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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. Cellular neural network modelling of soft tissue dynamics for surgical simulation.

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

    2017-07-20

    Currently, the mechanical dynamics of soft tissue deformation is achieved by numerical time integrations such as the explicit or implicit integration; however, the explicit integration is stable only under a small time step, whereas the implicit integration is computationally expensive in spite of the accommodation of a large time step. This paper presents a cellular neural network method for stable simulation of soft tissue deformation dynamics. The non-rigid motion equation is formulated as a cellular neural network with local connectivity of cells, and thus the dynamics of soft tissue deformation is transformed into the neural dynamics of the cellular neural network. Results show that the proposed method can achieve good accuracy at a small time step. It still remains stable at a large time step, while maintaining the computational efficiency of the explicit integration. The proposed method can achieve stable soft tissue deformation with efficiency of explicit integration for surgical simulation.

  3. Bioprinting for Neural Tissue Engineering.

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    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. Neuronal regeneration in the newt: a model to study the partly reconstruction of the neural tissue in real and simulated weightles sness

    Science.gov (United States)

    Anton, H.; Grigoryan, E.; Mitashov, V.

    The micro -"g" effect on nervous tissue regeneration in newts has been investigated by our group for many years. It has been performed in real and in simulated microgravity with a clinostat. During limb regeneration the motor - and sensory nerves regrow perfectly within the newly formed limb. Like in `1g' conditions they are responsible for the initiation of blastema formation and continuity of g owth andr differentiation. Except for a general acceleration of growth and differentiation processes no differences became visible. Tail regeneration, which is perfectly regulated in newts during their whole life, includes the restoration of the spinal cord and dorsal root ganglia. They follow or initiate an accelerated growth. Up to the present the cellular derivation of the sensory neurones within the regenerate has not yet been clarified. But growth acceleration comprises the whole nervous system. That means a totally new formation of the sensory connection from the periphery to the whole spinal cord. Regeneration must be initiated by the outgrowth of nerve fibres into the wound area. This may be performed by the remaining cut sensory fibres of the last stump segment and should be followed by the differentiation of undifferentiated cells of neural crest origin nearby the amputation area. Such cells are present in the form of meningeal cells which are the origin of mantle and Schwann cells too. Corresponding to the well proved growth acceleration of lens, retina, connective tissue, muscle and skin, the real and simulated microgravity affects the nervous system in the same manner. Tissues and organs of adult organisms have no chance to remain unaffected by the microgravity effect. We try to find the trigger which initiates the accelerated proliferation of the stem cells of sensory neurons, mantle and sheath cells under micro-"g" conditions.

  5. Electrospun Nanofibrous Materials for Neural Tissue Engineering

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    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.

  6. Vectorized algorithms for spiking neural network simulation.

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    Brette, Romain; Goodman, Dan F M

    2011-06-01

    High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.

  7. Simulating neural systems with Xyce.

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    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.

  8. Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion

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    Michael Polanco

    2016-06-01

    Full Text Available The functional longevity of a neural probe is dependent upon its ability to minimize injury risk during the insertion and recording period in vivo, which could be related to motion-related strain between the probe and surrounding tissue. A series of finite element analyses was conducted to study the extent of the strain induced within the brain in an area around a neural probe. This study focuses on the transient behavior of neural probe and brain tissue interface with a viscoelastic model. Different stages of the interface from initial insertion of neural probe to full bonding of the probe by astro-glial sheath formation are simulated utilizing analytical tools to investigate the effects of relative motion between the neural probe and the brain while friction coefficients and kinematic frequencies are varied. The analyses can provide an in-depth look at the quantitative benefits behind using soft materials for neural probes.

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

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    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

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

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    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.

  11. Neural simulations on multi-core architectures

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    Hubert Eichner

    2009-07-01

    Full Text Available Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i. e. user-transparent load balancing.

  12. Neural crest specification: tissues, signals, and transcription factors.

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    Rogers, C D; Jayasena, C S; Nie, S; Bronner, M E

    2012-01-01

    The neural crest is a transient population of multipotent and migratory cells unique to vertebrate embryos. Initially derived from the borders of the neural plate, these cells undergo an epithelial to mesenchymal transition to leave the central nervous system, migrate extensively in the periphery, and differentiate into numerous diverse derivatives. These include but are not limited to craniofacial cartilage, pigment cells, and peripheral neurons and glia. Attractive for their similarities to stem cells and metastatic cancer cells, neural crest cells are a popular model system for studying cell/tissue interactions and signaling factors that influence cell fate decisions and lineage transitions. In this review, we discuss the mechanisms required for neural crest formation in various vertebrate species, focusing on the importance of signaling factors from adjacent tissues and conserved gene regulatory interactions, which are required for induction and specification of the ectodermal tissue that will become neural crest. Copyright © 2011 Wiley Periodicals, Inc.

  13. Program Aids Simulation Of Neural Networks

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    Baffes, Paul T.

    1990-01-01

    Computer program NETS - Tool for Development and Evaluation of Neural Networks - provides simulation of neural-network algorithms plus software environment for development of such algorithms. Enables user to customize patterns of connections between layers of network, and provides features for saving weight values of network, providing for more precise control over learning process. Consists of translating problem into format using input/output pairs, designing network configuration for problem, and finally training network with input/output pairs until acceptable error reached. Written in C.

  14. Neuronify: An Educational Simulator for Neural Circuits.

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    Dragly, Svenn-Arne; Hobbi Mobarhan, Milad; Våvang Solbrå, Andreas; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel; Einevoll, Gaute T

    2017-01-01

    Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

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

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    Durand, D.M

    2003-07-01

    Uniform electric fields applied to neural tissue can modulate neuronal excitability with a threshold value of about 1mV mm{sup -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{sup -1}. These results suggest that the threshold for this effect is clearly smaller than 1mV mm{sup -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 {approx}1mmV mm{sup -.} (author)

  16. Adhesion molecule-modified biomaterials for neural tissue engineering

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    Shreyas S Rao

    2009-06-01

    Full Text Available Adhesion molecules (AMs represent one class of biomolecules that promote central nervous system regeneration. These tethered molecules provide cues to regenerating neurons that recapitulate the native brain environment. Improving cell adhesive potential of non-adhesive biomaterials is therefore a common goal in neural tissue engineering. This review discusses common AMs used in neural biomaterials and the mechanism of cell attachment to these AMs. Methods to modify materials with AMs are discussed and compared. Additionally, patterning of AMs for achieving specific neuronal responses is explored.

  17. Simulating the neural correlates of stuttering.

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    den Ouden, Dirk-Bart; Montgomery, Allen; Adams, Charley

    2014-08-01

    For functional neuroimaging studies of stuttering, two challenges are (1) the elicitation of naturally stuttered versus fluent speech and (2) the separation of activation associated with abnormal motor execution from activation that reflects the cognitive substrates of stuttering. This paper reports on a proof-of-concept study, in which a single-subject approach was applied to address these two issues. A stuttering speaker used his insight into his own stuttering behavior to create a list of stutter-prone words versus a list of "fluent" words. He was then matched to a non-stuttering speaker, who imitated the specific articulatory and orofacial motor pattern of the stuttering speaker. Both study participants performed a functional MRI experiment of single word reading, each being presented with the same lexical items. Results suggest that the generally observed right-hemisphere lateralization appears to reflect a true neural correlate of stuttering. Some of the classically reported activation associated with stuttering appears to be driven more by nonspecific motor patterns than by cognitive substrates of stuttering, while anterior cingulate activation may reflect awareness of (upcoming) dysfluencies. This study shows that it is feasible to match stuttering speakers' utterances more closely to simulated stutters for the investigation of neural correlates of real stuttering. Significant main effects and contrast effects were obtained for the differences between fluent and stuttered speech, and right-hemisphere lateralization associated with real stuttered speech was shown in a single subject.

  18. Neural Crest Stem Cells from Dental Tissues: A New Hope for Dental and Neural Regeneration

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    Gaskon Ibarretxe

    2012-01-01

    Full Text Available Several stem cell sources persist in the adult human body, which opens the doors to both allogeneic and autologous cell therapies. Tooth tissues have proven to be a surprisingly rich and accessible source of neural crest-derived ectomesenchymal stem cells (EMSCs, which may be employed to repair disease-affected oral tissues in advanced regenerative dentistry. Additionally, one area of medicine that demands intensive research on new sources of stem cells is nervous system regeneration, since this constitutes a therapeutic hope for patients affected by highly invalidating conditions such as spinal cord injury, stroke, or neurodegenerative diseases. However, endogenous adult sources of neural stem cells present major drawbacks, such as their scarcity and complicated obtention. In this context, EMSCs from dental tissues emerge as good alternative candidates, since they are preserved in adult human individuals, and retain both high proliferation ability and a neural-like phenotype in vitro. In this paper, we discuss some important aspects of tissue regeneration by cell therapy and point out some advantages that EMSCs provide for dental and neural regeneration. We will finally review some of the latest research featuring experimental approaches and benefits of dental stem cell therapy.

  19. 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...

  20. Neural networks analysis on SSME vibration simulation data

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    Lo, Ching F.; Wu, Kewei

    1993-01-01

    The neural networks method is applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME to supplement the statistical method utilized in the prototype system. The investigation of neural networks analysis is conducted using SSME vibration data from a NASA developed numerical simulator. The limited application of neural networks to the HPFTP has also shown the effectiveness in diagnosing the anomalies of turbopump vibrations.

  1. Learning from large scale neural simulations

    DEFF Research Database (Denmark)

    Serban, Maria

    2017-01-01

    -possibly explanations of neural and cognitive phenomena, or they can be used to test explanatory hypotheses and identify sufficient causal factors in specific neurobiological mechanisms, or yet again they can be deployed exploratorily to evaluate the hierarchy of multiple neural and cognitive models that are put...

  2. Classifications of multispectral colorectal cancer tissues using convolution neural network

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    Hawraa Haj-Hassan

    2017-01-01

    Full Text Available Background: Colorectal cancer (CRC is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs to predict three tissue types related to the progression of CRC: benign hyperplasia (BH, intraepithelial neoplasia (IN, and carcinoma (Ca. Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca. An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

  3. Multifunctional nanowire scaffolds for neural tissue engineering applications

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    Bechara, Samuel Leo

    Unlike other regions of the body, the nervous system is extremely vulnerable to damage and injury because it has a limited ability to self-repair. Over 250,000 people in the United States have spinal cord injuries and due to the complicated pathophysiology of such injuries, there are few options available for functional regeneration of the spinal column. Furthermore, peripheral nerve damage is troublingly common in the United States, with an estimated 200,000 patients treated surgically each year. The current gold standard in treatment for peripheral nerve damage is a nerve autograft. This technique was pioneered over 45 years ago, but suffers from a major drawback. By transecting a nerve from another part of the body, function is regained at the expense of destroying a nerve connection elsewhere. Because of these issues, the investigation of different materials for regenerating nervous tissue is necessary. This work examines multi-functional nanowire scaffolds to provide physical and chemical guidance cues to neural stem cells to enhance cellular activity from a biomedical engineering perspective. These multi-functional scaffolds include a unique nanowire nano-topography to provide physical cues to guide cellular adhesion. The nanowires were then coated with an electrically conductive polymer to further enhance cellular activity. Finally, nerve growth factor was conjugated to the surface of the scaffolds to provide chemical cues for the neural stem cells. The results in this work suggest that these multifunctional nanowire scaffolds could be used in vivo to repair nervous system tissue.

  4. Live tissue versus simulation training for emergency procedures: Is simulation ready to replace live tissue?

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    Barnes, Stephen L; Bukoski, Alex; Kerby, Jeffrey D; Llerena, Luis; Armstrong, John H; Strayhorn, Catherine

    2016-10-01

    Training of emergency procedures is challenging and application is not routine in all health care settings. The debate over simulation as an alternative to live tissue training continues with legislation before Congress to banish live tissue training in the Department of Defense. Little evidence exists to objectify best practice. We sought to evaluate live tissue and simulation-based training practices in 12 life-saving emergency procedures. In the study, 742 subjects were randomized to live tissue or simulation-training. Assessments of self-efficacy, cognitive knowledge, and psychomotor performance were completed pre- and post-training. Affective response to training was assessed through electrodermal activity. Subject matter experts gap analysis of live tissue versus simulation completed the data set. Subjects demonstrated pre- to post-training gains in self-efficacy, cognitive knowledge, psychomotor performance, and affective response regardless of training modality (P Affective response was greatest in live tissue training (P abandonment of live tissue training is not warranted. We maintain that combined live tissue and simulation-based training add value and should be continued. Congressional mandates may accelerate simulation development and improve performance. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Expression of Intermediate Filament Nestin in Blood Vessels of Neural and Non-neural Tissues

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    Jaroslav Mokrý

    2008-01-01

    Full Text Available Our previous findings performed in rat tissues demonstrated that intermediate filament nestin is expressed in endothelial cells of newly formed blood vessels of developing organs and neural transplants. The aim of the present study was to identify other cellular markers expressed in nestin-positive (nestin+ blood vessels. To reach this goal we performed double immunofluorescent study to co-localize nestin with endothelium-specific markers (CD31, CD34 II, vimentin or markers of perivascular cells (GFAP, SMA in paraffin-embedded sections of normal human brain tissue, low- and high-grade gliomas, postinfarcted heart and samples of non-neural tumours. Our findings documented that all the samples examined contained blood vessels with different ratio of nestin+ endothelial cells. Double immunostaining provided unambiguous evidence that endothelial cells expressed nestin and allowed them to distinguish from other nestin+ elements (perivascular astrocytic endfeet, undifferentiated tumour cells, smooth muscle cells and pericytes. Nestin+ endothelium was not confined only to newly formed capillaries but was also observed in blood vessels of larger calibres, frequently in arterioles and venules. We conclude that nestin represents a reliable vascular marker that is expressed in endothelial cells. Elevation of nestin expression likely corresponds to reorganization of intermediate filament network in the cytoskeleton of endothelial cells in the course of their maturation or adaptation to changes in growing tissues.

  6. A neural network simulation package in CLIPS

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    Bhatnagar, Himanshu; Krolak, Patrick D.; Mcgee, Brenda J.; Coleman, John

    1990-01-01

    The intrinsic similarity between the firing of a rule and the firing of a neuron has been captured in this research to provide a neural network development system within an existing production system (CLIPS). A very important by-product of this research has been the emergence of an integrated technique of using rule based systems in conjunction with the neural networks to solve complex problems. The systems provides a tool kit for an integrated use of the two techniques and is also extendible to accommodate other AI techniques like the semantic networks, connectionist networks, and even the petri nets. This integrated technique can be very useful in solving complex AI problems.

  7. 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...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear 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...

  8. Deep convolutional neural network approach for forehead tissue thickness estimation

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    Manit Jirapong

    2017-09-01

    Full Text Available In this paper, we presented a deep convolutional neural network (CNN approach for forehead tissue thickness estimation. We use down sampled NIR laser backscattering images acquired from a novel marker-less near-infrared laser-based head tracking system, combined with the beam’s incident angle parameter. These two-channel augmented images were constructed for the CNN input, while a single node output layer represents the estimated value of the forehead tissue thickness. The models were – separately for each subject – trained and tested on datasets acquired from 30 subjects (high resolution MRI data is used as ground truth. To speed up training, we used a pre-trained network from the first subject to bootstrap training for each of the other subjects. We could show a clear improvement for the tissue thickness estimation (mean RMSE of 0.096 mm. This proposed CNN model outperformed previous support vector regression (mean RMSE of 0.155 mm or Gaussian processes learning approaches (mean RMSE of 0.114 mm and eliminated their restrictions for future research.

  9. How Tissue Mechanical Properties Affect Enteric Neural Crest Cell Migration

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    Chevalier, N. R.; Gazguez, E.; Bidault, L.; Guilbert, T.; Vias, C.; Vian, E.; Watanabe, Y.; Muller, L.; Germain, S.; Bondurand, N.; Dufour, S.; Fleury, V.

    2016-02-01

    Neural crest cells (NCCs) are a population of multipotent cells that migrate extensively during vertebrate development. Alterations to neural crest ontogenesis cause several diseases, including cancers and congenital defects, such as Hirschprung disease, which results from incomplete colonization of the colon by enteric NCCs (ENCCs). We investigated the influence of the stiffness and structure of the environment on ENCC migration in vitro and during colonization of the gastrointestinal tract in chicken and mouse embryos. We showed using tensile stretching and atomic force microscopy (AFM) that the mesenchyme of the gut was initially soft but gradually stiffened during the period of ENCC colonization. Second-harmonic generation (SHG) microscopy revealed that this stiffening was associated with a gradual organization and enrichment of collagen fibers in the developing gut. Ex-vivo 2D cell migration assays showed that ENCCs migrated on substrates with very low levels of stiffness. In 3D collagen gels, the speed of the ENCC migratory front decreased with increasing gel stiffness, whereas no correlation was found between porosity and ENCC migration behavior. Metalloprotease inhibition experiments showed that ENCCs actively degraded collagen in order to progress. These results shed light on the role of the mechanical properties of tissues in ENCC migration during development.

  10. FPGA Simulation Engine for Customized Construction of Neural Microcircuits

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    Blair, Hugh T.; Cong, Jason; Wu, Di

    2014-01-01

    In this paper we describe an FPGA-based platform for high-performance and low-power simulation of neural microcircuits composed from integrate-and-fire (IAF) neurons. Based on high-level synthesis, our platform uses design templates to map hierarchies of neuron model to logic fabrics. This approach bypasses high design complexity and enables easy optimization and design space exploration. We demonstrate the benefits of our platform by simulating a variety of neural microcircuits that perform oscillatory path integration, which evidence suggests may be a critical building block of the navigation system inside a rodent’s brain. Experiments show that our FPGA simulation engine for oscillatory neural microcircuits can achieve up to 39× speedup compared to software benchmarks on commodity CPU, and 232× energy reduction compared to embedded ARM core. PMID:25584120

  11. Analytic Modeling of Neural Tissue: I. A Spherical Bidomain.

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    Schwartz, Benjamin L; Chauhan, Munish; Sadleir, Rosalind J

    2016-12-01

    Presented here is a model of neural tissue in a conductive medium stimulated by externally injected currents. The tissue is described as a conductively isotropic bidomain, i.e. comprised of intra and extracellular regions that occupy the same space, as well as the membrane that divides them, and the injection currents are described as a pair of source and sink points. The problem is solved in three spatial dimensions and defined in spherical coordinates [Formula: see text]. The system of coupled partial differential equations is solved by recasting the problem to be in terms of the membrane and a monodomain, interpreted as a weighted average of the intra and extracellular domains. The membrane and monodomain are defined by the scalar Helmholtz and Laplace equations, respectively, which are both separable in spherical coordinates. Product solutions are thus assumed and given through certain transcendental functions. From these electrical potentials, analytic expressions for current density are derived and from those fields the magnetic flux density is calculated. Numerical examples are considered wherein the interstitial conductivity is varied, as well as the limiting case of the problem simplifying to two dimensions due to azimuthal independence. Finally, future modeling work is discussed.

  12. Polypyrrole-coated electrospun PLGA nanofibers for neural tissue applications.

    Science.gov (United States)

    Lee, Jae Y; Bashur, Chris A; Goldstein, Aaron S; Schmidt, Christine E

    2009-09-01

    Electrospinning is a promising approach to create nanofiber structures that are capable of supporting adhesion and guiding extension of neurons for nerve regeneration. Concurrently, electrical stimulation of neurons in the absence of topographical features also has been shown to guide axonal extension. Therefore, the goal of this study was to form electrically conductive nanofiber structures and to examine the combined effect of nanofiber structures and electrical stimulation. Conductive meshes were produced by growing polypyrrole (PPy) on random and aligned electrospun poly(lactic-co-glycolic acid) (PLGA) nanofibers, as confirmed by scanning electron micrographs and X-ray photon spectroscopy. PPy-PLGA electrospun meshes supported the growth and differentiation of rat pheochromocytoma 12 (PC12) cells and hippocampal neurons comparable to non-coated PLGA control meshes, suggesting that PPy-PLGA may be suitable as conductive nanofibers for neuronal tissue scaffolds. Electrical stimulation studies showed that PC12 cells, stimulated with a potential of 10 mV/cm on PPy-PLGA scaffolds, exhibited 40-50% longer neurites and 40-90% more neurite formation compared to unstimulated cells on the same scaffolds. In addition, stimulation of the cells on aligned PPy-PLGA fibers resulted in longer neurites and more neurite-bearing cells than stimulation on random PPy-PLGA fibers, suggesting a combined effect of electrical stimulation and topographical guidance and the potential use of these scaffolds for neural tissue applications.

  13. Polypyrrole-Coated Electrospun PLGA Nanofibers for Neural Tissue Applications

    Science.gov (United States)

    Lee, Jae Young; Bashur, Chris A.; Goldstein, Aaron S.; Schmidt, Christine E.

    2009-01-01

    Electrospinning is a promising approach to create nanofiber structures that are capable of supporting adhesion and guiding extension of neurons for nerve regeneration. Concurrently, electrical stimulation of neurons in the absence of topographical features also has been shown to guide axonal extension. Therefore, the goal of this study was to form electrically conductive nanofiber structures and to examine the combined effect of nanofiber structures and electrical stimulation. Conductive meshes were produced by growing polypyrrole (PPy) on random and aligned electrospun poly(lactic-co-glycolic acid) (PLGA) nanofibers, as confirmed by scanning electron micrographs and X-ray photon spectroscopy. PPy-PLGA electrospun meshes supported the growth and differentiation of rat pheochromocytoma 12 (PC12) cells and hippocampal neurons comparable to non-coated PLGA control meshes, suggesting that PPy-PLGA may be suitable as conductive nanofibers for neuronal tissue scaffolds. Electrical stimulation studies showed that PC12 cells, stimulated with a potential of 10 mV/cm on PPy-PLGA scaffolds, exhibited 40–50% longer neurites and 40–90% more neurite formation compared to unstimulated cells on the same scaffolds. In addition, stimulation of the cells on aligned PPy-PLGA fibers resulted in longer neurites and more neurite-bearing cells than stimulation on random PPy-PLGA fibers, suggesting a combined effect of electrical stimulation and topographical guidance and the potential use of these scaffolds for neural tissue applications. PMID:19501901

  14. Hybrid neural network bushing model for vehicle dynamics simulation

    Energy Technology Data Exchange (ETDEWEB)

    Sohn, Jeong Hyun [Pukyong National University, Busan (Korea, Republic of); Lee, Seung Kyu [Hyosung Corporation, Changwon (Korea, Republic of); Yoo, Wan Suk [Pusan National University, Busan (Korea, Republic of)

    2008-12-15

    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

  15. A Neural Network Model for Dynamics Simulation | Bholoa ...

    African Journals Online (AJOL)

    University of Mauritius Research Journal. Journal Home · ABOUT · Advanced Search · Current Issue · Archives · Journal Home > Vol 15, No 1 (2009) >. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register. A Neural Network Model for Dynamics Simulation. Ajeevsing ...

  16. Artificial Neural Network Metamodels of Stochastic Computer Simulations

    Science.gov (United States)

    1994-08-10

    23 Haddock, J. and O’Keefe, R., "Using Artificial Intelligence to Facilitate Manufacturing Systems Simulation," Computers & Industrial Engineering , Vol...Feedforward Neural Networks," Computers & Industrial Engineering , Vol. 21, No. 1- 4, (1991), pp. 247-251. 87 Proceedings of the 1992 Summer Computer...Using Simulation Experiments," Computers & Industrial Engineering , Vol. 22, No. 2 (1992), pp. 195-209. 119 Kuei, C. and Madu, C., "Polynomial

  17. Part 2: Prediktion, Simulering og Regulering med Neurale Netværk. Prediction, Simulation and Control using Neural Network

    DEFF Research Database (Denmark)

    Schiøler, Henrik

    til Del 1, idet de to rapporter kan opfattes som en enhed. Herefter introduceres de grundlæggende begreber inden for prediktion, samt for mål og integralteorien. Det beskrives, hvorledes neurale net kan fungere som ulinære prediktionsmodeller og den nødvendige teori for Multi Lags Perceptronen (MLP......) samt alternative strukturer baseret på Parzen Window estimationsmetoden, præsenteres med detaljerne af analysen henlagt til appendices. Herefter demonstreres ved en simpel test, hvorledes de forskellige nettyper fungerer i prediktionsanvendelser. Herefter er neurale net anvendt til simulering behandlet...... på tilsvarende måde, dog i en lidt forkortet udgave. Til sidst behandles, hvorledes de behandlede nettyper anvendes i en regulatorstruktur baseret på såkaldte Sliding mode control. Teorien for de neurale net er her den samme som for simulering. Det konkluderes at de alternative strukturer, baseret på...

  18. Central neural control of thermoregulation and brown adipose tissue.

    Science.gov (United States)

    Morrison, Shaun F

    2016-04-01

    Central neural circuits orchestrate the homeostatic repertoire that maintains body temperature during environmental temperature challenges and alters body temperature during the inflammatory response. This review summarizes the experimental underpinnings of our current model of the CNS pathways controlling the principal thermoeffectors for body temperature regulation: cutaneous vasoconstriction controlling heat loss, and shivering and brown adipose tissue for thermogenesis. The activation of these effectors is regulated by parallel but distinct, effector-specific, core efferent pathways within the CNS that share a common peripheral thermal sensory input. Via the lateral parabrachial nucleus, skin thermal afferent input reaches the hypothalamic preoptic area to inhibit warm-sensitive, inhibitory output neurons which control heat production by inhibiting thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to thermogenesis-controlling premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation of spinal circuits necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus sympathetic premotor neurons controlling cutaneous vasoconstriction. The model proposed for central thermoregulatory control provides a useful platform for further understanding of the functional organization of central thermoregulation and elucidating the hypothalamic circuitry and neurotransmitters involved in body temperature regulation. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. On the design of script languages for neural simulation.

    Science.gov (United States)

    Brette, Romain

    2012-01-01

    In neural network simulators, models are specified according to a language, either specific or based on a general programming language (e.g. Python). There are also ongoing efforts to develop standardized languages, for example NeuroML. When designing these languages, efforts are often focused on expressivity, that is, on maximizing the number of model types than can be described and simulated. I argue that a complementary goal should be to minimize the cognitive effort required on the part of the user to use the language. I try to formalize this notion with the concept of "language entropy", and I propose a few practical guidelines to minimize the entropy of languages for neural simulation.

  20. 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.

  1. Enhanced expression of FNDC5 in human embryonic stem cell-derived neural cells along with relevant embryonic neural tissues.

    Science.gov (United States)

    Ghahrizjani, Fatemeh Ahmadi; Ghaedi, Kamran; Salamian, Ahmad; Tanhaei, Somayeh; Nejati, Alireza Shoaraye; Salehi, Hossein; Nabiuni, Mohammad; Baharvand, Hossein; Nasr-Esfahani, Mohammad Hossein

    2015-02-25

    Availability of human embryonic stem cells (hESCs) has enhanced the capability of basic and clinical research in the context of human neural differentiation. Derivation of neural progenitor (NP) cells from hESCs facilitates the process of human embryonic development through the generation of neuronal subtypes. We have recently indicated that fibronectin type III domain containing 5 protein (FNDC5) expression is required for appropriate neural differentiation of mouse embryonic stem cells (mESCs). Bioinformatics analyses have shown the presence of three isoforms for human FNDC5 mRNA. To differentiate which isoform of FNDC5 is involved in the process of human neural differentiation, we have used hESCs as an in vitro model for neural differentiation by retinoic acid (RA) induction. The hESC line, Royan H5, was differentiated into a neural lineage in defined adherent culture treated by RA and basic fibroblast growth factor (bFGF). We collected all cell types that included hESCs, rosette structures, and neural cells in an attempt to assess the expression of FNDC5 isoforms. There was a contiguous increase in all three FNDC5 isoforms during the neural differentiation process. Furthermore, the highest level of expression of the isoforms was significantly observed in neural cells compared to hESCs and the rosette structures known as neural precursor cells (NPCs). High expression levels of FNDC5 in human fetal brain and spinal cord tissues have suggested the involvement of this gene in neural tube development. Additional research is necessary to determine the major function of FDNC5 in this process. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. 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.

  3. 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.)

  4. Fast neural net simulation with a DSP processor array.

    Science.gov (United States)

    Muller, U A; Gunzinger, A; Guggenbuhl, W

    1995-01-01

    This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer. A 60-processor system, named MUSIC (multiprocessor system with intelligent communication), is operational and runs the backpropagation algorithm at a speed of 330 million connection updates per second (continuous weight update) using 32-b floating-point precision. This is equal to 1.4 Gflops sustained performance. The complete system with 3.8 Gflops peak performance consumes less than 800 W of electrical power and fits into a 19-in rack. While reaching the speed of modern supercomputers, MUSIC still can be used as a personal desktop computer at a researcher's own disposal. In neural net simulation, this gives a computing performance to a single user which was unthinkable before. The system's real-time interfaces make it especially useful for embedded applications.

  5. [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.

  6. Artificial neural network based approach to EEG signal simulation.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2012-06-01

    In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.

  7. 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

  8. 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.

  9. Correlated EEG Signals Simulation Based on Artificial Neural Networks.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2017-08-01

    In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

  10. Code generation: a strategy for neural network simulators.

    Science.gov (United States)

    Goodman, Dan F M

    2010-10-01

    We demonstrate a technique for the design of neural network simulation software, runtime code generation. This technique can be used to give the user complete flexibility in specifying the mathematical model for their simulation in a high level way, along with the speed of code written in a low level language such as C+ +. It can also be used to write code only once but target different hardware platforms, including inexpensive high performance graphics processing units (GPUs). Code generation can be naturally combined with computer algebra systems to provide further simplification and optimisation of the generated code. The technique is quite general and could be applied to any simulation package. We demonstrate it with the 'Brian' simulator ( http://www.briansimulator.org ).

  11. Simulation of morphologically structured photo-thermal neural stimulation

    Science.gov (United States)

    Weissler, Y.; Farah, N.; Shoham, S.

    2017-10-01

    Objective. Rational design of next-generation techniques for photo-thermal excitation requires the development of tools capable of modeling the effects of spatially- and temporally-dependent temperature distribution on cellular neuronal structures. Approach. We present a new computer simulation tool for predicting the effects of arbitrary spatiotemporally-structured photo-thermal stimulation on 3D, morphologically realistic neurons. The new simulation tool is based on interfacing two generic platforms, NEURON and MATLAB and is therefore suited for capturing different kinds of stimuli and neural models. Main results. Simulation results are validated using photo-absorber induced neuro-thermal stimulation (PAINTS) empirical results, and advanced features are explored. Significance. The new simulation tool could have an important role in understanding and investigating complex optical stimulation at the single-cell and network levels.

