tissues spatial fluorescence distribution by the Monte Carlo simulation

    International Nuclear Information System (INIS)

    Churmakov, D Y; Meglinski, I V; Piletsky, S A; Greenhalgh, D A

    2003-01-01

    A novel Monte Carlo technique of simulation of spatial fluorescence distribution within the human skin is presented. The computational model of skin takes into account the spatial distribution of fluorophores, which would arise due to the structure of collagen fibres, compared to the epidermis and stratum corneum where the distribution of fluorophores is assumed to be homogeneous. The results of simulation suggest that distribution of auto-fluorescence is significantly suppressed in the near-infrared spectral region, whereas the spatial distribution of fluorescence sources within a sensor layer embedded in the epidermis is localized at an 'effective' depth

  14. Analysis of skin tissues spatial fluorescence distribution by the Monte Carlo simulation

    Energy Technology Data Exchange (ETDEWEB)

    Churmakov, D Y [School of Engineering, Cranfield University, Cranfield, MK43 0AL (United Kingdom); Meglinski, I V [School of Engineering, Cranfield University, Cranfield, MK43 0AL (United Kingdom); Piletsky, S A [Institute of BioScience and Technology, Cranfield University, Silsoe, MK45 4DT (United Kingdom); Greenhalgh, D A [School of Engineering, Cranfield University, Cranfield, MK43 0AL (United Kingdom)

    2003-07-21

    A novel Monte Carlo technique of simulation of spatial fluorescence distribution within the human skin is presented. The computational model of skin takes into account the spatial distribution of fluorophores, which would arise due to the structure of collagen fibres, compared to the epidermis and stratum corneum where the distribution of fluorophores is assumed to be homogeneous. The results of simulation suggest that distribution of auto-fluorescence is significantly suppressed in the near-infrared spectral region, whereas the spatial distribution of fluorescence sources within a sensor layer embedded in the epidermis is localized at an 'effective' depth.

  15. Analysis of skin tissues spatial fluorescence distribution by the Monte Carlo simulation

    Science.gov (United States)

    Y Churmakov, D.; Meglinski, I. V.; Piletsky, S. A.; Greenhalgh, D. A.

    2003-07-01

    A novel Monte Carlo technique of simulation of spatial fluorescence distribution within the human skin is presented. The computational model of skin takes into account the spatial distribution of fluorophores, which would arise due to the structure of collagen fibres, compared to the epidermis and stratum corneum where the distribution of fluorophores is assumed to be homogeneous. The results of simulation suggest that distribution of auto-fluorescence is significantly suppressed in the near-infrared spectral region, whereas the spatial distribution of fluorescence sources within a sensor layer embedded in the epidermis is localized at an `effective' depth.

  16. Simulation of Snowmelt Runoff Using SRM Model and Comparison With Neural Networks ANN and ANFIS (Case Study: Kardeh dam basin

    Directory of Open Access Journals (Sweden)

    morteza akbari

    2017-03-01

    of the basin with 2962 meters above sea level. Kardeh dam was primarily constructed on the Kardehriver for providing drinking and agriculture water demand with an annual volume rate of 21.23 million cubic meters. Satellite image: To estimate the level of snow cover, the satellite Landsat ETM+ data at path 35-159, rows 34-159 over the period 2001-2002 were used. Surfaces covered with snow were separated bysnow distinction normalized index (NDSI, But due to the lack of training data for image classification (areas with snow and no snow, the k-means unsupervised classification algorithm was used. Extracting the data from the meteorological and hydrological Since only a gauging station exists at the Kardeh dam site, the daily discharge data recorded at these stations was used. To extract meteorological parameters such as precipitation and temperature data, the records of the three stations Golmakan, Mashhad and Ghouchan, as the stations closest to the dam basin Kardeh were used. The purpose of this study was to simulate snowmelt runoff using SRM hydrological models and to compare the results with the outputs of the neural network models such as the ANN and the ANFIS model. Flow simulation was carried out using SRM, ANN model with the Multilayer Perceptron with back-propagation algorithm, and Sugeno type ANFIS. To evaluate the performance of the models in addition to the standard statistics such as mean square error or mean absolute percentage error, the regression coefficient measures and the difference in volume were used. The results showed that all three models are almost similar in terms of statistical parameters MSE and R and the differences were negligible. SRM model: SRM model is a daily hydrological model. This equation is composed of different components including 14 parameters. The input values were calculated based on the equations of degree-day factor. The evaluation of the model was performed with flow subside factor, coefficient and subtracting volume

  17. Combined Monte Carlo and path-integral method for simulated library of time-resolved reflectance curves from layered tissue models

    Science.gov (United States)

    Wilson, Robert H.; Vishwanath, Karthik; Mycek, Mary-Ann

    2009-02-01

    Monte Carlo (MC) simulations are considered the "gold standard" for mathematical description of photon transport in tissue, but they can require large computation times. Therefore, it is important to develop simple and efficient methods for accelerating MC simulations, especially when a large "library" of related simulations is needed. A semi-analytical method involving MC simulations and a path-integral (PI) based scaling technique generated time-resolved reflectance curves from layered tissue models. First, a zero-absorption MC simulation was run for a tissue model with fixed scattering properties in each layer. Then, a closed-form expression for the average classical path of a photon in tissue was used to determine the percentage of time that the photon spent in each layer, to create a weighted Beer-Lambert factor to scale the time-resolved reflectance of the simulated zero-absorption tissue model. This method is a unique alternative to other scaling techniques in that it does not require the path length or number of collisions of each photon to be stored during the initial simulation. Effects of various layer thicknesses and absorption and scattering coefficients on the accuracy of the method will be discussed.

  18. Cyclic deformation-induced solute transport in tissue scaffolds with computer designed, interconnected, pore networks: experiments and simulations.

    Science.gov (United States)

    Den Buijs, Jorn Op; Dragomir-Daescu, Dan; Ritman, Erik L

    2009-08-01

    Nutrient supply and waste removal in porous tissue engineering scaffolds decrease from the periphery to the center, leading to limited depth of ingrowth of new tissue into the scaffold. However, as many tissues experience cyclic physiological strains, this may provide a mechanism to enhance solute transport in vivo before vascularization of the scaffold. The hypothesis of this study was that pore cross-sectional geometry and interconnectivity are of major importance for the effectiveness of cyclic deformation-induced solute transport. Transparent elastic polyurethane scaffolds, with computer-programmed design of pore networks in the form of interconnected channels, were fabricated using a 3D printing and injection molding technique. The scaffold pores were loaded with a colored tracer for optical contrast, cyclically compressed with deformations of 10 and 15% of the original undeformed height at 1.0 Hz. Digital imaging was used to quantify the spatial distribution of the tracer concentration within the pores. Numerical simulations of a fluid-structure interaction model of deformation-induced solute transport were compared to the experimental data. The results of experiments and modeling agreed well and showed that pore interconnectivity heavily influences deformation-induced solute transport. Pore cross-sectional geometry appears to be of less relative importance in interconnected pore networks. Validated computer models of solute transport can be used to design optimal scaffold pore geometries that will enhance the convective transport of nutrients inside the scaffold and the removal of waste, thus improving the cell survivability deep inside the scaffold.

  19. On Improved Least Squares Regression and Artificial Neural Network Meta-Models for Simulation via Control Variates

    Science.gov (United States)

    2016-09-15

    Requirements for the Degree of Doctor of Philosophy in Operations Research Michael P. Gibb, B.S., M.S. Captain, USAF September 2016 DISTRIBUTION...Bidstrup, P. Kohl, and G. May. Modeling the properties of PECVD silicon dioxide films using optimized back-propagation neural networks. IEEE Trans

  20. 3D-printed soft-tissue physical models of renal malignancies for individualized surgical simulation: a feasibility study.

    Science.gov (United States)

    Maddox, Michael M; Feibus, Allison; Liu, James; Wang, Julie; Thomas, Raju; Silberstein, Jonathan L

    2018-03-01

    To construct patient-specific physical three-dimensional (3D) models of renal units with materials that approximates the properties of renal tissue to allow pre-operative and robotic training surgical simulation, 3D physical kidney models were created (3DSystems, Rock Hill, SC) using computerized tomography to segment structures of interest (parenchyma, vasculature, collection system, and tumor). Images were converted to a 3D surface mesh file for fabrication using a multi-jet 3D printer. A novel construction technique was employed to approximate normal renal tissue texture, printers selectively deposited photopolymer material forming the outer shell of the kidney, and subsequently, an agarose gel solution was injected into the inner cavity recreating the spongier renal parenchyma. We constructed seven models of renal units with suspected malignancies. Partial nephrectomy and renorrhaphy were performed on each of the replicas. Subsequently all patients successfully underwent robotic partial nephrectomy. Average tumor diameter was 4.4 cm, warm ischemia time was 25 min, RENAL nephrometry score was 7.4, and surgical margins were negative. A comparison was made between the seven cases and the Tulane Urology prospectively maintained robotic partial nephrectomy database. Patients with surgical models had larger tumors, higher nephrometry score, longer warm ischemic time, fewer positive surgical margins, shorter hospitalization, and fewer post-operative complications; however, the only significant finding was lower estimated blood loss (186 cc vs 236; p = 0.01). In this feasibility study, pre-operative resectable physical 3D models can be constructed and used as patient-specific surgical simulation tools; further study will need to demonstrate if this results in improvement of surgical outcomes and robotic simulation education.

  1. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  2. Mechanical Stimulation of Adipose-Derived Stem Cells for Functional Tissue Engineering of the Musculoskeletal System via Cyclic Hydrostatic Pressure, Simulated Microgravity, and Cyclic Tensile Strain.

    Science.gov (United States)

    Nordberg, Rachel C; Bodle, Josie C; Loboa, Elizabeth G

    2018-01-01

    It is critical that human adipose stem cell (hASC) tissue-engineering therapies possess appropriate mechanical properties in order to restore function of the load bearing tissues of the musculoskeletal system. In an effort to elucidate the hASC response to mechanical stimulation and develop mechanically robust tissue engineered constructs, recent research has utilized a variety of mechanical loading paradigms including cyclic tensile strain, cyclic hydrostatic pressure, and mechanical unloading in simulated microgravity. This chapter describes methods for applying these mechanical stimuli to hASC to direct differentiation for functional tissue engineering of the musculoskeletal system.

  3. POD for Real-Time Simulation of Hyperelastic Soft Biological Tissue Using the Point Collocation Method of Finite Spheres

    Directory of Open Access Journals (Sweden)

    Suleiman Banihani

    2013-01-01

    Full Text Available The point collocation method of finite spheres (PCMFS is used to model the hyperelastic response of soft biological tissue in real time within the framework of virtual surgery simulation. The proper orthogonal decomposition (POD model order reduction (MOR technique was used to achieve reduced-order model of the problem, minimizing computational cost. The PCMFS is a physics-based meshfree numerical technique for real-time simulation of surgical procedures where the approximation functions are applied directly on the strong form of the boundary value problem without the need for integration, increasing computational efficiency. Since computational speed has a significant role in simulation of surgical procedures, the proposed technique was able to model realistic nonlinear behavior of organs in real time. Numerical results are shown to demonstrate the effectiveness of the new methodology through a comparison between full and reduced analyses for several nonlinear problems. It is shown that the proposed technique was able to achieve good agreement with the full model; moreover, the computational and data storage costs were significantly reduced.

  4. FLUKA and PENELOPE simulations of 10keV to 10MeV photons in LYSO and soft tissue

    CERN Document Server

    Chin, M P W; Fassò, A; Ferrari, A; Ortega, P G; Sala, P R

    2014-01-01

    Monte Carlo simulations of electromagnetic particle interactions and transport by FLUKA and PENELOPE were compared. 10 key to 10 MeV incident photon beams impinged a LYSO crystal and a soft-tissue phantom. Central-axis as well as off-axis depth doses agreed within 1 s.d.; no systematic under- or overestimate of the pulse height spectra was observed from 100 keV to 10 MeV for both materials, agreement was within 5\\%. Simulation of photon and electron transport and interactions at this level of precision and reliability is of significant impact, for instance, on treatment monitoring of hadrontherapy where a code like FLUKA is needed to simulate the full suite of particles and interactions (not just electromagnetic). At the interaction-by-interaction level, apart from known differences in condensed history techniques, two-quanta positron annihilation at rest was found to differ between the two codes. PENELOPE produced a 511 key sharp line, whereas FLUKA produced visible acolinearity, a feature recently implemen...

  5. Tissue classifications in Monte Carlo simulations of patient dose for photon beam tumor treatments

    Science.gov (United States)

    Lin, Mu-Han; Chao, Tsi-Chian; Lee, Chung-Chi; Tung-Chieh Chang, Joseph; Tung, Chuan-Jong

    2010-07-01

    The purpose of this work was to study the calculated dose uncertainties induced by the material classification that determined the interaction cross-sections and the water-to-material stopping-power ratios. Calculations were made for a head- and neck-cancer patient treated with five intensity-modulated radiotherapy fields using 6 MV photon beams. The patient's CT images were reconstructed into two voxelized patient phantoms based on different CT-to-material classification schemes. Comparisons of the depth-dose curve of the anterior-to-posterior field and the dose-volume-histogram of the treatment plan were used to evaluate the dose uncertainties from such schemes. The results indicated that any misassignment of tissue materials could lead to a substantial dose difference, which would affect the treatment outcome. To assure an appropriate material assignment, it is desirable to have different conversion tables for various parts of the body. The assignment of stopping-power ratio should be based on the chemical composition and the density of the material.

  6. Simulating the swelling and deformation behaviour in soft tissues using a convective thermal analogy

    Directory of Open Access Journals (Sweden)

    Herzog Walter

    2002-12-01

    Full Text Available Abstract Background It is generally accepted that cartilage adaptation and degeneration are mechanically mediated. Investigating the swelling behaviour of cartilage is important because the stress and strain state of cartilage is associated with the swelling and deformation behaviour. It is well accepted that the swelling of soft tissues is associated with mechanical, chemical, and electrical events. Method The purpose of the present study was to implement the triphasic theory into a commercial finite element tool (ABAQUS to solve practical problems in cartilage mechanics. Because of the mathematical identity between thermal and mass diffusion processes, the triphasic model was transferred into a convective thermal diffusion process in the commercial finite element software. The problem was solved using an iterative procedure. Results The proposed approach was validated using the one-dimensional numerical solutions and the experimental results of confined compression of articular cartilage described in the literature. The time-history of the force response of a cartilage specimen in confined compression, which was subjected to swelling caused by a sudden change of saline concentration, was predicted using the proposed approach and compared with the published experimental data. Conclusion The advantage of the proposed thermal analogy technique over previous studies is that it accounts for the convective diffusion of ion concentrations and the Donnan osmotic pressure in the interstitial fluid.

  7. Tissue classifications in Monte Carlo simulations of patient dose for photon beam tumor treatments

    International Nuclear Information System (INIS)

    Lin, Mu-Han; Chao, Tsi-Chian; Lee, Chung-Chi; Tung-Chieh Chang, Joseph; Tung, Chuan-Jong

    2010-01-01

    The purpose of this work was to study the calculated dose uncertainties induced by the material classification that determined the interaction cross-sections and the water-to-material stopping-power ratios. Calculations were made for a head- and neck-cancer patient treated with five intensity-modulated radiotherapy fields using 6 MV photon beams. The patient's CT images were reconstructed into two voxelized patient phantoms based on different CT-to-material classification schemes. Comparisons of the depth-dose curve of the anterior-to-posterior field and the dose-volume-histogram of the treatment plan were used to evaluate the dose uncertainties from such schemes. The results indicated that any misassignment of tissue materials could lead to a substantial dose difference, which would affect the treatment outcome. To assure an appropriate material assignment, it is desirable to have different conversion tables for various parts of the body. The assignment of stopping-power ratio should be based on the chemical composition and the density of the material.