  12. Uniform neural tissue models produced on synthetic hydrogels using standard culture techniques.

    Science.gov (United States)

    Barry, Christopher; Schmitz, Matthew T; Propson, Nicholas E; Hou, Zhonggang; Zhang, Jue; Nguyen, Bao K; Bolin, Jennifer M; Jiang, Peng; McIntosh, Brian E; Probasco, Mitchell D; Swanson, Scott; Stewart, Ron; Thomson, James A; Schwartz, Michael P; Murphy, William L

    2017-11-01

    The aim of the present study was to test sample reproducibility for model neural tissues formed on synthetic hydrogels. Human embryonic stem (ES) cell-derived precursor cells were cultured on synthetic poly(ethylene glycol) (PEG) hydrogels to promote differentiation and self-organization into model neural tissue constructs. Neural progenitor, vascular, and microglial precursor cells were combined on PEG hydrogels to mimic developmental timing, which produced multicomponent neural constructs with 3D neuronal and glial organization, organized vascular networks, and microglia with ramified morphologies. Spearman's rank correlation analysis of global gene expression profiles and a comparison of coefficient of variation for expressed genes demonstrated that replicate neural constructs were highly uniform to at least day 21 for samples from independent experiments. We also demonstrate that model neural tissues formed on PEG hydrogels using a simplified neural differentiation protocol correlated more strongly to in vivo brain development than samples cultured on tissue culture polystyrene surfaces alone. These results provide a proof-of-concept demonstration that 3D cellular models that mimic aspects of human brain development can be produced from human pluripotent stem cells with high sample uniformity between experiments by using standard culture techniques, cryopreserved cell stocks, and a synthetic extracellular matrix. Impact statement Pluripotent stem (PS) cells have been characterized by an inherent ability to self-organize into 3D "organoids" resembling stomach, intestine, liver, kidney, and brain tissues, offering a potentially powerful tool for modeling human development and disease. However, organoid formation must be quantitatively reproducible for applications such as drug and toxicity screening. Here, we report a strategy to produce uniform neural tissue constructs with reproducible global gene expression profiles for replicate samples from multiple

  13. 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.

  14. Neural network simulation of the industrial producer price index dynamical series

    OpenAIRE

    Soshnikov, L. E.

    2013-01-01

    This paper is devoted the simulation and forecast of dynamical series of the economical indicators. Multilayer perceptron and Radial basis function neural networks have been used. The neural networks model results are compared with the econometrical modeling.

  15. Leader neurons in leaky integrate and fire neural network simulations.

    Science.gov (United States)

    Zbinden, Cyrille

    2011-10-01

    In this paper, we highlight the topological properties of leader neurons whose existence is an experimental fact. Several experimental studies show the existence of leader neurons in population bursts of activity in 2D living neural networks (Eytan and Marom, J Neurosci 26(33):8465-8476, 2006; Eckmann et al., New J Phys 10(015011), 2008). A leader neuron is defined as a neuron which fires at the beginning of a burst (respectively network spike) more often than we expect by chance considering its mean firing rate. This means that leader neurons have some burst triggering power beyond a chance-level statistical effect. In this study, we characterize these leader neuron properties. This naturally leads us to simulate neural 2D networks. To build our simulations, we choose the leaky integrate and fire (lIF) neuron model (Gerstner and Kistler 2002; Cessac, J Math Biol 56(3):311-345, 2008), which allows fast simulations (Izhikevich, IEEE Trans Neural Netw 15(5):1063-1070, 2004; Gerstner and Naud, Science 326:379-380, 2009). The dynamics of our lIF model has got stable leader neurons in the burst population that we simulate. These leader neurons are excitatory neurons and have a low membrane potential firing threshold. Except for these two first properties, the conditions required for a neuron to be a leader neuron are difficult to identify and seem to depend on several parameters involved in the simulations themselves. However, a detailed linear analysis shows a trend of the properties required for a neuron to be a leader neuron. Our main finding is: A leader neuron sends signals to many excitatory neurons as well as to few inhibitory neurons and a leader neuron receives only signals from few other excitatory neurons. Our linear analysis exhibits five essential properties of leader neurons each with different relative importance. This means that considering a given neural network with a fixed mean number of connections per neuron, our analysis gives us a way of

  16. Efficiently passing messages in distributed spiking neural network simulation.

    Science.gov (United States)

    Thibeault, Corey M; Minkovich, Kirill; O'Brien, Michael J; Harris, Frederick C; Srinivasa, Narayan

    2013-01-01

    Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI). A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid method for spike exchange was implemented and benchmarked.

  17. 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.

  18. 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

    2017-08-31

    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.

  19. 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.

  20. 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.

  1. 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

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

    Science.gov (United States)

    Subramanian, Anuradha; Krishnan, Uma Maheswari; Sethuraman, Swaminathan

    2009-11-25

    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.

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

    Directory of Open Access Journals (Sweden)

    Julien eVitay

    2015-07-01

    Full Text Available 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.

  4. USE OF NEURAL NETWORK SIMULATION TO MONITOR PATIENTS UNDERGOING RADICAL PROSTATECTOMY

    National Research Council Canada - National Science Library

    I. V. Lukyanov; N. A. Demchenko

    2014-01-01

    .... Based on neural network simulation, the Department of Urology, Russian Medical Academy of Postgraduate Education, has developed an accounting prognostic system to monitor the postoperative course...

  5. 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

  6. 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.

  7. 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.

  8. Neural system modeling and simulation using Hybrid Functional Petri Net.

    Science.gov (United States)

    Tang, Yin; Wang, Fei

    2012-02-01

    The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.

  9. Artificial neural network simulator for SOFC performance prediction

    Science.gov (United States)

    Arriagada, Jaime; Olausson, Pernilla; Selimovic, Azra

    This paper describes the development of a novel modelling tool for evaluation of solid oxide fuel cell (SOFC) performance. An artificial neural network (ANN) is trained with a reduced amount of data generated by a validated cell model, and it is then capable of learning the generic functional relationship between inputs and outputs of the system. Once the network is trained, the ANN-driven simulator can predict different operational parameters of the SOFC (i.e. gas flows, operational voltages, current density, etc.) avoiding the detailed description of the fuel cell processes. The highly parallel connectivity within the ANN further reduces the computational time. In a real case, the necessary data for training the ANN simulator would be extracted from experiments. This simulator could be suitable for different applications in the fuel cell field, such as, the construction of performance maps and operating point optimisation and analysis. All this is performed with minimum time demand and good accuracy. This intelligent model together with the operational conditions may provide useful insight into SOFC operating characteristics and improved means of selecting operating conditions, reducing costs and the need for extensive experiments.

  10. 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.

  11. 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...

  12. Effects of simulated neural mobilization on fluid movement in cadaveric peripheral nerve sections: implications for the treatment of neuropathic pain and dysfunction.

    Science.gov (United States)

    Gilbert, Kerry K; Roger James, C; Apte, Gail; Brown, Cynthia; Sizer, Phillip S; Brismée, Jean-Michel; Smith, Michael P

    2015-09-01

    Neural mobilization techniques are used clinically to treat neuropathic pain and dysfunction. While selected studies report efficacy of these techniques, the mechanisms of benefit are speculative. The purpose of this study was to evaluate the effects of in vitro simulated stretch/relax neural mobilization cycles on fluid dispersion within sections of unembalmed cadaveric peripheral nerve tissue. Bilateral sciatic nerve sections were harvested from six cadavers. Matched pairs of nerve sections were secured in a tissue tester and injected with a plasma/Toluidine Blue dye solution. Once the initial dye spread stabilized, the experimental nerve sections underwent 25 stretch/relaxation cycles (e.g. simulated neural mobilization) produced by a mechanical tissue tester. Post-test dye spread measurements were compared to pre-test measurements as well as control findings (no simulated mobilization). Data were analyzed using paired t-tests. Individual dye spread measurements were reliable [ICC(3,1) = 0·99]. The post-test intraneural fluid movement (dye spread) in the experimental section increased significantly with simulated neural mobilization compared to pre-test measurements (3·2±2·1 mm; P = 0·015) and control measurements (3·3±2·7 mm; P = 0·013). Repetitive simulated neural mobilization, incorporating stretch/relax cycles, of excised cadaveric peripheral nerve tissue produced an increase in intraneural fluid dispersion. Neural mobilization may alter nerve tissue environment, promoting improved function and nerve health, by dispersing tissue fluid and diminishing intraneural swelling and/or pressure.

  13. 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...

  14. Novel nanofibrous spiral scaffolds for neural tissue engineering

    Science.gov (United States)

    Valmikinathan, Chandra M.; Tian, Jingjing; Wang, Junping; Yu, Xiaojun

    2008-12-01

    Due to several drawbacks associated with autografts and allografts, tissue-engineering approaches have been widely used to repair peripheral nerve injuries. Most of the traditional tissue-engineered scaffolds in use are either tubular (single or multi-lumen) or hydrogel-based cylindrical grafts, which provide limited surface area for cell attachment and regeneration. Here, we show a novel poly(lactide-co-glycotide) (PLGA) microsphere-based spiral scaffold design with a nanofibrous surface that has enhanced surface areas and possesses sufficient mechanical properties and porosities to support the nerve regeneration process. These scaffolds have an open architecture that goes evenly throughout the scaffolds hence leaving enough volume for media influx and deeper cell penetration into the scaffolds. The in vitro tests conducted using Schwann cells show that the nanofibrous spiral scaffolds promote higher cell attachment and proliferation when compared to contemporary tubular scaffolds or nanofiber-based tubular scaffolds. Also, the nanofiber coating on the surfaces enhances the surface area, mimics the extracellular matrix and provides unidirectional alignment of cells along its direction. Hence, we propose that these scaffolds could alleviate some drawbacks in current nerve grafts and could potentially be used in nerve regeneration.

  15. Evaluation of Raman spectra of human brain tumor tissue using the learning vector quantization neural network

    Science.gov (United States)

    Liu, Tuo; Chen, Changshui; Shi, Xingzhe; Liu, Chengyong

    2016-05-01

    The Raman spectra of tissue of 20 brain tumor patients was recorded using a confocal microlaser Raman spectroscope with 785 nm excitation in vitro. A total of 133 spectra were investigated. Spectra peaks from normal white matter tissue and tumor tissue were analyzed. Algorithms, such as principal component analysis, linear discriminant analysis, and the support vector machine, are commonly used to analyze spectral data. However, in this study, we employed the learning vector quantization (LVQ) neural network, which is typically used for pattern recognition. By applying the proposed method, a normal diagnosis accuracy of 85.7% and a glioma diagnosis accuracy of 89.5% were achieved. The LVQ neural network is a recent approach to excavating Raman spectra information. Moreover, it is fast and convenient, does not require the spectra peak counterpart, and achieves a relatively high accuracy. It can be used in brain tumor prognostics and in helping to optimize the cutting margins of gliomas.

  16. Imaging regenerating bone tissue based on neural networks applied to micro-diffraction measurements

    Energy Technology Data Exchange (ETDEWEB)

    Campi, G.; Pezzotti, G. [Institute of Crystallography, CNR, via Salaria Km 29.300, I-00015, Monterotondo Roma (Italy); Fratini, M. [Centro Fermi -Museo Storico della Fisica e Centro Studi e Ricerche ' Enrico Fermi' , Roma (Italy); Ricci, A. [Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, D-22607 Hamburg (Germany); Burghammer, M. [European Synchrotron Radiation Facility, B. P. 220, F-38043 Grenoble Cedex (France); Cancedda, R.; Mastrogiacomo, M. [Istituto Nazionale per la Ricerca sul Cancro, and Dipartimento di Medicina Sperimentale dell' Università di Genova and AUO San Martino Istituto Nazionale per la Ricerca sul Cancro, Largo R. Benzi 10, 16132, Genova (Italy); Bukreeva, I.; Cedola, A. [Institute for Chemical and Physical Process, CNR, c/o Physics Dep. at Sapienza University, P-le A. Moro 5, 00185, Roma (Italy)

    2013-12-16

    We monitored bone regeneration in a tissue engineering approach. To visualize and understand the structural evolution, the samples have been measured by X-ray micro-diffraction. We find that bone tissue regeneration proceeds through a multi-step mechanism, each step providing a specific diffraction signal. The large amount of data have been classified according to their structure and associated to the process they came from combining Neural Networks algorithms with least square pattern analysis. In this way, we obtain spatial maps of the different components of the tissues visualizing the complex kinetic at the base of the bone regeneration.

  17. Growing Tissues in Real and Simulated Microgravity: New Methods for Tissue Engineering

    OpenAIRE

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

    2014-01-01

    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...

  18. 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.

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

    NARCIS (Netherlands)

    Grimm, D.; Wehland, M.; Pietsch, J.; Aleshcheva, G.; Wise, P.; van Loon, J.; Ulbrich, C.; Magnusson, N.E.; Infanger, M.; Bauer, J.

    2014-01-01

    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

  20. Growing Tissues in Real and Simulated Microgravity: New Methods for Tissue Engineering

    NARCIS (Netherlands)

    Grimm, D.; Wehland, M.; Pietsch, J.; Aleshcheva, G.; Wise, P.; van Loon, J.J.W.A.; Ulbrich, C.; Magnusson, N.E.; Infanger, M.; Bauer, J.

    2014-01-01

    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

  1. Designing laboratory wind simulations using artificial neural networks

    Science.gov (United States)

    Križan, Josip; Gašparac, Goran; Kozmar, Hrvoje; Antonić, Oleg; Grisogono, Branko

    2015-05-01

    While experiments in boundary layer wind tunnels remain to be a major research tool in wind engineering and environmental aerodynamics, designing the modeling hardware required for a proper atmospheric boundary layer (ABL) simulation can be costly and time consuming. Hence, possibilities are sought to speed-up this process and make it more time-efficient. In this study, two artificial neural networks (ANNs) are developed to determine an optimal design of the Counihan hardware, i.e., castellated barrier wall, vortex generators, and surface roughness, in order to simulate the ABL flow developing above urban, suburban, and rural terrains, as previous ANN models were created for one terrain type only. A standard procedure is used in developing those two ANNs in order to further enhance best-practice possibilities rather than to improve existing ANN designing methodology. In total, experimental results obtained using 23 different hardware setups are used when creating ANNs. In those tests, basic barrier height, barrier castellation height, spacing density, and height of surface roughness elements are the parameters that were varied to create satisfactory ABL simulations. The first ANN was used for the estimation of mean wind velocity, turbulent Reynolds stress, turbulence intensity, and length scales, while the second one was used for the estimation of the power spectral density of velocity fluctuations. This extensive set of studied flow and turbulence parameters is unmatched in comparison to the previous relevant studies, as it includes here turbulence intensity and power spectral density of velocity fluctuations in all three directions, as well as the Reynolds stress profiles and turbulence length scales. Modeling results agree well with experiments for all terrain types, particularly in the lower ABL within the height range of the most engineering structures, while exhibiting sensitivity to abrupt changes and data scattering in profiles of wind-tunnel results. The

  2. Knockdown of tissue nonspecific alkaline phosphatase impairs neural stem cell proliferation and differentiation.

    Science.gov (United States)

    Kermer, Vanessa; Ritter, Mathias; Albuquerque, Boris; Leib, Christoph; Stanke, Matthias; Zimmermann, Herbert

    2010-11-26

    In the adult mammalian brain the subependymal layer of the lateral ventricles houses neural stem cells giving rise to young neurons migrating towards the olfactory bulb. The molecular cues controlling essential functions within the neurogenesis pathway such as proliferation, short and long distance migration, differentiation and functional integration are poorly understood. Neural progenitors in situ express the tissue nonspecific form of alkaline phosphatase (TNAP), a cell surface-located nonspecific phosphomonoesterase capable of hydrolyzing extracellular nucleotides. To gain insight into the functional role of TNAP in cultured multipotent neural stem cells we applied a knockdown protocol using RNA interference with shRNA and retroviral infection. We show that TNAP knockdown reduces cell proliferation and differentiation into neurons or oligodendrocytes. This effect is abrogated by addition of alkaline phosphatase to the culture medium. Our results suggest that TNAP is essential for NSC proliferation and differentiation in vitro and possibly also in vivo. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  3. The role of simulation in the design of a neural network chip

    Science.gov (United States)

    Desai, Utpal; Roppel, Thaddeus A.; Padgett, Mary L.

    1993-01-01

    An iterative, simulation-based design procedure for a neural network chip is introduced. For this design procedure, the goal is to produce a chip layout for a neural network in which the weights are determined by transistor gate width-to-length ratios. In a given iteration, the current layout is simulated using the circuit simulator SPICE, and layout adjustments are made based on conventional gradient-decent methods. After the iteration converges, the chip is fabricated. Monte Carlo analysis is used to predict the effect of statistical fabrication process variations on the overall performance of the neural network chip.

  4. Simulation of Foam Divot Weight on External Tank Utilizing Least Squares and Neural Network Methods

    Science.gov (United States)

    Chamis, Christos C.; Coroneos, Rula M.

    2007-01-01

    Simulation of divot weight in the insulating foam, associated with the external tank of the U.S. space shuttle, has been evaluated using least squares and neural network concepts. The simulation required models based on fundamental considerations that can be used to predict under what conditions voids form, the size of the voids, and subsequent divot ejection mechanisms. The quadratic neural networks were found to be satisfactory for the simulation of foam divot weight in various tests associated with the external tank. Both linear least squares method and the nonlinear neural network predicted identical results.

  5. Determination of platinum by radiochemical neutron activation analysis in neural tissues from rats, monkeys and patients treated with cisplatin

    DEFF Research Database (Denmark)

    Rietz, B.; Krarup-Hansen, A.; Rorth, M.

    2001-01-01

    of the animals mentioned and in the neural tissues of human patients. For the determination of platinum in the tissues radiochemical neutron activation analysis has been used. The detection limit is 1 ng Pt g(-1). The platinum results indicate that platinum becomes accumulated in the dorsal root ganglia......Cisplatin is one of the most used antineoplastic drugs, essential for the treatment of germ cell tumours. Its use in medical treatment of cancer patients often causes chronic peripheral neuropathy in these patients. The distribution of cisplatin in neural tissues is, therefore, of great interest....... Rats and monkeys were used as animal models for the study of sensory changes in different neural tissues, like spinal cord (ventral and dorsal part), dorsal root ganglia and sural nerve. The study was combined with quantitative measurements of the content of platinum in the neural tissues...

  6. Microinjection of membrane-impermeable molecules into single neural stem cells in brain tissue.

    Science.gov (United States)

    Wong, Fong Kuan; Haffner, Christiane; Huttner, Wieland B; Taverna, Elena

    2014-05-01

    This microinjection protocol allows the manipulation and tracking of neural stem and progenitor cells in tissue at single-cell resolution. We demonstrate how to apply microinjection to organotypic brain slices obtained from mice and ferrets; however, our technique is not limited to mouse and ferret embryos, but provides a means of introducing a wide variety of membrane-impermeable molecules (e.g., nucleic acids, proteins, hydrophilic compounds) into neural stem and progenitor cells of any developing mammalian brain. Microinjection experiments are conducted by using a phase-contrast microscope equipped with epifluorescence, a transjector and a micromanipulator. The procedure normally takes ∼2 h for an experienced researcher, and the entire protocol, including tissue processing, can be performed within 1 week. Thus, microinjection is a unique and versatile method for changing and tracking the fate of a cell in organotypic slice culture.

  7. Trigger Points, Pressure Pain Hyperalgesia, and Mechanosensitivity of Neural Tissue in Women with Chronic Pelvic Pain.

    Science.gov (United States)

    Fuentes-Márquez, Pedro; Valenza, Marie Carmen; Cabrera-Martos, Irene; Ríos-Sánchez, Ana; Ocón-Hernández, Olga

    2017-08-25

    This study aims to evaluate the presence of myofascial trigger points (TrPs), widespread pressure pain sensitivity, and mechanosensitivity of neural tissue in women with chronic pelvic pain. Case-control study. Faculty of Health Sciences. Forty women with chronic pelvic pain between age 18 and 60 years and 40 matched healthy controls were included in the study. TrPs were bilaterally explored in gluteus maximus, gluteus medius, gluteus minimus, quadratus lumborum, and adductor magnus muscles. The referred pain reproduced lumbopelvic symptoms. Pressure pain thresholds (PPTs) were also bilaterally assessed over the Pfannenstiel incision point on the abdominal, C5-C6 zygapophyseal joint, second metacarpal, and tibialis anterior muscle. Mechanosensitivity of neural tissue was assessed with the neurodynamics tests of slump and the straight-leg raising. Significant between-group differences were found in TrP presence in patients with chronic pelvic pain (P Neurodynamics show a significantly decreased value in women with CPP. Patients with chronic pelvic pain presented a high percentage of TrPs that reproduce their symptoms. Patients also showed a widespread pressure pain hyperalgesia and more mechanosensitive neural tissue due to a decrease on the range of motion related to neurodynamics.

  8. On random walks and entropy in diffusion-weighted magnetic resonance imaging studies of neural tissue.

    Science.gov (United States)

    Ingo, Carson; Magin, Richard L; Colon-Perez, Luis; Triplett, William; Mareci, Thomas H

    2014-02-01

    In diffusion-weighted MRI studies of neural tissue, the classical model assumes the statistical mechanics of Brownian motion and predicts a monoexponential signal decay. However, there have been numerous reports of signal decays that are not monoexponential, particularly in the white matter. We modeled diffusion in neural tissue from the perspective of the continuous time random walk. The characteristic diffusion decay is represented by the Mittag-Leffler function, which relaxes a priori assumptions about the governing statistics. We then used entropy as a measure of the anomalous features for the characteristic function. Diffusion-weighted MRI experiments were performed on a fixed rat brain using an imaging spectrometer at 17.6 T with b-values arrayed up to 25,000 s/mm(2). Additionally, we examined the impact of varying either the gradient strength, q, or mixing time, Δ, on the observed diffusion dynamics. In white and gray matter regions, the Mittag-Leffler and entropy parameters demonstrated new information regarding subdiffusion and produced different image contrast from that of the classical diffusion coefficient. The choice of weighting on q and Δ produced different image contrast within the regions of interest. We propose these parameters have the potential as biomarkers for morphology in neural tissue. Copyright © 2013 Wiley Periodicals, Inc.

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

    National Research Council Canada - National Science Library

    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...

  10. Application of Neural Network and Simulation Modeling to Evaluate Russian Banks’ Performance

    OpenAIRE

    Sharma, Satish; Shebalkov, Mikhail

    2013-01-01

    This paper presents an application of neural network and simulation modeling to analyze and predict the performance of 883 Russian Banks over the period 2000-2010. Correlation analysis was performed to obtain key financial indicators which reflect the leverage, liquidity, profitability and size of Banks. Neural network was trained over the entire dataset, and then simulation modeling was performed generating values which are distributed with Largest Extreme Value and Loglogistic distributions...

  11. Two-dimensional neural field simulator with parameter interface and 3D visualization

    OpenAIRE

    Nichols, Eric; Hutt, Axel

    2014-01-01

    International audience; A simulator calculating two-dimensional dynamic neural fields with multiple order derivatives is presented in this work. The simulated neural fields are of the type ... where I, L and S are respectively a field's input, spatial delay kernel with axonal transmission speed c and nonlinear firing rate function S = S0 / (1 + exp(-α(V-Θ)). A Fast Fourier Transform in space is used to accelerate the integral calculation. The stochastic differential equation is useful for stu...

  12. Comparison of the acute effects of hemostatic agents on neural tissues in spine surgery: Histologic analysis in rat models

    Directory of Open Access Journals (Sweden)

    Gokhan Meric

    2016-03-01

    Conclusion: Both gelatin sponge and oxidized cellulose did not increase the cellular necrosis of neural tissues. However, oxidized cellulose may lead to an increased local inflammatory reaction. [Arch Clin Exp Surg 2016; 5(1.000: 21-26

  13. A multiscale modelling of bone ultrastructure elastic proprieties using finite elements simulation and neural network method.

    Science.gov (United States)

    Barkaoui, Abdelwahed; Tlili, Brahim; Vercher-Martínez, Ana; Hambli, Ridha

    2016-10-01

    Bone is a living material with a complex hierarchical structure which entails exceptional mechanical properties, including high fracture toughness, specific stiffness and strength. Bone tissue is essentially composed by two phases distributed in approximately 30-70%: an organic phase (mainly type I collagen and cells) and an inorganic phase (hydroxyapatite-HA-and water). The nanostructure of bone can be represented throughout three scale levels where different repetitive structural units or building blocks are found: at the first level, collagen molecules are arranged in a pentameric structure where mineral crystals grow in specific sites. This primary bone structure constitutes the mineralized collagen microfibril. A structural organization of inter-digitating microfibrils forms the mineralized collagen fibril which represents the second scale level. The third scale level corresponds to the mineralized collagen fibre which is composed by the binding of fibrils. The hierarchical nature of the bone tissue is largely responsible of their significant mechanical properties; consequently, this is a current outstanding research topic. Scarce works in literature correlates the elastic properties in the three scale levels at the bone nanoscale. The main goal of this work is to estimate the elastic properties of the bone tissue in a multiscale approach including a sensitivity analysis of the elastic behaviour at each length scale. This proposal is achieved by means of a novel hybrid multiscale modelling that involves neural network (NN) computations and finite elements method (FEM) analysis. The elastic properties are estimated using a neural network simulation that previously has been trained with the database results of the finite element models. In the results of this work, parametric analysis and averaged elastic constants for each length scale are provided. Likewise, the influence of the elastic constants of the tissue constituents is also depicted. Results highlight

  14. 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.

  15. 3-D Bioprinting of Neural Tissue for Applications in Cell Therapy and Drug Screening.

    Science.gov (United States)

    Thomas, Michaela; Willerth, Stephanie M

    2017-01-01

    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.

  16. Tissue mimicking simulations for temporal enhanced ultrasound-based tissue typing

    Science.gov (United States)

    Bayat, Sharareh; Imani, Farhad; Gerardo, Carlos D.; Nir, Guy; Azizi, Shekoofeh; Yan, Pingkun; Tahmasebi, Amir; Wilson, Storey; Iczkowski, Kenneth A.; Lucia, M. Scott; Goldenberg, Larry; Salcudean, Septimiu E.; Mousavi, Parvin; Abolmaesumi, Purang

    2017-03-01

    Temporal enhanced ultrasound (TeUS) is an imaging approach where a sequence of temporal ultrasound data is acquired and analyzed for tissue typing. Previously, in a series of in vivo and ex vivo studies we have demonstrated that, this approach is effective for detecting prostate and breast cancers. Evidences derived from our experiments suggest that both ultrasound-signal related factors such as induced heat and tissue-related factors such as the distribution and micro-vibration of scatterers lead to tissue typing information in TeUS. In this work, we simulate mechanical micro-vibrations of scatterers in tissue-mimicking phantoms that have various scatterer densities reflecting benign and cancerous tissue structures. Finite element modeling (FEM) is used for this purpose where the vertexes are scatterers representing cell nuclei. The initial positions of scatterers are determined by the distribution of nuclei segmented from actual digital histology scans of prostate cancer patients. Subsequently, we generate ultrasound images of the simulated tissue structure using the Field II package resulting in a temporal enhanced ultrasound. We demonstrate that the micro-vibrations of scatterers are captured by temporal ultrasound data and this information can be exploited for tissue typing.

  17. 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.

  18. 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.

  19. 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

    ABSTRACT 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. PMID:26709633

  20. 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}.

  1. Binary tissue classification on wound images with neural networks and bayesian classifiers.

    Science.gov (United States)

    Veredas, Francisco; Mesa, Héctor; Morente, Laura

    2010-02-01

    A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, or friction. Diagnosis, treatment, and care of pressure ulcers are costly for health services. Accurate wound evaluation is a critical task for optimizing the efficacy of treatment and care. Clinicians usually evaluate each pressure ulcer by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. In this paper, a hybrid approach based on neural networks and Bayesian classifiers is used in the design of a computational system for automatic tissue identification in wound images. A mean shift procedure and a region-growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multilayer perceptrons is trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes which are determined by clinical experts. This training procedure is driven by a k-fold cross-validation method. Finally, a Bayesian committee machine is formed by training a Bayesian classifier to combine the classifications of the k neural networks. Specific heuristics based on the wound topology are designed to significantly improve the results of the classification. We obtain high efficiency rates from a binary cascade approach for tissue identification. Results are compared with other similar machine-learning approaches, including multiclass Bayesian committee machine classifiers and support vector machines. The different techniques analyzed in this paper show high global classification accuracy rates. Our binary cascade approach gives high global performance rates (average sensitivity =78.7% , specificity =94.7% , and accuracy =91.5% ) and shows the highest average sensitivity score ( =86.3%) when detecting

  2. 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.

  3. 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.

  4. A case for spiking neural network simulation based on configurable multiple-FPGA systems.

    Science.gov (United States)

    Yang, Shufan; Wu, Qiang; Li, Renfa

    2011-09-01

    Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

  5. Numerical simulation with finite element and artificial neural network ...

    Indian Academy of Sciences (India)

    Further, this database after the neural network training; is used to analyse measured material properties of different test pieces. The ANN predictions are reconfirmed with contact type finite element analysis for an arbitrary selected test sample. The methodology evolved in this work can be extended to predict material ...

  6. 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.

  7. A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors.

    Science.gov (United States)

    Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L; Nicolau, Alex; Veidenbaum, Alexander V

    2009-01-01

    Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for various neural engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Compute Unified Device Architecture (CUDA) Graphics Processing Units (GPUs) can provide a low-cost, programmable, and high-performance computing platform for simulation of SNNs. In this paper we demonstrate an efficient, biologically realistic, large-scale SNN simulator that runs on a single GPU. The SNN model includes Izhikevich spiking neurons, detailed models of synaptic plasticity and variable axonal delay. We allow user-defined configuration of the GPU-SNN model by means of a high-level programming interface written in C++ but similar to the PyNN programming interface specification. PyNN is a common programming interface developed by the neuronal simulation community to allow a single script to run on various simulators. The GPU implementation (on NVIDIA GTX-280 with 1 GB of memory) is up to 26 times faster than a CPU version for the simulation of 100K neurons with 50 Million synaptic connections, firing at an average rate of 7 Hz. For simulation of 10 Million synaptic connections and 100K neurons, the GPU SNN model is only 1.5 times slower than real-time. Further, we present a collection of new techniques related to parallelism extraction, mapping of irregular communication, and network representation for effective simulation of SNNs on GPUs. The fidelity of the simulation results was validated on CPU simulations using firing rate, synaptic weight distribution, and inter-spike interval analysis. Our simulator is publicly available to the modeling community so that researchers will have easy access to large-scale SNN simulations.

  8. 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

  9. Simulating dynamic plastic continuous neural networks by finite elements.

    Science.gov (United States)

    Joghataie, Abdolreza; Torghabehi, Omid Oliyan

    2014-08-01

    We introduce dynamic plastic continuous neural network (DPCNN), which is comprised of neurons distributed in a nonlinear plastic medium where wire-like connections of neural networks are replaced with the continuous medium. We use finite element method to model the dynamic phenomenon of information processing within the DPCNNs. During the training, instead of weights, the properties of the continuous material at its different locations and some properties of neurons are modified. Input and output can be vectors and/or continuous functions over lines and/or areas. Delay and feedback from neurons to themselves and from outputs occur in the DPCNNs. We model a simple form of the DPCNN where the medium is a rectangular plate of bilinear material, and the neurons continuously fire a signal, which is a function of the horizontal displacement.

  10. Earth Station Neural Network Control Methodology and Simulation

    OpenAIRE

    Hanaa T. El-Madany; Faten H. Fahmy; Ninet M. A. El-Rahman; Hassen T. Dorrah

    2012-01-01

    Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematic...

  11. A viscoelastic model to simulate soft tissue materials

    Science.gov (United States)

    Espinoza Ortiz, J. S.; Lagos, R. E.

    2015-09-01

    Continuum mechanic theories are frequently used to simulate the mechanical behavior of elastic and viscous materials, specifically soft tissues typically showing incompressibility, nonlinear deformation under stress, fading memory and insensitivity to the strain-rate. The time dependence of a viscoelastic material could be better understood by considering it as composed by an elastic solid and a viscous fluid. Different types of mechanical devices can be constructed provided a particular configuration of elastic springs and dashpots. In this work our aim is to probe many of the soft tissue mechanical behavior, by considering a Kelvin's device coupled to a set of in parallel Maxwell's devices. Then, the resulting model composed of a long series of modified Kelvin bodies must span a broad range of characteristic times resulting in a suitable model for soft tissue simulation. Under driving static and dynamic stress applied to a 2-Dim system, its time dependence strain response is computed. We obtain a set of coupled Volterra integral equations solved via the extended trapezoidal rule scheme, and the Newton-Raphson method to solve nonlinear coupled equations.

  12. The effect of ionic diffusion on extracellular potentials in neural tissue

    CERN Document Server

    Halnes, Geir; Keller, Daniel; Pettersen, Klas H; Eivenoll, Gaute T

    2015-01-01

    In computational neuroscience, it is common to use the simplifying assumption that diffusive currents are negligible compared to Ohmic currents. However, endured periods of intense neural signaling may cause local ion concentration changes in the millimolar range. Theoretical studies have identified scenarios where steep concentration gradients give rise to diffusive currents that are of comparable magnitude with Ohmic currents, and where the simplifying assumption that diffusion can be neglected does not hold. We here propose a novel formalism for computing (1) the ion concentration dynamics and (2) the electrical potential in the extracellular space surrounding multi-compartmental neuron models or networks of such (e.g., the Blue-Brain simulator). We use this formalism to explore the effects that diffusive currents can have on the extracellular (ECS) potential surrounding a small population of active cortical neurons. Our key findings are: (i) Sustained periods of neuronal output (simulations were run for 8...

  13. 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.

  14. Stem Cell Bioprinting: Functional 3D Neural Mini-Tissues from Printed Gel-Based Bioink and Human Neural Stem Cells (Adv. Healthcare Mater. 12/2016).

    Science.gov (United States)

    Gu, Qi; Tomaskovic-Crook, Eva; Lozano, Rodrigo; Chen, Yu; Kapsa, Robert M; Zhou, Qi; Wallace, Gordon G; Crook, Jeremy M

    2016-06-01

    On page 1429 G. G. Wallace, J. M. Crook, and co-workers report the first example of fabricating neural tissue by 3D bioprinting human neural stem cells. A novel polysaccharide based bioink preserves stem cell viability and function within the printed construct, enabling self-renewal and differentiation to neurons and supporting neuroglia. Neurons are predominantly GABAergic, establish networks, are spontaneously active, and show a bicuculline induced increased calcium response. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

    Science.gov (United States)

    Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

    2008-01-01

    The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations. PMID:19543450

  16. Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System

    Directory of Open Access Journals (Sweden)

    ASTROV, I.

    2007-04-01

    Full Text Available This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.

  17. Multiscale methodology for bone remodelling simulation using coupled finite element and neural network computation.

    Science.gov (United States)

    Hambli, Ridha; Katerchi, Houda; Benhamou, Claude-Laurent

    2011-02-01

    The aim of this paper is to develop a multiscale hierarchical hybrid model based on finite element analysis and neural network computation to link mesoscopic scale (trabecular network level) and macroscopic (whole bone level) to simulate the process of bone remodelling. As whole bone simulation, including the 3D reconstruction of trabecular level bone, is time consuming, finite element calculation is only performed at the macroscopic level, whilst trained neural networks are employed as numerical substitutes for the finite element code needed for the mesoscale prediction. The bone mechanical properties are updated at the macroscopic scale depending on the morphological and mechanical adaptation at the mesoscopic scale computed by the trained neural network. The digital image-based modelling technique using μ-CT and voxel finite element analysis is used to capture volume elements representative of 2 mm³ at the mesoscale level of the femoral head. The input data for the artificial neural network are a set of bone material parameters, boundary conditions and the applied stress. The output data are the updated bone properties and some trabecular bone factors. The current approach is the first model, to our knowledge, that incorporates both finite element analysis and neural network computation to rapidly simulate multilevel bone adaptation.

  18. Fast simulation and optimization tool to explore selective neural stimulation

    Directory of Open Access Journals (Sweden)

    Mélissa Dali

    2016-06-01

    Full Text Available In functional electrical stimulation, selective stimulation of axons is desirable to activate a specific target, in particular muscular function. This implies to simulate a fascicule without activating neighboring ones i.e. to be spatially selective. Spatial selectivity is achieved by the use of multicontact cuff electrodes over which the stimulation current is distributed. Because of the large number of parameters involved, numerical simulations provide a way to find and optimize electrode configuration. The present work offers a computation effective scheme and associated tool chain capable of simulating electrode-nerve interface and find the best spread of current to achieve spatial selectivity.

  19. 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

    platform is designed and tested. The purpose of setting up the network is to reduce calculation time in a fatigue life analysis. Therefore, the networks trained on different error functions are compared with respect to accuracy of rain flow counts of stress cycles over a number of time series simulations......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...... for regression is the mean square error. This paper looks into the possibility of improving the performance of neural networks by selecting or defining error functions that are tailor-made for a specific objective. A neural network trained to simulate tension forces in an anchor chain on a floating offshore...

  20. High power fuel cell simulator based on artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Chavez-Ramirez, Abraham U.; Munoz-Guerrero, Roberto [Departamento de Ingenieria Electrica, CINVESTAV-IPN. Av. Instituto Politecnico Nacional No. 2508, D.F. CP 07360 (Mexico); Duron-Torres, S.M. [Unidad Academica de Ciencias Quimicas, Universidad Autonoma de Zacatecas, Campus Siglo XXI, Edif. 6 (Mexico); Ferraro, M.; Brunaccini, G.; Sergi, F.; Antonucci, V. [CNR-ITAE, Via Salita S. Lucia sopra Contesse 5-98126 Messina (Italy); Arriaga, L.G. [Centro de Investigacion y Desarrollo Tecnologico en Electroquimica S.C., Parque Tecnologico Queretaro, Sanfandila, Pedro Escobedo, Queretaro (Mexico)

    2010-11-15

    Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs - 2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications. (author)

  1. 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.

  2. NEVESIM: Event-Driven Neural Simulation Framework with a Python Interface

    Directory of Open Access Journals (Sweden)

    Dejan ePecevski

    2014-08-01

    Full Text Available 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.

  3. 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…

  4. 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

  5. Simulation of emotions of agents in virtual environments using neural networks

    NARCIS (Netherlands)

    van Kesteren, A.-J.; van Kesteren, A.J.; op den Akker, Hendrikus J.A.; Poel, Mannes; Jokinen, K.; Heylen, Dirk K.J.; Nijholt, Antinus

    2000-01-01

    A distributed architecture for a system simulating the emotional state of an agent acting in a virtual environment is presented. The system is an implementation of an event appraisal model of emotional behaviour and uses neural networks to learn how the emotional state should be influenced by the

  6. Neural networks simulation of a discrete model of continious effects of irrelevant stimuli

    NARCIS (Netherlands)

    Molenaar, P.C.M.

    1990-01-01

    Presents a general simulation method based on minimal neural network representations of nonmathematical, structural models of information processes. The time-dependent behavior of each component in a given structural model is represented by a simple, noncommittal equation that does not affect the

  7. Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware

    Science.gov (United States)

    Knight, James C.; Tully, Philip J.; Kaplan, Bernhard A.; Lansner, Anders; Furber, Steve B.

    2016-01-01

    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 2.0 × 104 neurons and 5.1 × 107 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 45× 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. PMID:27092061

  8. 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.

  9. Influence of Anatomical Detail and Tissue Conductivity Variations in Simulations of Multi-Contact Nerve Cuff Recordings.

    Science.gov (United States)

    Garai, Purbasha; Koh, Ryan G L; Schuettler, Martin; Stieglitz, Thomas; Zariffa, Jose

    2017-09-01

    Accurate simulations of peripheral nerve recordings are needed to develop improved neuroprostheses. Previous models of peripheral nerves contained simplifications whose effects have not been investigated. We created a novel detailed finite element (FE) model of a peripheral nerve, and used it to carry out a sensitivity analysis of several model parameters. To construct the model, in vivo recordings were obtained in a rat sciatic nerve using an 8-channel nerve cuff electrode, after which the nerve was imaged using magnetic resonance imaging (MRI). The FE model was constructed based on the MRI data, and included progressive branching of the fascicles. Neural pathways were defined in the model for the tibial, peroneal and sural fascicles. The locations of these pathways were selected so as to maximize the correlations between the simulated and in vivo recordings. The sensitivity analysis showed that varying the conductivities of neural tissues had little influence on the ability of the model to reproduce the recording patterns obtained experimentally. On the other hand, the increased anatomical detail did substantially alter the recording patterns observed, demonstrating that incorporating fascicular branching is an important consideration in models of nerve cuff recordings. The model used in this study constitutes an improved simulation tool and can be used in the design of neural interfaces.

  10. Neuromechanic: a computational platform for simulation and analysis of the neural control of movement

    Science.gov (United States)

    Bunderson, Nathan E.; Bingham, Jeffrey T.; Sohn, M. Hongchul; Ting, Lena H.; Burkholder, Thomas J.

    2015-01-01

    Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states as well as muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization and stability analysis tools to provide structural insights into the neural control of movement. PMID:23027632

  11. An Efficient Neural Network Based Modeling Method for Automotive EMC Simulation

    Science.gov (United States)

    Frank, Florian; Weigel, Robert

    2011-09-01

    This paper presents a newly developed methodology for VHDL-AMS model integration into SPICE-based EMC simulations. To this end the VHDL-AMS model, which is available in a compiled version only, is characterized under typical loading conditions, and afterwards a neural network based technique is applied to convert characteristic voltage and current data into an equivalent circuit in SPICE syntax. After the explanation of the whole method and the presentation of a newly developed switched state space dynamic neural network model, the entire analysis process is demonstrated using a typical application from automotive industry.

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

    Directory of Open Access Journals (Sweden)

    Kit eCheung

    2016-01-01

    Full Text Available 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 optimised 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 approximately 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.

  13. 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.

  14. 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.

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

    Science.gov (United States)

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

    2016-01-01

    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. PMID:26834542

  16. PAX: A mixed hardware/software simulation platform for spiking neural networks.

    Science.gov (United States)

    Renaud, S; Tomas, J; Lewis, N; Bornat, Y; Daouzli, A; Rudolph, M; Destexhe, A; Saïghi, S

    2010-09-01

    Many hardware-based solutions now exist for the simulation of bio-like neural networks. Less conventional than software-based systems, these types of simulators generally combine digital and analog forms of computation. In this paper we present a mixed hardware-software platform, specifically designed for the simulation of spiking neural networks, using conductance-based models of neurons and synaptic connections with dynamic adaptation rules (Spike-Timing-Dependent Plasticity). The neurons and networks are configurable, and are computed in 'biological real time' by which we mean that the difference between simulated time and simulation time is guaranteed lower than 50 mus. After presenting the issues and context involved in the design and use of hardware-based spiking neural networks, we describe the analog neuromimetic integrated circuits which form the core of the platform. We then explain the organization and computation principles of the modules within the platform, and present experimental results which validate the system. Designed as a tool for computational neuroscience, the platform is exploited in collaborative research projects together with neurobiology and computer science partners. Copyright 2010 Elsevier Ltd. All rights reserved.

  17. 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.

  18. 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.

  19. A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling.

    Science.gov (United States)

    Hoang, Roger V; Tanna, Devyani; Jayet Bray, Laurence C; Dascalu, Sergiu M; Harris, Frederick C

    2013-01-01

    Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, 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 have shown 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 simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards.

  20. 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.

  1. 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.

  2. Comparison of IT Neural Response Statistics with Simulations

    Directory of Open Access Journals (Sweden)

    Qiulei Dong

    2017-07-01

    Full Text Available Lehky et al. (2011 provided a statistical analysis on the responses of the recorded 674 neurons to 806 image stimuli in anterior inferotemporalm (AIT cortex of two monkeys. In terms of kurtosis and Pareto tail index, they observed that the population sparseness of both unnormalized and normalized responses is always larger than their single-neuron selectivity, hence concluded that the critical features for individual neurons in primate AIT cortex are not very complex, but there is an indefinitely large number of them. In this work, we explore an “inverse problem” by simulation, that is, by simulating each neuron indeed only responds to a very limited number of stimuli among a very large number of neurons and stimuli, to assess whether the population sparseness is always larger than the single-neuron selectivity. Our simulation results show that the population sparseness exceeds the single-neuron selectivity in most cases even if the number of neurons and stimuli are much larger than several hundreds, which confirms the observations in Lehky et al. (2011. In addition, we found that the variances of the computed kurtosis and Pareto tail index are quite large in some cases, which reveals some limitations of these two criteria when used for neuron response evaluation.

  3. 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.

  4. Low Energy Defibrillation in Human Cardiac Tissue: A Simulation Study

    Science.gov (United States)

    Morgan, Stuart W.; Plank, Gernot; Biktasheva, Irina V.; Biktashev, Vadim N.

    2009-02-01

    We aim to assess the effectiveness of feedback controlled resonant drift pacing as a method for low energy defibrillation. Antitachycardia pacing is the only low energy defibrillation approach to have gained clinical significance, but it is still suboptimal. Low energy defibrillation would avoid adverse side effects associated with high voltage shocks and allow the application of ICD therapy where it is not tolerated today. We present results of computer simulations of a bidomain model of cardiac tissue with human atrial ionic kinetics. Re-entry was initiated and low energy shocks were applied with the same period as the re-entry, using feedback to maintain resonance. We demonstrate that such stimulation can move the core of re-entrant patterns, in the direction depending on location of electrodes and a time delay in the feedback. Termination of re-entry is achieved with shock strength one order of magnitude weaker than in conventional single-shock defibrillation. We conclude that resonant drift pacing can terminate re-entry at a fraction of the shock strength currently used for defibrillation and can potentially work where antitachycardia pacing fails, due to the feedback mechanisms. Success depends on a number of details which these numerical simulations have uncovered. \\emph{Keywords} Re-entry; Bidomain model; Resonant drift; ICD; Defibrillation; Antitachycardia pacing; Feedback.

  5. 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

  6. Simulated apoptosis/neurogenesis regulates learning and memory capabilities of adaptive neural networks.

    Science.gov (United States)

    Chambers, R Andrew; Potenza, Marc N; Hoffman, Ralph E; Miranker, Willard

    2004-04-01

    Characterization of neuronal death and neurogenesis in the adult brain of birds, humans, and other mammals raises the possibility that neuronal turnover represents a special form of neuroplasticity associated with stress responses, cognition, and the pathophysiology and treatment of psychiatric disorders. Multilayer neural network models capable of learning alphabetic character representations via incremental synaptic connection strength changes were used to assess additional learning and memory effects incurred by simulation of coordinated apoptotic and neurogenic events in the middle layer. Using a consistent incremental learning capability across all neurons and experimental conditions, increasing the number of middle layer neurons undergoing turnover increased network learning capacity for new information, and increased forgetting of old information. Simulations also showed that specific patterns of neural turnover based on individual neuronal connection characteristics, or the temporal-spatial pattern of neurons chosen for turnover during new learning impacts new learning performance. These simulations predict that apoptotic and neurogenic events could act together to produce specific learning and memory effects beyond those provided by ongoing mechanisms of connection plasticity in neuronal populations. Regulation of rates as well as patterns of neuronal turnover may serve an important function in tuning the informatic properties of plastic networks according to novel informational demands. Analogous regulation in the hippocampus may provide for adaptive cognitive and emotional responses to novel and stressful contexts, or operate suboptimally as a basis for psychiatric disorders. The implications of these elementary simulations for future biological and neural modeling research on apoptosis and neurogenesis are discussed.

  7. Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

    Science.gov (United States)

    Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

    2016-09-01

    The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

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

    OpenAIRE

    Dae Woo Park

    2016-01-01

    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...

  9. 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.

  10. On Improved Least Squares Regression and Artificial Neural Network Meta-Models for Simulation via Control Variates

    Science.gov (United States)

    2016-09-15

    neural network using applications across varied industries . Alam et al. [2] showed the factorial design did not perform as well as other designs (mentioned...composite design with a neural network using applications across varied industries . Alam et al. [2] showed the central composite design did not perform as...ON IMPROVED LEAST SQUARES REGRESSION & ARTIFICIAL NEURAL NETWORK META-MODELS FOR SIMULATION VIA CONTROL VARIATES DISSERTATION Michael P. Gibb

  11. Simulation, State Estimation and Control of Nonlinear Superheater Attemporator using Neural Networks

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Sørensen, O.

    2000-01-01

    This paper considers the use of neural networks for nonlinear state estimation, system identification and control. As a case study we use data taken from a nonlinear injection valve for a superheater attemporator at a power plant. One neural network is trained as a nonlinear simulation model......-by-sample linearizations and state estimates provided by the observer network. Simulation studies show that the nonlinear observer-based control loop performs better than a similar control loop based on a linear observer....... of the process, then another network is trained to act as a combined state and parameter estimator for the process. The observer network incorporates smoothing of the parameter estimates in the form of regularization. A pole placement controller is designed which takes advantage of the sample...

  12. Simulation, State Estimation and Control of Nonlinear Superheater Attemporator using Neural Networks

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Sørensen, O.

    1999-01-01

    This paper considers the use of neural networks for nonlinear state estimation, system identification and control. As a case study we use data taken from a nonlinear injection valve for a superheater attemporator at a power plant. One neural network is trained as a nonlinear simulation model......-by-sample linearizations and state estimates provided by the observer network. Simulation studies show that the nonlinear observer-based control loop performs better than a similar control loop based on a linear observer....... of the process, then another network is trained to act as a combined state and parameter estimator for the process. The observer network incorporates smoothing of the parameter estimates in the form of regularization. A pole placement controller is designed which takes advantage of the sample...

  13. Modeling and Simulation of Road Traffic Noise Using Artificial Neural Network and Regression.

    Science.gov (United States)

    Honarmand, M; Mousavi, S M

    2014-04-01

    Modeling and simulation of noise pollution has been done in a large city, where the population is over 2 millions. Two models of artificial neural network and regression were developed to predict in-city road traffic noise pollution with using the data of noise measurements and vehicle counts at three points of the city for a period of 12 hours. The MATLAB and DATAFIT softwares were used for simulation. The predicted results of noise level were compared with the measured noise levels in three stations. The values of normalized bias, sum of squared errors, mean of squared errors, root mean of squared errors, and squared correlation coefficient calculated for each model show the results of two models are suitable, and the predictions of artificial neural network are closer to the experimental data.

  14. Acquiring Efficient Locomotion in a Simulated Quadruped through Evolving Random and Predefined Neural Networks

    DEFF Research Database (Denmark)

    Veenstra, Frank; Struck, Alexander; Krauledat, Matthias

    2015-01-01

    The acquisition and optimization of dynamically stable locomotion is important to engender fast and energy efficient locomotion in animals. Conventional optimization strategies tend to have difficulties in acquiring dynamically stable gaits in legged robots. In this paper, an evolving neural...... network (ENN) was implemented with the aim to optimize the locomotive behavior of a four-legged simulated robot. In the initial generation, individuals had neural networks (NNs) that were either predefined or randomly initialized. Additional investigations show that the efficiency of applying additional...... sensors to the simulated quadruped improved the performance of the ENN slightly. Promising results were seen in the evolutionary runs where the initial predefined NNs of the population contributed to slight movements of the limbs. This paper shows how a predefined ENNs linked to bio-inspired sensors can...

  15. New tissue dissociation protocol for scaled-up production of neural stem cells in suspension bioreactors.

    Science.gov (United States)

    Sen, Arindom; Kallos, Michael S; Behie, Leo A

    2004-01-01

    The successful dissociation of mammalian neural stem cell (NSC) aggregates (neurospheres) into a single-cell suspension is an important procedure when expanding NSCs for clinical use, or when performing important assays such as clonal analyses. Until now, researchers have had to rely primarily on destructive mechanical methods such as trituration with a pipette tip to break apart the aggregates. In this study we report on a new chemical dissociation procedure that is efficient, cost effective, reproducible, and much less harmful to murine NSCs than both mechanical and enzymatic techniques. This method, involving the manipulation of environmental pH levels, resulted in 40% higher measured cell densities and 15-20% higher viabilities compared with mechanical dissociation. Moreover, chemical dissociation resulted in the production of significantly less cellular debris. Chemical dissociation was found to have no adverse effects on the long-term proliferation of the NSCs, which retained the ability to proliferate, form neurospheres, self-renew, and exhibit multipotentiality. This chemical method represents a new approach for the dissociation of tissues.

  16. 3D Normal Human Neural Progenitor Tissue-Like Assemblies: A Model of Persistent VZV Infection

    Science.gov (United States)

    Goodwin, Thomas J.

    2013-01-01

    Varicella-zoster virus (VZV) is a neurotropic human alphaherpesvirus that causes varicella upon primary infection, establishes latency in multiple ganglionic neurons, and can reactivate to cause zoster. Live attenuated VZV vaccines are available; however, they can also establish latent infections and reactivate. Studies of VZV latency have been limited to the analyses of human ganglia removed at autopsy, as the virus is strictly a human pathogen. Recently, terminally differentiated human neurons have received much attention as a means to study the interaction between VZV and human neurons; however, the short life-span of these cells in culture has limited their application. Herein, we describe the construction of a model of normal human neural progenitor cells (NHNP) in tissue-like assemblies (TLAs), which can be successfully maintained for at least 180 days in three-dimensional (3D) culture, and exhibit an expression profile similar to that of human trigeminal ganglia. Infection of NHNP TLAs with cell-free VZV resulted in a persistent infection that was maintained for three months, during which the virus genome remained stable. Immediate-early, early and late VZV genes were transcribed, and low-levels of infectious VZV were recurrently detected in the culture supernatant. Our data suggest that NHNP TLAs are an effective system to investigate long-term interactions of VZV with complex assemblies of human neuronal cells.

  17. 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.

  18. Expression of the synaptic vesicle proteins VAMPs/synaptobrevins 1 and 2 in non-neural tissues

    DEFF Research Database (Denmark)

    Ralston, E; Beushausen, S; Ploug, Thorkil

    1994-01-01

    for Vp/Syb 2 detected a protein in the endoplasmic reticulum-Golgi area of skeletal muscle. Thus Vp/Sybs 1 and 2 are not restricted to the nervous system but appear to be co-expressed with cellubrevin in many different tissues. This redundancy of Vp/Sybs in a single cell may be required to control......The VAMPs/synaptobrevins (Vp/Sybs) are small integral membrane proteins. Two isoforms, Vp/Syb 1 and Vp/Syb 2, are considered to be specific to neural tissue. They are associated with synaptic vesicles and are believed to play an important role in neurotransmitter release. A third isoform......, cellubrevin, has recently been found in non-neural tissues. We now report that the distribution of Vp/Syb 1 and Vp/Syb 2 is wider than previously thought. RNA transcripts for both Vp/Syb 1 and Vp/Syb 2 were found in rat skeletal muscle and in several other rat non-neural tissues, and antibodies specific...

  19. Unified-theory-of-reinforcement neural networks do not simulate the blocking effect.

    Science.gov (United States)

    Calvin, Nicholas T; J McDowell, J

    2015-11-01

    For the last 20 years the unified theory of reinforcement (Donahoe et al., 1993) has been used to develop computer simulations to evaluate its plausibility as an account for behavior. The unified theory of reinforcement states that operant and respondent learning occurs via the same neural mechanisms. As part of a larger project to evaluate the operant behavior predicted by the theory, this project was the first replication of neural network models based on the unified theory of reinforcement. In the process of replicating these neural network models it became apparent that a previously published finding, namely, that the networks simulate the blocking phenomenon (Donahoe et al., 1993), was a misinterpretation of the data. We show that the apparent blocking produced by these networks is an artifact of the inability of these networks to generate the same conditioned response to multiple stimuli. The piecemeal approach to evaluate the unified theory of reinforcement via simulation is critiqued and alternatives are discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Tissue motion in blood velocity estimation and its simulation

    DEFF Research Database (Denmark)

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

    1998-01-01

    . 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......Determination of blood velocities for color flow mapping systems involves both stationary echo cancelling and velocity estimation. Often the stationary echo cancelling filter is the limiting factor in color flow mapping and the optimization and further development of this filter is crucial...... 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...

  1. 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

  2. Simulation of Neurocomputing Based on Photophobic Reactions of Euglena: Toward Microbe-Based Neural Network Computing

    Science.gov (United States)

    Ozasa, Kazunari; Aono, Masashi; Maeda, Mizuo; Hara, Masahiko

    In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.

  3. 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

  4. Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

    Science.gov (United States)

    Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad

    2016-11-01

    In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.

  5. 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.

  6. 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.

  7. Novel high-viscosity polyacrylamidated chitosan for neural tissue engineering: fabrication of anisotropic neurodurable scaffold via molecular disposition of persulfate-mediated polymer slicing and complexation.

    Science.gov (United States)

    Kumar, Pradeep; Choonara, Yahya E; du Toit, Lisa C; Modi, Girish; Naidoo, Dinesh; Pillay, Viness

    2012-10-29

    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, superior hydrophilicity as well as

  8. 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

  9. 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

  10. Use of a neural network to extract a missile flight model for simulation purposes

    Science.gov (United States)

    Pascale, Danny; Volckaert, Guy

    1996-03-01

    A neural network is used to extract the flight model of guided, short to medium range, tripod and shoulder-fired missile systems which is then integrated into a training simulator. The simulator uses injected video to replace the optical sight and is fitted with a multi-axis positioning system which senses the gunner's movement. The movement creates an image shift and affects the input data to the missile control algorithm. Accurate flight dynamics are a key to efficient training, particularly in the case of closed loop guided systems. However, flight model data is not always available, either because it is proprietary, or because it is too complex to embed in a real time simulator. A solution is to reverse engineer the flight model by analyzing the missile's response when submitted to typical input conditions. Training data can be extracted from either recorded video or from a combination of weapon and missile positioning data. The video camera can be located either on the weapon or attached to a through-sight adapter. No knowledge of the missile flight transfer function is used in the process. The data is fed to a three-layer back-propagation type neural network. The network is configured within a standard spreadsheet application and is optimized with the built-in solver functions. The structure of the network, the selected inputs and outputs, as well as training data, output data after training, and output data when embedded in the simulator are presented.

  11. 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

  12. Transplantation of tissue engineering neural network and formation of neuronal relay into the transected rat spinal cord.

    Science.gov (United States)

    Lai, Bi-Qin; Che, Ming-Tian; Du, Bao-Ling; Zeng, Xiang; Ma, Yuan-Huan; Feng, Bo; Qiu, Xue-Chen; Zhang, Ke; Liu, Shu; Shen, Hui-Yong; Wu, Jin-Lang; Ling, Eng-Ang; Zeng, Yuan-Shan

    2016-12-01

    Severe spinal cord injury (SCI) causes loss of neural connectivity and permanent functional deficits. Re-establishment of new neuronal relay circuits after SCI is therefore of paramount importance. The present study tested our hypothesis if co-culture of neurotrophin-3 (NT-3) gene-modified Schwann cells (SCs, NT-3-SCs) and TrkC (NT-3 receptor) gene-modified neural stem cells (NSCs, TrkC-NSCs) in a gelatin sponge scaffold could construct a tissue engineering neural network for re-establishing an anatomical neuronal relay after rat spinal cord transection. Eight weeks after transplantation, the neural network created a favorable microenvironment for axonal regeneration and for survival and synaptogenesis of NSC-derived neurons. Biotin conjugates of cholera toxin B subunit (b-CTB, a transneuronal tracer) was injected into the crushed sciatic nerve to label spinal cord neurons. Remarkably, not only ascending and descending nerve fibers, but also propriospinal neurons, made contacts with b-CTB positive NSC-derived neurons. Moreover, b-CTB positive NSC-derived neurons extended their axons making contacts with the motor neurons located in areas caudal to the injury/graft site of spinal cord. Further study showed that NT-3/TrkC interactions activated the PI3K/AKT/mTOR pathway and PI3K/AKT/CREB pathway affecting synaptogenesis of NSC-derived neurons. Together, our findings suggest that NT-3-mediated TrkC signaling plays an essential role in constructing a tissue engineering neural network thus representing a promising avenue for effective exogenous neuronal relay-based treatment for SCI. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Simulation and stability analysis of neural network based control scheme for switched linear systems.

    Science.gov (United States)

    Singh, H P; Sukavanam, N

    2012-01-01

    This paper proposes a new adaptive neural network based control scheme for switched linear systems with parametric uncertainty and external disturbance. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network is employed to learn this upper bound. The adaptive learning algorithm is derived from Lyapunov stability analysis so that the system response under arbitrary switching laws is guaranteed uniformly ultimately bounded. A comparative simulation study with robust controller given in [Zhang L, Lu Y, Chen Y, Mastorakis NE. Robust uniformly ultimate boundedness control for uncertain switched linear systems. Computers and Mathematics with Applications 2008; 56: 1709-14] is presented. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  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. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Djeffal, F. [LEA, Department of Electronics, University of Batna 05000 (Algeria)], E-mail: faycaldzdz@hotmail.com; Dibi, Z. [LEA, Department of Electronics, University of Batna 05000 (Algeria)], E-mail: zohirdibi@univ-batna.dz; Hafiane, M.L.; Arar, D. [LEA, Department of Electronics, University of Batna 05000 (Algeria)

    2007-09-15

    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{sub 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.

  17. 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.

  18. Self-Organizing 3D Human Neural Tissue Derived from Induced Pluripotent Stem Cells Recapitulate Alzheimer's Disease Phenotypes.

    Directory of Open Access Journals (Sweden)

    Waseem K Raja

    Full Text Available The dismal success rate of clinical trials for Alzheimer's disease (AD motivates us to develop model systems of AD pathology that have higher predictive validity. The advent of induced pluripotent stem cells (iPSCs allows us to model pathology and study disease mechanisms directly in human neural cells from healthy individual as well as AD patients. However, two-dimensional culture systems do not recapitulate the complexity of neural tissue, and phenotypes such as extracellular protein aggregation are difficult to observe. We report brain organoids that use pluripotent stem cells derived from AD patients and recapitulate AD-like pathologies such as amyloid aggregation, hyperphosphorylated tau protein, and endosome abnormalities. These pathologies are observed in an age-dependent manner in organoids derived from multiple familial AD (fAD patients harboring amyloid precursor protein (APP duplication or presenilin1 (PSEN1 mutation, compared to controls. The incidence of AD pathology was consistent amongst several fAD lines, which carried different mutations. Although these are complex assemblies of neural tissue, they are also highly amenable to experimental manipulation. We find that treatment of patient-derived organoids with β- and γ-secretase inhibitors significantly reduces amyloid and tau pathology. Moreover, these results show the potential of this model system to greatly increase the translatability of pre-clinical drug discovery in AD.

  19. The characterization of neural tissue ablation rate and corresponding heat affected zone of a 2 micron Tm3+ doped fiber laser(Conference Presentation)

    Science.gov (United States)

    Marques, Andrew J.; Jivraj, Jamil; Reyes, Robnier; Ramjist, Joel; Gu, Xijia J.; Yang, Victor X. D.