  8. Accounting for large deformations in real-time simulations of soft tissues based on reduced-order models.

    Science.gov (United States)

    Niroomandi, S; Alfaro, I; Cueto, E; Chinesta, F

    2012-01-01

    Model reduction techniques have shown to constitute a valuable tool for real-time simulation in surgical environments and other fields. However, some limitations, imposed by real-time constraints, have not yet been overcome. One of such limitations is the severe limitation in time (established in 500Hz of frequency for the resolution) that precludes the employ of Newton-like schemes for solving non-linear models as the ones usually employed for modeling biological tissues. In this work we present a technique able to deal with geometrically non-linear models, based on the employ of model reduction techniques, together with an efficient non-linear solver. Examples of the performance of the technique over some examples will be given. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  9. 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.

  10. Simulation of nonlinear transient elastography: finite element model for the propagation of shear waves in homogeneous soft tissues.

    Science.gov (United States)

    Ye, W; Bel-Brunon, A; Catheline, S; Combescure, A; Rochette, M

    2018-01-01

    In this study, visco-hyperelastic Landau's model, which is widely used in acoustical physic field, is introduced into a finite element formulation. It is designed to model the nonlinear behaviour of finite amplitude shear waves in soft solids, typically, in biological tissues. This law is used in finite element models based on elastography, experiments reported in Jacob et al, the simulations results show a good agreement with the experimental study: It is observed in both that a plane shear wave generates only odd harmonics and a nonplane wave generates both odd and even harmonics in the spectral domain. In the second part, a parametric study is performed to analyse the influence of different factors on the generation of odd harmonics of plane wave. A quantitative relation is fitted between the odd harmonic amplitudes and the non-linear elastic parameter of Landau's model, which provides a practical guideline to identify the non-linearity of homogeneous tissues using elastography experiment. Copyright © 2017 John Wiley & Sons, Ltd.

  11. Temperature simulations in hyperthermia treatment planning of the head and neck region. Rigorous optimization of tissue properties

    Energy Technology Data Exchange (ETDEWEB)

    Verhaart, Rene F.; Rijnen, Zef; Verduijn, Gerda M.; Paulides, Margarethus M. [Erasmus MC - Cancer Institute, Department of Radiation Oncology, Hyperthermia Unit, Rotterdam (Netherlands); Fortunati, Valerio; Walsum, Theo van; Veenland, Jifke F. [Erasmus MC, Departments of Medical Informatics and Radiology, Biomedical Imaging Group Rotterdam, Rotterdam (Netherlands)

    2014-12-15

    Hyperthermia treatment planning (HTP) is used in the head and neck region (H and N) for pretreatment optimization, decision making, and real-time HTP-guided adaptive application of hyperthermia. In current clinical practice, HTP is based on power-absorption predictions, but thermal dose-effect relationships advocate its extension to temperature predictions. Exploitation of temperature simulations requires region- and temperature-specific thermal tissue properties due to the strong thermoregulatory response of H and N tissues. The purpose of our work was to develop a technique for patient group-specific optimization of thermal tissue properties based on invasively measured temperatures, and to evaluate the accuracy achievable. Data from 17 treated patients were used to optimize the perfusion and thermal conductivity values for the Pennes bioheat equation-based thermal model. A leave-one-out approach was applied to accurately assess the difference between measured and simulated temperature (∇T). The improvement in ∇T for optimized thermal property values was assessed by comparison with the ∇T for values from the literature, i.e., baseline and under thermal stress. The optimized perfusion and conductivity values of tumor, muscle, and fat led to an improvement in simulation accuracy (∇T: 2.1 ± 1.2 C) compared with the accuracy for baseline (∇T: 12.7 ± 11.1 C) or thermal stress (∇T: 4.4 ± 3.5 C) property values. The presented technique leads to patient group-specific temperature property values that effectively improve simulation accuracy for the challenging H and N region, thereby making simulations an elegant addition to invasive measurements. The rigorous leave-one-out assessment indicates that improvements in accuracy are required to rely only on temperature-based HTP in the clinic. (orig.) [German] Die Hyperthermiebehandlungsplanung (HTP, ''hyperthermia treatment planning'') wird in der Kopf- und Halsregion zur Optimierung der

  12. Modelling the propagation of terahertz radiation through a tissue simulating phantom

    International Nuclear Information System (INIS)

    Walker, Gillian C; Berry, Elizabeth; Smye, Stephen W; Zinov'ev, Nick N; Fitzgerald, Anthony J; Miles, Robert E; Chamberlain, Martyn; Smith, Michael A

    2004-01-01

    Terahertz (THz) frequency radiation, 0.1 THz to 20 THz, is being investigated for biomedical imaging applications following the introduction of pulsed THz sources that produce picosecond pulses and function at room temperature. Owing to the broadband nature of the radiation, spectral and temporal information is available from radiation that has interacted with a sample; this information is exploited in the development of biomedical imaging tools and sensors. In this work, models to aid interpretation of broadband THz spectra were developed and evaluated. THz radiation lies on the boundary between regions best considered using a deterministic electromagnetic approach and those better analysed using a stochastic approach incorporating quantum mechanical effects, so two computational models to simulate the propagation of THz radiation in an absorbing medium were compared. The first was a thin film analysis and the second a stochastic Monte Carlo model. The Cole-Cole model was used to predict the variation with frequency of the physical properties of the sample and scattering was neglected. The two models were compared with measurements from a highly absorbing water-based phantom. The Monte Carlo model gave a prediction closer to experiment over 0.1 to 3 THz. Knowledge of the frequency-dependent physical properties, including the scattering characteristics, of the absorbing media is necessary. The thin film model is computationally simple to implement but is restricted by the geometry of the sample it can describe. The Monte Carlo framework, despite being initially more complex, provides greater flexibility to investigate more complicated sample geometries

  13. Elaboration of a neural network for classification of Taylors bubbles in vertical pipes using Monte Carlo simulation for the training phase

    Energy Technology Data Exchange (ETDEWEB)

    Schuabb, Pablo G.; Medeiros, Jose A.C.C.; Schirru, Roberto, E-mail: pablogs@poli.ufrj.br, E-mail: canedo@lmp.ufrj.br, E-mail: schirru@lmp.ufrj.br [Corrdenacao dos Programas de Pos-Graduacao em Engenharia (PEN/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear

    2015-07-01

    The increase of the diameter of a spherical bubble deforms its shape, after which it moves along the vertical center of the pipeline. The Taylor's flow has bubbles with the form of a bullet and increases in the bubble's volume are seen by an enlargement of their length making that kind of bubble easily identified using gamma ray attenuation which is simulated via the software MCNPX that uses the Monte Carlo probabilistic method to simulate radiation-matter interactions. The simulations show that there exists a relation among the counts of a detector and the rising movement of a Taylor's Bubble. A database could be made to answer queries on the dimensions of a Taylor bubble for given readings of a detector, approach that would require a huge database. To make that association an Artificial Neural Network is proposed. The network can be trained with a finite number of samples that is enough to make the network able to classify data of not known bubbles simulated via MCNPX or measured on field. (author)

  14. Artificial Neural Network-Based Constitutive Relationship of Inconel 718 Superalloy Construction and Its Application in Accuracy Improvement of Numerical Simulation

    Directory of Open Access Journals (Sweden)

    Junya Lv

    2017-01-01

    Full Text Available The application of accurate constitutive relationship in finite element simulation would significantly contribute to accurate simulation results, which play critical roles in process design and optimization. In this investigation, the true stress-strain data of an Inconel 718 superalloy were obtained from a series of isothermal compression tests conducted in a wide temperature range of 1153–1353 K and strain rate range of 0.01–10 s−1 on a Gleeble 3500 testing machine (DSI, St. Paul, DE, USA. Then the constitutive relationship was modeled by an optimally-constructed and well-trained back-propagation artificial neural network (ANN. The evaluation of the ANN model revealed that it has admirable performance in characterizing and predicting the flow behaviors of Inconel 718 superalloy. Consequently, the developed ANN model was used to predict abundant stress-strain data beyond the limited experimental conditions and construct the continuous mapping relationship for temperature, strain rate, strain and stress. Finally, the constructed ANN was implanted in a finite element solver though the interface of “URPFLO” subroutine to simulate the isothermal compression tests. The results show that the integration of finite element method with ANN model can significantly promote the accuracy improvement of numerical simulations for hot forming processes.

  15. Elaboration of a neural network for classification of Taylors bubbles in vertical pipes using Monte Carlo simulation for the training phase

    International Nuclear Information System (INIS)

    Schuabb, Pablo G.; Medeiros, Jose A.C.C.; Schirru, Roberto

    2015-01-01

    The increase of the diameter of a spherical bubble deforms its shape, after which it moves along the vertical center of the pipeline. The Taylor's flow has bubbles with the form of a bullet and increases in the bubble's volume are seen by an enlargement of their length making that kind of bubble easily identified using gamma ray attenuation which is simulated via the software MCNPX that uses the Monte Carlo probabilistic method to simulate radiation-matter interactions. The simulations show that there exists a relation among the counts of a detector and the rising movement of a Taylor's Bubble. A database could be made to answer queries on the dimensions of a Taylor bubble for given readings of a detector, approach that would require a huge database. To make that association an Artificial Neural Network is proposed. The network can be trained with a finite number of samples that is enough to make the network able to classify data of not known bubbles simulated via MCNPX or measured on field. (author)

  16. Control of uncertain systems by feedback linearization with neural networks augmentation. Part II. Controller validation by numerical simulation

    Directory of Open Access Journals (Sweden)

    Adrian TOADER

    2010-09-01

    Full Text Available The paper was conceived in two parts. Part I, previously published in this journal, highlighted the main steps of adaptive output feedback control for non-affine uncertain systems, having a known relative degree. The main paradigm of this approach was the feedback linearization (dynamic inversion with neural network augmentation. Meanwhile, based on new contributions of the authors, a new paradigm, that of robust servomechanism problem solution, has been added to the controller architecture. The current Part II of the paper presents the validation of the controller hereby obtained by using the longitudinal channel of a hovering VTOL-type aircraft as mathematical model.

  17. Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method.

    Science.gov (United States)

    Rezvani, Alireza; Khalili, Abbas; Mazareie, Alireza; Gandomkar, Majid

    2016-07-01

    Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  18. ICG-assisted blood vessel detection during stereotactic neurosurgery: simulation study on excitation power limitations due to thermal effects in human brain tissue.

    Science.gov (United States)

    Rühm, Adrian; Göbel, Werner; Sroka, Ronald; Stepp, Herbert

    2014-09-01

    Intraoperative blood vessel detection based on intraluminal indocyanin-green (ICG) would allow to minimize the risk of blood vessel perforation during stereotactic brain tumor biopsy. For a fiber-based approach compatible with clinical conditions, the maximum tolerable excitation light power was derived from simulations of the thermal heat load on the tissue. Using the simulation software LITCIT, the temperature distribution in human brain tissue was calculated as a function of time for realistic single-fiber probes (0.72mm active diameter, numerical aperture 0.35, optional focusing to 0.29mm diameter) and for the optimum ICG excitation wavelength of 785nm. The asymptotic maximum temperature in the simulated tissue region was derived for different radiant fluxes at the distal fiber end. Worst case values were assumed for all other parameters. In addition to homogeneous (normal and tumor) brain tissue with homogeneous blood perfusion, models with localized extra blood vessels incorporated ahead of the distal fiber end were investigated. If one demands that destruction of normal brain tissue must be excluded by limiting the tissue heating to 42°C, then the radiant flux at the distal fiber end must be limited to 33mW with and 43mW without focusing. When considering extra blood vessels of 0.1mm diameter incorporated into homogeneously perfused brain tissue, the tolerable radiant flux is reduced to 22mW with and 32mW without focusing. The threshold value according to legal laser safety regulations for human skin tissue is 28.5mW. For the envisaged modality of blood vessel detection, light power limits for an application-relevant fiber configuration were determined and found to be roughly consistent with present legal regulations for skin tissue. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. 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.

  20. Analysis of the dynamic behavior of structures using the high-rate GNSS-PPP method combined with a wavelet-neural model: Numerical simulation and experimental tests

    Science.gov (United States)

    Kaloop, Mosbeh R.; Yigit, Cemal O.; Hu, Jong W.

    2018-03-01

    Recently, the high rate global navigation satellite system-precise point positioning (GNSS-PPP) technique has been used to detect the dynamic behavior of structures. This study aimed to increase the accuracy of the extraction oscillation properties of structural movements based on the high-rate (10 Hz) GNSS-PPP monitoring technique. A developmental model based on the combination of wavelet package transformation (WPT) de-noising and neural network prediction (NN) was proposed to improve the dynamic behavior of structures for GNSS-PPP method. A complicated numerical simulation involving highly noisy data and 13 experimental cases with different loads were utilized to confirm the efficiency of the proposed model design and the monitoring technique in detecting the dynamic behavior of structures. The results revealed that, when combined with the proposed model, GNSS-PPP method can be used to accurately detect the dynamic behavior of engineering structures as an alternative to relative GNSS method.

  1. Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks

    Science.gov (United States)

    Steen, Kim Arild; Green, Ole; Karstoft, Henrik

    2017-01-01

    Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover, grass, and weeds in red, green, and blue (RGB) images of clover-grass mixtures. The estimated clover fractions of the dry matter from the images were found to be highly correlated with the real clover fractions of the dry matter, making this a cheap and non-destructive way of monitoring clover-grass fields. The network was trained solely on simulated top-down images of clover-grass fields. This enables the network to distinguish clover, grass, and weed pixels in real images. The use of simulated images for training reduces the manual labor to a few hours, as compared to more than 3000 h when all the real images are annotated for training. The network was tested on images with varied clover/grass ratios and achieved an overall pixel classification accuracy of 83.4%, while estimating the dry matter clover fraction with a standard deviation of 7.8%. PMID:29258215

  2. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

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

  3. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

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

  4. Dynamics of neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

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

  5. Modeling of the removal of arsenic species from simulated groundwater containing As, Fe, and Mn: a neural network based approach

    Energy Technology Data Exchange (ETDEWEB)

    Mondal, Prasenjit; Mohanty, Bikash; Balomajumder, Chandrajit [Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand (India); Saraswati, Samir [Department of Mechanical Engineering, Motital Nehru National Institute of Technology, Allahabad, Uttar Pradesh (India)

    2012-03-15

    The present paper deals with the modeling of the removal of total arsenic As(T), trivalent arsenic As(III), and pentavalent arsenic As(V) from synthetic solutions containing total arsenic (0.167-2.0 mg/L), Fe (0.9-2.7 mg/L), and Mn (0.2-0.6 mg/L) in a batch reactor using Fe impregnated granular activated charcoal (GAC-Fe). Mass ratio of As(III) and As(V) in the solution was 1:1. Multi-layer neural network (MLNN) has been used and full factorial design technique has been applied for the selection of input data set. The developed models are able to predict the adsorption of arsenic species with an error limit of -0.3 to +1.7%. Combination of MLNN with design of experiment has been able to generalize the MLNN with less number of experimental points. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  6. Beveled fiber-optic probe couples a ball lens for improving depth-resolved fluorescence measurements of layered tissue: Monte Carlo simulations

    International Nuclear Information System (INIS)

    Jaillon, Franck; Zheng Wei; Huang Zhiwei

    2008-01-01

    In this study, we evaluate the feasibility of designing a beveled fiber-optic probe coupled with a ball lens for improving depth-resolved fluorescence measurements of epithelial tissue using Monte Carlo (MC) simulations. The results show that by using the probe configuration with a beveled tip collection fiber and a flat tip excitation fiber associated with a ball lens, discrimination of fluorescence signals generated in different tissue depths is achievable. In comparison with a flat-tip collection fiber, the use of a large bevel angled collection fiber enables a better differentiation between the shallow and deep tissue layers by changing the excitation-collection fiber separations. This work suggests that the beveled fiber-optic probe coupled with a ball lens has the potential to facilitate depth-resolved fluorescence measurements of epithelial tissues

  7. Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations.

    Science.gov (United States)

    Šiljić, Aleksandra; Antanasijević, Davor; Perić-Grujić, Aleksandra; Ristić, Mirjana; Pocajt, Viktor

    2015-03-01

    Biological oxygen demand (BOD) is the most significant water quality parameter and indicates water pollution with respect to the present biodegradable organic matter content. European countries are therefore obliged to report annual BOD values to Eurostat; however, BOD data at the national level is only available for 28 of 35 listed European countries for the period prior to 2008, among which 46% of data is missing. This paper describes the development of an artificial neural network model for the forecasting of annual BOD values at the national level, using widely available sustainability and economical/industrial parameters as inputs. The initial general regression neural network (GRNN) model was trained, validated and tested utilizing 20 inputs. The number of inputs was reduced to 15 using the Monte Carlo simulation technique as the input selection method. The best results were achieved with the GRNN model utilizing 25% less inputs than the initial model and a comparison with a multiple linear regression model trained and tested using the same input variables using multiple statistical performance indicators confirmed the advantage of the GRNN model. Sensitivity analysis has shown that inputs with the greatest effect on the GRNN model were (in descending order) precipitation, rural population with access to improved water sources, treatment capacity of wastewater treatment plants (urban) and treatment of municipal waste, with the last two having an equal effect. Finally, it was concluded that the developed GRNN model can be useful as a tool to support the decision-making process on sustainable development at a regional, national and international level.

  8. A Wearable Goggle Navigation System for Dual-Mode Optical and Ultrasound Localization of Suspicious Lesions: Validation Studies Using Tissue-Simulating Phantoms and an Ex Vivo Human Breast Tissue Model.

    Directory of Open Access Journals (Sweden)

    Zeshu Zhang

    Full Text Available Surgical resection remains the primary curative treatment for many early-stage cancers, including breast cancer. The development of intraoperative guidance systems for identifying all sites of disease and improving the likelihood of complete surgical resection is an area of active ongoing research, as this can lead to a decrease in the need of subsequent additional surgical procedures. We develop a wearable goggle navigation system for dual-mode optical and ultrasound imaging of suspicious lesions. The system consists of a light source module, a monochromatic CCD camera, an ultrasound system, a Google Glass, and a host computer. It is tested in tissue-simulating phantoms and an ex vivo human breast tissue model. Our experiments demonstrate that the surgical navigation system provides useful guidance for localization and core needle biopsy of simulated tumor within the tissue-simulating phantom, as well as a core needle biopsy and subsequent excision of Indocyanine Green (ICG-fluorescing sentinel lymph nodes. Our experiments support the contention that this wearable goggle navigation system can be potentially very useful and fully integrated by the surgeon for optimizing many aspects of oncologic surgery. Further engineering optimization and additional in vivo clinical validation work is necessary before such a surgical navigation system can be fully realized in the everyday clinical setting.

  9. Classical algorithms for automated parameter-search methods in compartmental neural models - A critical survey based on simulations using neuron

    International Nuclear Information System (INIS)

    Mutihac, R.; Mutihac, R.C.; Cicuttin, A.

    2001-09-01

    gradient-descent techniques are adequate if the parameter space is low-dimensional, relatively smooth, and has a few local minima (e.g., parameterizing single-neuron compartmental models). Only the fast algorithms and/or a decent (low) number of model parameters are candidates for automated parameter search because of practical reasons. Eventually, the size of the parameter space may be reduced and/or parallel supercomputers may be used. Data overfitting may negatively affect the generalization ability of the model. Bayesian methods include Occam's factor, which set the preference for simpler models. Proliferation of (neural) models raises the question of rigorous criteria for comparing the overall performance of various models designed to match the same type of data. Bayesian methods provide the best framework to assess the neural models quantitatively. Paradoxically, parameter-search methods may sometimes be more useful when they fail by discarding unrealistic mechanisms used in the model design, rather than fitting experimental data to an alleged model

  10. On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra

    Science.gov (United States)

    Dafonte, C.; Fustes, D.; Manteiga, M.; Garabato, D.; Álvarez, M. A.; Ulla, A.; Allende Prieto, C.

    2016-10-01

    Aims: We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in this case) to the given inputs (spectra). Such a model can be integrated in a Bayesian framework to estimate the posterior distribution of the outputs. Methods: The architecture of the GANN follows the same scheme as a normal ANN, but with the inputs and outputs inverted. We train the network with the set of atmospheric parameters (Teff, log g, [Fe/H] and [α/ Fe]), obtaining the stellar spectra for such inputs. The residuals between the spectra in the grid and the estimated spectra are minimized using a validation dataset to keep solutions as general as possible. Results: The performance of both conventional ANNs and GANNs to estimate the stellar parameters as a function of the star brightness is presented and compared for different Galactic populations. GANNs provide significantly improved parameterizations for early and intermediate spectral types with rich and intermediate metallicities. The behaviour of both algorithms is very similar for our sample of late-type stars, obtaining residuals in the derivation of [Fe/H] and [α/ Fe] below 0.1 dex for stars with Gaia magnitude Grvs satellite. Conclusions: Uncertainty estimation of computed astrophysical parameters is crucial for the validation of the parameterization itself and for the subsequent exploitation by the astronomical community. GANNs produce not only the parameters for a given spectrum, but a goodness-of-fit between the observed spectrum and the predicted one for a given set of parameters. Moreover, they allow us to obtain the full posterior distribution over the astrophysical parameters space once a noise model is assumed. This can be used for novelty detection and quality assessment.

  11. Simulations

    CERN Document Server

    Ngada, Narcisse

    2015-06-15

    The complexity and cost of building and running high-power electrical systems make the use of simulations unavoidable. The simulations available today provide great understanding about how systems really operate. This paper helps the reader to gain an insight into simulation in the field of power converters for particle accelerators. Starting with the definition and basic principles of simulation, two simulation types, as well as their leading tools, are presented: analog and numerical simulations. Some practical applications of each simulation type are also considered. The final conclusion then summarizes the main important items to keep in mind before opting for a simulation tool or before performing a simulation.

  12. Piecewise parabolic method for simulating one-dimensional shear shock wave propagation in tissue-mimicking phantoms

    Science.gov (United States)

    Tripathi, B. B.; Espíndola, D.; Pinton, G. F.

    2017-11-01

    The recent discovery of shear shock wave generation and propagation in the porcine brain suggests that this new shock phenomenology may be responsible for a broad range of traumatic injuries. Blast-induced head movement can indirectly lead to shear wave generation in the brain, which could be a primary mechanism for injury. Shear shock waves amplify the local acceleration deep in the brain by up to a factor of 8.5, which may tear and damage neurons. Currently, there are numerical methods that can model compressional shock waves, such as comparatively well-studied blast waves, but there are no numerical full-wave solvers that can simulate nonlinear shear shock waves in soft solids. Unlike simplified representations, e.g., retarded time, full-wave representations describe fundamental physical behavior such as reflection and heterogeneities. Here we present a piecewise parabolic method-based solver for one-dimensional linearly polarized nonlinear shear wave in a homogeneous medium and with empirical frequency-dependent attenuation. This method has the advantage of being higher order and more directly extendable to multiple dimensions and heterogeneous media. The proposed numerical scheme is validated analytically and experimentally and compared to other shock capturing methods. A Riemann step-shock problem is used to characterize the numerical dissipation. This dissipation is then tuned to be negligible with respect to the physical attenuation by choosing an appropriate grid spacing. The numerical results are compared to ultrasound-based experiments that measure planar polarized shear shock wave propagation in a tissue-mimicking gelatin phantom. Good agreement is found between numerical results and experiment across a 40 mm propagation distance. We anticipate that the proposed method will be a starting point for the development of a two- and three-dimensional full-wave code for the propagation of nonlinear shear waves in heterogeneous media.

  13. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    NJD

    Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous ... process by training a number of neural networks. .... Matlab® version 6.1 was employed for building principal component ... provide a fair simulation of calibration data set with some degree.

  14. SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

    Energy Technology Data Exchange (ETDEWEB)

    Khazaee, M [shahid beheshti university, Tehran, Tehran (Iran, Islamic Republic of); Asl, A Kamali [Shahid Beheshti University, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of); Geramifar, P [Shariati Hospital, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of)

    2015-06-15

    Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine.

  15. SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

    International Nuclear Information System (INIS)

    Khazaee, M; Asl, A Kamali; Geramifar, P

    2015-01-01

    Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine

  16. Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis

    DEFF Research Database (Denmark)

    Karemore, Gopal Raghunath; Mascarenhas, Kim Komal; Patil, Choudhary

    2008-01-01

    In the present work we discuss the potential of recently developed classification algorithm, Learning Vector Quantization (LVQ), for the analysis of Laser Induced Fluorescence (LIF) Spectra, recorded from normal and malignant bladder tissue samples. The algorithm is prototype based and inherently...

  17. Mechanics of neurulation: From classical to current perspectives on the physical mechanics that shape, fold, and form the neural tube.

    Science.gov (United States)

    Vijayraghavan, Deepthi S; Davidson, Lance A

    2017-01-30

    Neural tube defects arise from mechanical failures in the process of neurulation. At the most fundamental level, formation of the neural tube relies on coordinated, complex tissue movements that mechanically transform the flat neural epithelium into a lumenized epithelial tube (Davidson, 2012). The nature of this mechanical transformation has mystified embryologists, geneticists, and clinicians for more than 100 years. Early embryologists pondered the physical mechanisms that guide this transformation. Detailed observations of cell and tissue movements as well as experimental embryological manipulations allowed researchers to generate and test elementary hypotheses of the intrinsic and extrinsic forces acting on the neural tissue. Current research has turned toward understanding the molecular mechanisms underlying neurulation. Genetic and molecular perturbation have identified a multitude of subcellular components that correlate with cell behaviors and tissue movements during neural tube formation. In this review, we focus on methods and conceptual frameworks that have been applied to the study of amphibian neurulation that can be used to determine how molecular and physical mechanisms are integrated and responsible for neurulation. We will describe how qualitative descriptions and quantitative measurements of strain, force generation, and tissue material properties as well as simulations can be used to understand how embryos use morphogenetic programs to drive neurulation. Birth Defects Research 109:153-168, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  18. MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

    Directory of Open Access Journals (Sweden)

    Artur Popko

    2013-06-01

    Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

  19. Neural network to diagnose lining condition

    Science.gov (United States)

    Yemelyanov, V. A.; Yemelyanova, N. Y.; Nedelkin, A. A.; Zarudnaya, M. V.

    2018-03-01

    The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.

  20. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  1. Artificial neural network-based predictive model for bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products.

    Science.gov (United States)

    Lou, W; Nakai, S

    2001-04-01

    The data of Devilieghere et al. (Int. J. Food Microbiol. 1999, 46, 57--70) on bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products was processed for estimating maximum specific growth rate mu(max) and lag phase lambda of Lactobacillus sake using artificial neural networks-based model (ANNM) computation. The comparison between ANNM and response surface methodology (RSM) model showed that the accuracy of ANNM prediction was higher than that of RSM. Two-dimensional and three-dimensional plots of the response surfaces revealed that the relationships of water activity a(w), temperature T, and dissolved CO(2) concentration with mu(max) and lambda were complicated, not just linear or second-order relations. Furthermore, it was possible to compute the sensitivity of the model outputs against each input parameter by using ANNM. The results showed that mu(max) was most sensitive to a(w), T, and dissolved CO(2) in this order; whereas lambda was sensitive to T the most, followed by a(w), and dissolved CO(2) concentrations.

  2. The AutoAssociative Neural Network in signal analysis: II. Application to on-line monitoring of a simulated BWR component

    International Nuclear Information System (INIS)

    Marseguerra, M.; Zoia, A.

    2005-01-01

    In this paper, Robust AutoAssociative Neural Networks (RAANN) are applied to a series of signals produced by the Halden simulator of the 1200MWe BWR Forsmark-3 plant in Sweden. The applications concern: - correction of drifts and gross errors in sensors, for diagnostic and control purposes, - cluster analysis, to individuate a failed component and the intensity of the failure, - forecasting system signals, for safety or economic purposes, - reconstruction of unmeasured signals (virtual sensors). In the attainment of the above results, the geometric interpretation of the mapping performed by the network, propounded in Part I of this work, has provided a reasoned choice of the most critical free parameter, i.e., the number f of nodes of the bottleneck layer, thus allowing a deep understanding of the network functioning and also avoiding the traditional and troubling procedure of selection by trial-and-error. The theoretical basis of this analysis, discussed in details in the companion paper, is founded on the idea of dimension and in particular of fractal dimension, which has been used as a numerical estimator of f

  3. Tissue interfaces dosimetry in small field radiotherapy with alanine/EPR mini dosimeters and Monte Carlo-Penelope simulation

    Energy Technology Data Exchange (ETDEWEB)

    Vega R, J. L.; Nicolucci, P.; Baffa, O. [Universidade de Sao Paulo, FFCLRP, Departamento de Fisica, Av. Bandeirantes 3900, Bairro Monte Alegre, 14040-901 Ribeirao Preto, Sao Paulo (Brazil); Chen, F. [Universidade Federale do ABC, CCNH, Rua Santa Adelia 166, Bangu, 09210-170 Santo Andre, Sao Paulo (Brazil); Apaza V, D. G., E-mail: josevegaramirez@yahoo.es [Universidad Nacional de San Agustin de Arequipa, Departamento de Fisica, Arequipa (Peru)

    2014-08-15

    The dosimetry system based on alanine mini dosimeters plus K-Band EPR spectrometer was tested in the tissue-interface dosimetry through the percentage depth-dose (Pdd) determination for 3 x 3 cm{sup 2} and 1 x 1 cm{sup 2} radiation fields sizes. The alanine mini dosimeters were produced by mechanical pressure from a mixture of 95% L-alanine and 5% polyvinyl alcohol (Pva) acting as binder. Nominal dimensions of these mini dosimeters were 1 mm diameter and 3 mm length as well as 3 - 4 mg mass. The EPR spectra of the mini dosimeters were registered using a K-Band (24 GHz) EPR spectrometer. The mini dosimeters were placed in a nonhomogeneous phantom and irradiated with 20 Gy in a 6 MV PRIMUS Siemens linear accelerator, with a source-to-surface distance of 100 cm using the small fields previously mentioned. The cylindrical non-homogeneous phantom was comprised of several disk-shaped plates of different materials in the sequence acrylic-bone cork-bone-acrylic, with dimensions 15 cm diameter and 1 cm thick. The plates were placed in descending order, starting from top with four acrylic plates followed by two bone plates plus eight cork plates plus two bone plates and finally, four acrylic plates (4-2-8-2-4). Pdd curves from the treatment planning system and from Monte Carlo simulation with Penelope code were determined. Mini dosimeters Pdd results show good agreement with Penelope, better than 95% for the cork homogeneous region and 97.7% in the bone heterogeneous region. In the first interface region, between acrylic and bone, it can see a dose increment of 0.6% for mini dosimeters compared to Penelope. At the second interface, between bone and cork, there is 9.1% of dose increment for mini dosimeter relative to Penelope. For the third (cork-bone) and fourth (bone-acrylic) interfaces, the dose increment for mini dosimeters compared to Penelope was 4.1% both. (Author)

  4. Tissue interfaces dosimetry in small field radiotherapy with alanine/EPR mini dosimeters and Monte Carlo-Penelope simulation

    International Nuclear Information System (INIS)

    Vega R, J. L.; Nicolucci, P.; Baffa, O.; Chen, F.; Apaza V, D. G.