    2017-02-01

    Tissue removal using electrocautery is standard practice in neurosurgery since tissue can be cut and cauterized simultaneously. Thermally mediated tissue ablation using lasers can potentially possess the same benefits but with increased precision. However, given the critical nature of the spine, brain, and nerves, the effects of direct photo-thermal interaction on neural tissue needs to be known, yielding not only high precision of tissue removal but also increased control of peripheral heat damage. The proposed use of lasers as a neurosurgical tool requires that a common ground is found between ablation rates and resulting peripheral heat damage. Most surgical laser systems rely on the conversion of light energy into heat resulting in both desirable and undesirable thermal damage to the targeted tissue. Classifying the distribution of thermal energy in neural tissue, and thus characterizing the extent of undesirable thermal damage, can prove to be exceptionally challenging considering its highly inhomogenous composition when compared to other tissues such as muscle and bone. Here we present the characterization of neural tissue ablation rate and heat affected zone of a 1.94 micron thulium doped fiber laser for neural tissue ablation. In-Vivo ablation of porcine cerebral cortex is performed. Ablation volumes are studied in association with laser parameters. Histological samples are taken and examined to characterize the extent of peripheral heat damage.

  20. 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.

  1. 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.

  2. ALK5-mediated transforming growth factor β signaling in neural crest cells controls craniofacial muscle development via tissue-tissue interactions.

    Science.gov (United States)

    Han, Arum; Zhao, Hu; Li, Jingyuan; Pelikan, Richard; Chai, Yang

    2014-08-01

    The development of the craniofacial muscles requires reciprocal interactions with surrounding craniofacial tissues that originate from cranial neural crest cells (CNCCs). However, the molecular mechanism involved in the tissue-tissue interactions between CNCCs and muscle progenitors during craniofacial muscle development is largely unknown. In the current study, we address how CNCCs regulate the development of the tongue and other craniofacial muscles using Wnt1-Cre; Alk5(fl/fl) mice, in which loss of Alk5 in CNCCs results in severely disrupted muscle formation. We found that Bmp4 is responsible for reduced proliferation of the myogenic progenitor cells in Wnt1-Cre; Alk5(fl/fl) mice during early myogenesis. In addition, Fgf4 and Fgf6 ligands were reduced in Wnt1-Cre; Alk5(fl/fl) mice and are critical for differentiation of the myogenic cells. Addition of Bmp4 or Fgf ligands rescues the proliferation and differentiation defects in the craniofacial muscles of Alk5 mutant mice in vitro. Taken together, our results indicate that CNCCs play critical roles in controlling craniofacial myogenic proliferation and differentiation through tissue-tissue interactions. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

  3. Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

    Science.gov (United States)

    Jafari, Mehdi; Kasaei, Shohreh

    2012-01-01

    Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

  4. Simulation of planar soft tissues using a structural constitutive model: Finite element implementation and validation.

    Science.gov (United States)

    Fan, Rong; Sacks, Michael S

    2014-06-27

    Computational implementation of physical and physiologically realistic constitutive models is critical for numerical simulation of soft biological tissues in a variety of biomedical applications. It is well established that the highly nonlinear and anisotropic mechanical behaviors of soft tissues are an emergent behavior of the underlying tissue microstructure. In the present study, we have implemented a structural constitutive model into a finite element framework specialized for membrane tissues. We noted that starting with a single element subjected to uniaxial tension, the non-fibrous tissue matrix must be present to prevent unrealistic tissue deformations. Flexural simulations were used to set the non-fibrous matrix modulus because fibers have little effects on tissue deformation under three-point bending. Multiple deformation modes were simulated, including strip biaxial, planar biaxial with two attachment methods, and membrane inflation. Detailed comparisons with experimental data were undertaken to insure faithful simulations of both the macro-level stress-strain insights into adaptations of the fiber architecture under stress, such as fiber reorientation and fiber recruitment. Results indicated a high degree of fidelity and demonstrated interesting microstructural adaptions to stress and the important role of the underlying tissue matrix. Moreover, we apparently resolve a discrepancy in our 1997 study (Billiar and Sacks, 1997. J. Biomech. 30 (7), 753-756) where we observed that under strip biaxial stretch the simulated fiber splay responses were not in good agreement with the experimental results, suggesting non-affine deformations may have occurred. However, by correctly accounting for the isotropic phase of the measured fiber splay, good agreement was obtained. While not the final word, these simulations suggest that affine fiber kinematics for planar collagenous tissues is a reasonable assumption at the macro level. Simulation tools such as these are

  5. Simulation tests of the optimization method of Hopfield and Tank using neural networks

    Science.gov (United States)

    Paielli, Russell A.

    1988-01-01

    The method proposed by Hopfield and Tank for using the Hopfield neural network with continuous valued neurons to solve the traveling salesman problem is tested by simulation. Several researchers have apparently been unable to successfully repeat the numerical simulation documented by Hopfield and Tank. However, as suggested to the author by Adams, it appears that the reason for those difficulties is that a key parameter value is reported erroneously (by four orders of magnitude) in the original paper. When a reasonable value is used for that parameter, the network performs generally as claimed. Additionally, a new method of using feedback to control the input bias currents to the amplifiers is proposed and successfully tested. This eliminates the need to set the input currents by trial and error.

  6. Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Omar Payán-Serrano

    2017-05-01

    Full Text Available The aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF structures subjected to dynamics wind load using Artificial Neural Networks (ANNs through the combination of several structural and turbulent wind parameters. The maximum story drift of 1600 MDOF structures under 16 simulated wind conditions are computed with the purpose of generating the data set for the networks training with the Levenberg–Marquardt method. The Shinozuka and Newmark methods are used to simulate the turbulent wind and dynamic response, respectively. In order to optimize the computational time required for the dynamic analyses, an array format based on the Shinozuka method is presented to perform the parallel computing. Finally, it is observed that the already trained ANNs allow for predicting adequately the maximum story drift with a correlation close to 99%.

  7. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Magnier, Laurent; Haghighat, Fariborz [Department of Building, Civil and Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. W., BE-351, Montreal, Quebec H3G 1M8 (Canada)

    2010-03-15

    Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption. (author)

  8. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems.

    Science.gov (United States)

    Behler, Jörg

    2017-10-09

    Modern simulation techniques have reached a level of maturity which allows a wide range of problems in chemistry and materials science to be addressed. Unfortunately, the application of first principles methods with predictive power is still limited to rather small systems, and despite the rapid evolution of computer hardware no fundamental change in this situation can be expected. Consequently, the development of more efficient but equally reliable atomistic potentials to reach an atomic level understanding of complex systems has received considerable attention in recent years. A promising new development has been the introduction of machine learning (ML) methods to describe the atomic interactions. Once trained with electronic structure data, ML potentials can accelerate computer simulations by several orders of magnitude, while preserving quantum mechanical accuracy. This Review considers the methodology of an important class of ML potentials that employs artificial neural networks. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. The influence of a depressed scapular alignment on upper limb neural tissue mechanosensitivity and local pressure pain sensitivity.

    Science.gov (United States)

    Martínez-Merinero, Patricia; Lluch, Enriqe; Gallezo-Izquierdo, Tomas; Pecos-Martín, Daniel; Plaza-Manzano, Gustavo; Nuñez-Nagy, Susana; Falla, Deborah

    2017-06-01

    A depressed scapular alignment could lead to prolonged and repetitive stress or compression of the brachial plexus, resulting in sensitization of neural tissue. However, no study has investigated the influence of alignment of the scapulae on sensitization of upper limb neural tissue in otherwise asymptomatic people. In this case-control study, we investigate the influence of a depressed scapular alignment on mechanosensitivity of the upper limb peripheral nervous system as well as pressure pain thresholds (PPT). Asymptomatic individuals with neutral vertical scapular alignment (n = 25) or depressed scapular alignment (n = 25) participated. We measured the upper limb neurodynamic test (ULNT1), including assessment of symptom response and elbow range of motion (ROM), and PPT measured over upper limb peripheral nerve trunks, the upper trapezius muscle and overlying cervical zygapophyseal joints. Subjects with a depressed scapular reported significantly greater pain intensity (t = 5.7, p < 0.0001) and reduced elbow extension ROM (t = -2.7, p < 0.01) during the ULNT1 compared to those with a normal scapular orientation. Regardless of the location tested, the group presenting with a depressed scapular had significantly lower PPT compared to those with a normal scapular orientation (PPT averaged across all sites: normal orientation: 3.3 ± 0.6 kg/cm(2), depressed scapular: 2.1 ± 0.5 kg/cm(2), p < 0.00001). Despite being asymptomatic, people with a depressed scapular have greater neck and upper limb neural tissue mechanosensitivity when compared to people with a normal scapular orientation. This study offers insight into the potential development of neck-arm pain due to a depressed scapular position. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Artificial neural network model for simulation of water distribution in sprinkle irrigation

    Directory of Open Access Journals (Sweden)

    Paulo L. de Menezes

    2015-09-01

    Full Text Available ABSTRACTDetermining uniformity coefficients of sprinkle irrigation systems, in general, depends on field trials, which require time and financial resources. One alternative to reduce time and expense is the use of simulations. The objective of this study was to develop an artificial neural network (ANN to simulate sprinkler precipitation, using the values ​​of operating pressure, wind speed, wind direction and sprinkler nozzle diameter as the input parameters. Field trials were performed with one sprinkler operating in a grid of 16 x 16, collectors with spacing of 1.5 m and different combinations of nozzles, pressures, and wind conditions. The ANN model showed good results in the simulation of precipitation, with Spearman's correlation coefficient (rs ranging from 0.92 to 0.97 and Willmott agreement index (d from 0.950 to 0.991, between the observed and simulated values for ten analysed trials. The ANN model shows promise in the simulation of precipitation in sprinkle irrigation systems.

  11. Photoacoustic design parameter optimization for deep tissue imaging by numerical simulation

    Science.gov (United States)

    Wang, Zhaohui; Ha, Seunghan; Kim, Kang

    2012-02-01

    A new design of light illumination scheme for deep tissue photoacoustic (PA) imaging, a light catcher, is proposed and evaluated by in silico simulation. Finite element (FE)-based numerical simulation model was developed for photoacoustic (PA) imaging in soft tissues. In this in silico simulation using a commercially available FE simulation package (COMSOL MultiphysicsTM, COMSOL Inc., USA), a short-pulsed laser point source (pulse length of 5 ns) was placed in water on the tissue surface. Overall, four sets of simulation models were integrated together to describe the physical principles of PA imaging. Light energy transmission through background tissues from the laser source to the target tissue or contrast agent was described by diffusion equation. The absorption of light energy and its conversion to heat by target tissue or contrast agent was modeled using bio-heat equation. The heat then causes the stress and strain change, and the resulting displacement of the target surface produces acoustic pressure. The created wide-band acoustic pressure will propagate through background tissues to the ultrasound detector, which is governed by acoustic wave equation. Both optical and acoustical parameters in soft tissues such as scattering, absorption, and attenuation are incorporated in tissue models. PA imaging performance with different design parameters of the laser source and energy delivery scheme was investigated. The laser light illumination into the deep tissues can be significantly improved by up to 134.8% increase of fluence rate by introducing a designed compact light catcher with highly reflecting inner surface surrounding the light source. The optimized parameters through this simulation will guide the design of PA system for deep tissue imaging, and help to form the base protocols of experimental evaluations in vitro and in vivo.

  12. Microscopic observations of needle and soft-tissue simulant interactions

    NARCIS (Netherlands)

    Jahya, Alex; Misra, Sarthak

    2011-01-01

    Currently, physicians have no means of correctly estimating the needle tip location during percutaneous needle insertion. A model of needle-tissue interaction that predicts the needle tip location would assist physicians in pre-operative planning and hence improve needle targeting accuracy. This

  13. eLoom and Flatland: specification, simulation and visualization engines for the study of arbitrary hierarchical neural architectures.

    Science.gov (United States)

    Caudell, Thomas P; Xiao, Yunhai; Healy, Michael J

    2003-01-01

    eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), that enables users to specify and simulate neural network models. Its specification language and libraries enables users to construct and simulate arbitrary, potentially hierarchical network structures on serial and parallel processing systems. In addition, eLoom is integrated with UNM's Flatland, an open source virtual environments development tool to provide real-time visualizations of the network structure and activity. Visualization is a useful method for understanding both learning and computation in artificial neural networks. Through 3D animated pictorially representations of the state and flow of information in the network, a better understanding of network functionality is achieved. ART-1, LAPART-II, MLP, and SOM neural networks are presented to illustrate eLoom and Flatland's capabilities.

  14. 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

  15. 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.

  16. 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.

  17. 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...

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

    Science.gov (United States)

    Shen, Lin; Yang, Weitao

    2018-02-13

    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 complex environment but very time consuming. The computational cost on 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 way. 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 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 reaction dynamics, which provides a useful tool to study chemical or biochemical systems in solution or enzymes.

  19. Three-dimensional fuse deposition modeling of tissue-simulating phantom for biomedical optical imaging.

    Science.gov (United States)

    Dong, Erbao; Zhao, Zuhua; Wang, Minjie; Xie, Yanjun; Li, Shidi; Shao, Pengfei; Cheng, Liuquan; Xu, Ronald X

    2015-01-01

    Biomedical optical devices are widely used for clinical detection of various tissue anomalies. However, optical measurements have limited accuracy and traceability, partially owing to the lack of effective calibration methods that simulate the actual tissue conditions. To facilitate standardized calibration and performance evaluation of medical optical devices, we develop a three-dimensional fuse deposition modeling (FDM) technique for freeform fabrication of tissue-simulating phantoms. The FDM system uses transparent gel wax as the base material, titanium dioxide (TiO2 ) powder as the scattering ingredient, and graphite powder as the absorption ingredient. The ingredients are preheated, mixed, and deposited at the designated ratios layer-by-layer to simulate tissue structural and optical heterogeneities. By printing the sections of human brain model based on magnetic resonance images, we demonstrate the capability for simulating tissue structural heterogeneities. By measuring optical properties of multilayered phantoms and comparing with numerical simulation, we demonstrate the feasibility for simulating tissue optical properties. By creating a rat head phantom with embedded vasculature, we demonstrate the potential for mimicking physiologic processes of a living system.

  20. Doppler Monte Carlo simulations of light scattering in tissue to support laser-Doppler perfusion measurements

    NARCIS (Netherlands)

    de Mul, F.F.M.; Steenbergen, Wiendelt; Greve, Jan

    1999-01-01

    Doppler Monte Carlo (DMC) simulations of the transport of light through turbid media, e.g., tissue, can be used to predict or to interpret measurements of the blood perfusion of tissue by laser‐Doppler perfusion flowmetry. We describe the physical and mathematical background of Doppler Monte Carlo

  1. Simulation of human atherosclerotic femoral plaque tissue: the influence of plaque material model on numerical results

    Science.gov (United States)

    2015-01-01

    Background Due to the limited number of experimental studies that mechanically characterise human atherosclerotic plaque tissue from the femoral arteries, a recent trend has emerged in current literature whereby one set of material data based on aortic plaque tissue is employed to numerically represent diseased femoral artery tissue. This study aims to generate novel vessel-appropriate material models for femoral plaque tissue and assess the influence of using material models based on experimental data generated from aortic plaque testing to represent diseased femoral arterial tissue. Methods Novel material models based on experimental data generated from testing of atherosclerotic femoral artery tissue are developed and a computational analysis of the revascularisation of a quarter model idealised diseased femoral artery from a 90% diameter stenosis to a 10% diameter stenosis is performed using these novel material models. The simulation is also performed using material models based on experimental data obtained from aortic plaque testing in order to examine the effect of employing vessel appropriate material models versus those currently employed in literature to represent femoral plaque tissue. Results Simulations that employ material models based on atherosclerotic aortic tissue exhibit much higher maximum principal stresses within the plaque than simulations that employ material models based on atherosclerotic femoral tissue. Specifically, employing a material model based on calcified aortic tissue, instead of one based on heavily calcified femoral tissue, to represent diseased femoral arterial vessels results in a 487 fold increase in maximum principal stress within the plaque at a depth of 0.8 mm from the lumen. Conclusions Large differences are induced on numerical results as a consequence of employing material models based on aortic plaque, in place of material models based on femoral plaque, to represent a diseased femoral vessel. Due to these large

  2. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity

    Directory of Open Access Journals (Sweden)

    Wiebke ePotjans

    2010-11-01

    Full Text Available A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e. on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity.

  3. Automated cell-specific laser detection and ablation of neural circuits in neonatal brain tissue

    Science.gov (United States)

    Wang, Xueying; Hayes, John A; Picardo, Maria Cristina D; Del Negro, Christopher A

    2013-01-01

    A key feature of neurodegenerative disease is the pathological loss of neurons that participate in generating behaviour. To investigate network properties of neural circuits and provide a complementary tool to study neurodegeneration in vitro or in situ, we developed an automated cell-specific laser detection and ablation system. The instrument consists of a two-photon and visible-wavelength confocal imaging setup, controlled by executive software, that identifies neurons in preparations based on genetically encoded fluorescent proteins or Ca2+ imaging, and then sequentially ablates cell targets while monitoring network function concurrently. Pathological changes in network function can be directly attributed to ablated cells, which are logged in real time. Here, we investigated brainstem respiratory circuits to demonstrate single-cell precision in ablation during physiological network activity, but the technique could be applied to interrogate network properties in neural systems that retain network functionality in reduced preparations in vitro or in situ. PMID:23440965

  4. Absorbed radiation by various tissues during simulated endodontic radiography

    Energy Technology Data Exchange (ETDEWEB)

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

    1989-06-01

    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.

  5. Efficient Simulation of Wing Modal Response: Application of 2nd Order Shape Sensitivities and Neural Networks

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    2000-01-01

    At the preliminary design stage of a wing structure, an efficient simulation, one needing little computation but yielding adequately accurate results for various response quantities, is essential in the search of optimal design in a vast design space. In the present paper, methods of using sensitivities up to 2nd order, and direct application of neural networks are explored. The example problem is how to decide the natural frequencies of a wing given the shape variables of the structure. It is shown that when sensitivities cannot be obtained analytically, the finite difference approach is usually more reliable than a semi-analytical approach provided an appropriate step size is used. The use of second order sensitivities is proved of being able to yield much better results than the case where only the first order sensitivities are used. When neural networks are trained to relate the wing natural frequencies to the shape variables, a negligible computation effort is needed to accurately determine the natural frequencies of a new design.

  6. Different genetic algorithms and the evolution of specialization: a study with groups of simulated neural robots.

    Science.gov (United States)

    Ferrauto, Tomassino; Parisi, Domenico; Di Stefano, Gabriele; Baldassarre, Gianluca

    2013-01-01

    Organisms that live in groups, from microbial symbionts to social insects and schooling fish, exhibit a number of highly efficient cooperative behaviors, often based on role taking and specialization. These behaviors are relevant not only for the biologist but also for the engineer interested in decentralized collective robotics. We address these phenomena by carrying out experiments with groups of two simulated robots controlled by neural networks whose connection weights are evolved by using genetic algorithms. These algorithms and controllers are well suited to autonomously find solutions for decentralized collective robotic tasks based on principles of self-organization. The article first presents a taxonomy of role-taking and specialization mechanisms related to evolved neural network controllers. Then it introduces two cooperation tasks, which can be accomplished by either role taking or specialization, and uses these tasks to compare four different genetic algorithms to evaluate their capacity to evolve a suitable behavioral strategy, which depends on the task demands. Interestingly, only one of the four algorithms, which appears to have more biological plausibility, is capable of evolving role taking or specialization when they are needed. The results are relevant for both collective robotics and biology, as they can provide useful hints on the different processes that can lead to the emergence of specialization in robots and organisms.

  7. 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.

  8. Phase-dependent stimulation effects on bursting activity in a neural network cortical simulation.

    Science.gov (United States)

    Anderson, William S; Kudela, Pawel; Weinberg, Seth; Bergey, Gregory K; Franaszczuk, Piotr J

    2009-03-01

    A neural network simulation with realistic cortical architecture has been used to study synchronized bursting as a seizure representation. This model has the property that bursting epochs arise and cease spontaneously, and bursting epochs can be induced by external stimulation. We have used this simulation to study the time-frequency properties of the evolving bursting activity, as well as effects due to network stimulation. The model represents a cortical region of 1.6 mm x 1.6mm, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. There are a total of 65,536 modeled single compartment neurons that operate according to a version of Hodgkin-Huxley dynamics. The intercellular wiring is based on histological studies and our previous modeling efforts. The bursting phase is characterized by a flat frequency spectrum. Stimulation pulses are applied to this modeled network, with an electric field provided by a 1mm radius circular electrode represented mathematically in the simulation. A phase dependence to the post-stimulation quiescence is demonstrated, with local relative maxima in efficacy occurring before or during the network depolarization phase in the underlying activity. Brief periods of network insensitivity to stimulation are also demonstrated. The phase dependence was irregular and did not reach statistical significance when averaged over the full 2.5s of simulated bursting investigated. This result provides comparison with previous in vivo studies which have also demonstrated increased efficacy of stimulation when pulses are applied at the peak of the local field potential during cortical after discharges. The network bursting is synchronous when comparing the different neuron classes represented up to an uncertainty of 10 ms. Studies performed with an excitatory chandelier cell component demonstrated increased synchronous bursting in the model, as predicted from

  9. PERFORMANCE, RELIABILITY, AND IMPROVEMENT OF A TISSUE-SPECIFIC METABOLIC SIMULATOR

    Science.gov (United States)

    A methodology is described that has been used to build and enhance a simulator for rat liver metabolism providing reliable predictions within a large chemical domain. The tissue metabolism simulator (TIMES) utilizes a heuristic algorithm to generate plausible metabolic maps using...

  10. A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

    Science.gov (United States)

    Naveros, Francisco; Luque, Niceto R; Garrido, Jesús A; Carrillo, Richard R; Anguita, Mancia; Ros, Eduardo

    2015-07-01

    Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

  11. Spatial Estimation, Data Assimilation and Stochastic Conditional Simulation using the Counterpropagation Artificial Neural Network

    Science.gov (United States)

    Besaw, L. E.; Rizzo, D. M.; Boitnoitt, G. N.

    2006-12-01

    Accurate, yet cost effective, sites characterization and analysis of uncertainty are the first steps in remediation efforts at sites with subsurface contamination. From the time of source identification to the monitoring and assessment of a remediation design, the management objectives change, resulting in increased costs and the need for additional data acquisition. Parameter estimation is a key component in reliable site characterization, contaminant flow and transport predictions, plume delineation and many other data management goals. We implement a data-driven parameter estimation technique using a counterpropagation Artificial Neural Network (ANN) that is able to incorporate multiple types of data. This method is applied to estimates of geophysical properties measured on a slab of Berea sandstone and delineation of the leachate plume migrating from a landfill in upstate N.Y. The estimates generated by the ANN have been found to be statistically similar to estimates generated using conventional geostatistical kriging methods. The associated parameter uncertainty in site characterization, due to sparsely distributed samples (spatial or temporal) and incomplete site knowledge, is of major concern in resource mining and environmental engineering. We also illustrate the ability of the ANN method to perform conditional simulation using the spatial structure of parameters identified with semi-variogram analysis. This method allows for the generation of simulations that respect the observed measurement data, as well as the data's underlying spatial structure. The method of conditional simulation is used in a 3-dimensional application to estimate the uncertainty of soil lithology.

  12. Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions

    Science.gov (United States)

    Clark, John W.; Rafelski, Johann; Winston, Jeffrey V.

    1985-07-01

    Aspects of brain function are examined in terms of a nonlinear dynamical system of highly interconnected neuron-like binary decision elements. The model neurons operate synchronously in discrete time, according to deterministic or probabilistic equations of motion. Plasticity of the nervous system, which underlies such cognitive collective phenomena as adaptive development, learning, and memory, is represented by temporal modification of interneuronal connection strengths depending on momentary or recent neural activity. A formal basis is presented for the construction of local plasticity algorithms, or connection-modification routines, spanning a large class. To build an intuitive understanding of the behavior of discrete-time network models, extensive computer simulations have been carried out (a) for nets with fixed, quasirandom connectivity and (b) for nets with connections that evolve under one or another choice of plasticity algorithm. From the former experiments, insights are gained concerning the spontaneous emergence of order in the form of cyclic modes of neuronal activity. In the course of the latter experiments, a simple plasticity routine (“brainwashing,” or “anti-learning”) was identified which, applied to nets with initially quasirandom connectivity, creates model networks which provide more felicitous starting points for computer experiments on the engramming of content-addressable memories and on learning more generally. The potential relevance of this algorithm to developmental neurobiology and to sleep states is discussed. The model considered is at the same time a synthesis of earlier synchronous neural-network models and an elaboration upon them; accordingly, the present article offers both a focused review of the dynamical properties of such systems and a selection of new findings derived from computer simulation.

  13. Exponential distance distribution of connected neurons in simulations of two-dimensional in vitro neural network development

    Science.gov (United States)

    Lv, Zhi-Song; Zhu, Chen-Ping; Nie, Pei; Zhao, Jing; Yang, Hui-Jie; Wang, Yan-Jun; Hu, Chin-Kun

    2017-06-01

    The distribution of the geometric distances of connected neurons is a practical factor underlying neural networks in the brain. It can affect the brain's dynamic properties at the ground level. Karbowski derived a power-law decay distribution that has not yet been verified by experiment. In this work, we check its validity using simulations with a phenomenological model. Based on the in vitro two-dimensional development of neural networks in culture vessels by Ito, we match the synapse number saturation time to obtain suitable parameters for the development process, then determine the distribution of distances between connected neurons under such conditions. Our simulations obtain a clear exponential distribution instead of a power-law one, which indicates that Karbowski's conclusion is invalid, at least for the case of in vitro neural network development in two-dimensional culture vessels.

  14. A self-organizing neural network for the traveling salesman problem that is competitive with simulated annealing.

    Science.gov (United States)

    Budinich, M

    1996-02-15

    Unsupervised learning applied to an unstructured neural network can give approximate solutions to the traveling salesman problem. For 50 cities in the plane this algorithm performs like the elastic net of Durbin and Willshaw (1987) and it improves when increasing the number of cities to get better than simulated annealing for problems with more than 500 cities. In all the tests this algorithm requires a fraction of the time taken by simulated annealing.

  15. The shock response and suitability of Synbone® as a tissue simulant

    Science.gov (United States)

    Appleby-Thomas, G. J.; Fitzmaurice, B. C.; Hameed, A.; Wood, D. C.; Gibson, M. C.; Painter, J.

    2017-01-01

    The applicability of various materials as human tissue analogues has been a topic of increasing interest in recent years. It allows for more cost-effective experiments to be carried out, but also avoids ethical issues that would arise from using real human tissue. Synbone®, a porous polyurethane material, is commonly used in ballistic experiments as a bone simulant, but until now has not been characterised in terms of its dynamic behaviour. Here, the Hugoniot equation-of-state (EOS) for Synbone® has been derived via a series of plate-impact experiments; highlighting the importance of the underlying material structure in terms of material collapse under high strain-rates. A compaction model was also used for a more extensive analysis of Synbone® and for further comparison of this material to solid polyurethane. This work - following on from previous in-house studies of other tissue analogues - has provided useful data for future simulation of this material. In addition, comparison to dynamic data for other tissue and simulant materials has highlighted the importance of considering tissue as non-monolithic; each layer of tissue should ideally be represented by its own simulant in ballistic experiments. The equation-of-state (EOS) of Synbone® was found to be Us = 0.33up + 0.97; up up<0.95 mm μs-1 , while the compaction Hugoniot curve tended towards the Hugoniot for polyurethane at higher pressures.

  16. 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.

  17. A neural-network-based model for the dynamic simulation of the tire/suspension system while traversing road irregularities.

    Science.gov (United States)

    Guarneri, Paolo; Rocca, Gianpiero; Gobbi, Massimiliano

    2008-09-01

    This paper deals with the simulation of the tire/suspension dynamics by using recurrent neural networks (RNNs). RNNs are derived from the multilayer feedforward neural networks, by adding feedback connections between output and input layers. The optimal network architecture derives from a parametric analysis based on the optimal tradeoff between network accuracy and size. The neural network can be trained with experimental data obtained in the laboratory from simulated road profiles (cleats). The results obtained from the neural network demonstrate good agreement with the experimental results over a wide range of operation conditions. The NN model can be effectively applied as a part of vehicle system model to accurately predict elastic bushings and tire dynamics behavior. Although the neural network model, as a black-box model, does not provide a good insight of the physical behavior of the tire/suspension system, it is a useful tool for assessing vehicle ride and noise, vibration, harshness (NVH) performance due to its good computational efficiency and accuracy.

  18. Reorganization of pathological control functions of memory-A neural model for tissue healing by shock waves

    Science.gov (United States)

    Wess, Othmar

    2005-04-01

    Since 1980 shock waves have proven effective in the field of extracorporeal lithotripsy. More than 10 years ago shock waves were successfully applied for various indications such as chronic pain, non-unions and, recently, for angina pectoris. These fields do not profit from the disintegration power but from stimulating and healing effects of shock waves. Increased metabolism and neo-vascularization are reported after shock wave application. According to C. J. Wang, a biological cascade is initiated, starting with a stimulating effect of physical energy resulting in increased circulation and metabolism. Pathological memory of neural control patterns is considered the reason for different pathologies characterized by insufficient metabolism. This paper presents a neural model for reorganization of pathological reflex patterns. The model acts on associative memory functions of the brain based on modification of synaptic junctions. Accordingly, pathological memory effects of the autonomous nervous system are reorganized by repeated application of shock waves followed by development of normal reflex patterns. Physiologic control of muscle and vascular tone is followed by increased metabolism and tissue repair. The memory model may explain hyper-stimulation effects in pain therapy.

  19. Clinical decision-making augmented by simulation training: neural correlates demonstrated by functional imaging: a pilot study.

    Science.gov (United States)

    Goon, S S H; Stamatakis, E A; Adapa, R M; Kasahara, M; Bishop, S; Wood, D F; Wheeler, D W; Menon, D K; Gupta, A K

    2014-01-01

    Investigation of the neuroanatomical basis of clinical decision-making, and whether this differs when students are trained via online training or simulation training, could provide valuable insight into the means by which simulation training might be beneficial. The aim of this pilot prospective parallel group cohort study was to investigate the neural correlates of clinical decision-making, and to determine if simulation as opposed to online training influences these neural correlates. Twelve third-year medical students were randomized into two groups and received simulation-based or online-based training on anaphylaxis. This was followed by functional magnetic resonance imaging scanning to detect brain activation patterns while answering multiple choice questions (MCQs) related to anaphylaxis, and unrelated non-clinical (control) questions. Performance in the MCQs, salivary cortisol levels, heart rate, and arterial pressure were also measured. Comparing neural responses to clinical and non-clinical questions (in all participants), significant areas of activation were seen in the ventral anterior cingulate cortex and medial prefrontal cortex. These areas were activated in the online group when answering action-based questions related to their training, but not in the simulation group. The simulation group tended to react more quickly and accurately to clinical MCQs than the online group, but statistical significance was not reached. The activation areas seen could indicate increased stress when answering clinical questions compared with general non-clinical questions, and in the online group when answering action-based clinical questions. These findings suggest simulation training attenuates neural responses related to stress when making clinical decisions.

  20. 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.

  1. 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.)

  2. Computer simulation of the breast subcutaneous and retromammary tissue for use in virtual clinical trials

    Science.gov (United States)

    Bakic, Predrag R.; Pokrajac, David D.; Maidment, Andrew D. A.

    2017-03-01

    Computer simulation of breast anatomy is an essential component of Virtual Clinical Trials, a preclinical approach to validate breast imaging systems. Realism of breast phantoms affects simulation studies and their acceptance among researchers. Previously, we developed a simulation of tissue compartments defined by the hierarchy of Cooper's ligaments, based upon recursive partitioning using octrees. In this work, we optimize the simulation parameters to represent realistically the breast subcutaneous and retromammary tissue regions. As seen in clinical images, the subcutaneous and retromammary regions contain predominantly adipose tissue organized into relatively large compartments, as opposed to the predominantly glandular breast interior. To mimic such organization, we divided the phantom volume into "subcutaneous", "retromammary", and "interior" regions. Within each region, parameters controlling the size and orientation of tissue compartments were selected separately. In this preliminary study, we varied parameter values and calculated the corresponding average compartment volume in each region. The proposed method was evaluated using anatomic descriptors at both radiological and pathological spatial scales. We simulated the subcutaneous region as spanning 20% of the breast diameter, comparable to published analysis of breast CT images. We simulated tissue compartments with the average volume of 0.94 cm3, 0.89 cm3 and 0.31 cm3 in the subcutaneous, retromammary and interior regions, respectively. Those average volumes match within 12% the values reported from histological analysis. Future evaluation will include a comparison of simulated and clinical parenchymal descriptors. The proposed method will be extended to automate the parameter optimization, and simulate detailed spatial variation, to further improve the realism.

  3. 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".

  4. Tissue normalizing capacity as a key determinant of carcinogenesis: an in silico simulation.

    Science.gov (United States)

    Cao, Wenhu

    2015-03-01

    A perturbed microenvironment is at the core of carcinogenesis. Here, we used a 2D cellular automata model to simulate how cancers are generated in epithelial tissue. We applied several mathematical rules to simulate tissue renewal and surrounding cell control. Under the simulation, we showed that the average value of surrounding normal cells could be an indicator for the tissue normalizing capacity (TNC). Further, we found the incidence of carcinogenesis correlated inversely with the TNC. Interestingly, we also found that multi-round mutagenesis could gradually disturb the TNC when compared to one-round mutagenesis: cancer incidence increased significantly compared to one-round mutagenesis. Our model suggests that the genetic alterations (mutations) by themselves were not sufficient to initiate cancer. The perturbation of TNC could be a key process leading to carcinogenesis.

  5. Numerical Simulation of Anisotropic Tissue Growth Using a Total Lagrangian Formulation

    Directory of Open Access Journals (Sweden)

    Grand Roman Joldes

    2015-01-01

    Full Text Available This paper describes a new method for simulating tissue growth which can handle anisotropic changes in volume. The method takes advantage of the total Lagrangian formulation which allows the computation of nodal forces for each element in a finite element mesh based on a theoretical stress-free configuration, obtained by considering the unconstrained anisotropic growth of the considered element. The method allows the modelling of shrinking (atrophy, swelling, or tissue growth and the computation of the resulting mechanical stresses in the surrounding tissue. The steady-state solution is obtained using an explicit integration method and dynamic relaxation. The proposed method allows the coupling of continuum mechanical simulations with underlying growth mechanisms, offering a tool for the multiscale study of tissue growth.

  6. Chaotic Simulated Annealing by A Neural Network Model with Transient Chaos

    CERN Document Server

    Chen, L; Chen, Luonan; Aihara, Kazuyuki

    1997-01-01

    We propose a neural network model with transient chaos, or a transiently chaotic neural network (TCNN) as an approximation method for combinatorial optimization problem, by introducing transiently chaotic dynamics into neural networks. Unlike conventional neural networks only with point attractors, the proposed neural network has richer and more flexible dynamics, so that it can be expected to have higher ability of searching for globally optimal or near-optimal solutions. A significant property of this model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing, and eventually vanishes with autonomous decreasing of a bifurcation parameter corresponding to the "temperature" in usual annealing process. Therefore, the neural network gradually approaches, through the transient chaos, to dynamical structure similar to such conventional models as the Hopfield neural network which converges to a stable equilibrium point. Since the optimization process of the transiently chaoti...

  7. 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

    2017-07-21

    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, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  8. Cytokine gene signatures in neural tissue of horses with equine protozoal myeloencephalitis or equine herpes type 1 myeloencephalopathy.

    Science.gov (United States)

    Pusterla, N; Wilson, W D; Conrad, P A; Barr, B C; Ferraro, G L; Daft, B M; Leutenegger, C M

    2006-09-09

    This study was designed to determine the relative levels of gene transcription of selected pathogens and cytokines in the brain and spinal cord of 12 horses with equine protozoal myeloencephalitis (EPM), 11 with equine herpesvirus type 1 (EHV-1) myeloencephalopathy, and 12 healthy control horses by applying a real time pcr to the formalin-fixed and paraffin-embedded tissues. Total rna was extracted from each tissue, transcribed to complementary dna (cDNA) and assayed for Sarcocystis neurona, Neospora hughesi, EHV-1, equine GAPDH (housekeeping gene), tumour necrosis factor (TNF)-alpha, interferon (IFN)-gamma, interleukin (IL)-1beta, IL-2, IL-4, IL-6, IL-8, IL-10 AND IL-12 p40. S neurona cdna was detected in the neural tissue from all 12 horses with EPM, and two of them also had amplifiable cDNA of N hughesi. The relative levels of transcription of protozoal cdna ranged from 1 to 461 times baseline (mean 123). All the horses with ehv-1 myeloencephalopathy had positive viral signals by PCR with relative levels of transcription ranging from 1 to 1618 times baseline (mean 275). All the control horses tested negative for S neurona, N hughesi and EHV-1 cdna. The cytokine profiles of each disease indicated a balance between pro- and anti-inflammatory markers. In the horses with epm the pro-inflammatory Th1 cytokines (IL-8, TNF-alpha and IFN-gamma) were commonly expressed but the anti-inflammatory Th2 cytokines (IL-4, IL-6 AND IL-10) were absent or rare. In the horses with ehv-1 the proinflammatory cytokine IL-8 was commonly expressed, but IL-10 and IFN-gamma were not, and TNF-alpha was rare. Tissue from the control horses expressed only the gene GAPDH.

  9. USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA

    Directory of Open Access Journals (Sweden)

    M. Ahmadlou

    2015-12-01

    Full Text Available 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.

  10. Polypyrrole/Alginate Hybrid Hydrogels: Electrically Conductive and Soft Biomaterials for Human Mesenchymal Stem Cell Culture and Potential Neural Tissue Engineering Applications.

    Science.gov (United States)

    Yang, Sumi; Jang, LindyK; Kim, Semin; Yang, Jongcheol; Yang, Kisuk; Cho, Seung-Woo; Lee, Jae Young

    2016-11-01

    Electrically conductive biomaterials that can efficiently deliver electrical signals to cells or improve electrical communication among cells have received considerable attention for potential tissue engineering applications. Conductive hydrogels are desirable particularly for neural applications, as they can provide electrical signals and soft microenvironments that can mimic native nerve tissues. In this study, conductive and soft polypyrrole/alginate (PPy/Alg) hydrogels are developed by chemically polymerizing PPy within ionically cross-linked alginate hydrogel networks. The synthesized hydrogels exhibit a Young's modulus of 20-200 kPa. Electrical conductance of the PPy/Alg hydrogels could be enhanced by more than one order of magnitude compared to that of pristine alginate hydrogels. In vitro studies with human bone marrow-derived mesenchymal stem cells (hMSCs) reveal that cell adhesion and growth are promoted on the PPy/Alg hydrogels. Additionally, the PPy/Alg hydrogels support and greatly enhance the expression of neural differentiation markers (i.e., Tuj1 and MAP2) of hMSCs compared to tissue culture plate controls. Subcutaneous implantation of the hydrogels for eight weeks induces mild inflammatory reactions. These soft and conductive hydrogels will serve as a useful platform to study the effects of electrical and mechanical signals on stem cells and/or neural cells and to develop multifunctional neural tissue engineering scaffolds. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. 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....

  12. Engineered neural tissue with Schwann cell differentiated human dental pulp stem cells: potential for peripheral nerve repair?

    Science.gov (United States)

    Sanen, Kathleen; Martens, Wendy; Georgiou, Melanie; Ameloot, Marcel; Lambrichts, Ivo; Phillips, James

    2017-01-04

    Despite the spontaneous regenerative capacity of the peripheral nervous system, large gap peripheral nerve injuries (PNIs) require bridging strategies. The limitations and suboptimal results obtained with autografts or hollow nerve conduits in the clinic urge the need for alternative treatments. Recently, we have described promising neuroregenerative capacities of Schwann cells derived from differentiated human dental pulp stem cells (d-hDPSCs) in vitro. Here, we extended the in vitro assays to show the pro-angiogenic effects of d-hDPSCs, such as enhanced endothelial cell proliferation, migration and differentiation. In addition, for the first time we evaluated the performance of d-hDPSCs in an in vivo rat model of PNI. Eight weeks after transplantation of NeuraWrap™ conduits filled with engineered neural tissue (EngNT) containing aligned d-hDPSCs in 15-mm rat sciatic nerve defects, immunohistochemistry and ultrastructural analysis revealed ingrowing neurites, myelinated nerve fibres and blood vessels along the construct. Although further research is required to optimize the delivery of this EngNT, our findings suggest that d-hDPSCs are able to exert a positive effect in the regeneration of nerve tissue in vivo. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  13. 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.

  14. EFFECTIVENESS OF NEURAL TISSUE MOBILISATION ON PAIN, PAIN FREE PASSIVE SLR RANGE OF MOTION AND FUNCTIONAL DISABILITY IN LOW BACK ACHE SUBJECTS WITH SCIATICA

    Directory of Open Access Journals (Sweden)

    V. B. Geethika

    2015-10-01

    Full Text Available Background: Low back pain is a common, benign, and self-limiting disease that affects almost all persons, with a lifetime prevalence of up to 84%. In contrast, sciatica affects only 40 % of all persons in the Western industrialized countries. In sciatica, pain radiates down the legs, below the knee along the distribution of sciatic nerve. Nerve root compression is the most common cause of sciatica. Neuro dynamics or Neural Tissue Mobilization is relatively new approach in treatment of neuro musculoskeletal disorders. The aim of the study to determine the effectiveness of Neural Tissue mobilization on pain, pain free passive SLR ROM &functional disability in LBA subjects with Sciatica. Objective of the study is to study and compare the effectiveness of Neural tissue mobilization in LBA subjects with sciatica in terms of pain, pain free SLR ROM and Oswestry Disability Index. Methods: 30 subjects were selected by simple random sampling and assigned in to Control(n=15 &Experimental group(n=15.The subjects in control group were given conventional physiotherapy and those in Experimental group were given Neural Tissue Mobilization in addition to conventional therapy. All the participants were assessed with VAS, ODI and pain free passive SLR ROM. Results: After the analysis, the results were found to be significant improvement in pain, pain free SLR ROM, ODI in both groups (p< 0.00.But there is a high significance in Experimental group when compared to control group. Conclusion: Results suggest that NEURAL TISSUE MOBILIZATION along with conventional therapy is more effective in reducing pain, decreasing disability and improving SLR ROM.

  15. 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.

  16. Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge

    Directory of Open Access Journals (Sweden)

    M. Mohan Raju

    2011-01-01

    Full Text Available The present study demonstrates the application of artificial neural networks (ANNs in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (, determination coefficient, or Nash Sutcliff's efficiency (DC. Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.

  17. Application of Self-Organizing Artificial Neural Networks on Simulated Diffusion Tensor Images

    Directory of Open Access Journals (Sweden)

    Dilek Göksel-Duru

    2013-01-01

    Full Text Available Diffusion tensor magnetic resonance imaging (DTMRI as a noninvasive modality providing in vivo anatomical information allows determination of fiber connections which leads to brain mapping. The success of DTMRI is very much algorithm dependent, and its verification is of great importance due to limited availability of a gold standard in the literature. In this study, unsupervised artificial neural network class, namely, self-organizing maps, is employed to discover the underlying fiber tracts. A common artificial diffusion tensor resource, named “phantom images for simulating tractography errors” (PISTE, is used for the accuracy verification and acceptability of the proposed approach. Four different tract geometries with varying SNRs and fractional anisotropy are investigated. The proposed method, SOFMAT, is able to define the predetermined fiber paths successfully with a standard deviation of (0.8–1.9 × 10−3 depending on the trajectory and the SNR value selected. The results illustrate the capability of SOFMAT to reconstruct complex fiber tract configurations. The ability of SOFMAT to detect fiber paths in low anisotropy regions, which physiologically may correspond to either grey matter or pathology (abnormality and uncertainty areas in real data, is an advantage of the method for future studies.

  18. 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.

  19. Total mass attenuation coefficient evaluation of ten materials commonly used to simulate human tissue

    Science.gov (United States)

    Ferreira, C. C.; Ximenes, R. E.; Garcia, C. A. B.; Vieira, J. W.; Maia, A. F.

    2010-11-01

    To study the doses received by patient submitted to ionizing radiation, several materials are used to simulate the human tissue and organs. The total mass attenuation coefficient is a reasonable way for evaluating the usage in dosimetry of these materials. The total mass attenuation coefficient is determined by photon energy and constituent elements of the material. Currently, the human phantoms are composed by a unique material that presents characteristics similar to the mean proprieties of the different tissues within the region. Therefore, the phantoms are usually homogeneous and filled with a material similar to soft tissue. We studied ten materials used as soft tissue-simulating. These materials were named: bolus, nylon®, orange articulation wax, red articulation wax, PMMA, modelling clay, bee wax, paraffin 1, paraffin 2 and pitch. The objective of this study was to verify the best material to simulate the human cerebral tissue. We determined the elementary composition, mass density and, therefore, calculated the total mass attenuation coefficient of each material. The results were compared to the values established by the International Commission on Radiation Units and Measurements - ICRU, report n° 44, and by the International Commission on Radiation Protection - ICRP, report n° 89, to determine the best material for this energy interval. These results indicate that new head phantoms can be constructed with nylon®.

  20. 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

  1. Realistic tool-tissue interaction models for surgical simulation and planning

    NARCIS (Netherlands)

    Misra, Sarthak

    2009-01-01

    Surgical simulators present a safe and potentially effective method for surgical training, and can also be used in pre- and intra-operative surgical planning. Realistic modeling of medical interventions involving tool-tissue interactions has been considered to be a key requirement in the development

  2. The evaluation of new multi-material human soft tissue simulants for sports impact surrogates.

    Science.gov (United States)

    Payne, Thomas; Mitchell, Séan; Bibb, Richard; Waters, Mark

    2015-01-01

    Previous sports impact reconstructions have highlighted the inadequacies in current measures to evaluate the effectiveness of personal protective equipment (PPE) and emphasised the need for improved impact surrogates that provide a more biofidelic representation of human impact response. The skin, muscle and subcutaneous adipose tissues were considered to constitute the structures primarily governing the mechanical behaviour of the human body segment. A preceding study by Payne et al. (in press) investigated the formulation and characterisation of muscle tissue simulants. The present study investigates the development of bespoke blends of additive cure polydimethysiloxane (PDMS) silicones to represent both skin and adipose tissues using the same processes previously reported. These simulants were characterised mechanically through a range of strain rates and a range of hyperelastic and viscoelastic constitutive models were evaluated to describe their behaviour. To explore the worth of the silicone simulants, finite element (FE) models were developed using anthropometric parameters representative of the human thigh segment, derived from the Visible Human Project. The multi-material silicone construction was validated experimentally and compared with both organic tissue data from literature and commonly used single material simulants: Dow Corning Silastic 3480 series silicones and ballistics gelatin when subject to a representative sports specific knee impact. Superior biofidelic performance is reported for the PDMS silicone formulations and surrogate predictions. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. 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.

  4. 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.

  5. Heat transition during magnetic heating treatment: Study with tissue models and simulation

    Energy Technology Data Exchange (ETDEWEB)

    Henrich, Franziska [Technische Universität Dresden, Institute of Fluid Mechanics, Chair of Magnetofluiddynamics, 01062 Dresden (Germany); Max Planck Institute for Polymer Research, Physics at Interfaces, 55128 Mainz (Germany); Rahn, Helene, E-mail: helene.rahn@tu-dresden.de [Technische Universität Dresden, Institute of Fluid Mechanics, Chair of Magnetofluiddynamics, 01062 Dresden (Germany); Odenbach, Stefan [Technische Universität Dresden, Institute of Fluid Mechanics, Chair of Magnetofluiddynamics, 01062 Dresden (Germany)

    2015-04-15

    The magnetic heating treatment (MHT) is well known as a promising therapy for cancer diseases. Depending on concentration and specific heating power of the magnetic material as well as on parameters of the magnetic field, temperatures between 43 and 55 °C can be reached. This paper deals with the evaluation of heat distribution around such a heat source in a tissue model, thereby focusing on the heat transfer from tissue enriched with magnetic nanoparticles to regions of no or little enrichment of magnetic nanoparticles. We examined the temperature distribution with several tissue phantoms made of polyurethane (PUR) with similar thermal conductivity coefficient as biological tissue. These phantoms are composed of a cylinder with one sphere embedded, enriched with magnetic fluid. Thereby the spheres have different diameters in order to study the influence of the surface-to-volume ratio. The phantoms were exposed to an alternating magnetic field. The magnetically induced heat increase within the phantoms was measured with thermocouples. Those were placed at defined positions inside the phantoms. Based on the measured results a 3-dimensional simulation of each phantom was built. We achieved an agreement between the measured and simulated temperatures for all phantoms produced in this experimental study. The established experiment theoretically allows a prediction of temperature profiles in tumors and the surrounding tissue for the potential cancer treatment and therefore an optimization of e.g. the respective magnetic nanoparticles concentrations for the desirable rise of temperature. - Highlights: • Four phantoms built to measure the temperature distribution during magnetic heating. • Simulations have been carried out based on experimental data. • Measured and simulated temperature distribution for different magnetic field strength. • Temperature profiles for with ferrofluid enriched areas of different size. • Comparison of experimental and simulated data.

  6. Iterative Radial Basis Functions Neural Networks as Metamodels of Stochastic Simulations of the Quality of Search Engines in the World Wide Web.

    Science.gov (United States)

    Meghabghab, George

    2001-01-01

    Discusses the evaluation of search engines and uses neural networks in stochastic simulation of the number of rejected Web pages per search query. Topics include the iterative radial basis functions (RBF) neural network; precision; response time; coverage; Boolean logic; regression models; crawling algorithms; and implications for search engine…

  7. 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.

  8. COMPUTER-SIMULATED NEURAL NETWORKS - AN APPROPRIATE MODEL FOR MOTOR DEVELOPMENT

    NARCIS (Netherlands)

    VOS, JE; SCHEEPSTRA, KA

    The idea of an artificial neural network is introduced in a historical context, and the essential aspect of it, viz., the modifiable synapse, is compared to the aspect of plasticity in the natural nervous system. Based on such an artificial neural network, a model is presented for the way in which

  9. Star poly(ethylene glycol) as a tunable scaffold for neural tissue engineering

    Science.gov (United States)

    Zustiak, Silviya Petrova

    The primary focus of this work was to develop a novel synthetic hydrogel scaffold as an in vitro model to enable future detailed studies of how neurons grow in environments with controllable diffusion profiles of soluble cues and tunable neuronmatrix interactions. The development of in vitro models that enable elucidation of the mechanisms of system performance is a recently emerging goal of tissue engineering. The design of three-dimensional (3D) scaffolds in particular, is motivated by the need to develop model systems that better mimic native tissue as compared to conventional two-dimensional (2D) cell culture substrates. An ideal scaffold is degradable, porous, biocompatible, with mechanical properties to match those of the tissues of interest and with a suitable surface chemistry for cell attachment, proliferation, and differentiation. Although naturally derived materials are more versatile in providing complex biological cues, synthetic polymers are preferable for the design of in vitro models as they provide wider range of properties, controllable degradation rates, and easier processing. Most importantly, their mechanical properties can be decoupled from their biological properties, a crucial issue in interpreting cell responses. The synthetic material provides the structural backbone of the scaffold while biochemical function is added via incorporation of ligands or proteins aimed at triggering specific cell behaviors. As presented in this dissertation, we have developed and characterized a new synthetic 3D hydrogel scaffold from cross-linked poly(ethylene glycol) (PEG). PEG was selected because it is hydrophilic, non-toxic, biocompatible, and inert to protein adhesion. The chosen cross-linking chemistry was a highly specific reaction that occurred under physiological conditions so that cells could be embedded within the gel prior to cross-linking. Controllable degradability was imparted via series of hydrolytically degradable PEG cross-linkers. Thorough

  10. 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…

  11. Numerical Simulation and Artificial Neural Network Modeling for Predicting Welding-Induced Distortion in Butt-Welded 304L Stainless Steel Plates

    Science.gov (United States)

    Narayanareddy, V. V.; Chandrasekhar, N.; Vasudevan, M.; Muthukumaran, S.; Vasantharaja, P.

    2016-02-01

    In the present study, artificial neural network modeling has been employed for predicting welding-induced angular distortions in autogenous butt-welded 304L stainless steel plates. The input data for the neural network have been obtained from a series of three-dimensional finite element simulations of TIG welding for a wide range of plate dimensions. Thermo-elasto-plastic analysis was carried out for 304L stainless steel plates during autogenous TIG welding employing double ellipsoidal heat source. The simulated thermal cycles were validated by measuring thermal cycles using thermocouples at predetermined positions, and the simulated distortion values were validated by measuring distortion using vertical height gauge for three cases. There was a good agreement between the model predictions and the measured values. Then, a multilayer feed-forward back propagation neural network has been developed using the numerically simulated data. Artificial neural network model developed in the present study predicted the angular distortion accurately.

  12. GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues.

    Science.gov (United States)

    Ren, Nunu; Liang, Jimin; Qu, Xiaochao; Li, Jianfeng; Lu, Bingjia; Tian, Jie

    2010-03-29

    As the most accurate model for simulating light propagation in heterogeneous tissues, Monte Carlo (MC) method has been widely used in the field of optical molecular imaging. However, MC method is time-consuming due to the calculations of a large number of photons propagation in tissues. The structural complexity of the heterogeneous tissues further increases the computational time. In this paper we present a parallel implementation for MC simulation of light propagation in heterogeneous tissues whose surfaces are constructed by different number of triangle meshes. On the basis of graphics processing units (GPU), the code is implemented with compute unified device architecture (CUDA) platform and optimized to reduce the access latency as much as possible by making full use of the constant memory and texture memory on GPU. We test the implementation in the homogeneous and heterogeneous mouse models with a NVIDIA GTX 260 card and a 2.40GHz Intel Xeon CPU. The experimental results demonstrate the feasibility and efficiency of the parallel MC simulation on GPU.

  13. Preoperative surgical rehearsal using cadaveric fresh tissue surgical simulation increases resident operative confidence.

    Science.gov (United States)

    Weber, Erin L; Leland, Hyuma A; Azadgoli, Beina; Minneti, Michael; Carey, Joseph N

    2017-08-01

    Rehearsal is an essential part of mastering any technical skill. The efficacy of surgical rehearsal is currently limited by low fidelity simulation models. Fresh cadaver models, however, offer maximal surgical simulation. We hypothesize that preoperative surgical rehearsal using fresh tissue surgical simulation will improve resident confidence and serve as an important adjunct to current training methods. Preoperative rehearsal of surgical procedures was performed by plastic surgery residents using fresh cadavers in a simulated operative environment. Rehearsal was designed to mimic the clinical operation, complete with a surgical technician to assist. A retrospective, web-based survey was used to assess resident perception of pre- and post-procedure confidence, preparation, technique, speed, safety, and anatomical knowledge on a 5-point scale (1= not confident, 5= very confident). Twenty-six rehearsals were performed by 9 residents (PGY 1-7) an average of 4.7±2.1 days prior to performance of the scheduled operation. Surveys demonstrated a median pre-simulation confidence score of 2 and a post-rehearsal score of 4 (Pimprovement in confidence and performance was greatest when simulation was performed within 3 days of the scheduled case. All residents felt that cadaveric simulation was better than standard preparation methods of self-directed reading or discussion with other surgeons. All residents believed that their technique, speed, safety, and anatomical knowledge improved as a result of simulation. Fresh tissue-based preoperative surgical rehearsal was effectively implemented in the residency program. Resident confidence and perception of technique improved. Survey results suggest that cadaveric simulation is beneficial for all levels of residents. We believe that implementation of preoperative surgical rehearsal is an effective adjunct to surgical training at all skill levels in the current environment of decreased work hours.

  14. An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms

    Science.gov (United States)

    Yaşar, Hüseyin; Ceylan, Murat

    2015-03-01

    Breast cancer is one of the types of cancer which is most commonly seen in women. Density of breast is an important indicator for the risk of cancer. In addition, densities of tissue may harden the diagnosis by hiding the abnormalities occurring on the breast. For this reason, during the process of diagnosis, the process of automatic classification of breast density has a significant importance. In this study, a new system with the base of Artificial Neural Network (ANN) and multiple resolution analysis is suggested. Wavelet and curvelet analyses having the most common use have been used as multi resolution analysis. 4 pieces of statistics which are minimum value, maximum value, mean value and standard deviation have been extracted from the images which have been eluted to their sub-bands via multi resolution analysis. For the purpose of testing the success of the system, 322 pieces of images which are in MIAS database have been used. The obtained results for different backgrounds are so satisfying; and the highest classification values have been obtained as 97.16 % with Wavelet transform and ANN for fatty background and 79.80 % with Wavelet transform and ANN for fatty-glanduar background. The same results have been obtained using Wavelet transform and ANN and Curvelet transform and ANN for dense background and accuracy rate of 84.82 % have been reached. The results of mean classification have been obtained, for three pieces of tissue types (fatty, fatty-glanduar, dense), in sequence as 84.47 % with the use of ANN, 85.71 % with the use of curvelet analysis and ANN; and 87.26 % with the use of wavelet analysis and ANN.

  15. Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

    Science.gov (United States)

    Jiménez del Toro, Oscar; Atzori, Manfredo; Otálora, Sebastian; Andersson, Mats; Eurén, Kristian; Hedlund, Martin; Rönnquist, Peter; Müller, Henning

    2017-03-01

    The Gleason grading system was developed for assessing prostate histopathology slides. It is correlated to the outcome and incidence of relapse in prostate cancer. Although this grading is part of a standard protocol performed by pathologists, visual inspection of whole slide images (WSIs) has an inherent subjectivity when evaluated by different pathologists. Computer aided pathology has been proposed to generate an objective and reproducible assessment that can help pathologists in their evaluation of new tissue samples. Deep convolutional neural networks are a promising approach for the automatic classification of histopathology images and can hierarchically learn subtle visual features from the data. However, a large number of manual annotations from pathologists are commonly required to obtain sufficient statistical generalization when training new models that can evaluate the daily generated large amounts of pathology data. A fully automatic approach that detects prostatectomy WSIs with high-grade Gleason score is proposed. We evaluate the performance of various deep learning architectures training them with patches extracted from automatically generated regions-of-interest rather than from manually segmented ones. Relevant parameters for training the deep learning model such as size and number of patches as well as the inclusion or not of data augmentation are compared between the tested deep learning architectures. 235 prostate tissue WSIs with their pathology report from the publicly available TCGA data set were used. An accuracy of 78% was obtained in a balanced set of 46 unseen test images with different Gleason grades in a 2-class decision: high vs. low Gleason grade. Grades 7-8, which represent the boundary decision of the proposed task, were particularly well classified. The method is scalable to larger data sets with straightforward re-training of the model to include data from multiple sources, scanners and acquisition techniques. Automatically

  16. Simulating tissue oxygenation by encapsulating hemoglobin in polymer microcapsules (Conference Presentation)

    Science.gov (United States)

    Liu, Guangli; Wu, Qiang; Shen, Shuwei; Zhao, Gang; Dong, Erbao; Xu, Ronald X.

    2017-03-01

    We describe a combination of liquid-jet microencapsulation and molding techniques to fabricate tissue-simulating phantoms that mimick functional characteristics of tissue oxygen saturation (StO2). Chicken hemoglobin (Hb) was encapsulated inside a photocurable resin by a coaxial flow focusing process. The microdroplets were cured by ultraviolet (UV) illumination to form Hb loaded polymersome microdroplets. The microdroplets were further freeze-dried to form semipermeable solid microcapules with an outer transparent polymeric shell and an inner core of Hb. The diameter of the microcapsules ranged from 50 to100 μm. The absorption spectrum of the microcapsules was measured by a UV/VIS spectrophotometer over a wavelength range from 400 nm to 1100 nm. To fabricate the tissue-simulating phantom, the Hb loaded microcapsules were dispersed in transparent polydimethylsiloxane (PDMS). The optical properties of the phantom were determined by an vertical double integrating sphere with a reconstruction algorithm. The experimental results showed that the tissue-simulating phantom exhibited the spectral characteristics closely resembling that of oxy-hemoglobin. The phantom had a long-term optical stability when stored in 4 ℃, indicating that microencapsulation effectively protected Hb and improved its shelf time. With the Hb loaded microcapsules, we will produce skin-simulating phantoms for quantitative validation of multispectral imaging techniques. To the best of the authors' knowledge, no solid phantom is able to mimick living tissue oxygenation with good agreement. Therefore, our work provided an engineering platform for validating and calibrating spectral optical devices in biomedical applications.

  17. 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.

  18. 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.

  19. A reduction in DNA damage in neural tissue and peripheral blood of old mice treated with caffeine.

    Science.gov (United States)

    Damiani, Adriani Paganini; Garcez, Michelle Lima; Letieli de Abreu, Larissa; Tavares, Taís Helena; Rodrigues Boeck, Carina; Moraes de Andrade, Vanessa

    2017-01-01

    Studies on caffeine consumption have shown a negative correlation with development of some diseases with subsequent beneficial manifestations. Our aim was to assess the effects of caffeine on peripheral blood and neural tissue DNA in young adult and aged mice. Male Swiss mice (age 2-3 or 16-18 months, respectively) were treated with a caffeine solution (0.3 g/l) for 4 weeks, while controls received water. After the treatments, blood and hippocampal cells (for a comet assay) and femurs (for a micronucleus [MN] test) were collected. The comet assay of peripheral blood and hippocampal cells demonstrated no significant differences between caffeine-treated and control young adult mice in terms of DNA damage index (DI) and frequency. In contrast, when comparing young adult with aged animals, significant differences were observed in DNA damage in blood and hippocampal cells. The differences between aged animals (with or without caffeine) consisted of a significant decrease in DNA DI in the group that received caffeine. In the MN test, an increase in frequency of micronucleated polychromatic (PCE) erythrocytes was noted in aged animals that received water compared to young adult mice. In addition, comparing treated with control aged murine groups, a decrease in frequency of MN was found in PCE erythrocytes of caffeine-treated mice. Chronic caffeine consumption was neither genotoxic nor mutagenic at the dose tested; however, it appears that caffeine actually protected mice from genotoxicity and mutagenicity, consequences attributed to aging.

  20. Neural restrictive silencer factor and choline acetyltransferase expression in cerebral tissue of Alzheimer’s Disease patients: A pilot study

    Science.gov (United States)

    González-Castañeda, Rocío E.; Sánchez-González, Víctor J.; Flores-Soto, Mario; Vázquez-Camacho, Gonzalo; Macías-Islas, Miguel A.; Ortiz, Genaro G.