    2014-08-01

    The dosimetry system based on alanine mini dosimeters plus K-Band EPR spectrometer was tested in the tissue-interface dosimetry through the percentage depth-dose (Pdd) determination for 3 x 3 cm 2 and 1 x 1 cm 2 radiation fields sizes. The alanine mini dosimeters were produced by mechanical pressure from a mixture of 95% L-alanine and 5% polyvinyl alcohol (Pva) acting as binder. Nominal dimensions of these mini dosimeters were 1 mm diameter and 3 mm length as well as 3 - 4 mg mass. The EPR spectra of the mini dosimeters were registered using a K-Band (24 GHz) EPR spectrometer. The mini dosimeters were placed in a nonhomogeneous phantom and irradiated with 20 Gy in a 6 MV PRIMUS Siemens linear accelerator, with a source-to-surface distance of 100 cm using the small fields previously mentioned. The cylindrical non-homogeneous phantom was comprised of several disk-shaped plates of different materials in the sequence acrylic-bone cork-bone-acrylic, with dimensions 15 cm diameter and 1 cm thick. The plates were placed in descending order, starting from top with four acrylic plates followed by two bone plates plus eight cork plates plus two bone plates and finally, four acrylic plates (4-2-8-2-4). Pdd curves from the treatment planning system and from Monte Carlo simulation with Penelope code were determined. Mini dosimeters Pdd results show good agreement with Penelope, better than 95% for the cork homogeneous region and 97.7% in the bone heterogeneous region. In the first interface region, between acrylic and bone, it can see a dose increment of 0.6% for mini dosimeters compared to Penelope. At the second interface, between bone and cork, there is 9.1% of dose increment for mini dosimeter relative to Penelope. For the third (cork-bone) and fourth (bone-acrylic) interfaces, the dose increment for mini dosimeters compared to Penelope was 4.1% both. (Author)

  5. Chondroitin sulfate effects on neural stem cell differentiation.

    Science.gov (United States)

    Canning, David R; Brelsford, Natalie R; Lovett, Neil W

    2016-01-01

    We have investigated the role chondroitin sulfate has on cell interactions during neural plate formation in the early chick embryo. Using tissue culture isolates from the prospective neural plate, we have measured neural gene expression profiles associated with neural stem cell differentiation. Removal of chondroitin sulfate from stage 4 neural plate tissue leads to altered associations of N-cadherin-positive neural progenitors and causes changes in the normal sequence of neural marker gene expression. Absence of chondroitin sulfate in the neural plate leads to reduced Sox2 expression and is accompanied by an increase in the expression of anterior markers of neural regionalization. Results obtained in this study suggest that the presence of chondroitin sulfate in the anterior chick embryo is instrumental in maintaining cells in the neural precursor state.

  6. Uptake and bio-reactivity of polystyrene nanoparticles is affected by surface modifications, ageing and LPS adsorption: in vitro studies on neural tissue cells

    Science.gov (United States)

    Murali, Kumarasamy; Kenesei, Kata; Li, Yang; Demeter, Kornél; Környei, Zsuzsanna; Madarász, Emilia

    2015-02-01

    Because of their capacity of crossing an intact blood-brain barrier and reaching the brain through an injured barrier or via the nasal epithelium, nanoparticles have been considered as vehicles to deliver drugs and as contrast materials for brain imaging. The potential neurotoxicity of nanoparticles, however, is not fully explored. Using particles with a biologically inert polystyrene core material, we investigated the role of the chemical composition of particle surfaces in the in vitro interaction with different neural cell types. PS NPs within a size-range of 45-70 nm influenced the metabolic activity of cells depending on the cell-type, but caused toxicity only at extremely high particle concentrations. Neurons did not internalize particles, while microglial cells ingested a large amount of carboxylated but almost no PEGylated NPs. PEGylation reduced the protein adsorption, toxicity and cellular uptake of NPs. After storage (shelf-life >6 months), the toxicity and cellular uptake of NPs increased. The altered biological activity of ``aged'' NPs was due to particle aggregation and due to the adsorption of bioactive compounds on NP surfaces. Aggregation by increasing the size and sedimentation velocity of NPs results in increased cell-targeted NP doses. The ready endotoxin adsorption which cannot be prevented by PEG coating, can render the particles toxic. The age-dependent changes in otherwise harmless NPs could be the important sources for variability in the effects of NPs, and could explain the contradictory data obtained with ``identical'' NPs.Because of their capacity of crossing an intact blood-brain barrier and reaching the brain through an injured barrier or via the nasal epithelium, nanoparticles have been considered as vehicles to deliver drugs and as contrast materials for brain imaging. The potential neurotoxicity of nanoparticles, however, is not fully explored. Using particles with a biologically inert polystyrene core material, we investigated the

  7. Neural Networks for Non-linear Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1994-01-01

    This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....

  8. An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation.

    Science.gov (United States)

    Zhang, Xiaoyan; Kim, Daeseung; Shen, Shunyao; Yuan, Peng; Liu, Siting; Tang, Zhen; Zhang, Guangming; Zhou, Xiaobo; Gateno, Jaime; Liebschner, Michael A K; Xia, James J

    2018-04-01

    Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical

  9. Experimental evaluation of neural probe’s insertion induced injury based on digital image correlation method

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Wenguang, E-mail: zhwg@sjtu.edu.cn; Ma, Yakun; Li, Zhengwei [State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240 (China)

    2016-01-15

    Purpose: The application of neural probes in clinic has been challenged by probes’ short lifetime when implanted into brain tissue. The primary goal is to develop an evaluation system for testing brain tissue injury induced by neural probe’s insertion using microscope based digital image correlation method. Methods: A brain tissue phantom made of silicone rubber with speckle pattern on its surface was fabricated. To obtain the optimal speckle pattern, mean intensity gradient parameter was used for quality assessment. The designed testing system consists of three modules: (a) load module for simulating neural electrode implantation process; (b) data acquisition module to capture micrographs of speckle pattern and to obtain reactive forces during the insertion of the probe; (c) postprocessing module for extracting tissue deformation information from the captured speckle patterns. On the basis of the evaluation system, the effects of probe wedge angle, insertion speed, and probe streamline on insertion induced tissue injury were investigated. Results: The optimal quality speckle pattern can be attained by the following fabrication parameters: spin coating rate—1000 r/min, silicone rubber component A: silicone rubber component B: softener: graphite = 5 ml: 5 ml: 2 ml: 0.6 g. The probe wedge angle has a significant effect on tissue injury. Compared to wedge angle 40° and 20°, maximum principal strain of 60° wedge angle was increased by 40.3% and 87.5%, respectively; compared with a relatively higher speed (500 μm/s), the maximum principle strain within the tissue induced by slow insertion speed (100 μm/s) was increased by 14.3%; insertion force required by probe with convex streamline was smaller than the force of traditional probe. Based on the experimental results, a novel neural probe that has a rounded tip covered by a biodegradable silk protein coating with convex streamline was proposed, which has both lower insertion and micromotion induced tissue

  10. Simulation and performance evaluation of fiber optic sensor for detection of hepatic malignancies in human liver tissues

    Science.gov (United States)

    Sharma, Anuj K.; Gupta, Jyoti; Basu, Rikmantra

    2018-01-01

    A fiber optic sensor is proposed for the identification of healthy and cancerous liver tissues through determination of their corresponding refractive index values. Existing experimental results describing variation of complex refractive index of liver tissues in near infrared (NIR) spectral region are considered for theoretical calculations. The intensity interrogation method with chalcogenide fiber is considered. The sensor's performance is closely analyzed in terms of its sensitivity at multiple operating wavelengths falling in NIR region. Operating at shorter NIR wavelengths leads to greater sensitivity. The effect of design parameters (sensing region length and fiber core diameter), different launching conditions, and fiber glass materials on sensor's performance is examined. The proposed sensor has the potential to provide high sensitivity of liver tissue detection.

  11. Monte Carlo simulation of the interaction of X-ray spectrum with human tissue, in the energies range of diagnostic radiology

    International Nuclear Information System (INIS)

    Cayllahua Q, L. F.; Apaza V, G.; Vega R, J. L.

    2015-10-01

    Full text: This paper is an approach to an increasingly complete knowledge about the nature of the processes that occur during a simple examination of radiological diagnosis; know as X-rays are produced and how they will put their energy into the tissue of patients when they are subjected to an examination of radiological diagnosis. First, using the MCNP code an X-rays tube was simulated, where electrons are emitted from a filament (cathode) which travel a certain distance with a certain kinetic energy and then be stopped suddenly in the tungsten target. The X-rays emitted as a result of this interaction, are previously filtered through the inherent filter of Pyrex glass and then by a thin aluminum foil before quantification as an X-rays spectrum. 6 spectra (for 60, 80, 100, 120 and 140 KeV) were obtained. Second, using the Penelope code was simulated the interaction of the X-rays spectrum, obtained in the first part with human tissue, putting as simile of human tissue water phantoms of different thicknesses. As final result: dose of energy deposited (in 2 and 3-dimensional) and reflected, absorbed and transmitted photons spectra. (Author)

  12. The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: A comparison of CT and CT-MRI based tissue segmentation on simulated temperature

    International Nuclear Information System (INIS)

    Verhaart, René F.; Paulides, Margarethus M.; Fortunati, Valerio; Walsum, Theo van; Veenland, Jifke F.; Verduijn, Gerda M.; Lugt, Aad van der

    2014-01-01

    models based on CT (T max : 38.0 °C) and CT and MRI (T max : 38.1 °C) result in similar simulated temperatures, while CT and MRI db (T max : 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly. Conclusions: Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia

  13. The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: A comparison of CT and CT-MRI based tissue segmentation on simulated temperature

    Energy Technology Data Exchange (ETDEWEB)

    Verhaart, René F., E-mail: r.f.verhaart@erasmusmc.nl; Paulides, Margarethus M. [Hyperthermia Unit, Department of Radiation Oncology, Erasmus MC - Cancer Institute, Groene Hilledijk 301, Rotterdam 3008 AE (Netherlands); Fortunati, Valerio; Walsum, Theo van; Veenland, Jifke F. [Biomedical Imaging Group of Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE (Netherlands); Verduijn, Gerda M. [Department of Radiation Oncology, Erasmus MC - Cancer Institute, Groene Hilledijk 301, Rotterdam 3008 AE (Netherlands); Lugt, Aad van der [Department of Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE (Netherlands)

    2014-12-15

    ). Patient models based on CT (T{sub max}: 38.0 °C) and CT and MRI (T{sub max}: 38.1 °C) result in similar simulated temperatures, while CT and MRI{sub db} (T{sub max}: 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly. Conclusions: Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia.

  14. The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: a comparison of CT and CT-MRI based tissue segmentation on simulated temperature.

    Science.gov (United States)

    Verhaart, René F; Fortunati, Valerio; Verduijn, Gerda M; van der Lugt, Aad; van Walsum, Theo; Veenland, Jifke F; Paulides, Margarethus M

    2014-12-01

    (Tmax: 38.1 °C) result in similar simulated temperatures, while CT and MRIdb (Tmax: 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly. Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia.

  15. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  16. Comparing the magnetic resonant coupling radiofrequency stimulation to the traditional approaches: Ex-vivo tissue voltage measurement and electromagnetic simulation analysis

    Science.gov (United States)

    Yeung, Sai Ho; Pradhan, Raunaq; Feng, Xiaohua; Zheng, Yuanjin

    2015-09-01

    Recently, the design concept of magnetic resonant coupling has been adapted to electromagnetic therapy applications such as non-invasive radiofrequency (RF) stimulation. This technique can significantly increase the electric field radiated from the magnetic coil at the stimulation target, and hence enhancing the current flowing through the nerve, thus enabling stimulation. In this paper, the developed magnetic resonant coupling (MRC) stimulation, magnetic stimulation (MS) and transcutaneous electrical nerve stimulation (TENS) are compared. The differences between the MRC RF stimulation and other techniques are presented in terms of the operating mechanism, ex-vivo tissue voltage measurement and electromagnetic simulation analysis. The ev-vivo tissue voltage measurement experiment is performed on the compared devices based on measuring the voltage induced by electromagnetic induction at the tissue. The focusing effect, E field and voltage induced across the tissue, and the attenuation due to the increase of separation between the coil and the target are analyzed. The electromagnetic stimulation will also be performed to obtain the electric field and magnetic field distribution around the biological medium. The electric field intensity is proportional to the induced current and the magnetic field is corresponding to the electromagnetic induction across the biological medium. The comparison between the MRC RF stimulator and the MS and TENS devices revealed that the MRC RF stimulator has several advantages over the others for the applications of inducing current in the biological medium for stimulation purposes.

  17. Comparing the magnetic resonant coupling radiofrequency stimulation to the traditional approaches: Ex-vivo tissue voltage measurement and electromagnetic simulation analysis

    Directory of Open Access Journals (Sweden)

    Sai Ho Yeung

    2015-09-01

    Full Text Available Recently, the design concept of magnetic resonant coupling has been adapted to electromagnetic therapy applications such as non-invasive radiofrequency (RF stimulation. This technique can significantly increase the electric field radiated from the magnetic coil at the stimulation target, and hence enhancing the current flowing through the nerve, thus enabling stimulation. In this paper, the developed magnetic resonant coupling (MRC stimulation, magnetic stimulation (MS and transcutaneous electrical nerve stimulation (TENS are compared. The differences between the MRC RF stimulation and other techniques are presented in terms of the operating mechanism, ex-vivo tissue voltage measurement and electromagnetic simulation analysis. The ev-vivo tissue voltage measurement experiment is performed on the compared devices based on measuring the voltage induced by electromagnetic induction at the tissue. The focusing effect, E field and voltage induced across the tissue, and the attenuation due to the increase of separation between the coil and the target are analyzed. The electromagnetic stimulation will also be performed to obtain the electric field and magnetic field distribution around the biological medium. The electric field intensity is proportional to the induced current and the magnetic field is corresponding to the electromagnetic induction across the biological medium. The comparison between the MRC RF stimulator and the MS and TENS devices revealed that the MRC RF stimulator has several advantages over the others for the applications of inducing current in the biological medium for stimulation purposes.

  18. Comparing the magnetic resonant coupling radiofrequency stimulation to the traditional approaches: Ex-vivo tissue voltage measurement and electromagnetic simulation analysis

    Energy Technology Data Exchange (ETDEWEB)

    Yeung, Sai Ho; Pradhan, Raunaq; Feng, Xiaohua; Zheng, Yuanjin [School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 (Singapore)

    2015-09-15

    Recently, the design concept of magnetic resonant coupling has been adapted to electromagnetic therapy applications such as non-invasive radiofrequency (RF) stimulation. This technique can significantly increase the electric field radiated from the magnetic coil at the stimulation target, and hence enhancing the current flowing through the nerve, thus enabling stimulation. In this paper, the developed magnetic resonant coupling (MRC) stimulation, magnetic stimulation (MS) and transcutaneous electrical nerve stimulation (TENS) are compared. The differences between the MRC RF stimulation and other techniques are presented in terms of the operating mechanism, ex-vivo tissue voltage measurement and electromagnetic simulation analysis. The ev-vivo tissue voltage measurement experiment is performed on the compared devices based on measuring the voltage induced by electromagnetic induction at the tissue. The focusing effect, E field and voltage induced across the tissue, and the attenuation due to the increase of separation between the coil and the target are analyzed. The electromagnetic stimulation will also be performed to obtain the electric field and magnetic field distribution around the biological medium. The electric field intensity is proportional to the induced current and the magnetic field is corresponding to the electromagnetic induction across the biological medium. The comparison between the MRC RF stimulator and the MS and TENS devices revealed that the MRC RF stimulator has several advantages over the others for the applications of inducing current in the biological medium for stimulation purposes.