    2013-01-01

    Decreased Choline Acetyltransferase (ChAT) brain level is one of the main biochemical disorders in Alzheimer’s Disease (AD). In rodents, recent data show that the CHAT gene can be regulated by a neural restrictive silencer factor (NRSF). The aim of the present work was to evaluate the gene and protein expression of CHAT and NRSF in frontal, temporal, entorhinal and parietal cortices of AD patient brains. Four brains from patients with AD and four brains from subjects without dementia were studied. Cerebral tissues were obtained and processed by the guanidine isothiocyanate method for RNA extraction. CHAT and NRSF gene and protein expression were determined by reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting. CHAT gene expression levels were 39% lower in AD patients as compared to the control group (p 0.05, U test) than in the control subjects. These findings suggest for the first time that in the brain of AD patients high NRSF protein levels are related to low CHAT gene expression levels. PMID:23569405

  1. Development of a Multi-Functional Biopolymer Scaffold for Neural Tissue Engineering

    Science.gov (United States)

    Francis, Nicola Louise

    Spinal cord injury (SCI) affects approximately 270,000 people in the U.S., with approximately 12,000 new cases occurring every year. Several strategies have been investigated to enhance axonal regeneration after SCI, however, the resulting growth can be random and disorganized. Bioengineered scaffolds provide a physical substrate for the guidance of regenerating axons towards their targets, and can be produced by freeze casting. This technique involves the controlled directional solidification of an aqueous solution or suspension, resulting in a linearly aligned porous structure caused by ice templating. In this thesis, freeze casting was used to create novel porous chitosan-alginate (C/A) scaffolds with longitudinally aligned channels and a compressive modulus (5.08 ± 0.61 kPa) comparable to that of native spinal cord tissue. These C/A scaffolds supported the viability, attachment, and directionally oriented growth of chick dorsal root ganglia (DRG) neurites in vitro, with surface adsorptions of polycations and laminin promoting significantly longer neurite growth than the uncoated scaffolds (pvitro, while chABC was released for up to 35 days. However, up to 85% of biomolecules emained entrapped within the scaffold walls, due to limitation of diffusion by the scaffold wall mesh size. Release of bioactive chABC and neurotrophins from the multifunctional scaffolds promoted the growth of DRG neurites through an in vitro barrier of chondroitin sulfate proteoglycans, a main inhibitory component of the growth-inhibiting glial scar in the injured spinal cord. The present data suggest these multi-functional scaffolds are suitable for use and future testing in vivo as a combination strategy for spinal cord repair due to their ability to promote the directionally oriented growth of neurites and their ability to provide the sustained release of therapeutic bioactive molecules for the stimulation of axonal growth through the glial scar.

  2. Risk assessment of water quality using Monte Carlo simulation and artificial neural network method.

    Science.gov (United States)

    Jiang, Yunchao; Nan, Zhongren; Yang, Sucai

    2013-06-15

    There is always uncertainty in any water quality risk assessment. A Monte Carlo simulation (MCS) is regarded as a flexible, efficient method for characterizing such uncertainties. However, the required computational effort for MCS-based risk assessment is great, particularly when the number of random variables is large and the complicated water quality models have to be calculated by a computationally expensive numerical method, such as the finite element method (FEM). To address this issue, this paper presents an improved method that incorporates an artificial neural network (ANN) into the MCS to enhance the computational efficiency of conventional risk assessment. The conventional risk assessment uses the FEM to create multiple water quality models, which can be time consuming or cumbersome. In this paper, an ANN model was used as a substitute for the iterative FEM runs, and thus, the number of water quality models that must be calculated can be dramatically reduced. A case study on the chemical oxygen demand (COD) pollution risks in the Lanzhou section of the Yellow River in China was taken as a reference. Compared with the conventional risk assessment method, the ANN-MCS-based method can save much computational effort without a loss of accuracy. The results show that the proposed method in this paper is more applicable to assess water quality risks. Because the characteristics of this ANN-MCS-based technique are quite general, it is hoped that the technique can also be applied to other MCS-based uncertainty analysis in the environmental field. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. A comparison of live tissue training and high-fidelity patient simulator: A pilot study in battlefield trauma training.

    Science.gov (United States)

    Savage, Erin C; Tenn, Catherine; Vartanian, Oshin; Blackler, Kristen; Sullivan-Kwantes, Wendy; Garrett, Michelle; Blais, Ann-Renee; Jarmasz, Jerzy; Peng, Henry; Pannell, Dylan; Tien, Homer C

    2015-10-01

    Trauma procedural and management skills are often learned on live tissue. However, there is increasing pressure to use simulators because their fidelity improves and as ethical concerns increase. We randomized military medical technicians (medics) to training on either simulators or live tissue to learn combat casualty care skills to determine if the choice of modality was associated with differences in skill uptake. Twenty medics were randomized to trauma training using either simulators or live tissue. Medics were trained to perform five combat casualty care tasks (surgical airway, needle decompression, tourniquet application, wound packing, and intraosseous line insertion). We measured skill uptake using a structured assessment tool. The medics also completed exit questionnaires and interviews to determine which modality they preferred. We found no difference between groups trained with live tissue versus simulators in how they completed each combat casualty care skill. However, we did find that the modality of assessment affected the assessment score. Finally, we found that medics preferred trauma training on live tissue because of the fidelity of tissue handling in live tissue models. However, they also felt that training on simulators also provided additional training value. We found no difference in performance between medics trained on simulators versus live tissue models. Even so, medics preferred live tissue training over simulation. However, more studies are required, and future studies need to address the measurement bias of measuring outcomes in the same model on which the study participants are trained. Therapeutic/care management study, level II.

  4. Numerical simulation of microwave ablation incorporating tissue contraction based on thermal dose

    Science.gov (United States)

    Liu, Dong; Brace, Christopher L.

    2017-03-01

    Tissue contraction plays an important role during high temperature tumor ablation, particularly during device characterization, treatment planning and imaging follow up. We measured such contraction in 18 ex vivo bovine liver samples during microwave ablation by tracking fiducial motion on CT imaging. Contraction was then described using a thermal dose dependent model and a negative thermal expansion coefficient based on the empirical data. FEM simulations with integrated electromagnetic wave propagation, heat transfer, and structural mechanics were evaluated using temperature-dependent dielectric properties and the negative thermal expansion models. Simulated temperature and displacement curves were then compared with the ex vivo experimental results on different continuous output powers. The optimized thermal dose model indicated over 50% volumetric contraction occurred at the temperature over 102.1 °C. The numerical simulation results on temperature and contraction-induced displacement showed a good agreement with experimental results. At microwave powers of 55 W, the mean errors on temperature between simulation and experimental results were 8.25%, 2.19% and 5.67% at 5 mm, 10 mm and 20 mm radially from the antenna, respectively. The simulated displacements had mean errors of 16.60%, 14.08% and 23.45% at the same radial locations. Compared to the experimental results, the simulations at the other microwave powers had larger errors with 10-40% mean errors at 40 W, and 10-30% mean errors at 25 W. The proposed model is able to predict temperature elevation and simulate tissue deformation during microwave ablation, and therefore may be incorporated into treatment planning and clinical translation from numerical simulations.

  5. The evolution of simulation techniques for dynamic bone tissue engineering in bioreactors.

    Science.gov (United States)

    Vetsch, Jolanda Rita; Müller, Ralph; Hofmann, Sandra

    2015-08-01

    Bone tissue engineering aims to overcome the drawbacks of current bone regeneration techniques in orthopaedics. Bioreactors are widely used in the field of bone tissue engineering, as they help support efficient nutrition of cultured cells with the possible combination of applying mechanical stimuli. Beneficial influencing parameters of in vitro cultures are difficult to find and are mostly determined by trial and error, which is associated with significant time and money spent. Mathematical simulations can support the finding of optimal parameters. Simulations have evolved over the last 20 years from simple analytical models to complex and detailed computational models. They allow researchers to simulate the mechanical as well as the biological environment experienced by cells seeded on scaffolds in a bioreactor. Based on the simulation results, it is possible to give recommendations about specific parameters for bone bioreactor cultures, such as scaffold geometries, scaffold mechanical properties, the level of applied mechanical loading or nutrient concentrations. This article reviews the evolution in simulating various aspects of dynamic bone culture in bioreactors and reveals future research directions. Copyright © 2013 John Wiley & Sons, Ltd.

  6. 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 RF...... developed models for the motions and incorporated them into the RF simulation program Field II, thereby obtaining realistic simulated data. A powerful tool for evaluation of different filters and estimators is then available. The model parameters can be varied according to the physical situation...

  7. A simulation study for the application of two different neural network control algorithms on an electrohydraulic system

    Science.gov (United States)

    İstif, İlyas

    2005-11-01

    This paper studies a servo-valve controlled hydraulic cylinder system which is mostly used in industrial applications such as robotics, computer numerical control (CNC) machines and transportations. The system model consists of combination of two models: The first model involves nonlinear flow equations of the servo-valve, which are widely available in the literature. The second model employed in the system is a tailored asymmetric cylinder model. A fourth order nonlinear system model is then obtained by combining these two models. Two different neural network control algorithms are applied to the system. The first algorithm is "Neural Network Predictive Control (NNPC)," which employs identified neural network model to predict the future output of the system. The second algorithm is "Nonlinear Autoregressive Moving Average (NARMA-L2)" control, which transforms nonlinear system dynamics into linear system dynamics by eliminating the nonlinearities. On the simulation, NNPC and NARMA-L2 control are applied to the system model by using Matlab's Simulik simulation package and position control of the system is realized. A discussion regarding the advantages and disadvantages of the two control algorithms are also provided in the paper.

  8. 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.......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 able...

  9. Artificial Neural Systems Application to the Simulation of Air Combat Decision Making

    Science.gov (United States)

    1992-04-01

    unit, the CPU , whereas neural networks utilize the effects of many, simple processing elements. Traditional computing is done in a step-by-step, serial...Nielsen Neurocomputers (HNC). The ANZA-Plus coprocessor is part of an 80386 -based computer system which is optimized for training and executing neural...host computer for this program is a Zenith 386/16 system running under the DOS 3.31 operating system. The 80386 microprocessor in this machine operates

  10. Comparison of Simulation Algorithms for the Hopfield Neural Network: An Application of Economic Dispatch

    OpenAIRE

    Altun, Tankut Yalçınöz and Halis

    2014-01-01

    This paper is mainly concerned with an investigation of the suitability of Hopfield neural network structures in solving the power economic dispatch problem. For Hopfield neural network applications to this problem three important questions have been answered: what the size of the power system is; how efficient the computational method; and how to handle constraints. A new mapping process is formulated and a computational method for obtaining the weights and biases is described. A few simulat...

  11. 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.

  12. 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.

  13. [Computer simulation programs as an alternative for classical nerve, muscle and heart experiments using frog tissues].

    Science.gov (United States)

    Breves, G; Schröder, B

    2000-03-01

    Courses in Physiology include different methodical approaches such as exercises with living animals, experiments using organs or tissues from killed or slaughtered animals, application of diagnostic techniques in humans and theoretical seminars. In addition to these classical approaches computer programs for multimedia simulation of nerve, muscle and heart physiology are now a regular component of courses in Physiology at the School of Veterinary Medicine in Hannover. It is the aim of the present paper to give the first experiences about these new components.

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

    Science.gov (United States)

    Shih, Tzu-Ching; Chen, Jeon-Hor; Liu, Dongxu; Nie, Ke; Sun, Lizhi; Lin, Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying

    2010-07-01

    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® 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® 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 different compression

  15. Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex.

    Science.gov (United States)

    Lang, Stefan; Dercksen, Vincent J; Sakmann, Bert; Oberlaender, Marcel

    2011-11-01

    The three-dimensional (3D) structure of neural circuits represents an essential constraint for information flow in the brain. Methods to directly monitor streams of excitation, at subcellular and millisecond resolution, are at present lacking. Here, we describe a pipeline of tools that allow investigating information flow by simulating electrical signals that propagate through anatomically realistic models of average neural networks. The pipeline comprises three blocks. First, we review tools that allow fast and automated acquisition of 3D anatomical data, such as neuron soma distributions or reconstructions of dendrites and axons from in vivo labeled cells. Second, we introduce NeuroNet, a tool for assembling the 3D structure and wiring of average neural networks. Finally, we introduce a simulation framework, NeuroDUNE, to investigate structure-function relationships within networks of full-compartmental neuron models at subcellular, cellular and network levels. We illustrate the pipeline by simulations of a reconstructed excitatory network formed between the thalamus and spiny stellate neurons in layer 4 (L4ss) of a cortical barrel column in rat vibrissal cortex. Exciting the ensemble of L4ss neurons with realistic input from an ensemble of thalamic neurons revealed that the location-specific thalamocortical connectivity may result in location-specific spiking of cortical cells. Specifically, a radial decay in spiking probability toward the column borders could be a general feature of signal flow in a barrel column. Our simulations provide insights of how anatomical parameters, such as the subcellular organization of synapses, may constrain spiking responses at the cellular and network levels. Copyright © 2011 Elsevier Ltd. All rights reserved.

  16. Acoustic-based proton range verification in heterogeneous tissue: simulation studies

    Science.gov (United States)

    Jones, Kevin C.; Nie, Wei; Chu, James C. H.; Turian, Julius V.; Kassaee, Alireza; Sehgal, Chandra M.; Avery, Stephen

    2018-01-01

    Acoustic-based proton range verification (protoacoustics) is a potential in vivo technique for determining the Bragg peak position. Previous measurements and simulations have been restricted to homogeneous water tanks. Here, a CT-based simulation method is proposed and applied to a liver and prostate case to model the effects of tissue heterogeneity on the protoacoustic amplitude and time-of-flight range verification accuracy. For the liver case, posterior irradiation with a single proton pencil beam was simulated for detectors placed on the skin. In the prostate case, a transrectal probe measured the protoacoustic pressure generated by irradiation with five separate anterior proton beams. After calculating the proton beam dose deposition, each CT voxel’s material properties were mapped based on Hounsfield Unit values, and thermoacoustically-generated acoustic wave propagation was simulated with the k-Wave MATLAB toolbox. By comparing the simulation results for the original liver CT to homogenized variants, the effects of heterogeneity were assessed. For the liver case, 1.4 cGy of dose at the Bragg peak generated 50 mPa of pressure (13 cm distal), a 2×  lower amplitude than simulated in a homogeneous water tank. Protoacoustic triangulation of the Bragg peak based on multiple detector measurements resulted in 0.4 mm accuracy for a δ-function proton pulse irradiation of the liver. For the prostate case, higher amplitudes are simulated (92–1004 mPa) for closer detectors (verification to heterogeneous tissue will result in decreased signal amplitudes relative to homogeneous water tank measurements, but accurate range verification is still expected to be possible.

  17. 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.

  18. 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...

  19. Monte Carlo simulation of light propagation in skin tissue phantoms using a parallel computing method

    Science.gov (United States)

    Wu, Di M.; Zhao, S. S.; Lu, Jun Q.; Hu, Xin-Hua

    2000-06-01

    In Monte Carlo simulations of light propagating in biological tissues, photons propagating in the media are described as classic particles being scattered and absorbed randomly in the media, and their path are tracked individually. To obtain any statistically significant results, however, a large number of photons is needed in the simulations and the calculations are time consuming and sometime impossible with existing computing resource, especially when considering the inhomogeneous boundary conditions. To overcome this difficulty, we have implemented a parallel computing technique into our Monte Carlo simulations. And this moment is well justified due to the nature of the Monte Carlo simulation. Utilizing the PVM (Parallel Virtual Machine, a parallel computing software package), parallel codes in both C and Fortran have been developed on the massive parallel computer of Cray T3E and a local PC-network running Unix/Sun Solaris. Our results show that parallel computing can significantly reduce the running time and make efficient usage of low cost personal computers. In this report, we present a numerical study of light propagation in a slab phantom of skin tissue using the parallel computing technique.

  20. Incorporation of fresh tissue surgical simulation into plastic surgery education: maximizing extraclinical surgical experience.

    Science.gov (United States)

    Sheckter, Clifford C; Kane, Justin T; Minneti, Michael; Garner, Warren; Sullivan, Maura; Talving, Peep; Sherman, Randy; Urata, Mark; Carey, Joseph N

    2013-01-01

    As interest in surgical simulation grows, plastic surgical educators are pressed to provide realistic surgical experience outside of the operating suite. Simulation models of plastic surgery procedures have been developed, but they are incomparable to the dissection of fresh tissue. We evolved a fresh tissue dissection (FTD) and simulation program with emphasis on surgical technique and simulation of clinical surgery. We hypothesized that resident confidence could be improved by adding FTD to our resident curriculum. Over a 5-year period, FTD was incorporated into the curriculum. Participants included clinical medical students, postgraduate year 1 to 7 residents, and attending surgeons. Participants performed dissections and procedures with structured emphasis on anatomical detail, surgical technique, and rehearsal of operative sequence. Resident confidence was evaluated using retrospective pretest and posttest analysis with a 5-point scale, ranging from 1 (least confident) to 5 (most confident). Confidence was evaluated according to postgraduate year level, anatomical region, and procedure. A total of 103 dissection days occurred, and a total of 192 dissections were reported, representing 73 different procedures. Overall, resident predissection confidence was 1.90±1.02 and postdissection confidence was 4.20±0.94 (pSurgery. Published by Elsevier Inc. All rights reserved.

  1. Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling.

    Science.gov (United States)

    Struzyna, Laura A; Adewole, Dayo O; Gordián-Vélez, Wisberty J; Grovola, Michael R; Burrell, Justin C; Katiyar, Kritika S; Petrov, Dmitriy; Harris, James P; Cullen, D Kacy

    2017-05-31

    Functional recovery rarely occurs following injury or disease-induced degeneration within the central nervous system (CNS) due to the inhibitory environment and the limited capacity for neurogenesis. We are developing a strategy to simultaneously address neuronal and axonal pathway loss within the damaged CNS. This manuscript presents the fabrication protocol for micro-tissue engineered neural networks (micro-TENNs), implantable constructs consisting of neurons and aligned axonal tracts spanning the extracellular matrix (ECM) lumen of a preformed hydrogel cylinder hundreds of microns in diameter that may extend centimeters in length. Neuronal aggregates are delimited to the extremes of the three-dimensional encasement and are spanned by axonal projections. Micro-TENNs are uniquely poised as a strategy for CNS reconstruction, emulating aspects of brain connectome cytoarchitecture and potentially providing means for network replacement. The neuronal aggregates may synapse with host tissue to form new functional relays to restore and/or modulate missing or damaged circuitry. These constructs may also act as pro-regenerative "living scaffolds" capable of exploiting developmental mechanisms for cell migration and axonal pathfinding, providing synergistic structural and soluble cues based on the state of regeneration. Micro-TENNs are fabricated by pouring liquid hydrogel into a cylindrical mold containing a longitudinally centered needle. Once the hydrogel has gelled, the needle is removed, leaving a hollow micro-column. An ECM solution is added to the lumen to provide an environment suitable for neuronal adhesion and axonal outgrowth. Dissociated neurons are mechanically aggregated for precise seeding within one or both ends of the micro-column. This methodology reliably produces self-contained miniature constructs with long-projecting axonal tracts that may recapitulate features of brain neuroanatomy. Synaptic immunolabeling and genetically encoded calcium

  2. 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

    2015-09-01

    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.

  3. Neural tissue-spheres

    DEFF Research Database (Denmark)

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

    2007-01-01

    -spheres (NTS) in EGF and FGF2 containing medium. The spheres were cut into quarters when passaged every 10-15th day, avoiding mechanical or enzymatic dissociation in order to minimize cellular trauma and preserve intercellular contacts. For analysis of regional differences within the forebrain SVZ, NTS were...

  4. Neural Network Based Prediction of Conformational Free Energies - A New Route toward Coarse-Grained Simulation Models.

    Science.gov (United States)

    Lemke, Tobias; Peter, Christine

    2017-12-12

    Coarse-grained (CG) simulation models have become very popular tools to study complex molecular systems with great computational efficiency on length and time scales that are inaccessible to simulations at atomistic resolution. In so-called bottom-up coarse-graining strategies, the interactions in the CG model are devised such that an accurate representation of an atomistic sampling of configurational phase space is achieved. This means the coarse-graining methods use the underlying multibody potential of mean force (i.e., free-energy surface) derived from the atomistic simulation as parametrization target. Here, we present a new method where a neural network (NN) is used to extract high-dimensional free energy surfaces (FES) from molecular dynamics (MD) simulation trajectories. These FES are used for simulations on a CG level of resolution. The method is applied to simulating homo-oligo-peptides (oligo-glutamic-acid (oligo-glu) and oligo-aspartic-acid (oligo-asp)) of different lengths. We show that the NN not only is able to correctly describe the free-energy surface for oligomer lengths that it was trained on but also is able to predict the conformational sampling of longer chains.

  5. 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

  6. Optical simulation of laser beam phase-shaping focusing optimization in biological tissues

    Science.gov (United States)

    Gomes, Ricardo; Vieira, Pedro; Coelho, João. M. P.

    2013-11-01

    In this paper we report the development of an optical simulator that can be used in the development of methodologies for compensate/decrease the light scattering effect of most biological tissues through phase-shaping methods. In fact, scattering has long been a major limitation for the medical applications of lasers where in-depth tissues concerns due to the turbid nature of most biological media in the human body. In developing the simulator, two different approaches were followed: one using multiple identical beams directed to the same target area and the other using a phase-shaped beam. In the multiple identical beams approach (used mainly to illustrate the limiting effect of scattering on the beam's propagation) there was no improvement in the beam focus at 1 mm compared to a single beam layout but, in phase-shaped beam approach, a 8x improvement on the radius of the beam at the same depth was achieved. The models were created using the optical design software Zemax and numerical algorithms created in Matlab programming language to shape the beam wavefront. A dedicated toolbox allowed communication between both programs. The use of the two software's proves to be a simple and powerful solution combining the best of the two and allowing a significant potential for adapting the simulations to new systems and thus allow to assess their response and define critical engineering parameters prior to laboratorial implementation.

  7. 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...

  8. 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.

  9. The neural code in developing cultured networks: experiments and advanced simulation models

    NARCIS (Netherlands)

    Rutten, Wim; Gritsun, T.; Stoyanova, Irina; le Feber, Jakob

    2012-01-01

    Understanding the neural code of cultured neuronal networks may help to forward our understanding of human brain processes.The most striking property of spontaneously firing cultures is their regular bursting activity, a burst being defined as synchronized firing of groups of neurons spread

  10. Transplanted neurally modified bone marrow-derived mesenchymal stem cells promote tissue protection and locomotor recovery in spinal cord injured rats.

    Science.gov (United States)

    Alexanian, Arshak R; Fehlings, Michael G; Zhang, Zhiying; Maiman, Dennis J

    2011-01-01

    Stem cell-based therapy for repair and replacement of lost neural cells is a promising treatment for central nervous system (CNS) diseases. Bone marrow (BM)-derived mesenchymal stem cells (MSCs) can differentiate into neural phenotypes and be isolated and expanded for autotransplantation with no risk of rejection. The authors examined whether transplanted neurally induced human MSCs (NI hMSCs), developed by a new procedure, can survive, differentiate, and promote tissue protection and functional recovery in injured spinal cord (ISC) rats. Neural induction was achieved by exposing cells simultaneously to inhibitors of DNA methylation, histone deacetylation, and pharmacological agents that increased cAMP levels. Three groups of adult female Sprague-Dawley rats were injected immediately rostral and caudal to the midline lesion with phosphate-buffered saline, MSCs, or NI hMSCs, 1 week after a spinal cord impact injury at T-8. Functional outcome was measured using the Basso Beattie Bresnahan (BBB) locomotor rating scale and thermal sensitivity test on a weekly basis up to 12 weeks postinjury. Graft integration and anatomy of spinal cord was assessed by stereological, histochemical, and immunohistochemical techniques. The transplanted NI hMSCs survived, differentiated, and significantly improved locomotor recovery of ISC rats. Transplantation also reduced the volume of lesion cavity and white matter loss. This method of hMSC modification may provide an alternative source of autologous adult stem cells for CNS repair.

  11. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

    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

  12. 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.)

  13. Stochastic pacing effect on cardiac alternans--simulation study of a 2D human ventricular tissue.

    Science.gov (United States)

    Dvir, Hila; Zlochiver, Sharon

    2013-01-01

    The physiological heart rate is not deterministic but rather varies in time; those variations are termed heart rate variability (HRV). It is well known that low HRV is often seen in patients prone to arrhythmias. The ability of HRV to predict arrhythmia events is traditionally attributed to an impaired balance between the autonomic sympathetic and parasympathetic tone. However, there is no concrete model that directly relates low HRV to the electrical conduction in the cardiac tissue and to arrhythmogenic dynamic properties. We simulated stochastic cardiac pacing with Gaussian distribution using 2D human ventricular tissue model. Conduction stabilization was obtained with stochastic pacing owing to reduced propensity of the appearance of action potential duration (APD) discordant alternans and reduced APD spatial heterogeneity.

  14. Simulation of the oxygen distribution in a tumor tissue using residual algorithms

    Directory of Open Access Journals (Sweden)

    William La Cruz

    2015-09-01

    Full Text Available In this paper the use of recent residual algorithms for the simulation of the oxygen distribution in a tumor tissue in 2-D is proposed. The oxygen distribution in a tumor is considered a reaction-diffusion problem in steady state, whose mathematical model is a nonlinear partial differential equation, which is numerically solved using the traditional methods for systems of nonlinear equations (Newton's method, Broyden method, inexact Newton methods, etc.. Unlike of these traditional methods that require the use of derivatives and a large memory storage capacity, the proposed residual algorithms are derivative-free methods with low memory storage. The preliminary numerical results indicate that the proposed methods allows efficiently determine the distribution of oxygen in a tumor tissue to synthetic problems.

  15. 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.

  16. 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.

  17. 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.

  18. Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks.

    Science.gov (United States)

    Henker, Stephan; Partzsch, Johannes; Schüffny, René

    2012-04-01

    With the various simulators for spiking neural networks developed in recent years, a variety of numerical solution methods for the underlying differential equations are available. In this article, we introduce an approach to systematically assess the accuracy of these methods. In contrast to previous investigations, our approach focuses on a completely deterministic comparison and uses an analytically solved model as a reference. This enables the identification of typical sources of numerical inaccuracies in state-of-the-art simulation methods. In particular, with our approach we can separate the error of the numerical integration from the timing error of spike detection and propagation, the latter being prominent in simulations with fixed timestep. To verify the correctness of the testing procedure, we relate the numerical deviations to theoretical predictions for the employed numerical methods. Finally, we give an example of the influence of simulation artefacts on network behaviour and spike-timing-dependent plasticity (STDP), underlining the importance of spike-time accuracy for the simulation of STDP.

  19. 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. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

  20. 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

  1. A rectangular tetrahedral adaptive mesh based corotated finite element model for interactive soft tissue simulation.

    Science.gov (United States)

    Tagawa, Kazuyoshi; Yamada, Takahiro; Tanaka, Hiromi T

    2013-01-01

    In this paper, we propose a rectangular tetrahedral adaptive mesh based corotated finite element model for interactive soft tissue simulation. Our approach consists of several computation reduction techniques. They are as follows: 1) an efficient calculation approach for computing internal forces of nodes of elastic objects to take advantage of the rectangularity of the tetrahedral adaptive mesh; 2) fast shape matching approach by using a new scaling of polar decomposition; 3) an approach for the reduction of the number of times of shape matching by using the hierarchical structure. We implemented the approach into our surgery simulator and compared the accuracy of the deformation and the computation time among 1) proposed approach, 2) L-FE), and 3) NL-FEM. Finally, we show the effectiveness of our proposed approach.

  2. Tissue-scale, personalized modeling and simulation of prostate cancer growth

    Science.gov (United States)

    Lorenzo, Guillermo; Scott, Michael A.; Tew, Kevin; Hughes, Thomas J. R.; Zhang, Yongjie Jessica; Liu, Lei; Vilanova, Guillermo; Gomez, Hector

    2016-11-01

    Recently, mathematical modeling and simulation of diseases and their treatments have enabled the prediction of clinical outcomes and the design of optimal therapies on a personalized (i.e., patient-specific) basis. This new trend in medical research has been termed “predictive medicine.” Prostate cancer (PCa) is a major health problem and an ideal candidate to explore tissue-scale, personalized modeling of cancer growth for two main reasons: First, it is a small organ, and, second, tumor growth can be estimated by measuring serum prostate-specific antigen (PSA, a PCa biomarker in blood), which may enable in vivo validation. In this paper, we present a simple continuous model that reproduces the growth patterns of PCa. We use the phase-field method to account for the transformation of healthy cells to cancer cells and use diffusion-reaction equations to compute nutrient consumption and PSA production. To accurately and efficiently compute tumor growth, our simulations leverage isogeometric analysis (IGA). Our model is shown to reproduce a known shape instability from a spheroidal pattern to fingered growth. Results of our computations indicate that such shift is a tumor response to escape starvation, hypoxia, and, eventually, necrosis. Thus, branching enables the tumor to minimize the distance from inner cells to external nutrients, contributing to cancer survival and further development. We have also used our model to perform tissue-scale, personalized simulation of a PCa patient, based on prostatic anatomy extracted from computed tomography images. This simulation shows tumor progression similar to that seen in clinical practice.

  3. Tissue-scale, personalized modeling and simulation of prostate cancer growth.

    Science.gov (United States)

    Lorenzo, Guillermo; Scott, Michael A; Tew, Kevin; Hughes, Thomas J R; Zhang, Yongjie Jessica; Liu, Lei; Vilanova, Guillermo; Gomez, Hector

    2016-11-29

    Recently, mathematical modeling and simulation of diseases and their treatments have enabled the prediction of clinical outcomes and the design of optimal therapies on a personalized (i.e., patient-specific) basis. This new trend in medical research has been termed "predictive medicine." Prostate cancer (PCa) is a major health problem and an ideal candidate to explore tissue-scale, personalized modeling of cancer growth for two main reasons: First, it is a small organ, and, second, tumor growth can be estimated by measuring serum prostate-specific antigen (PSA, a PCa biomarker in blood), which may enable in vivo validation. In this paper, we present a simple continuous model that reproduces the growth patterns of PCa. We use the phase-field method to account for the transformation of healthy cells to cancer cells and use diffusion-reaction equations to compute nutrient consumption and PSA production. To accurately and efficiently compute tumor growth, our simulations leverage isogeometric analysis (IGA). Our model is shown to reproduce a known shape instability from a spheroidal pattern to fingered growth. Results of our computations indicate that such shift is a tumor response to escape starvation, hypoxia, and, eventually, necrosis. Thus, branching enables the tumor to minimize the distance from inner cells to external nutrients, contributing to cancer survival and further development. We have also used our model to perform tissue-scale, personalized simulation of a PCa patient, based on prostatic anatomy extracted from computed tomography images. This simulation shows tumor progression similar to that seen in clinical practice.

  4. 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

  5. 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.

  6. Study of the possibility of determining mass flow of water from neutron activation measurements with flow simulations and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Linden, P.; Pazsit, I. [Department of Reactor Physics, Chalmers University of Technology, Goeteborg (Sweden)

    1998-08-01

    Mass flow of water in a pipe can be measured in a non-intrusive way by the pulsed neutron activation (PNA) technique. From such measurements, mass flow can be estimated by various techniques of time averaging, performed on the time-resolved detector signal(s). However, time averaging methods have a few percent systematic error, which, in addition, is not a constant but varies with flow and measurement parameters. Achieving a precision better than 1% from PNA measurements is a hitherto unsolved task. In this paper a methodology is suggested to solve this task and is tested by simulation methods. The method is based on the use of artificial neural networks to determine mass flow rate from the time resolved detector signal. To achieve this, the network needs to be trained on a large number of real detector data. It is suggested that these data should be obtained by advanced numerical simulation of the PNA measurement. In this paper we use a simplified simulation model for a feasibility study of the methodology. It is shown that a neural network is capable to determine the mass flow rate with a precision of about 0.5%. (orig.) [Deutsch] Das Stroemungsverhalten von Wasser in einer Leitung kann nicht-invasiv durch gepulste Neutronenaktivierungsverfahren (PNA) gemessen werden. Durch solche Messungen kann das Stroemungsverhalten mit Hilfe verschiedener Techniken der Mittelung ueber zeitabhaengige Detektorsignale bestimmt werden. Der systematische Fehler von Zeitmittelungsmethoden liegt jedoch bei ein paar Prozent und ist zusaetzlich nicht konstant, sondern variiert mit den Stroemungs- und Messparametern. Das Erreichen einer Genauigkeit von besser als 1% ist also bei Neutronenaktivierungsverfahren ein bisher ungeloestes Problem. In der vorliegenden Arbeit wird eine Methode zur Loesung dieser Aufgabe vorgeschlagen und mit Hilfe von Simulationsverfahren getestet. Die Methode basiert auf der Verwendung von kuenstlichen, neuralen Netzwerken zur Bestimmung des

  7. Chlorophyll a simulation in a lake ecosystem using a model with wavelet analysis and artificial neural network.

    Science.gov (United States)

    Wang, Fei; Wang, Xuan; Chen, Bin; Zhao, Ying; Yang, Zhifeng

    2013-05-01

    Accurate and reliable forecasting is important for the sustainable management of ecosystems. Chlorophyll a (Chl a) simulation and forecasting can provide early warning information and enable managers to make appropriate decisions for protecting lake ecosystems. In this study, we proposed a method for Chl a simulation in a lake that coupled the wavelet analysis and the artificial neural networks (WA-ANN). The proposed method had the advantage of data preprocessing, which reduced noise and managed nonstationary data. Fourteen variables were included in the developed and validated model, relating to hydrologic, ecological and meteorologic time series data from January 2000 to December 2009 at the Lake Baiyangdian study area, North China. The performance of the proposed WA-ANN model for monthly Chl a simulation in the lake ecosystem was compared with a multiple stepwise linear regression (MSLR) model, an autoregressive integrated moving average (ARIMA) model and a regular ANN model. The results showed that the WA-ANN model was suitable for Chl a simulation providing a more accurate performance than the MSLR, ARIMA, and ANN models. We recommend that the proposed method be widely applied to further facilitate the development and implementation of lake ecosystem management.

  8. Chlorophyll a Simulation in a Lake Ecosystem Using a Model with Wavelet Analysis and Artificial Neural Network

    Science.gov (United States)

    Wang, Fei; Wang, Xuan; Chen, Bin; Zhao, Ying; Yang, Zhifeng

    2013-05-01

    Accurate and reliable forecasting is important for the sustainable management of ecosystems. Chlorophyll a (Chl a) simulation and forecasting can provide early warning information and enable managers to make appropriate decisions for protecting lake ecosystems. In this study, we proposed a method for Chl a simulation in a lake that coupled the wavelet analysis and the artificial neural networks (WA-ANN). The proposed method had the advantage of data preprocessing, which reduced noise and managed nonstationary data. Fourteen variables were included in the developed and validated model, relating to hydrologic, ecological and meteorologic time series data from January 2000 to December 2009 at the Lake Baiyangdian study area, North China. The performance of the proposed WA-ANN model for monthly Chl a simulation in the lake ecosystem was compared with a multiple stepwise linear regression (MSLR) model, an autoregressive integrated moving average (ARIMA) model and a regular ANN model. The results showed that the WA-ANN model was suitable for Chl a simulation providing a more accurate performance than the MSLR, ARIMA, and ANN models. We recommend that the proposed method be widely applied to further facilitate the development and implementation of lake ecosystem management.

  9. A novel 3D modelling and simulation technique in thermotherapy predictive analysis on biological tissue

    Science.gov (United States)

    Fanjul-Vélez, F.; Arce-Diego, J. L.; Romanov, Oleg G.; Tolstik, Alexei L.

    2007-07-01

    Optical techniques applied to biological tissue allow the development of new tools in medical praxis, either in tissue characterization or treatment. Examples of the latter are Photodynamic Therapy (PDT) or Low Intensity Laser Treatment (LILT), and also a promising technique called thermotherapy, that tries to control temperature increase in a pathological tissue in order to reduce or even eliminate pathological effects. The application of thermotherapy requires a previous analysis in order to avoid collateral damage to the patient, and also to choose the appropriate optical source parameters. Among different implementations of opto-thermal models, the one we use consists of a three dimensional Beer-Lambert law for the optical part, and a bio-heat equation, that models heat transference, conduction, convection, radiation, blood perfusion and vaporization, solved via a numerical spatial-temporal explicit finite difference approach, for the thermal part. The usual drawback of the numerical method of the thermal model is that convergence constraints make spatial and temporal steps very small, with the natural consequence of slow processing. In this work, a new algorithm implementation is used for the bio-heat equation solution, in such a way that the simulation time decreases considerably. Thermal damage based on the Arrhenius integral damage is also considered.

  10. Neural stem cell differentiation by electrical stimulation using a cross-linked PEDOT substrate: Expanding the use of biocompatible conjugated conductive polymers for neural tissue engineering.

    Science.gov (United States)

    Pires, Filipa; Ferreira, Quirina; Rodrigues, Carlos A V; Morgado, Jorge; Ferreira, Frederico Castelo

    2015-06-01

    The use of conjugated polymers allows versatile interactions between cells and flexible processable materials, while providing a platform for electrical stimulation, which is particularly relevant when targeting differentiation of neural stem cells and further application for therapy or drug screening. Materials were tested for cytotoxicity following the ISO10993-5. PSS was cross-linked. ReNcellVM neural stem cells (NSC) were seeded in laminin coated surfaces, cultured for 4 days in the presence of EGF (20 ng/mL), FGF-2 (20 ng/mL) and B27 (20 μg/mL) and differentiated over eight additional days in the absence of those factors under 100Hz pulsed DC electrical stimulation, 1V with 10 ms pulses. NSC and neuron elongation aspect ratio as well as neurite length were assessed using ImageJ. Cells were immune-stained for Tuj1 and GFAP. F8T2, MEH-PPV, P3HT and cross-linked PSS (x PSS) were assessed as non-cytotoxic. L929 fibroblast population was 1.3 higher for x PSS than for glass control, while F8T2 presents moderate proliferation. The population of neurons (Tuj1) was 1.6 times higher with longer neurites (73 vs 108 μm) for cells cultured under electrical stimulus, with cultured NSC. Such stimulus led also to longer neurons. x PSS was, for the first time, used to elongate human NSC through the application of pulsed current, impacting on their differentiation towards neurons and contributing to longer neurites. The range of conductive conjugated polymers known as non-cytotoxic was expanded. x PSS was introduced as a stable material, easily processed from solution, to interface with biological systems, in particular NSC, without the need of in-situ polymerization. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. In vitro simulation of pathological bone conditions to predict clinical outcome of bone tissue engineered materials

    Science.gov (United States)

    Nguyen, Duong Thuy Thi

    According to the Centers for Disease Control, the geriatric population of ≥65 years of age will increase to 51.5 million in 2020; 40% of white women and 13% of white men will be at risk for fragility fractures or fractures sustained under normal stress and loading conditions due to bone disease, leading to hospitalization and surgical treatment. Fracture management strategies can be divided into pharmaceutical therapy, surgical intervention, and tissue regeneration for fracture prevention, fracture stabilization, and fracture site regeneration, respectively. However, these strategies fail to accommodate the pathological nature of fragility fractures, leading to unwanted side effects, implant failures, and non-unions. Compromised innate bone healing reactions of patients with bone diseases are exacerbated with protective bone therapy. Once these patients sustain a fracture, bone healing is a challenge, especially when fracture stabilization is unsuccessful. Traditional stabilizing screw and plate systems were designed with emphasis on bone mechanics rather than biology. Bone grafts are often used with fixation devices to provide skeletal continuity at the fracture gap. Current bone grafts include autologous bone tissue and donor bone tissue; however, the quality and quantity demanded by fragility fractures sustained by high-risk geriatric patients and patients with bone diseases are not met. Consequently, bone tissue engineering strategies are advancing towards functionalized bone substitutes to provide fracture reconstruction while effectively mediating bone healing in normal and diseased fracture environments. In order to target fragility fractures, fracture management strategies should be tailored to allow bone regeneration and fracture stabilization with bioactive bone substitutes designed for the pathological environment. The clinical outcome of these materials must be predictable within various disease environments. Initial development of a targeted

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units.

    Science.gov (United States)

    Igarashi, Jun; Shouno, Osamu; Fukai, Tomoki; Tsujino, Hiroshi

    2011-11-01

    Real-time simulation of a biologically realistic spiking neural network is necessary for evaluation of its capacity to interact with real environments. However, the real-time simulation of such a neural network is difficult due to its high computational costs that arise from two factors: (1) vast network size and (2) the complicated dynamics of biologically realistic neurons. In order to address these problems, mainly the latter, we chose to use general purpose computing on graphics processing units (GPGPUs) for simulation of such a neural network, taking advantage of the powerful computational capability of a graphics processing unit (GPU). As a target for real-time simulation, we used a model of the basal ganglia that has been developed according to electrophysiological and anatomical knowledge. The model consists of heterogeneous populations of 370 spiking model neurons, including computationally heavy conductance-based models, connected by 11,002 synapses. Simulation of the model has not yet been performed in real-time using a general computing server. By parallelization of the model on the NVIDIA Geforce GTX 280 GPU in data-parallel and task-parallel fashion, faster-than-real-time simulation was robustly realized with only one-third of the GPU's total computational resources. Furthermore, we used the GPU's full computational resources to perform faster-than-real-time simulation of three instances of the basal ganglia model; these instances consisted of 1100 neurons and 33,006 synapses and were synchronized at each calculation step. Finally, we developed software for simultaneous visualization of faster-than-real-time simulation output. These results suggest the potential power of GPGPU techniques in real-time simulation of realistic neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.

  17. 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.

  18. Fabrication of Nerve Growth Factor Encapsulated Aligned Poly(ε-Caprolactone Nanofibers and Their Assessment as a Potential Neural Tissue Engineering Scaffold

    Directory of Open Access Journals (Sweden)

    Jue Hu

    2016-02-01

    Full Text Available Peripheral nerve injury is a serious clinical problem to be solved. There has been no breakthrough so far and neural tissue engineering offers a promising approach to promote the regeneration of peripheral neural injuries. In this study, emulsion electrospinning technique was introduced as a flexible and promising technique for the fabrication of random (R and aligned (A Poly(ε-caprolactone (PCL-Nerve Growth Factor (NGF&Bovine Serum Albumin (BSA nanofibrous scaffolds [(R/A-PCL-NGF&BSA], where NGF and BSA were encapsulated in the core while PCL form the shell. Random and aligned pure PCL, PCL-BSA, and PCL-NGF nanofibers were also produced for comparison. The scaffolds were characterized by Field Emission Scanning Electron Microscopy (FESEM and water contact angle test. Release study showed that, with the addition of stabilizer BSA, a sustained release of NGF from emulsion electrospun PCL nanofibers was observed over 28 days. [3-(4,5-dimethylthiazol-2-yl-5-(3-carboxymethoxyphenyl-2-(4-sulfophenyl-2H-tetrazolium, inner salt; MTS] assay revealed that (R/A-PCL-NGF and (R/A-PCL-NGF&BSA scaffolds favored cell growth and showed no cytotoxicity to PC12 cells. Laser scanning confocal microscope images exhibited that the A-PCL-NGF&BSA scaffold increased the length of neurites and directed neurites extension along the fiber axis, indicating that the A-PCL-NGF&BSA scaffold has a potential for guiding nerve tissue growth and promoting nerve regeneration.

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

    Science.gov (United States)

    Praet, Jelle; Santermans, Eva; Reekmans, Kristien; de Vocht, Nathalie; Le Blon, Debbie; Hoornaert, Chloé; Daans, Jasmijn; Goossens, Herman; Berneman, Zwi; Hens, Niel; Van der Linden, Annemie; Ponsaerts, Peter

    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 culture eGFP(+) neural and fibroblast(-like) stem cells from embryonic mouse tissue. Second, we describe flow cytometric procedures to determine cell viability, eGFP transgene expression, and the expression of different stem cell lineage markers. Third, we explain how to induce reproducible demyelination in the CNS of mice by means of cuprizone administration, a validated mouse model for human multiple sclerosis. Fourth, the technical procedures for cell grafting in the CNS are explained in detail. Finally, an optimized and validated workflow for the quantitative histological analysis of cell graft survival and endogenous astroglial and microglial responses is provided.

  20. Poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate)-poly(vinyl alcohol)/poly(acrylic acid) interpenetrating polymer networks for improving optrode-neural tissue interface in optogenetics.

    Science.gov (United States)

    Lu, Yi; Li, Yanling; Pan, Jianqing; Wei, Pengfei; Liu, Nan; Wu, Bifeng; Cheng, Jinbo; Lu, Caiyi; Wang, Liping

    2012-01-01

    The field of optogenetics has been successfully used to understand the mechanisms of neuropsychiatric diseases through the precise spatial and temporal control of specific groups of neurons in a neural circuitry. However, it remains a great challenge to integrate optogenetic modulation with electrophysiological and behavioral read out methods as a means to explore the causal, temporally precise, and behaviorally relevant interactions of neurons in the specific circuits of freely behaving animals. In this study, an eight-channel chronically implantable optrode array was fabricated and modified with poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate)-poly(vinyl alcohol)/poly(acrylic acid) interpenetrating polymer networks (PEDOT/PSS-PVA/PAA IPNs) for improving the optrode-neural tissue interface. The conducting polymer-hydrogel IPN films exhibited a significantly higher capacitance and lower electrochemical impedance at 1 kHz as compared to unmodified optrode sites and showed significantly improved mechanical and electrochemical stability as compared to pure conducting polymer films. The cell attachment and neurite outgrowth of rat pheochromocytoma (PC12) cells on the IPN films were clearly observed through calcein-AM staining. Furthermore, the optrode arrays were chronically implanted into the hippocampus of SD rats after the lentiviral expression of synapsin-ChR2-EYFP, and light-evoked, frequency-dependant action potentials were obtained in freely moving animals. The electrical recording results suggested that the modified optrode arrays showed significantly reduced impedance and RMS noise and an improved SNR as compared to unmodified sites, which may have benefited from the improved electrochemical performance and biocompatibility of the deposited IPN films. All these characteristics are greatly desired in optogenetic applications, and the fabrication method of conducting polymer-hydrogel IPNs can be easily integrated with other modification methods to build a

  1. Tensile stress generation by optical breakdown in tissue: Experimental investigations and numerical simulations

    Energy Technology Data Exchange (ETDEWEB)

    Vogel, A. [Medizinisches Laserzentrum Luebeck (Germany); Scammon, R.J.; Godwin, R.P. [Los Alamos National Lab., NM (United States)

    1999-03-01

    Biological tissue is more susceptible to damage from tensile stress than to compressive stress. Tensile stress may arise through the thermoelastic response of laser-irradiated media. Optical breakdown, however, has to date been exclusively associated with compressive stress. The authors show that this is appropriate for water, but not for tissues for which the elastic-plastic material response needs to be considered. The acoustic transients following optical breakdown in water and cornea were measured with a fast hydrophone and the cavitation bubble dynamics, which is closely linked to the stress wave generation, was documented by flash photography. Breakdown in water produced a monopolar acoustic signal and a bubble oscillation in which the expansion and collapse phases were symmetric. Breakdown in cornea produced a bipolar acoustic signal coupled with a pronounced shortening of the bubble expansion phase and a considerable prolongation of its collapse phase. The tensile stress wave is related to the abrupt end of the bubble expansion. Numerical simulations using the MESA-2D code were performed assuming elastic-plastic material behavior in a wide range of values for the shear modulus and yield strength. The calculations revealed that consideration of the elastic-plastic material response is essential to reproduce the experimentally observed bipolar stress waves. The tensile stress evolves during the outward propagation of the acoustic transient and reaches an amplitude of 30--40% of the compressive pulse.

  2. Simulated Thrombin Generation in the Presence of Surface-Bound Heparin and Circulating Tissue Factor.

    Science.gov (United States)

    Dydek, E Victoria; Chaikof, Elliot L

    2016-04-01

    An expanded computational model of surface induced thrombin generation was developed that includes hemodynamic effects, 22 biochemical reactions and 44 distinct chemical species. Surface binding of factors V, VIII, IX, and X was included in order to more accurately simulate the formation of the surface complexes tenase and prothrombinase. In order to model these reactions, the non-activated, activated and inactivated forms were all considered. This model was used to investigate the impact of surface bound heparin on thrombin generation with and without the additive effects of thrombomodulin (TM). In total, 104 heparin/TM pairings were evaluated (52 under venous conditions, 52 under arterial conditions), the results demonstrating the synergistic ability of heparin and TM to reduce thrombin generation. Additionally, the role of circulating tissue factor (TF(p)) was investigated and compared to that of surface-bound tissue factor (TF(s)). The numerical results suggest that circulating TF has the power to amplify thrombin generation once the coagulation cascade is already initiated by surface-bound TF. TF(p) concentrations as low as 0.01 nM were found to have a significant impact on total thrombin generation.

  3. Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry

    Science.gov (United States)

    Pogue, Brian W.; Patterson, Michael S.

    2006-07-01

    Optical spectroscopy, imaging, and therapy tissue phantoms must have the scattering and absorption properties that are characteristic of human tissues, and over the past few decades, many useful models have been created. In this work, an overview of their composition and properties is outlined, by separating matrix, scattering, and absorbing materials, and discussing the benefits and weaknesses in each category. Matrix materials typically are water, gelatin, agar, polyester or epoxy and polyurethane resin, room-temperature vulcanizing (RTV) silicone, or polyvinyl alcohol gels. The water and hydrogel materials provide a soft medium that is biologically and biochemically compatible with addition of organic molecules, and are optimal for scientific laboratory studies. Polyester, polyurethane, and silicone phantoms are essentially permanent matrix compositions that are suitable for routine calibration and testing of established systems. The most common three choices for scatters have been: (1.) lipid based emulsions, (2.) titanium or aluminum oxide powders, and (3.) polymer microspheres. The choice of absorbers varies widely from hemoglobin and cells for biological simulation, to molecular dyes and ink as less biological but more stable absorbers. This review is an attempt to indicate which sets of phantoms are optimal for specific applications, and provide links to studies that characterize main phantom material properties and recipes.

  4. Neural and Behavioral Evidence for the Role of Mental Simulation in Meaning in Life

    Science.gov (United States)

    Waytz, Adam; Hershfield, Hal E; Tamir, Diana I

    2014-01-01

    Mental simulation, the process of self-projection into alternate temporal, spatial, social, or hypothetical realities is a distinctively human capacity. Numerous lines of research also suggest that the tendency for mental simulation is associated with enhanced meaning. The present research tests this association specifically examining the relationship between two forms of simulation (temporal and spatial) and meaning in life. Study 1 uses neuroimaging to demonstrate that enhanced connectivity in the medial temporal lobe network, a subnetwork of the brain’s default network implicated in prospection and retrospection, correlates with self-reported meaning in life. Study 2 demonstrates that experimentally inducing people to think about the past or future versus the present enhances self-reported meaning in life, through the generation of more meaningful events. Study 3 demonstrates that experimentally inducing people to think specifically versus generally about the past or future enhances self-reported meaning in life. Study 4 turns to spatial simulation to demonstrate that experimentally inducing people to think specifically about an alternate spatial location (from the present) increases meaning derived from this simulation compared to thinking generally about another location or specifically about one’s present location. Study 5 demonstrates that experimentally inducing people to think about an alternate spatial location versus one’s present location enhances meaning in life, through meaning derived from this simulation. Study 6 demonstrates that simply asking people to imagine completing a measure of meaning in life in an alternate location compared to asking them to do so in their present location enhances reports of meaning. This research sheds light on an important determinant of meaning in life and suggests that undirected mental simulation benefits psychological well-being. PMID:25603379

  5. Detection and modelling of contacts in explicit finite-element simulation of soft tissue biomechanics.

    Science.gov (United States)

    Johnsen, S F; Taylor, Z A; Han, L; Hu, Y; Clarkson, M J; Hawkes, D J; Ourselin, S

    2015-11-01

    Realistic modelling of soft tissue biomechanics and mechanical interactions between tissues is an important part of biomechanically-informed surgical image-guidance and surgical simulation. This submission details a contact-modelling pipeline suitable for implementation in explicit matrix-free FEM solvers. While these FEM algorithms have been shown to be very suitable for simulation of soft tissue biomechanics and successfully used in a number of image-guidance systems, contact modelling specifically for these solvers is rarely addressed, partly because the typically large number of time steps required with this class of FEM solvers has led to a perception of them being a poor choice for simulations requiring complex contact modelling. The presented algorithm is capable of handling most scenarios typically encountered in image-guidance. The contact forces are computed with an evolution of the Lagrange-multiplier method first used by Taylor and Flanagan in PRONTO 3D extended with spatio-temporal smoothing heuristics for improved stability and edge-edge collision handling, and a new friction model. For contact search, a bounding-volume hierarchy (BVH) is employed, which is capable of identifying self-collisions by means of the surface-normal bounding cone of Volino and Magnenat-Thalmann, in turn computed with a novel formula. The BVH is further optimised for the small time steps by reducing the number of bounding-volume refittings between iterations through identification of regions with mostly rigid motion and negligible deformation. Further optimisation is achieved by integrating the self-collision criterion in the BVH creation and updating algorithms. The effectiveness of the algorithm is demonstrated on a number of artificial test cases and meshes derived from medical image data. It is shown that the proposed algorithm reduces the cost of BVH refitting to the point where it becomes a negligible part of the overall computation time of the simulation. It is also

  6. A Pharmacokinetics-Neural Mass Model (PK-NMM) for the Simulation of EEG Activity during Propofol Anesthesia.

    Science.gov (United States)

    Liang, Zhenhu; Duan, Xuejing; Su, Cui; Voss, Logan; Sleigh, Jamie; Li, Xiaoli

    2015-01-01

    Modeling the effects of anesthetic drugs on brain activity is very helpful in understanding anesthesia mechanisms. The aim of this study was to set up a combined model to relate actual drug levels to EEG dynamics and behavioral states during propofol-induced anesthesia. We proposed a new combined theoretical model based on a pharmacokinetics (PK) model and a neural mass model (NMM), which we termed PK-NMM--with the aim of simulating electroencephalogram (EEG) activity during propofol-induced general anesthesia. The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen. The NMM model took C(eff) as the control parameter to produce simulated EEG-like (sEEG) data. For comparison, we used real prefrontal EEG (rEEG) data of nine volunteers undergoing propofol anesthesia from a previous experiment. To see how well the sEEG could describe the dynamic changes of neural activity during anesthesia, the rEEG data and the sEEG data were compared with respect to: power-frequency plots; nonlinear exponent (permutation entropy (PE)); and bispectral SynchFastSlow (SFS) parameters. We found that the PK-NMM model was able to reproduce anesthesia EEG-like signals based on the estimated drug concentration and patients' condition. The frequency spectrum indicated that the frequency power peak of the sEEG moved towards the low frequency band as anesthesia deepened. Different anesthetic states could be differentiated by the PE index. The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects. Additionally, SFS could track the depth of anesthesia and the SFS of rEEG and sEEG were highly correlated with a correlation coefficient of 0.77 ± 0.13. The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.

  7. Artificial neural network modification of simulation-based fitting: application to a protein-lipid system

    NARCIS (Netherlands)

    Nazarov, P.V.; Apanasovich, V.V.; Lutkovski, V.M.; Yatskou, M.M.; Koehorst, R.B.M.; Hemminga, M.A.

    2004-01-01

    Simulation-based fitting has been applied to data analysis and parameter determination of complex experimental systems in many areas of chemistry and biophysics. However, this method is limited because of the time costs of the calculations. In this paper it is proposed to approximate and substitute

  8. 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)

  9. 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

  10. Corticostriatal response selection in sentence production: Insights from neural network simulation with reservoir computing.

    Science.gov (United States)

    Hinaut, Xavier; Lance, Florian; Droin, Colas; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

    2015-11-01

    Language production requires selection of the appropriate sentence structure to accommodate the communication goal of the speaker - the transmission of a particular meaning. Here we consider event meanings, in terms of predicates and thematic roles, and we address the problem that a given event can be described from multiple perspectives, which poses a problem of response selection. We present a model of response selection in sentence production that is inspired by the primate corticostriatal system. The model is implemented in the context of reservoir computing where the reservoir - a recurrent neural network with fixed connections - corresponds to cortex, and the readout corresponds to the striatum. We demonstrate robust learning, and generalization properties of the model, and demonstrate its cross linguistic capabilities in English and Japanese. The results contribute to the argument that the corticostriatal system plays a role in response selection in language production, and to the stance that reservoir computing is a valid potential model of corticostriatal processing. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. 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.

  12. Mechanical roles of apical constriction, cell elongation, and cell migration during neural tube formation in Xenopus.

    Science.gov (United States)

    Inoue, Yasuhiro; Suzuki, Makoto; Watanabe, Tadashi; Yasue, Naoko; Tateo, Itsuki; Adachi, Taiji; Ueno, Naoto

    2016-12-01

    Neural tube closure is an important and necessary process during the development of the central nervous system. The formation of the neural tube structure from a flat sheet of neural epithelium requires several cell morphogenetic events and tissue dynamics to account for the mechanics of tissue deformation. Cell elongation changes cuboidal cells into columnar cells, and apical constriction then causes them to adopt apically narrow, wedge-like shapes. In addition, the neural plate in Xenopus is stratified, and the non-neural cells in the deep layer (deep cells) pull the overlying superficial cells, eventually bringing the two layers of cells to the midline. Thus, neural tube closure appears to be a complex event in which these three physical events are considered to play key mechanical roles. To test whether these three physical events are mechanically sufficient to drive neural tube formation, we employed a three-dimensional vertex model and used it to simulate the process of neural tube closure. The results suggest that apical constriction cued the bending of the neural plate by pursing the circumference of the apical surface of the neural cells. Neural cell elongation in concert with apical constriction further narrowed the apical surface of the cells and drove the rapid folding of the neural plate, but was insufficient for complete neural tube closure. Migration of the deep cells provided the additional tissue deformation necessary for closure. To validate the model, apical constriction and cell elongation were inhibited in Xenopus laevis embryos. The resulting cell and tissue shapes resembled the corresponding simulation results.

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

    Directory of Open Access Journals (Sweden)

    Kim Elmer K

    2012-07-01

    Full Text Available Abstract Background 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. Methods 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. Results 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

  14. Selective optical scattering characterisation of tissue malignancy using Mueller matrix polarimetry: a simulation study

    Science.gov (United States)

    Fathima, Adeeba; Sujatha, N.

    2016-04-01

    Quantitative Mueller polarimetry optically characterizes a medium and is reflected upon by the ultrastructural changes in it. Tissue morphology changes occur during advent of diseases like cancer neoplasia. This alters the Mueller matrix characterizing the tissue as an optical element. The nucleus size undergoes an approximate doubling during the development of cancer. Cell crowding during cancer increases the number density of the nuclei per unit volume. Modeling the cell nuclei as main scattering centers, a systematic computational study on how Mueller matrix elements vary for an increase in scatterer size and number density is performed. Simulation on polarized light transport of wavelength 633nm through a slab of size 3 mm comprising of spherical scatterers in a medium of refractive index 1.33 is carried out. Light propagation is modeled using Monte Carlo method and meridian plane method is adopted for tracking the polarization state change. The stokes vector of the outgoing light is tracked to calculate the Mueller matrix images of the light backscattered from the slab. The Mueller matrix elements as well as depolarization factors are derived. The depolarization index increases with scatterer size. Along with nucleus size, change in the cell number density is also expected in the different stages of the cancer growth. Volume fraction of the scatterers in medium is varied as an indicator of this number density change. Behavior of Mueller matrix with respect to change in scattering coefficient due to variation in scatterer size and volume fraction is studied. It is observed that the depolarization index derived from Mueller matrix has selective discrimination towards the change in scattering coefficient caused due to size change and volume fraction change respectively.

  15. Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.

    Science.gov (United States)

    Morrison, Abigail; Straube, Sirko; Plesser, Hans Ekkehard; Diesmann, Markus

    2007-01-01

    Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.

  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. The Extent of Tissue Damage in the Epidural Space by Ho / YAG Laser During Epiduroscopic Laser Neural Decompression.

    Science.gov (United States)

    Jo, Daehyun; Lee, Dong Joo

    2016-01-01

    Lasers have recently become very useful for epiduroscopy. As the use of lasers increases, the potential for unwanted complications with direct application of laser energy to nerve tissue has also increased. Even using the lowest laser power to test for nerve stimulation, there are still risks of laser ablation. However, there are no studies investigating tissue damage from laser procedures in the epidural space. This is a study on the risks of Ho/YAG laser usage during epiduroscopy. Observatory cadaver study. Department of anatomy and clinical research institute at the University Hospital. We used 5 cadavers for this study. After removing the dura and nerve root from the spinal column, laser energy from a Ho/YAG laser was applied directly to the dura and nerve root as well as in the virtual epidural space, which mimicked the conditions of epiduroscopy with the dura folded. Tissue destruction at all laser ablation sites was observed with the naked eye as well as with a microscope. Specimens were collected from each site of laser exposure, fixed in 10% neutral formalin, and dyed with H/E staining. Tissue destruction was observed in all laser ablation sites, regardless of the length of exposure and the power of the laser beam. A cadaver is not exactly the same as a living human because dura characteristics change and tissue damage can be influenced by dura thickness according to the spinal level. Even with low power and short duration, a laser can destroy tissue if the laser beam makes direct contact with the tissue.

  18. A cGMP-applicable expansion method for aggregates of human neural stem and progenitor cells derived from pluripotent stem cells or fetal brain tissue.

    Science.gov (United States)

    Shelley, Brandon C; Gowing, Geneviève; Svendsen, Clive N

    2014-06-15

    A cell expansion technique to amass large numbers of cells from a single specimen for research experiments and clinical trials would greatly benefit the stem cell community. Many current expansion methods are laborious and costly, and those involving complete dissociation may cause several stem and progenitor cell types to undergo differentiation or early senescence. To overcome these problems, we have developed an automated mechanical passaging method referred to as "chopping" that is simple and inexpensive. This technique avoids chemical or enzymatic dissociation into single cells and instead allows for the large-scale expansion of suspended, spheroid cultures that maintain constant cell/cell contact. The chopping method has primarily been used for fetal brain-derived neural progenitor cells or neurospheres, and has recently been published for use with neural stem cells derived from embryonic and induced pluripotent stem cells. The procedure involves seeding neurospheres onto a tissue culture Petri dish and subsequently passing a sharp, sterile blade through the cells effectively automating the tedious process of manually mechanically dissociating each sphere. Suspending cells in culture provides a favorable surface area-to-volume ratio; as over 500,000 cells can be grown within a single neurosphere of less than 0.5 mm in diameter. In one T175 flask, over 50 million cells can grow in suspension cultures compared to only 15 million in adherent cultures. Importantly, the chopping procedure has been used under current good manufacturing practice (cGMP), permitting mass quantity production of clinical-grade cell products.