  19. A Geant4-based Simulation to Evaluate the Feasibility of Using Nuclear Resonance Fluorescence (NRF) in Determining Atomic Compositions of Body Tissue in Cancer Diagnostics and Irradiation

    Science.gov (United States)

    Gilbo, Yekaterina; Wijesooriya, Krishni; Liyanage, Nilanga

    2017-01-01

    Customarily applied in homeland security for identifying concealed explosives and chemical weapons, NRF (Nuclear Resonance Fluorescence) may have high potential in determining atomic compositions of body tissue. High energy photons incident on a target excite the target nuclei causing characteristic re-emission of resonance photons. As the nuclei of each isotope have well-defined excitation energies, NRF uniquely indicates the isotopic content of the target. NRF radiation corresponding to nuclear isotopes present in the human body is emitted during radiotherapy based on Bremsstrahlung photons generated in a linear electron accelerator. We have developed a Geant4 simulation in order to help assess NRF capabilities in detecting, mapping, and characterizing tumors. We have imported a digital phantom into the simulation using anatomical data linked to known chemical compositions of various tissues. Work is ongoing to implement the University of Virginia's cancer center treatment setup and patient geometry, and to collect and analyze the simulation's physics quantities to evaluate the potential of NRF for medical imaging applications. Preliminary results will be presented.

  20. A simulation model for the prediction of tissue:plasma partition coefficients for drug residues in natural casings.

    NARCIS (Netherlands)

    Haritova, A.M.; Fink-Gremmels, J.

    2010-01-01

    Tissue residues arise from the exposure of animals to undesirable substances in animal feed materials and drinking water and to the therapeutic or zootechnical use of veterinary medicinal products. In the framework of this study, an advanced toxicokinetic model was developed to predict the

  1. HDR monotherapy for prostate cancer: A simulation study to determine the effect of catheter displacement on target coverage and normal tissue irradiation

    International Nuclear Information System (INIS)

    Kolkman-Deurloo, Inger-Karine K.; Roos, Martin A.; Aluwini, Shafak

    2011-01-01

    Purpose: The aim of this study was to systematically analyse the effect of catheter displacements both on target coverage and normal tissue irradiation in fractionated high dose rate (HDR) prostate brachytherapy, using a simulation study, and to define tolerances for catheter displacement ensuring that both target coverage and normal tissue doses remain clinically acceptable. Besides the effect of total implant displacement, also displacements of catheters belonging to selected template rows only were evaluated in terms of target coverage and normal tissue dose, in order to analyse the change in dose distribution as a function of catheter dwell weight and catheter location. Material and methods: Five representative implant geometries, with 17 catheters each, were selected. The clinical treatment plan was compared to treatment plans in which an entire implant displacement in caudal direction over 3, 5, 7 and 10 mm was simulated. Besides, treatment plans were simulated considering a displacement of either the central, most ventral or most dorsal catheter rows only, over 5 mm caudally. Results: Due to displacement of the entire implant the target coverage drops below the tolerance of 93% for all displacements studied. The effect of displacement of the entire implant on organs at risk strongly depended on the patient anatomy; e.g., for 80% of the implant geometries the V 80 of the rectum exceeded its tolerance for all displacements. The effect of displacement of catheters belonging to selected template rows depended strongly on the relative weight of each catheter row when considering the target coverage and on its location when considering the dose in the organs at risk. Conclusion: This study supports the need for a check of the catheter locations before each fraction and correction of deviations of the catheter position exceeding 3 mm.

  2. Three-Dimensional Normal Human Neural Progenitor Tissue-Like Assemblies: A Model for Persistent Varicell-Zoster Virus Infection and Platform to Study Viral Infectivity and Oxidative Stress and Damage

    Science.gov (United States)

    Goodwin, T. J.; McCarthy, M.; Osterrieder, N.; Cohrs, R. J.; Kaufer, B. B.

    2014-01-01

    The environment of space results in a multitude of challenges to the human physiology that present barriers to extended habitation and exploration. Over 40 years of investigation to define countermeasures to address space flight adaptation has left gaps in our knowledge regarding mitigation strategies partly due to the lack of investigative tools, monitoring strategies, and real time diagnostics to understand the central causative agent(s) responsible for physiologic adaptation and maintaining homeostasis. Spaceflight-adaptation syndrome is the combination of space environmental conditions and the synergistic reaction of the human physiology. Our work addresses the role of oxidative stress and damage (OSaD) as a negative and contributing Risk Factor (RF) in the following areas of combined spaceflight related dysregulation: i) radiation induced cellular damage [1], [2] ii) immune impacts and the inflammatory response [3], [4] and iii) varicella zoster virus (VZV) reactivation [5]. Varicella-zoster (VZV)/Chicken Pox virus is a neurotropic human alphaherpesvirus resulting in varicella upon primary infection, suppressed by the immune system becomes latent in ganglionic neurons, and reactivates under stress events to re-express in zoster and possibly shingles. Our laboratory has developed a complex threedimensional (3D) normal human neural tissue model that emulates several characteristics of the human trigeminal ganglia (TG) and allows the study of combinatorial experimentation which addresses, simultaneously, OSaD associated with Spaceflight adaptation and habitation [6].

  3. The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

    Science.gov (United States)

    Pla, Patrick; Monsoro-Burq, Anne H

    2018-05-28

    The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

  4. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan; Naous, Rawan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

    2015-01-01

    . Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards

  5. Cooperating attackers in neural cryptography.

    Science.gov (United States)

    Shacham, Lanir N; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang

    2004-06-01

    A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.

  6. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

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

  7. Neural networks for triggering

    International Nuclear Information System (INIS)

    Denby, B.; Campbell, M.; Bedeschi, F.; Chriss, N.; Bowers, C.; Nesti, F.

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab

  8. TH-CD-202-05: DECT Based Tissue Segmentation as Input to Monte Carlo Simulations for Proton Treatment Verification Using PET Imaging

    International Nuclear Information System (INIS)

    Berndt, B; Wuerl, M; Dedes, G; Landry, G; Parodi, K; Tessonnier, T; Schwarz, F; Kamp, F; Thieke, C; Belka, C; Reiser, M; Sommer, W; Bauer, J; Verhaegen, F

    2016-01-01

    Purpose: To improve agreement of predicted and measured positron emitter yields in patients, after proton irradiation for PET-based treatment verification, using a novel dual energy CT (DECT) tissue segmentation approach, overcoming known deficiencies from single energy CT (SECT). Methods: DECT head scans of 5 trauma patients were segmented and compared to existing decomposition methods with a first focus on the brain. For validation purposes, three brain equivalent solutions [water, white matter (WM) and grey matter (GM) – equivalent with respect to their reference carbon and oxygen contents and CT numbers at 90kVp and 150kVp] were prepared from water, ethanol, sucrose and salt. The activities of all brain solutions, measured during a PET scan after uniform proton irradiation, were compared to Monte Carlo simulations. Simulation inputs were various solution compositions obtained from different segmentation approaches from DECT, SECT scans, and known reference composition. Virtual GM solution salt concentration corrections were applied based on DECT measurements of solutions with varying salt concentration. Results: The novel tissue segmentation showed qualitative improvements in %C for patient brain scans (ground truth unavailable). The activity simulations based on reference solution compositions agree with the measurement within 3–5% (4–8Bq/ml). These reference simulations showed an absolute activity difference between WM (20%C) and GM (10%C) to H2O (0%C) of 43 Bq/ml and 22 Bq/ml, respectively. Activity differences between reference simulations and segmented ones varied from −6 to 1 Bq/ml for DECT and −79 to 8 Bq/ml for SECT. Conclusion: Compared to the conventionally used SECT segmentation, the DECT based segmentation indicates a qualitative and quantitative improvement. In controlled solutions, a MC input based on DECT segmentation leads to better agreement with the reference. Future work will address the anticipated improvement of quantification

  9. TH-CD-202-05: DECT Based Tissue Segmentation as Input to Monte Carlo Simulations for Proton Treatment Verification Using PET Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Berndt, B; Wuerl, M; Dedes, G; Landry, G; Parodi, K [Ludwig-Maximilians-Universitaet Muenchen, Garching, DE (Germany); Tessonnier, T [Ludwig-Maximilians-Universitaet Muenchen, Garching, DE (Germany); Universitaetsklinikum Heidelberg, Heidelberg, DE (Germany); Schwarz, F; Kamp, F; Thieke, C; Belka, C; Reiser, M; Sommer, W [LMU Munich, Munich, DE (Germany); Bauer, J [Universitaetsklinikum Heidelberg, Heidelberg, DE (Germany); Heidelberg Ion-Beam Therapy Center, Heidelberg, DE (Germany); Verhaegen, F [Maastro Clinic, Maastricht (Netherlands)

    2016-06-15

    Purpose: To improve agreement of predicted and measured positron emitter yields in patients, after proton irradiation for PET-based treatment verification, using a novel dual energy CT (DECT) tissue segmentation approach, overcoming known deficiencies from single energy CT (SECT). Methods: DECT head scans of 5 trauma patients were segmented and compared to existing decomposition methods with a first focus on the brain. For validation purposes, three brain equivalent solutions [water, white matter (WM) and grey matter (GM) – equivalent with respect to their reference carbon and oxygen contents and CT numbers at 90kVp and 150kVp] were prepared from water, ethanol, sucrose and salt. The activities of all brain solutions, measured during a PET scan after uniform proton irradiation, were compared to Monte Carlo simulations. Simulation inputs were various solution compositions obtained from different segmentation approaches from DECT, SECT scans, and known reference composition. Virtual GM solution salt concentration corrections were applied based on DECT measurements of solutions with varying salt concentration. Results: The novel tissue segmentation showed qualitative improvements in %C for patient brain scans (ground truth unavailable). The activity simulations based on reference solution compositions agree with the measurement within 3–5% (4–8Bq/ml). These reference simulations showed an absolute activity difference between WM (20%C) and GM (10%C) to H2O (0%C) of 43 Bq/ml and 22 Bq/ml, respectively. Activity differences between reference simulations and segmented ones varied from −6 to 1 Bq/ml for DECT and −79 to 8 Bq/ml for SECT. Conclusion: Compared to the conventionally used SECT segmentation, the DECT based segmentation indicates a qualitative and quantitative improvement. In controlled solutions, a MC input based on DECT segmentation leads to better agreement with the reference. Future work will address the anticipated improvement of quantification

  10. Simulation-Based Communication Skills Training for Experienced Clinicians to Improve Family Conversations About Organ and Tissue Donation.

    Science.gov (United States)

    Potter, Julie Elizabeth; Gatward, Jonathan J; Kelly, Michelle A; McKay, Leigh; McCann, Ellie; Elliott, Rosalind M; Perry, Lin

    2017-12-01

    The approach, communication skills, and confidence of clinicians responsible for raising deceased organ donation may influence families' donation decisions. The aim of this study was to increase the preparedness and confidence of intensive care clinicians allocated to work in a "designated requester" role. We conducted a posttest evaluation of an innovative simulation-based training program. Simulation-based training enabled clinicians to rehearse the "balanced approach" to family donation conversations (FDCs) in the designated requester role. Professional actors played family members in simulated clinical settings using authentic scenarios, with video-assisted reflective debriefing. Participants completed an evaluation after the workshop. Simple descriptive statistical analysis and content analysis were performed. Between January 2013 and July 2015, 25 workshops were undertaken with 86 participants; 82 (95.3%) returned evaluations. Respondents were registered practicing clinicians; over half (44/82; 53.7%) were intensivists. Most attended a single workshop. Evaluations were overwhelmingly positive with the majority rating workshops as outstanding (64/80; 80%). Scenario fidelity, competence of the actors, opportunity to practice and receive feedback on performance, and feedback from actors, both in and out of character, were particularly valued. Most (76/78; 97.4%) reported feeling more confident about their designated requester role. Simulation-based communication training for the designated requester role in FDCs increased the knowledge and confidence of clinicians to raise the topic of donation.

  11. Simulation

    DEFF Research Database (Denmark)

    Gould, Derek A; Chalmers, Nicholas; Johnson, Sheena J

    2012-01-01

    Recognition of the many limitations of traditional apprenticeship training is driving new approaches to learning medical procedural skills. Among simulation technologies and methods available today, computer-based systems are topical and bring the benefits of automated, repeatable, and reliable p...... performance assessments. Human factors research is central to simulator model development that is relevant to real-world imaging-guided interventional tasks and to the credentialing programs in which it would be used....

  12. DIANE, a simulation code for the interaction of neutrons with living tissues. Application to low doses of fast neutrons on human tumoral cells

    International Nuclear Information System (INIS)

    Nenot, M.L.

    2003-07-01

    Our work deals with the irradiation of cells and living tissues by 14 MeV neutrons at very low doses (a few 10 -2 Gy). Such experiments require an accurate knowledge of the values of neutron dose rates and fluences at the level of cell cultures. We have performed measurements of fluence rates through an activation method applied to gold and copper foils. The fluence rate is deduced from the gamma rays emitted by the irradiated foils. Neutron doses and dose rates have been measured through varied methods: PIN diodes, ionization tissue equivalent chambers, and Geiger-Mueller counters. We have designed the DIANE code to simulate the impact of energetic neutrons on cells. This code can be used with isolated cells or macroscopic tissues, it takes into account the roles of the ionisation electrons produced by recoil nuclei entering the cell. This point is all the more important since recent works have highlighted the impact of very low energy electrons on DNA. (A.C.)

  13. SU-F-T-62: Three-Dimensional Dosimetric Gamma Analysis for Impacts of Tissue Inhomogeneity Using Monte Carlo Simulation in Intracavitary Brachytheray for Cervix Carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Tran Thi Thao; Nakamoto, Takahiro; Shibayama, Yusuke [Graduate School of Medical Sciences, Kyushu University (Japan); Arimura, Hidetaka [Faculty of Medical Sciences, Kyushu University (Japan); Oku, Yoshifumi [Kagoshima University Hospital (Japan); Yoshiura, Takashi [Graduate School of Diagnostic Radiotherapy, Kagoshima University (Japan)

    2016-06-15

    Purpose: The aim of this study was to investigate the impacts of tissue inhomogeneity on dose distributions using a three-dimensional (3D) gamma analysis in cervical intracavitary brachytherapy using Monte Carlo (MC) simulations. Methods: MC simulations for comparison of dose calculations were performed in a water phantom and a series of CT images of a cervical cancer patient (stage: Ib; age: 27) by employing a MC code, Particle and Heavy Ion Transport Code System (PHIT) version 2.73. The {sup 192}Ir source was set at fifteen dwell positions, according to clinical practice, in an applicator consisting of a tandem and two ovoids. Dosimetric comparisons were performed for the dose distributions in the water phantom and CT images by using gamma index image and gamma pass rate (%). The gamma index is the minimum Euclidean distance between two 3D spatial dose distributions of the water phantom and CT images in a same space. The gamma pass rates (%) indicate the percentage of agreement points, which mean that two dose distributions are similar, within an acceptance criteria (3 mm/3%). The volumes of physical and clinical interests for the gamma analysis were a whole calculated volume and a region larger than t% of a dose (close to a target), respectively. Results: The gamma pass rates were 77.1% for a whole calculated volume and 92.1% for a region within 1% dose region. The differences of 7.7% to 22.9 % between two dose distributions in the water phantom and CT images were found around the applicator region and near the target. Conclusion: This work revealed the large difference on the dose distributions near the target in the presence of the tissue inhomogeneity. Therefore, the tissue inhomogeneity should be corrected in the dose calculation for clinical treatment.

  14. Numerical simulation of time fractional dual-phase-lag model of heat transfer within skin tissue during thermal therapy.

    Science.gov (United States)

    Kumar, Dinesh; Rai, K N

    2017-07-01

    In this paper, we investigated the thermal behavior in living biological tissues using time fractional dual-phase-lag bioheat transfer (DPLBHT) model subjected to Dirichelt boundary condition in presence of metabolic and electromagnetic heat sources during thermal therapy. We solved this bioheat transfer model using finite element Legendre wavelet Galerkin method (FELWGM) with help of block pulse function in sense of Caputo fractional order derivative. We compared the obtained results from FELWGM and exact method in a specific case, and found a high accuracy. Results are interpreted in the form of standard and anomalous cases for taking different order of time fractional DPLBHT model. The time to achieve hyperthermia position is discussed in both cases as standard and time fractional order derivative. The success of thermal therapy in the treatment of metastatic cancerous cell depends on time fractional order derivative to precise prediction and control of temperature. The effect of variability of parameters such as time fractional derivative, lagging times, blood perfusion coefficient, metabolic heat source and transmitted power on dimensionless temperature distribution in skin tissue is discussed in detail. The physiological parameters has been estimated, corresponding to the value of fractional order derivative for hyperthermia treatment therapy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Time-specific measurements of energy deposition from radiation fields in simulated sub-micron tissue volumes

    International Nuclear Information System (INIS)

    Famiano, M.A.

    1997-01-01

    A tissue-equivalent spherical proportional counter is used with a modified amplifier system to measure specific energy deposited from a uniform radiation field for short periods of time (∼1 micros to seconds) in order to extrapolate to dose in sub-micron tissue volumes. The energy deposited during these time intervals is compared to biological repair processes occurring within the same intervals after the initial energy deposition. The signal is integrated over a variable collection time which is adjusted with a square-wave pulse. Charge from particle passages is collected on the anode during the period in which the integrator is triggered, and the signal decays quickly to zero after the integrator feedback switch resets; the process repeats for every triggering pulse. Measurements of energy deposited from x rays, 137 Cs gamma rays, and electrons from a 90 Sr/ 90 Y source for various time intervals are taken. Spectral characteristics as a function of charge collection time are observed and frequency plots of specific energy and collection time-interval are presented. In addition, a threshold energy flux is selected for each radiation type at which the formation of radicals (based on current measurements) in mammalian cells equals the rate at which radicals are repaired

  16. Computer simulations of plasma-biomolecule and plasma-tissue interactions for a better insight in plasma medicine

    Science.gov (United States)

    Neyts, Erik C.; Yusupov, Maksudbek; Verlackt, Christof C.; Bogaerts, Annemie

    2014-07-01

    Plasma medicine is a rapidly evolving multidisciplinary field at the intersection of chemistry, biochemistry, physics, biology, medicine and bioengineering. It holds great potential in medical, health care, dentistry, surgical, food treatment and other applications. This multidisciplinary nature and variety of possible applications come along with an inherent and intrinsic complexity. Advancing plasma medicine to the stage that it becomes an everyday tool in its respective fields requires a fundamental understanding of the basic processes, which is lacking so far. However, some major advances have already been made through detailed experiments over the last 15 years. Complementary, computer simulations may provide insight that is difficult—if not impossible—to obtain through experiments. In this review, we aim to provide an overview of the various simulations that have been carried out in the context of plasma medicine so far, or that are relevant for plasma medicine. We focus our attention mostly on atomistic simulations dealing with plasma-biomolecule interactions. We also provide a perspective and tentative list of opportunities for future modelling studies that are likely to further advance the field.

  17. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  18. Dosimetric characterization of model Cs-1 Rev2 cesium-131 brachytherapy source in water phantoms and human tissues with MCNP5 Monte Carlo simulation

    International Nuclear Information System (INIS)

    Wang Jianhua; Zhang Hualin

    2008-01-01

    A recently developed alternative brachytherapy seed, Cs-1 Rev2 cesium-131, has begun to be used in clinical practice. The dosimetric characteristics of this source in various media, particularly in human tissues, have not been fully evaluated. The aim of this study was to calculate the dosimetric parameters for the Cs-1 Rev2 cesium-131 seed following the recommendations of the AAPM TG-43U1 report [Rivard et al., Med. Phys. 31, 633-674 (2004)] for new sources in brachytherapy applications. Dose rate constants, radial dose functions, and anisotropy functions of the source in water, Virtual Water, and relevant human soft tissues were calculated using MCNP5 Monte Carlo simulations following the TG-43U1 formalism. The results yielded dose rate constants of 1.048, 1.024, 1.041, and 1.044 cGy h -1 U -1 in water, Virtual Water, muscle, and prostate tissue, respectively. The conversion factor for this new source between water and Virtual Water was 1.02, between muscle and water was 1.006, and between prostate and water was 1.004. The authors' calculation of anisotropy functions in a Virtual Water phantom agreed closely with Murphy's measurements [Murphy et al., Med. Phys. 31, 1529-1538 (2004)]. Our calculations of the radial dose function in water and Virtual Water have good agreement with those in previous experimental and Monte Carlo studies. The TG-43U1 parameters for clinical applications in water, muscle, and prostate tissue are presented in this work

  19. Simulation

    CERN Document Server

    Ross, Sheldon

    2006-01-01

    Ross's Simulation, Fourth Edition introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes. This text explains how a computer can be used to generate random numbers, and how to use these random numbers to generate the behavior of a stochastic model over time. It presents the statist

  20. Dynamic training algorithm for dynamic neural networks

    International Nuclear Information System (INIS)

    Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

    1996-01-01

    The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

  1. Microtomography evaluation of dental tissue wear surface induced by in vitro simulated chewing cycles on human and composite teeth

    Directory of Open Access Journals (Sweden)

    Rossella Bedini

    2012-01-01

    Full Text Available In this study a 3D microtomography display of tooth surfaces after in vitro dental wear tests has been obtained. Natural teeth have been compared with prosthetic teeth, manufactured by three different polyceramic composite materials. The prosthetic dental element samples, similar to molars, have been placed in opposition to human teeth extracted by paradontology diseases. After microtomography analysis, samples have been subjected to in vitro fatigue test cycles by servo-hydraulic mechanical testing machine. After the fatigue test, each sample has been subjected again to microtomography analysis to obtain volumetric value changes and dental wear surface images. Wear surface images were obtained by 3D reconstruction software and volumetric value changes were measured by CT analyser software. The aim of this work has been to show the potential of microtomography technique to display very clear and reliable wear surface images. Microtomography analysis methods to evaluate volumetric value changes have been used to quantify dental tissue and composite material wear.

  2. 3D conformal MRI-controlled transurethral ultrasound prostate therapy: validation of numerical simulations and demonstration in tissue-mimicking gel phantoms.

    Science.gov (United States)

    Burtnyk, Mathieu; N'Djin, William Apoutou; Kobelevskiy, Ilya; Bronskill, Michael; Chopra, Rajiv

    2010-11-21

    MRI-controlled transurethral ultrasound therapy uses a linear array of transducer elements and active temperature feedback to create volumes of thermal coagulation shaped to predefined prostate geometries in 3D. The specific aims of this work were to demonstrate the accuracy and repeatability of producing large volumes of thermal coagulation (>10 cc) that conform to 3D human prostate shapes in a tissue-mimicking gel phantom, and to evaluate quantitatively the accuracy with which numerical simulations predict these 3D heating volumes under carefully controlled conditions. Eleven conformal 3D experiments were performed in a tissue-mimicking phantom within a 1.5T MR imager to obtain non-invasive temperature measurements during heating. Temperature feedback was used to control the rotation rate and ultrasound power of transurethral devices with up to five 3.5 × 5 mm active transducer elements. Heating patterns shaped to human prostate geometries were generated using devices operating at 4.7 or 8.0 MHz with surface acoustic intensities of up to 10 W cm(-2). Simulations were informed by transducer surface velocity measurements acquired with a scanning laser vibrometer enabling improved calculations of the acoustic pressure distribution in a gel phantom. Temperature dynamics were determined according to a FDTD solution to Pennes' BHTE. The 3D heating patterns produced in vitro were shaped very accurately to the prostate target volumes, within the spatial resolution of the MRI thermometry images. The volume of the treatment difference falling outside ± 1 mm of the target boundary was, on average, 0.21 cc or 1.5% of the prostate volume. The numerical simulations predicted the extent and shape of the coagulation boundary produced in gel to within (mean ± stdev [min, max]): 0.5 ± 0.4 [-1.0, 2.1] and -0.05 ± 0.4 [-1.2, 1.4] mm for the treatments at 4.7 and 8.0 MHz, respectively. The temperatures across all MRI thermometry images were predicted within -0.3 ± 1.6 °C and 0

  3. Turing Instability-Driven Biofabrication of Branching Tissue Structures: A Dynamic Simulation and Analysis Based on the Reaction–Diffusion Mechanism †

    Directory of Open Access Journals (Sweden)

    Xiaolu Zhu

    2018-03-01

    Full Text Available Four-dimensional (4D biofabrication techniques aim to dynamically produce and control three-dimensional (3D biological structures that would transform their shapes or functionalities with time, when a stimulus is imposed or cell post-printing self-assembly occurs. The evolution of 3D branching patterns via self-assembly of cells is critical for the 4D biofabrication of artificial organs or tissues with branched geometry. However, it is still unclear how the formation and evolution of these branching patterns are biologically encoded. Here, we study the biofabrication of lung branching structures utilizing a simulation model based on Turing instability that raises a dynamic reaction–diffusion (RD process of the biomolecules and cells. The simulation model incorporates partial differential equations of four variables, describing the tempo-spatial distribution of the variables in 3D over time. The simulation results present the formation and evolution process of 3D branching patterns over time and also interpret both the behaviors of side-branching and tip-splitting as the stalk grows and the fabrication style under an external concentration gradient of morphogen, through 3D visualization. This provides a theoretical framework for rationally guiding the 4D biofabrication of lung airway grafts via cellular self-organization, which would potentially reduce the complexity of future experimental research and number of trials.

  4. Bee waxes: a model of characterization for using as base simulator tissue in teletherapy with photons; Ceras de abelha: um modelo de caracterizacao para sua utilizacao como tecido simulador base em teleterapia com fotons

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Rogerio Matias Vidal da; Souza, Divanizia do Nascimento

    2011-10-26

    This paper presents a model of characterization and selection of bee waxes which makes possible to certify the usage viability of that base simulator tissue in the manufacture of appropriated objects for external radiotherapy with mega volt photon beams. The work was divide into three stages, where was evaluated physical and chemical properties besides the aspects related to the capacity of beam attenuation. All the process was carefully accompanied related to the wax origin such as the bee specimen and the flora surrounding the beehives. The chemical composition of the waxes is similar to others simulators usually used in radiotherapy. The behavior of mass attenuation coefficient in the radiotherapeutic energy range is comparable to other simulators, and consequently to the soft tissue. The proposed model is efficient and allows the affirmative that the usage of determined bee wax as base simulator tissue is convenient

  5. Regional deep hyperthermia: impact of observer variability in CT-based manual tissue segmentation on simulated temperature distribution

    Science.gov (United States)

    Aklan, Bassim; Hartmann, Josefin; Zink, Diana; Siavooshhaghighi, Hadi; Merten, Ricarda; Putz, Florian; Ott, Oliver; Fietkau, Rainer; Bert, Christoph

    2017-06-01

    The aim of this study was to systematically investigate the influence of the inter- and intra-observer segmentation variation of tumors and organs at risk on the simulated temperature coverage of the target. CT scans of six patients with tumors in the pelvic region acquired for radiotherapy treatment planning were used for hyperthermia treatment planning. To study the effect of inter-observer variation, three observers manually segmented in the CT images of each patient the following structures: fat, muscle, bone and the bladder. The gross tumor volumes (GTV) were contoured by three radiation oncology residents and used as the hyperthermia target volumes. For intra-observer variation, one of the observers of each group contoured the structures of each patient three times with a time span of one week between the segmentations. Moreover, the impact of segmentation variations in organs at risk (OARs) between the three inter-observers was investigated on simulated temperature distributions using only one GTV. The spatial overlap between individual segmentations was assessed by the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Additionally, the temperatures T90/T10 delivered to 90%/10% of the GTV, respectively, were assessed for each observer combination. The results of the segmentation similarity evaluation showed that the DSC of the inter-observer variation of fat, muscle, the bladder, bone and the target was 0.68  ±  0.12, 0.88  ±  0.05, 0.73  ±  0.14, 0.91  ±  0.04 and 0.64  ±  0.11, respectively. Similar results were found for the intra-observer variation. The MSD results were similar to the DSCs for both observer variations. A statistically significant difference (p  <  0.05) was found for T90 and T10 in the predicted target temperature due to the observer variability. The conclusion is that intra- and inter-observer variations have a significant impact on the temperature coverage of the

  6. Differentiation between non-neural and neural contributors to ankle joint stiffness in cerebral palsy

    NARCIS (Netherlands)

    De Gooijer-van de Groep, K.L.; De Vlugt, E.; De Groot, J.H.; Van der Heijden-Maessen, H.C.M.; Wielheesen, D.H.M.; Van Wijlen-Hempel, R.M.S.; Arendzen, J.H.; Meskers, C.G.M.

    2013-01-01

    Background Spastic paresis in cerebral palsy (CP) is characterized by increased joint stiffness that may be of neural origin, i.e. improper muscle activation caused by e.g. hyperreflexia or non-neural origin, i.e. altered tissue viscoelastic properties (clinically: “spasticity” vs. “contracture”).

  7. Neural Network to Solve Concave Games

    OpenAIRE

    Liu, Zixin; Wang, Nengfa

    2014-01-01

    The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given to illustrate the theoretical results.

  8. able utilizando redes neuronales artificiales; UTILIZATION OF ARTIFICIAL NEURAL NETWORK IN THE SIMULATION AND CONTROL OF WIND TURBINE GENERATORS WITH VARIABLE SPEED AND VARIABLE PITCH.

    Directory of Open Access Journals (Sweden)

    Osley López González

    2011-02-01

    , considered as a whole, must be able of respond with anadequate precision and speed in response to the randomness and variability of the wind.The relationship between the wind speed, the blade pitch and the generator speed in order to produce themaximum power and also be able to limit the output power for large wind speeds is a very complicated oneand it is very difficult to find its mathematical function.In this paper, the authors, utilizing the MATLABSIMULINK toolboxes, propose representing this functional relation by means of an Artificial Neural Network(ANN. The parameters and characteristics of an existing wind turbine generator are utilized and it isdemonstrated that it is possible to use an ANN in the simulation and control of a variable speed, variablepitch wind turbine that capture the maximum power from the wind.

  9. Optimization behavior of brainstem respiratory neurons. A cerebral neural network model.

    Science.gov (United States)

    Poon, C S

    1991-01-01

    A recent model of respiratory control suggested that the steady-state respiratory responses to CO2 and exercise may be governed by an optimal control law in the brainstem respiratory neurons. It was not certain, however, whether such complex optimization behavior could be accomplished by a realistic biological neural network. To test this hypothesis, we developed a hybrid computer-neural model in which the dynamics of the lung, brain and other tissue compartments were simulated on a digital computer. Mimicking the "controller" was a human subject who pedalled on a bicycle with varying speed (analog of ventilatory output) with a view to minimize an analog signal of the total cost of breathing (chemical and mechanical) which was computed interactively and displayed on an oscilloscope. In this manner, the visuomotor cortex served as a proxy (homolog) of the brainstem respiratory neurons in the model. Results in 4 subjects showed a linear steady-state ventilatory CO2 response to arterial PCO2 during simulated CO2 inhalation and a nearly isocapnic steady-state response during simulated exercise. Thus, neural optimization is a plausible mechanism for respiratory control during exercise and can be achieved by a neural network with cognitive computational ability without the need for an exercise stimulus.

  10. Studies in RF power communication, SAR, and temperature elevation in wireless implantable neural interfaces.

    Directory of Open Access Journals (Sweden)

    Yujuan Zhao

    Full Text Available Implantable neural interfaces are designed to provide a high spatial and temporal precision control signal implementing high degree of freedom real-time prosthetic systems. The development of a Radio Frequency (RF wireless neural interface has the potential to expand the number of applications as well as extend the robustness and longevity compared to wired neural interfaces. However, it is well known that RF signal is absorbed by the body and can result in tissue heating. In this work, numerical studies with analytical validations are performed to provide an assessment of power, heating and specific absorption rate (SAR associated with the wireless RF transmitting within the human head. The receiving antenna on the neural interface is designed with different geometries and modeled at a range of implanted depths within the brain in order to estimate the maximum receiving power without violating SAR and tissue temperature elevation safety regulations. Based on the size of the designed antenna, sets of frequencies between 1 GHz to 4 GHz have been investigated. As expected the simulations demonstrate that longer receiving antennas (dipole and lower working frequencies result in greater power availability prior to violating SAR regulations. For a 15 mm dipole antenna operating at 1.24 GHz on the surface of the brain, 730 uW of power could be harvested at the Federal Communications Commission (FCC SAR violation limit. At approximately 5 cm inside the head, this same antenna would receive 190 uW of power prior to violating SAR regulations. Finally, the 3-D bio-heat simulation results show that for all evaluated antennas and frequency combinations we reach FCC SAR limits well before 1 °C. It is clear that powering neural interfaces via RF is possible, but ultra-low power circuit designs combined with advanced simulation will be required to develop a functional antenna that meets all system requirements.

  11. A framework for plasticity implementation on the SpiNNaker neural architecture.

    Science.gov (United States)

    Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A; Furber, Steve B; Benosman, Ryad B

    2014-01-01

    Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

  12. Upregulated epidermal growth factor receptor expression following near-infrared irradiation simulating solar radiation in a three-dimensional reconstructed human corneal epithelial tissue culture model.

    Science.gov (United States)

    Tanaka, Yohei; Nakayama, Jun

    2016-01-01

    Humans are increasingly exposed to near-infrared (NIR) radiation from both natural (eg, solar) and artificial (eg, electrical appliances) sources. Although the biological effects of sun and ultraviolet (UV) exposure have been extensively investigated, the biological effect of NIR radiation is still unclear. We previously reported that NIR as well as UV induces photoaging and standard UV-blocking materials, such as sunglasses, do not sufficiently block NIR. The objective of this study was to investigate changes in gene expression in three-dimensional reconstructed corneal epithelial tissue culture exposed to broad-spectrum NIR irradiation to simulate solar NIR radiation that reaches human tissues. DNA microarray and quantitative real-time polymerase chain reaction analysis were used to assess gene expression levels in a three-dimensional reconstructed corneal epithelial model composed of normal human corneal epithelial cells exposed to water-filtered broad-spectrum NIR irradiation with a contact cooling (20°C). The water-filter allowed 1,000-1,800 nm wavelengths and excluded 1,400-1,500 nm wavelengths. A DNA microarray with >62,000 different probes showed 25 and 150 genes that were up- or downregulated by at least fourfold and twofold, respectively, after NIR irradiation. In particular, epidermal growth factor receptor (EGFR) was upregulated by 19.4-fold relative to control cells. Quantitative real-time polymerase chain reaction analysis revealed that two variants of EGFR in human corneal epithelial tissue were also significantly upregulated after five rounds of 10 J/cm(2) irradiation (Psolar energy reaching the Earth is in the NIR region, which cannot be adequately blocked by eyewear and thus can induce eye damage with intensive or long-term exposure, protection from both UV and NIR radiation may prevent changes in gene expression and in turn eye damage.

  13. Efficient Cancer Detection Using Multiple Neural Networks.

    Science.gov (United States)

    Shell, John; Gregory, William D

    2017-01-01

    The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

  14. Monte Carlo simulations for dose enhancement in cancer treatment using bismuth oxide nanoparticles implanted in brain soft tissue.

    Science.gov (United States)

    Taha, Eslam; Djouider, Fathi; Banoqitah, Essam

    2018-03-26

    The objective of this work is to study the dosimetric performances of bismuth oxide nanoparticles implanted in tumors in cancer radiotherapy. GEANT4 based Monte Carlo numerical simulations were performed to assess dose enhancement distributions in and around a 1 × 1 × 1 cm 3 tumor implanted with different concentrations of bismuth oxide and irradiated with low energies 125 I, 131 Cs, and 103 Pd radioactive sources. Dose contributions were considered from photoelectrons, Auger electrons, and characteristic X-rays. Our results show the dose enhancement increased with increasing both bismuth oxide concentration in the target and photon energy. A dose enhancement factor up to 18.55 was obtained for a concentration of 70 mg/g of bismuth oxide in the tumor when irradiated with 131 Cs source. This study showed that bismuth oxide nanoparticles are innovative agents that could be potentially applicable to in vivo cancer radiotherapy due to the fact that they induce a highly localized energy deposition within the tumor.

  15. Fuzzy neural network theory and application

    CERN Document Server

    Liu, Puyin

    2004-01-01

    This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he

  16. 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

  17. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  18. Neurophysiology and neural engineering: a review.

    Science.gov (United States)

    Prochazka, Arthur

    2017-08-01

    Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight. Copyright © 2017 the American Physiological Society.

  19. Measurement of fluorophore concentrations and fluorescence quantum yield in tissue-simulating phantoms using three diffusion models of steady-state spatially resolved fluorescence

    Energy Technology Data Exchange (ETDEWEB)

    Diamond, Kevin R; Farrell, Thomas J; Patterson, Michael S [Department of Medical Physics, Juravinski Cancer Centre and McMaster University, 699 Concession Street, Hamilton, Ontario L8V 5C2 (Canada)

    2003-12-21

    Steady-state diffusion theory models of fluorescence in tissue have been investigated for recovering fluorophore concentrations and fluorescence quantum yield. Spatially resolved fluorescence, excitation and emission reflectance were calculated by diffusion theory and Monte Carlo simulations, and measured using a multi-fibre probe on tissue-simulating phantoms containing either aluminium phthalocyanine tetrasulfonate (AlPcS{sub 4}), Photofrin or meso-tetra-(4-sulfonatophenyl)-porphine dihydrochloride (TPPS{sub 4}). The accuracy of the fluorophore concentration and fluorescence quantum yield recovered by three different models of spatially resolved fluorescence were compared. The models were based on: (a) weighted difference of the excitation and emission reflectance, (b) fluorescence due to a point excitation source or (c) fluorescence due to a pencil beam excitation source. When literature values for the fluorescence quantum yield were used for each of the fluorophores, the fluorophore absorption coefficient (and hence concentration) at the excitation wavelengthwas recovered with a root-mean-square accuracy of 11.4% using the point source model of fluorescence and 8.0% using the more complicated pencil beam excitation model. The accuracy was calculated over a broad range of optical properties and fluorophore concentrations. The weighted difference of reflectance model performed poorly, with a root-mean-square error in concentration of about 50%. Monte Carlo simulations suggest that there are some situations where the weighted difference of reflectance is as accurate as the other two models, although this was not confirmed experimentally. Estimates of the fluorescence quantum yield in multiple scattering media were also made by determining independently from the fitted absorption spectrum and applying the various diffusion theory models. The fluorescence quantum yields for AlPcS{sub 4} and TPPS{sub 4} were calculated to be 0.59 {+-} 0.03 and 0.121 {+-} 0

  20. Measurement of fluorophore concentrations and fluorescence quantum yield in tissue-simulating phantoms using three diffusion models of steady-state spatially resolved fluorescence

    International Nuclear Information System (INIS)

    Diamond, Kevin R; Farrell, Thomas J; Patterson, Michael S

    2003-01-01

    Steady-state diffusion theory models of fluorescence in tissue have been investigated for recovering fluorophore concentrations and fluorescence quantum yield. Spatially resolved fluorescence, excitation and emission reflectance were calculated by diffusion theory and Monte Carlo simulations, and measured using a multi-fibre probe on tissue-simulating phantoms containing either aluminium phthalocyanine tetrasulfonate (AlPcS 4 ), Photofrin or meso-tetra-(4-sulfonatophenyl)-porphine dihydrochloride (TPPS 4 ). The accuracy of the fluorophore concentration and fluorescence quantum yield recovered by three different models of spatially resolved fluorescence were compared. The models were based on: (a) weighted difference of the excitation and emission reflectance, (b) fluorescence due to a point excitation source or (c) fluorescence due to a pencil beam excitation source. When literature values for the fluorescence quantum yield were used for each of the fluorophores, the fluorophore absorption coefficient (and hence concentration) at the excitation wavelengthwas recovered with a root-mean-square accuracy of 11.4% using the point source model of fluorescence and 8.0% using the more complicated pencil beam excitation model. The accuracy was calculated over a broad range of optical properties and fluorophore concentrations. The weighted difference of reflectance model performed poorly, with a root-mean-square error in concentration of about 50%. Monte Carlo simulations suggest that there are some situations where the weighted difference of reflectance is as accurate as the other two models, although this was not confirmed experimentally. Estimates of the fluorescence quantum yield in multiple scattering media were also made by determining independently from the fitted absorption spectrum and applying the various diffusion theory models. The fluorescence quantum yields for AlPcS 4 and TPPS 4 were calculated to be 0.59 ± 0.03 and 0.121 ± 0.001 respectively using the point

  1. Artificial Neural Network-Based Three-dimensional Continuous Response Relationship Construction of 3Cr20Ni10W2 Heat-Resisting Alloy and Its Application in Finite Element Simulation

    Science.gov (United States)

    Li, Le; Wang, Li-yong

    2018-04-01

    The application of accurate constitutive relationship in finite element simulation would significantly contribute to accurate simulation results, which plays a critical role in process design and optimization. In this investigation, the true stress-strain data of 3Cr20Ni10W2 heat-resisting alloy were obtained from a series of isothermal compression tests conducted in a wide temperature range of 1203-1403 K and strain rate range of 0.01-10 s-1 on a Gleeble 1500 testing machine. Then the constitutive relationship was modeled by an optimally constructed and well-trained back-propagation artificial neural network (BP-ANN). The evaluation of the BP-ANN model revealed that it has admirable performance in characterizing and predicting the flow behaviors of 3Cr20Ni10W2 heat-resisting alloy. Meanwhile, a comparison between improved Arrhenius-type constitutive equation and BP-ANN model shows that the latter has higher accuracy. Consequently, the developed BP-ANN model was used to predict abundant stress-strain data beyond the limited experimental conditions and construct the three-dimensional continuous response relationship for temperature, strain rate, strain, and stress. Finally, the three-dimensional continuous response relationship was applied to the numerical simulation of isothermal compression tests. The results show that such constitutive relationship can significantly promote the accuracy improvement of numerical simulation for hot forming processes.

  2. Antenna analysis using neural networks

    Science.gov (United States)

    Smith, William T.

    1992-01-01

    Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

  3. Proposal of a model of mammalian neural induction

    Science.gov (United States)

    Levine, Ariel J.; Brivanlou, Ali H.

    2009-01-01

    How does the vertebrate embryo make a nervous system? This complex question has been at the center of developmental biology for many years. The earliest step in this process – the induction of neural tissue – is intimately linked to patterning of the entire early embryo, and the molecular and embryological basis these processes are beginning to emerge. Here, we analyze classic and cutting-edge findings on neural induction in the mouse. We find that data from genetics, tissue explants, tissue grafting, and molecular marker expression support a coherent framework for mammalian neural induction. In this model, the gastrula organizer of the mouse embryo inhibits BMP signaling to allow neural tissue to form as a default fate – in the absence of instructive signals. The first neural tissue induced is anterior and subsequent neural tissue is posteriorized to form the midbrain, hindbrain, and spinal cord. The anterior visceral endoderm protects the pre-specified anterior neural fate from similar posteriorization, allowing formation of forebrain. This model is very similar to the default model of neural induction in the frog, thus bridging the evolutionary gap between amphibians and mammals. PMID:17585896

  4. Tissue engineering of cartilage using a mechanobioreactor exerting simultaneous mechanical shear and compression to simulate the rolling action of articular joints.

    Science.gov (United States)

    Shahin, Kifah; Doran, Pauline M

    2012-04-01

    The effect of dynamic mechanical shear and compression on the synthesis of human tissue-engineered cartilage was investigated using a mechanobioreactor capable of simulating the rolling action of articular joints in a mixed fluid environment. Human chondrocytes seeded into polyglycolic acid (PGA) mesh or PGA-alginate scaffolds were precultured in shaking T-flasks or recirculation perfusion bioreactors for 2.5 or 4 weeks prior to mechanical stimulation in the mechanobioreactor. Constructs were subjected to intermittent unconfined shear and compressive loading at a frequency of 0.05 Hz using a peak-to-peak compressive strain amplitude of 2.2% superimposed on a static axial compressive strain of 6.5%. The mechanical treatment was carried out for up to 2.5 weeks using a loading regime of 10 min duration each day with the direction of the shear forces reversed after 5 min and release of all loading at the end of the daily treatment period. Compared with shaking T-flasks and mechanobioreactor control cultures without loading, mechanical treatment improved the amount and quality of cartilage produced. On a per cell basis, synthesis of both major structural components of cartilage, glycosaminoglycan (GAG) and collagen type II, was enhanced substantially by up to 5.3- and 10-fold, respectively, depending on the scaffold type and seeding cell density. Levels of collagen type II as a percentage of total collagen were also increased after mechanical treatment by up to 3.4-fold in PGA constructs. Mechanical treatment had a less pronounced effect on the composition of constructs precultured in perfusion bioreactors compared with perfusion culture controls. This work demonstrates that the quality of tissue-engineered cartilage can be enhanced significantly by application of simultaneous dynamic mechanical shear and compression, with the greatest benefits evident for synthesis of collagen type II. Copyright © 2011 Wiley Periodicals, Inc.

  5. Modelling organs, tissues, cells and devices using Matlab and Comsol multiphysics

    CERN Document Server

    Dokos, Socrates

    2017-01-01

    This book presents a theoretical and practical overview of computational modeling in bioengineering, focusing on a range of applications including electrical stimulation of neural and cardiac tissue, implantable drug delivery, cancer therapy, biomechanics, cardiovascular dynamics, as well as fluid-structure interaction for modelling of organs, tissues, cells and devices. It covers the basic principles of modeling and simulation with ordinary and partial differential equations using MATLAB and COMSOL Multiphysics numerical software. The target audience primarily comprises postgraduate students and researchers, but the book may also be beneficial for practitioners in the medical device industry.

  6. Upregulated epidermal growth factor receptor expression following near-infrared irradiation simulating solar radiation in a three-dimensional reconstructed human corneal epithelial tissue culture model

    Directory of Open Access Journals (Sweden)

    Tanaka Y

    2016-08-01

    Full Text Available Yohei Tanaka,1,2 Jun Nakayama2 1Department of Plastic Surgery, Clinica Tanaka Plastic, Reconstructive Surgery and Anti-aging Center, 2Department of Molecular Pathology, Shinshu University Graduate School of Medicine, Matsumoto, Nagano, Japan Background and objective: Humans are increasingly exposed to near-infrared (NIR radiation from both natural (eg, solar and artificial (eg, electrical appliances sources. Although the biological effects of sun and ultraviolet (UV exposure have been extensively investigated, the biological effect of NIR radiation is still unclear. We previously reported that NIR as well as UV induces photoaging and standard UV-blocking materials, such as sunglasses, do not sufficiently block NIR. The objective of this study was to investigate changes in gene expression in three-dimensional reconstructed corneal epithelial tissue culture exposed to broad-spectrum NIR irradiation to simulate solar NIR radiation that reaches human tissues.Materials and methods: DNA microarray and quantitative real-time polymerase chain reaction analysis were used to assess gene expression levels in a three-dimensional reconstructed corneal epithelial model composed of normal human corneal epithelial cells exposed to water-filtered broad-spectrum NIR irradiation with a contact cooling (20°C. The water-filter allowed 1,000–1,800 nm wavelengths and excluded 1,400–1,500 nm wavelengths.Results: A DNA microarray with >62,000 different probes showed 25 and 150 genes that were up- or downregulated by at least fourfold and twofold, respectively, after NIR irradiation. In particular, epidermal growth factor receptor (EGFR was upregulated by 19.4-fold relative to control cells. Quantitative real-time polymerase chain reaction analysis revealed that two variants of EGFR in human corneal epithelial tissue were also significantly upregulated after five rounds of 10 J/cm2 irradiation (P<0.05.Conclusion: We found that NIR irradiation induced the

  7. Dosimetric comparison on tissue interfaces with TLD dosimeters, L-alanine, EDR2 films and Penelope simulation for a Co-60 source and linear accelerator in radiotherapy

    International Nuclear Information System (INIS)

    Vega R, J. L.; Cayllahua, F.; Apaza, D. G.; Javier, H.

    2015-10-01

    Percentage depth dose curves were obtained with TLD-100 dosimeters, EDR2 films and Penelope simulation at the interfaces in an inhomogeneous mannequin, composed by equivalent materials to the human body built for this study, consisting of cylindrical plates of solid water-bone-lung-bone-solid water of 15 cm in diameter and 1 cm in height; plates were placed in descending way (4-2-8-2-4). Irradiated with Co-60 source (Theratron Equinox-100) for small radiation fields 3 x 3 cm 2 and 1 x 1 cm 2 at a surface source distance of 100 cm from mannequin. The TLD-100 dosimeters were placed in the center of each plate of mannequin irradiated at 10 Gy. The results were compared between these measurement techniques, giving good agreement in interfaces better than 97%. This study was compared with the same characteristics of another study realized with other equivalent materials to human body not homogeneous acrylic-bone-cork-bone-acrylic. The percentage depth dose curves were obtained with mini-dosimeters L-alanine of 1 mm in diameter and 3 mm in height and 3.5 to 4.0 mg of mass with spectrometer band K (EPR). The mini-dosimeters were irradiated with a lineal accelerator PRIMUS Siemens 6 MV. The results of percentage depth dose of L-alanine mini-dosimeters show a good agreement with the percentage depth dose curves of Penelope code, better than 97.7% in interfaces of tissues. (Author)

  8. Monte Carlo simulation of radiation transport and dose deposition from locally released gold nanoparticles labeled with 111In, 177Lu or 90Y incorporated into tissue implantable depots

    Science.gov (United States)

    Lai, Priscilla; Cai, Zhongli; Pignol, Jean-Philippe; Lechtman, Eli; Mashouf, Shahram; Lu, Yijie; Winnik, Mitchell A.; Jaffray, David A.; Reilly, Raymond M.

    2017-11-01

    Permanent seed implantation (PSI) brachytherapy is a highly conformal form of radiation therapy but is challenged with dose inhomogeneity due to its utilization of low energy radiation sources. Gold nanoparticles (AuNP) conjugated with electron emitting radionuclides have recently been developed as a novel form of brachytherapy and can aid in homogenizing dose through physical distribution of radiolabeled AuNP when injected intratumorally (IT) in suspension. However, the distribution is unpredictable and precise placement of many injections would be difficult. Previously, we reported the design of a nanoparticle depot (NPD) that can be implanted using PSI techniques and which facilitates controlled release of AuNP. We report here the 3D dose distribution resulting from a NPD incorporating AuNP labeled with electron emitters (90Y, 177Lu, 111In) of different energies using Monte Carlo based voxel level dosimetry. The MCNP5 Monte Carlo radiation transport code was used to assess differences in dose distribution from simulated NPD and conventional brachytherapy sources, positioned in breast tissue simulating material. We further compare these dose distributions in mice bearing subcutaneous human breast cancer xenografts implanted with 177Lu-AuNP NPD, or injected IT with 177Lu-AuNP in suspension. The radioactivity distributions were derived from registered SPECT/CT images and time-dependent dose was estimated. Results demonstrated that the dose distribution from NPD reduced the maximum dose 3-fold when compared to conventional seeds. For simulated NPD, as well as NPD implanted in vivo, 90Y delivered the most homogeneous dose distribution. The tumor radioactivity in mice IT injected with 177Lu-AuNP redistributed while radioactivity in the NPD remained confined to the implant site. The dose distribution from radiolabeled AuNP NPD were predictable and concentric in contrast to IT injected radiolabeled AuNP, which provided irregular and temporally variant dose distributions

  9. Identification of b-jets with a low pΤ muon using ATLAS Tile Calorimeter simulation data and artificial neural networks technique

    International Nuclear Information System (INIS)

    Astvatsaturov, A.; Budagov, Yu.; Shigaev, V.; Nessi, M.; Pantea, D.

    1996-01-01

    The possibility to enhance the capability of ATLAS Tile Calorimeter to identify low p Τ muons (2 Τ Τ =20 and 40 GeV/c in the central region 0 b g is 4-10 times higher in NND case compared to LTD. The results obtained are based on 2000 jets simulated with the use of ATLAS simulation programs. 8 refs., 13 figs., 2 tabs

  10. Runoff Modelling in Urban Storm Drainage by Neural Networks

    DEFF Research Database (Denmark)

    Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld

    1995-01-01

    A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....

  11. Dynamic decomposition of spatiotemporal neural signals.

    Directory of Open Access Journals (Sweden)

    Luca Ambrogioni

    2017-05-01

    Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

  12. Characterization of the angular memory effect of scattered light in biological tissues.

    Science.gov (United States)

    Schott, Sam; Bertolotti, Jacopo; Léger, Jean-Francois; Bourdieu, Laurent; Gigan, Sylvain

    2015-05-18

    High resolution optical microscopy is essential in neuroscience but suffers from scattering in biological tissues and therefore grants access to superficial brain layers only. Recently developed techniques use scattered photons for imaging by exploiting angular correlations in transmitted light and could potentially increase imaging depths. But those correlations ('angular memory effect') are of a very short range and should theoretically be only present behind and not inside scattering media. From measurements on neural tissues and complementary simulations, we find that strong forward scattering in biological tissues can enhance the memory effect range and thus the possible field-of-view by more than an order of magnitude compared to isotropic scattering for ∼1 mm thick tissue layers.

  13. Comparison of an adaptive neuro-fuzzy inference system and an artificial neural network in the cross-talk correction of simultaneous 99 m Tc / 201Tl SPECT imaging using a GATE Monte-Carlo simulation

    Science.gov (United States)

    Heidary, Saeed; Setayeshi, Saeed; Ghannadi-Maragheh, Mohammad

    2014-09-01

    The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) to estimate the cross-talk contamination of 99 m Tc / 201 Tl image acquisition in the 201 Tl energy window (77 ± 15% keV). GATE (Geant4 Application in Emission and Tomography) is employed due to its ability to simulate multiple radioactive sources concurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. In the real and the simulation studies, data acquisition is carried out using eight energy windows. The ANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training data set. Three indications are evaluated and compared. The ANFIS method yields better outcomes for two indications (Spearman's rank correlation coefficient and contrast) and the two phantom results in each category. The maximum image biasing, which is the third indication, is found to be 6% more than that for the ANN.

  14. Relationship Between Kinetics of Inflow and Outflow as the Basis of a Computer Simulation for Solving Compartmental Models: Example of Electrolyte Transfers in Cardiovascular Tissues

    Energy Technology Data Exchange (ETDEWEB)

    Llaurado, J. G. [Biomedical Engineering Group, Marquette University (United States); Marquette School of Medicine, Milwaukee (United States); Nuclear Medicine Service of Veterans Administration Center, Wood, WI (United States)

    1971-02-15

    A method commonly used for the study of the distribution of a substance among the different spaces ol a biological tissue is the continuous washout (outflow) and isotope counting of fragments of tissue previously incubated with a tracer. A first order kinetics compartmental system can be postulated and characterized by the transport rates (k) at which the substance of interest moves across its different compartments. Direct solution from the outflow data requires knowledge of the initial conditions for, or to have access for measurements in, each compartment. This cannot be fulfilled in most biological problems. In the course of studying {sup 22}Na distribution in segments of arteries a digital computer simulation approach was developed to solve the system. In the belief that the approach transcends this particular application, its mathematical basis is herein presented: the movement of radioactive tracer obeys Divides dq/d Divides = - Divides k Divides Divides q Divides + Divides r Divides (1) where |q| is a vector of response functions for each compartment, |k] is a square matrix of transport rate constants and |r| is a vector of input rates to the system. Solution of Eq. 1 is Divides q Divides = e{sup - Divides k Divides t} {integral}{sub 0}{sup t} e{sup Divides k Divides t} Divides r Divides dt + e{sup - Divides k Divides t} Divides q{sub 0} Divides (2) (i) For an inflow experiment, with 0 initial conditions and a constant unit input rate |r{sub u}| Divides q Divides = ( Divides I Divides - e{sup - Divides k Divides t}) Divides k Divides {sup -1} Divides r{sub u} Divides (3) as t --> {infinity}, Divides q{sub {infinity}} Divides = Divides k Divides {sup -1} Divides r{sub u} Divides , which replaced in Eq. 3, Divides q Divides = Divides q{sub {infinity}} Divides -e{sup - Divides k Divides t} Divides q{sub {infinity}} Divides (4) (ii) For an outflow experiment Divides r Divides = 0 and Eq. 2 becomes Divides q Divides = e{sup - Divides k Divides t} Divides q{sub 0

  15. Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System

    Directory of Open Access Journals (Sweden)

    Wahiba Yaïci

    2017-08-01

    Full Text Available In recent years, there has been a strong growth in solar power generation industries. The need for highly efficient and optimised solar thermal energy systems, stand-alone or grid connected photovoltaic systems, has substantially increased. This requires the development of efficient and reliable performance prediction capabilities of solar heat and power production over the day. This contribution investigates the effect of the number of input variables on both the accuracy and the reliability of the artificial neural network (ANN method for predicting the performance parameters of a solar energy system. This paper describes the ANN models and the optimisation process in detail for predicting performance. Comparison with experimental data from a solar energy system tested in Ottawa, Canada during two years under different weather conditions demonstrates the good prediction accuracy attainable with each of the models using reduced input variables. However, it is likely true that the degree of model accuracy would gradually decrease with reduced inputs. Overall, the results of this study demonstrate that the ANN technique is an effective approach for predicting the performance of highly non-linear energy systems. The suitability of the modelling approach using ANNs as a practical engineering tool in renewable energy system performance analysis and prediction is clearly demonstrated.

  16. c-T phase diagram and Landau free energies of (AgAu)55 nanoalloy via neural-network molecular dynamic simulations.

    Science.gov (United States)

    Chiriki, Siva; Jindal, Shweta; Bulusu, Satya S

    2017-10-21

    For understanding the structure, dynamics, and thermal stability of (AgAu) 55 nanoalloys, knowledge of the composition-temperature (c-T) phase diagram is essential due to the explicit dependence of properties on composition and temperature. Experimentally, generating the phase diagrams is very challenging, and therefore theoretical insight is necessary. We use an artificial neural network potential for (AgAu) 55 nanoalloys. Predicted global minimum structures for pure gold and gold rich compositions are lower in energy compared to previous reports by density functional theory. The present work based on c-T phase diagram, surface area, surface charge, probability of isomers, and Landau free energies supports the enhancement of catalytic property of Ag-Au nanoalloys by incorporation of Ag up to 24% by composition in Au nanoparticles as found experimentally. The phase diagram shows that there is a coexistence temperature range of 70 K for Ag 28 Au 27 compared to all other compositions. We propose the power spectrum coefficients derived from spherical harmonics as an order parameter to calculate Landau free energies.

  17. Pax7 lineage contributions to the mammalian neural crest.

    Directory of Open Access Journals (Sweden)

    Barbara Murdoch

    Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

  18. 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.

  19. Neural control of magnetic suspension systems

    Science.gov (United States)

    Gray, W. Steven

    1993-01-01

    The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

  20. Nonlinear programming with feedforward neural networks.

    Energy Technology Data Exchange (ETDEWEB)

    Reifman, J.

    1999-06-02

    We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.

  1. Neural networks, D0, and the SSC

    International Nuclear Information System (INIS)

    Barter, C.; Cutts, D.; Hoftun, J.S.; Partridge, R.A.; Sornborger, A.T.; Johnson, C.T.; Zeller, R.T.

    1989-01-01

    We outline several exploratory studies involving neural network simulations applied to pattern recognition in high energy physics. We describe the D0 data acquisition system and a natual means by which algorithms derived from neural networks techniques may be incorporated into recently developed hardware associated with the D0 MicroVAX farm nodes. Such applications to the event filtering needed by SSC detectors look interesting. 10 refs., 11 figs

  2. Neural Synchronization and Cryptography

    Science.gov (United States)

    Ruttor, Andreas

    2007-11-01

    Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.

  3. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  4. Epidural anaesthesia with levobupivacaine and ropivacaine : effects of age on the pharmacokinetics, neural blockade and haemodynamics

    NARCIS (Netherlands)

    Simon, Mischa J.G.

    2006-01-01

    Epidural neural blockade results from processes after the administration of a local anaesthetic in the epidural space until the uptake in neural tissue. The pharmacokinetics, neural blockade and haemodynamics after epidural anaesthesia may be influenced by several factors, with age as the most

  5. Different propagation speeds of recalled sequences in plastic spiking neural networks

    Science.gov (United States)

    Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

    2015-03-01

    Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in

  6. A three dimensional in vitro glial scar model to investigate the local strain effects from micromotion around neural implants.

    Science.gov (United States)

    Spencer, Kevin C; Sy, Jay C; Falcón-Banchs, Roberto; Cima, Michael J

    2017-02-28

    Glial scar formation remains a significant barrier to the long term success of neural probes. Micromotion coupled with mechanical mismatch between the probe and tissue is believed to be a key driver of the inflammatory response. In vitro glial scar models present an intermediate step prior to conventional in vivo histology experiments as they enable cell-device interactions to be tested on a shorter timescale, with the ability to conduct broader biochemical assays. No established in vitro models have incorporated methods to assess device performance with respect to mechanical factors. In this study, we describe an in vitro glial scar model that combines high-precision linear actuators to simulate axial micromotion around neural implants with a 3D primary neural cell culture in a collagen gel. Strain field measurements were conducted to visualize the local displacement within the gel in response to micromotion. Primary brain cell cultures were found to be mechanically responsive to micromotion after one week in culture. Astrocytes, as determined by immunohistochemical staining, were found to have significantly increased in cell areas and perimeters in response to micromotion compared to static control wells. These results demonstrate the importance of micromotion when considering the chronic response to neural implants. Going forward, this model provides advantages over existing in vitro models as it will enable critical mechanical design factors of neural implants to be evaluated prior to in vivo testing.

  7. Radioactive fallout and neural tube defects

    African Journals Online (AJOL)

    Nejat Akar

    2015-07-10

    Jul 10, 2015 ... It is a prenatal failure of the embryonic neural tube to close over the ... and the ability of radioisotopes to attach to cells, tissues, and ... The Egyptian Journal of Medical Human Genetics .... Stem Cells 1997;15(Suppl 2):255–60.

  8. Inverse-FEM Characterization of a Brain Tissue Phantom to Simulate