  19. Uncertainty analysis of a combined Artificial Neural Network - Fuzzy logic - Kriging system for spatial and temporal simulation of Hydraulic Head.

    Science.gov (United States)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2015-04-01

    The purpose of this study is to evaluate the uncertainty, using various methodologies, in a combined Artificial Neural Network (ANN) - Fuzzy logic - Kriging system, which can simulate spatially and temporally the hydraulic head in an aquifer. This system uses ANNs for the temporal prediction of hydraulic head in various locations, one ANN for every location with available data, and Kriging for the spatial interpolation of ANN's results. A fuzzy logic is used for the interconnection of these two methodologies. The full description of the initial system and its functionality can be found in Tapoglou et al. (2014). Two methodologies were used for the calculation of uncertainty for the implementation of the algorithm in a study area. First, the uncertainty of Kriging parameters was examined using a Bayesian bootstrap methodology. In this case the variogram is calculated first using the traditional methodology of Ordinary Kriging. Using the parameters derived and the covariance function of the model, the covariance matrix is constructed. A common method for testing a statistical model is the use of artificial data. Normal random numbers generation is the first step in this procedure and by multiplying them by the decomposed covariance matrix, correlated random numbers (sample set) can be calculated. These random values are then fitted into a variogram and the value in an unknown location is estimated using Kriging. The distribution of the simulated values using the Kriging of different correlated random values can be used in order to derive the prediction intervals of the process. In this study 500 variograms were constructed for every time step and prediction point, using the method described above, and their results are presented as the 95th and 5th percentile of the predictions. The second methodology involved the uncertainty of ANNs training. In this case, for all the data points 300 different trainings were implemented having different training datasets each time

  20. Serotonin-Sensitive Adenylate Cyclase in Neural Tissue and Its Similarity to the Serotonin Receptor: A Possible Site of Action of Lysergic Acid Diethylamide

    Science.gov (United States)

    Nathanson, James A.; Greengard, Paul

    1974-01-01

    An adenylate cyclase (EC 4.6.1.1) that is activated specifically by low concentrations of serotonin has been identified in homogenates of the thoracic ganglia of an insect nervous system. The activation of this enzyme by serotonin was selectively inhibited by extremely low concentrations of D-lysergic acid diethylamide (LSD), 2-bromo-LSD, and cyproheptadine, agents which are known to block certain serotonin receptors in vivo. The inhibition was competitive with respect to serotonin, and the calculated inhibitory constant of LSD for this serotonin-sensitive adenylate cyclase was 5 nM. The data are consistent with a model in which the serotonin receptor of neural tissue is intimately associated with a serotonin-sensitive adenylate cyclase which mediates serotonergic neurotransmission. The results are also compatible with the possibility that some of the physiological effects of LSD may be mediated through interaction with serotonin-sensitive adenylate cyclase. PMID:4595572

  1. The orphan G-protein-coupled receptor-encoding gene V28 is closely related to genes for chemokine receptors and is expressed in lymphoid and neural tissues.

    Science.gov (United States)

    Raport, C J; Schweickart, V L; Eddy, R L; Shows, T B; Gray, P W

    1995-10-03

    A polymerase chain reaction (PCR) strategy with degenerate primers was used to identify novel G-protein-coupled receptor-encoding genes from human genomic DNA. One of the isolated clones, termed V28, showed high sequence similarity to the genes encoding human chemokine receptors for monocyte chemoattractant protein 1 (MCP-1) and macrophage inflammatory protein 1 alpha (MIP-1 alpha)/RANTES, and to the rat orphan receptor-encoding gene RBS11. When RNA was analyzed by Northern blot, V28 was found to be most highly expressed in neural and lymphoid tissues. Myeloid cell lines, particularly THP.1 cells, showed especially high expression of V28. We have mapped V28 to human chromosome 3p21-3pter, near the MIP-1 alpha/RANTES receptor-encoding gene.

  2. 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%.

  3. 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.

  4. A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects.

    Science.gov (United States)

    De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S

    2011-08-01

    BACKGROUND: While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. METHODS: In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. RESULTS: We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. CONCLUSIONS: A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal

  5. The immediate effects of soft tissue mobilization versus therapeutic ultrasound for patients with neck and arm pain with evidence of neural mechanosensitivity: a randomized clinical trial.

    Science.gov (United States)

    Costello, Michael; Puentedura, Emilio 'Louie' J; Cleland, Josh; Ciccone, Charles D

    2016-07-01

    Randomized clinical trial. To investigate the immediate effects of soft tissue mobilization (STM) versus therapeutic ultrasound (US) in patients with neck and arm pain who demonstrate neural mechanical sensitivity. While experts have suggested that individuals with neck and arm pain associated with neural tissue mechanical sensitivity may benefit from STM, there has been little research to investigate this hypothesis. Twenty-three patients with neck and arm pain and a positive upper limb neurodynamic test (ULNT) were randomly assigned to receive STM or therapeutic US during a single session. Outcome measures were collected immediately before and after treatment, and at 2-4 day follow-up. Primary outcomes were the Global Rating of Change (GROC), range of motion (ROM) during the ULNT, and pain rating during the ULNT. Secondary measures included the Neck Disability Index (NDI), Patient-Specific Functional Scale (PSFS), Numeric Pain Rating Scale (NPRS), and active range of shoulder abduction motion combined with the wrist neutral or wrist extension. A greater proportion of patients in the STM group reported a significant improvement on the GROC immediately after treatment (P = 0·003, STM = 75%, US = 9%), and at 2-4 day follow-up (P = 0·027, STM = 58%, US = 9%). Patients who received STM demonstrated greater improvements in ROM during ULNT (P = 0·026), PSFS (P = 0·007), and shoulder active ROM combined with wrist extension (P = 0·028). Improvements in Numeric Pain Rating Scale and pain during the ULNT were observed only in the STM group. There was no difference between groups for the NDI or shoulder abduction ROM with wrist neutral. Patients with neck and arm pain demonstrated greater improvements in ULNT ROM, GROC, and PSFS, and pain following STM than after receiving therapeutic US. Therapy, level 1b.

  6. The immediate effects of soft tissue mobilization versus therapeutic ultrasound for patients with neck and arm pain with evidence of neural mechanosensitivity: a randomized clinical trial

    Science.gov (United States)

    Costello, Michael; Puentedura, Emilio ‘Louie’ J.; Cleland, Josh; Ciccone, Charles D.

    2016-01-01

    Study design Randomized clinical trial. Objectives To investigate the immediate effects of soft tissue mobilization (STM) versus therapeutic ultrasound (US) in patients with neck and arm pain who demonstrate neural mechanical sensitivity. Background While experts have suggested that individuals with neck and arm pain associated with neural tissue mechanical sensitivity may benefit from STM, there has been little research to investigate this hypothesis. Methods Twenty-three patients with neck and arm pain and a positive upper limb neurodynamic test (ULNT) were randomly assigned to receive STM or therapeutic US during a single session. Outcome measures were collected immediately before and after treatment, and at 2–4 day follow-up. Primary outcomes were the Global Rating of Change (GROC), range of motion (ROM) during the ULNT, and pain rating during the ULNT. Secondary measures included the Neck Disability Index (NDI), Patient-Specific Functional Scale (PSFS), Numeric Pain Rating Scale (NPRS), and active range of shoulder abduction motion combined with the wrist neutral or wrist extension. Results A greater proportion of patients in the STM group reported a significant improvement on the GROC immediately after treatment (P = 0·003, STM = 75%, US = 9%), and at 2–4 day follow-up (P = 0·027, STM = 58%, US = 9%). Patients who received STM demonstrated greater improvements in ROM during ULNT (P = 0·026), PSFS (P = 0·007), and shoulder active ROM combined with wrist extension (P = 0·028). Improvements in Numeric Pain Rating Scale and pain during the ULNT were observed only in the STM group. There was no difference between groups for the NDI or shoulder abduction ROM with wrist neutral. Conclusion Patients with neck and arm pain demonstrated greater improvements in ULNT ROM, GROC, and PSFS, and pain following STM than after receiving therapeutic US. Level of evidence Therapy, level 1b. PMID:27559283

  7. An automated and reproducible workflow for running and analyzing neural simulations using Lancet and IPython Notebook

    Directory of Open Access Journals (Sweden)

    Jean-Luc Richard Stevens

    2013-12-01

    Full Text Available Lancet is a new, simulator-independent Python utility for succinctlyspecifying, launching, and collating results from large batches ofinterrelated computationally demanding program runs. This paperdemonstrates how to combine Lancet with IPython Notebook to provide aflexible, lightweight, and agile workflow for fully reproduciblescientific research. This informal and pragmatic approach usesIPython Notebook to capture the steps in a scientific computation asit is gradually automated and made ready for publication, withoutmandating the use of any separate application that can constrainscientific exploration and innovation. The resulting notebookconcisely records each step involved in even very complexcomputational processes that led to a particular figure or numericalresult, allowing the complete chain of events to be replicatedautomatically.Lancet was originally designed to help solve problems in computationalneuroscience, such as analyzing the sensitivity of a complexsimulation to various parameters, or collecting the results frommultiple runs with different random starting points. However, becauseit is never possible to know in advance what tools might be requiredin future tasks, Lancet has been designed to be completely general,supporting any type of program as long as it can be launched as aprocess and can return output in the form of files. For instance,Lancet is also heavily used by one of the authors in a separateresearch group for launching batches of microprocessor simulations.This general design will allow Lancet to continue supporting a givenresearch project even as the underlying approaches and tools change.

  8. 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.

  9. Tissue-simulating phantoms for assessing potential near-infrared fluorescence imaging applications in breast cancer surgery.

    Science.gov (United States)

    Pleijhuis, Rick; Timmermans, Arwin; De Jong, Johannes; De Boer, Esther; Ntziachristos, Vasilis; Van Dam, Gooitzen

    2014-09-19

    Inaccuracies in intraoperative tumor localization and evaluation of surgical margin status result in suboptimal outcome of breast-conserving surgery (BCS). Optical imaging, in particular near-infrared fluorescence (NIRF) imaging, might reduce the frequency of positive surgical margins following BCS by providing the surgeon with a tool for pre- and intraoperative tumor localization in real-time. In the current study, the potential of NIRF-guided BCS is evaluated using tissue-simulating breast phantoms for reasons of standardization and training purposes. Breast phantoms with optical characteristics comparable to those of normal breast tissue were used to simulate breast conserving surgery. Tumor-simulating inclusions containing the fluorescent dye indocyanine green (ICG) were incorporated in the phantoms at predefined locations and imaged for pre- and intraoperative tumor localization, real-time NIRF-guided tumor resection, NIRF-guided evaluation on the extent of surgery, and postoperative assessment of surgical margins. A customized NIRF camera was used as a clinical prototype for imaging purposes. Breast phantoms containing tumor-simulating inclusions offer a simple, inexpensive, and versatile tool to simulate and evaluate intraoperative tumor imaging. The gelatinous phantoms have elastic properties similar to human tissue and can be cut using conventional surgical instruments. Moreover, the phantoms contain hemoglobin and intralipid for mimicking absorption and scattering of photons, respectively, creating uniform optical properties similar to human breast tissue. The main drawback of NIRF imaging is the limited penetration depth of photons when propagating through tissue, which hinders (noninvasive) imaging of deep-seated tumors with epi-illumination strategies.

  10. Spiking neural network simulation: numerical integration with the Parker-Sochacki method.

    Science.gov (United States)

    Stewart, Robert D; Bair, Wyeth

    2009-08-01

    Mathematical neuronal models are normally expressed using differential equations. The Parker-Sochacki method is a new technique for the numerical integration of differential equations applicable to many neuronal models. Using this method, the solution order can be adapted according to the local conditions at each time step, enabling adaptive error control without changing the integration timestep. The method has been limited to polynomial equations, but we present division and power operations that expand its scope. We apply the Parker-Sochacki method to the Izhikevich 'simple' model and a Hodgkin-Huxley type neuron, comparing the results with those obtained using the Runge-Kutta and Bulirsch-Stoer methods. Benchmark simulations demonstrate an improved speed/accuracy trade-off for the method relative to these established techniques.

  11. Why are friends special? Implementing a social interaction simulation task to probe the neural correlates of friendship.

    Science.gov (United States)

    Güroğlu, Berna; Haselager, Gerbert J T; van Lieshout, Cornelis F M; Takashima, Atsuko; Rijpkema, Mark; Fernández, Guillén

    2008-01-15

    Friendships form one of the most proximal contexts with a critical role in mental health and social and psychological development. Yet, the neurobiological basis of this crucial developmental factor is largely uninvestigated. In this study, we tested the hypothesis that the interaction with friends is associated with specific activity increases in brain areas known to be involved in interpersonal phenomena, such as empathy, and in reward expectancy. Using functional magnetic resonance imaging (fMRI), we assessed neural activity in a social interaction simulation task implementing the factors 'type of relationship' (peers vs. familiar celebrities) and 'emotional valence' (positive (liked), negative (disliked), and neutral (neither liked nor disliked)). In this design, all stimuli were selected individually for each of the 28 participants and positive peers constituted the friends. Participants were asked to approach a stimulus, to avoid it, or remain neutral. Behavioral results confirmed the expectations in the sense that the participants approached positive stimuli more often than they approached neutral, which were also more often approached than negative stimuli. Moreover, peers were more often approached than celebrities were. Imaging results revealed, among others, three regions of particular interest as selectively more strongly activated when subjects interacted with their friends than with other peers and celebrities: the amygdala and hippocampus, the nucleus accumbens, and the ventro-medial prefrontal cortex. These results might highlight the role of empathy and reward-related processes in friendship. Thus, we may have identified a potential mechanism by which friendships exert such a critical role in development and mental health.

  12. 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.

  13. Neural network and Monte Carlo simulation approach to investigate variability of copper concentration in phytoremediated contaminated soils.

    Science.gov (United States)

    Hattab, Nour; Hambli, Ridha; Motelica-Heino, Mikael; Mench, Michel

    2013-11-15

    The statistical variation of soil properties and their stochastic combinations may affect the extent of soil contamination by metals. This paper describes a method for the stochastic analysis of the effects of the variation in some selected soil factors (pH, DOC and EC) on the concentration of copper in dwarf bean leaves (phytoavailability) grown in the laboratory on contaminated soils treated with different amendments. The method is based on a hybrid modeling technique that combines an artificial neural network (ANN) and Monte Carlo Simulations (MCS). Because the repeated analyses required by MCS are time-consuming, the ANN is employed to predict the copper concentration in dwarf bean leaves in response to stochastic (random) combinations of soil inputs. The input data for the ANN are a set of selected soil parameters generated randomly according to a Gaussian distribution to represent the parameter variabilities. The output is the copper concentration in bean leaves. The results obtained by the stochastic (hybrid) ANN-MCS method show that the proposed approach may be applied (i) to perform a sensitivity analysis of soil factors in order to quantify the most important soil parameters including soil properties and amendments on a given metal concentration, (ii) to contribute toward the development of decision-making processes at a large field scale such as the delineation of contaminated sites. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. 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.

  15. Simulating the 'other-race' effect with autoassociative neural networks: further evidence in favor of the face-space model.

    Science.gov (United States)

    Caldara, Roberto; Hervé, Abdi

    2006-01-01

    Other-race (OR) faces are less accurately recognized than same-race (SR) faces, but faster classified by race. This phenomenon has often been reported as the 'other-race' effect (ORE). Valentine (1991 Quarterly Journal of Experimental Psychology A: Human Experimental Psychology 43 161-204) proposed a theoretical multidimensional face-space model that explained both of these results, in terms of variations in exemplar density between races. According to this model, SR faces are more widely distributed across the dimensions of the space than OR faces. However, this model does not quantify nor state the dimensions coded within this face space. The aim of the present study was to test the face-space explanation of the ORE with neural network simulations by quantifying its dimensions. We found the predicted density properties of Valentine's framework in the face-projection spaces of the autoassociative memories. This was supported by an interaction for exemplar density between the race of the learned face set and the race of the faces. In addition, the elaborated face representations showed optimal responses for SR but not for OR faces within SR face spaces when explored at the individual level, as gender errors occurred significantly more often in OR than in SR face-space representations. Altogether, our results add further evidence in favor of a statistical exemplar density explanation of the ORE as suggested by Valentine, and question the plausibility of such coding for faces in the framework of recent neuroimaging studies.

  16. 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

  17. 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.

  18. A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment

    OpenAIRE

    2009-01-01

    In this paper, we report an application of neural networks to simulate daily nitrate-nitrogen and suspended sediment fluxes from a small 7.1 km2 agricultural catchment (Melarchez), 70km east of Paris,France. Nitrate-nitrogen and sediment losses are only a few possible consequences of soil erosion and biochemical applications associated to human activities such as intensive agriculture. Stacked multilayer perceptrons models (MLPs) like the ones explored here are based on commonly available inp...

  19. Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution

    Science.gov (United States)

    Aichinger, Wolfgang; Krappe, Sebastian; Cetin, A. Enis; Cetin-Atalay, Rengul; Üner, Aysegül; Benz, Michaela; Wittenberg, Thomas; Stamminger, Marc; Münzenmayer, Christian

    2017-03-01

    The analysis and interpretation of histopathological samples and images is an important discipline in the diagnosis of various diseases, especially cancer. An important factor in prognosis and treatment with the aim of a precision medicine is the determination of so-called cancer stem cells (CSC) which are known for their resistance to chemotherapeutic treatment and involvement in tumor recurrence. Using immunohistochemistry with CSC markers like CD13, CD133 and others is one way to identify CSC. In our work we aim at identifying CSC presence on ubiquitous Hematoxilyn and Eosin (HE) staining as an inexpensive tool for routine histopathology based on their distinct morphological features. We present initial results of a new method based on color deconvolution (CD) and convolutional neural networks (CNN). This method performs favorably (accuracy 0.936) in comparison with a state-of-the-art method based on 1DSIFT and eigen-analysis feature sets evaluated on the same image database. We also show that accuracy of the CNN is improved by the CD pre-processing.

  20. Hybrid method for fast Monte Carlo simulation of diffuse reflectance from a multilayered tissue model with tumor-like heterogeneities.

    Science.gov (United States)

    Zhu, Caigang; Liu, Quan

    2012-01-01

    We present a hybrid method that combines a multilayered scaling method and a perturbation method to speed up the Monte Carlo simulation of diffuse reflectance from a multilayered tissue model with finite-size tumor-like heterogeneities. The proposed method consists of two steps. In the first step, a set of photon trajectory information generated from a baseline Monte Carlo simulation is utilized to scale the exit weight and exit distance of survival photons for the multilayered tissue model. In the second step, another set of photon trajectory information, including the locations of all collision events from the baseline simulation and the scaling result obtained from the first step, is employed by the perturbation Monte Carlo method to estimate diffuse reflectance from the multilayered tissue model with tumor-like heterogeneities. Our method is demonstrated to shorten simulation time by several orders of magnitude. Moreover, this hybrid method works for a larger range of probe configurations and tumor models than the scaling method or the perturbation method alone.

  1. Modeling of body tissues for Monte Carlo simulation of radiotherapy treatments planned with conventional x-ray CT systems

    Science.gov (United States)

    Kanematsu, Nobuyuki; Inaniwa, Taku; Nakao, Minoru

    2016-07-01

    In the conventional procedure for accurate Monte Carlo simulation of radiotherapy, a CT number given to each pixel of a patient image is directly converted to mass density and elemental composition using their respective functions that have been calibrated specifically for the relevant x-ray CT system. We propose an alternative approach that is a conversion in two steps: the first from CT number to density and the second from density to composition. Based on the latest compilation of standard tissues for reference adult male and female phantoms, we sorted the standard tissues into groups by mass density and defined the representative tissues by averaging the material properties per group. With these representative tissues, we formulated polyline relations between mass density and each of the following; electron density, stopping-power ratio and elemental densities. We also revised a procedure of stoichiometric calibration for CT-number conversion and demonstrated the two-step conversion method for a theoretically emulated CT system with hypothetical 80 keV photons. For the standard tissues, high correlation was generally observed between mass density and the other densities excluding those of C and O for the light spongiosa tissues between 1.0 g cm-3 and 1.1 g cm-3 occupying 1% of the human body mass. The polylines fitted to the dominant tissues were generally consistent with similar formulations in the literature. The two-step conversion procedure was demonstrated to be practical and will potentially facilitate Monte Carlo simulation for treatment planning and for retrospective analysis of treatment plans with little impact on the management of planning CT systems.

  2. A novel 3D modelling and simulation technique in thermotherapy predictive analysis on biological tissue

    OpenAIRE

    Fanjul Vélez, Félix; Arce Diego, José Luis; Romanov, Oleg G.; Tolstik, Alexei L.

    2007-01-01

    Optical techniques applied to biological tissue allow the development of new tools in medical praxis, either in tissue characterization or treatment. Examples of the latter are Photodynamic Therapy (PDT) or Low Intensity Laser Treatment (LILT), and also a promising technique called thermotherapy, that tries to control temperature increase in a pathological tissue in order to reduce or even eliminate pathological effects. The application of thermotherapy requires a previous analysis in order t...

  3. Growth of plant tissue cultures in simulated lunar soil: Implications for a lunar base Controlled Ecological Life Support System (CELSS)

    Science.gov (United States)

    Venketeswaran, S.

    1987-01-01

    Experiments to determine whether plant tissue cultures can be grown in the presence of simulated lunar soil (SLS) and the effect of simulated lunar soil on the growth and morphogenesis of such cultures, as well as the effect upon the germination of seeds and the development of seedlings were carried out . Preliminary results on seed germination and seedling growth of rice and calli growth of winged bean and soybean indicate that there is no toxicity or inhibition caused by SLS. SLS can be used as a support medium with supplements of certain major and micro elements.

  4. The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP.

    Science.gov (United States)

    Mustafa, Yasmen A; Jaid, Ghydaa M; Alwared, Abeer I; Ebrahim, Mothana

    2014-06-01

    The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H2O2/Fe(+2)) process. The reaction is influenced by the input concentration of hydrogen peroxide H2O2, amount of the iron catalyst Fe(+2), pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H2O2 = 400 mg/L, Fe(+2) = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72%. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R (2) = 0.9949). The sensitivity analysis showed that all studied variables (H2O2, Fe(+2), pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6%.

  5. Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis

    Science.gov (United States)

    Antanasijević, Davor; Pocajt, Viktor; Perić-Grujić, Aleksandra; Ristić, Mirjana

    2014-11-01

    This paper describes the training, validation, testing and uncertainty analysis of general regression neural network (GRNN) models for the forecasting of dissolved oxygen (DO) in the Danube River. The main objectives of this work were to determine the optimum data normalization and input selection techniques, the determination of the relative importance of uncertainty in different input variables, as well as the uncertainty analysis of model results using the Monte Carlo Simulation (MCS) technique. Min-max, median, z-score, sigmoid and tanh were validated as normalization techniques, whilst the variance inflation factor, correlation analysis and genetic algorithm were tested as input selection techniques. As inputs, the GRNN models used 19 water quality variables, measured in the river water each month at 17 different sites over a period of 9 years. The best results were obtained using min-max normalized data and the input selection based on the correlation between DO and dependent variables, which provided the most accurate GRNN model, and in combination the smallest number of inputs: Temperature, pH, HCO3-, SO42-, NO3-N, Hardness, Na, Cl-, Conductivity and Alkalinity. The results show that the correlation coefficient between measured and predicted DO values is 0.85. The inputs with the greatest effect on the GRNN model (arranged in descending order) were T, pH, HCO3-, SO42- and NO3-N. Of all inputs, variability of temperature had the greatest influence on the variability of DO content in river body, with the DO decreasing at a rate similar to the theoretical DO decreasing rate relating to temperature. The uncertainty analysis of the model results demonstrate that the GRNN can effectively forecast the DO content, since the distribution of model results are very similar to the corresponding distribution of real data.

  6. Estimation of tissue perfusion by dynamic contrast-enhanced imaging: simulation-based evaluation of the steepest slope method.

    Science.gov (United States)

    Brix, Gunnar; Zwick, Stefan; Griebel, Jürgen; Fink, Christian; Kiessling, Fabian

    2010-09-01

    Tissue perfusion is frequently determined from dynamic contrast-enhanced CT or MRI image series by means of the steepest slope method. It was thus the aim of this study to systematically evaluate the reliability of this analysis method on the basis of simulated tissue curves. 9600 tissue curves were simulated for four noise levels, three sampling intervals and a wide range of physiological parameters using an axially distributed reference model and subsequently analysed by the steepest slope method. Perfusion is systematically underestimated with errors becoming larger with increasing perfusion and decreasing intravascular volume. For curves sampled after rapid contrast injection with a temporal resolution of 0.72 s, the bias was less than 23% when the mean residence time of tracer molecules in the intravascular distribution space was greater than 6 s. Increasing the sampling interval and the noise level substantially reduces the accuracy and precision of estimates, respectively. The steepest slope method allows absolute quantification of tissue perfusion in a computationally simple and numerically robust manner. The achievable degree of accuracy and precision is considered to be adequate for most clinical applications.

  7. Wireless channel characterization for mm-size neural implants.

    Science.gov (United States)

    Mark, Michael; Bjorninen, Toni; Chen, Yuhui David; Venkatraman, Subramaniam; Ukkonen, Leena; Sydanheimo, Lauri; Carmena, Jose M; Rabaey, Jan M

    2010-01-01

    This paper discusses an approach to modeling and characterizing wireless channel properties for mm-size neural implants. Full-wave electromagnetic simulation was employed to model signal propagation characteristics in biological materials. Animal tests were carried out, proving the validity of the simulation model over a wide range of frequency from 100MHz to 6GHz. Finally, effects of variability and uncertainty in human anatomy and dielectric properties of tissues on these radio links are explored.

  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. 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.

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

    Science.gov (United States)

    Calì, Corrado; Baghabra, Jumana; Boges, Daniya J; Holst, Glendon R; Kreshuk, Anna; Hamprecht, Fred A; Srinivasan, Madhusudhanan; Lehväslaiho, Heikki; Magistretti, Pierre J

    2016-01-01

    Advances in the application of electron microscopy (EM) 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 us to observe the spatial arrangement of the smallest cellular features with unprecedented detail in full three-dimensions. 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 in which 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 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 the observation of a nonrandom distribution of glycogen, and led us to develop tools to quantitatively analyze glycogen clustering and proximity to other subcellular features. © 2015 Wiley Periodicals, Inc.

  12. Conducting Polymers for Neural Prosthetic and Neural Interface Applications

    Science.gov (United States)

    2015-01-01

    Neural interfacing devices are an artificial mechanism for restoring or supplementing the function of the nervous system lost as a result of injury or disease. Conducting polymers (CPs) are gaining significant attention due to their capacity to meet the performance criteria of a number of neuronal therapies including recording and stimulating neural activity, the regeneration of neural tissue and the delivery of bioactive molecules for mediating device-tissue interactions. CPs form a flexible platform technology that enables the development of tailored materials for a range of neuronal diagnostic and treatment therapies. In this review the application of CPs for neural prostheses and other neural interfacing devices are discussed, with a specific focus on neural recording, neural stimulation, neural regeneration, and therapeutic drug delivery. PMID:26414302

  13. [Alteration mechanisms of oxidative stress at periodontal tissues of rats in a simulated periodontitis and elaborate methods of their correction].

    Science.gov (United States)

    Хмиль, Елена В; Ляшенко, Лилия И; Янко, Наталия В; Хмиль, Дмитрий А; Каськова, Людмила Ф

    one of the peroxidation stress mechanisms is inducible NO synthase (iNOS) expression involved in the pathogenesis of periodontitis. to access the influence of isoform NO synthase (NOS) on alteration mechanisms of oxidative stress at periodontal tissues of 50 mature rats in a simulated periodontitis (SP). a SP at rats was induced by a high-carbohydrate, high-fat (HCHF) diet. Тreated SP rat groups were intragastrically administered with selective neuronal NOS (nNOS) inhibitor 7-nitroindazole, selective inducible NOS (iNOS) inhibitor aminoguanidine, and nitric oxide synthase substrate L-arginine. Oxidative stress level in the homogenated soft periodontal tissues was evaluated by TBARS (thiobarbituric acid reactive substances) level before and after 1,5 hours of incubation. Antioxidant response was evaluated by the increase in concentration of TBARS for incubation, аnd by antioxidant enzyme activity - superoxide dismutase and catalase. nNOS activity increase in a SP considerably limits oxidative stress activation at periodontal tissues, decreases antioxidant response, but heightens catalase activity. iNOS functional activity stimulates oxidative stress at periodontal tissues of rats, decreases antioxidant response. L-arginine in a MS effectively repaired antioxidant response at periodontal tissues that probably will give positive result at complex treatment of periodontitis and MS generally. in the near future, the appropriate regulation of NO activity by using NOS-active agents may provide a novel strategy for the periodontal disease prevention and correction in a MS.

  14. 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

  15. Effect of Butler's neural tissue mobilization and Mulligan's bent leg raise on pain and straight leg raise in patients of low back ache.

    Science.gov (United States)

    Tambekar, Neha; Sabnis, Shaila; Phadke, Apoorva; Bedekar, Nilima

    2016-04-01

    Low back ache (LBA) is a common musculoskeletal disorder sometimes associated with a positive limited Straight leg raise (SLR) test. Mulligan's bent leg raise (BLR) and Butler's neural tissue mobilization (NTM) are commonly used techniques for the treatment of low back ache where SLR is limited. The aim of this study was to evaluate the effect of both the techniques on pain and limited SLR in patients with LBA. Thirty one patients with LBA with radiculopathy were randomly allocated into 2 groups; BLR [n = 16] NTM [n = 15]. The outcome measures i.e. visual analogue scale (VAS) for pain and universal goniometer for measuring SLR range of motion (SROM) were assessed at the baseline, post intervention and after 24 h (follow up). Within group analysis using paired t-test revealed a significant difference between pre-treatment and post-treatment VAS and SROM score(p  0.05). The study showed that both techniques produce immediate improvement in pain and SLR range but this effect was not maintained during the follow up period. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Neural Crest Cell Implantation Restores Enteric Nervous System Function and Alters the Gastrointestinal Transcriptome in Human Tissue-Engineered Small Intestine.

    Science.gov (United States)

    Schlieve, Christopher R; Fowler, Kathryn L; Thornton, Matthew; Huang, Sha; Hajjali, Ibrahim; Hou, Xiaogang; Grubbs, Brendan; Spence, Jason R; Grikscheit, Tracy C

    2017-09-12

    Acquired or congenital disruption in enteric nervous system (ENS) development or function can lead to significant mechanical dysmotility. ENS restoration through cellular transplantation may provide a cure for enteric neuropathies. We have previously generated human pluripotent stem cell (hPSC)-derived tissue-engineered small intestine (TESI) from human intestinal organoids (HIOs). However, HIO-TESI fails to develop an ENS. The purpose of our study is to restore ENS components derived exclusively from hPSCs in HIO-TESI. hPSC-derived enteric neural crest cell (ENCC) supplementation of HIO-TESI establishes submucosal and myenteric ganglia, repopulates various subclasses of neurons, and restores neuroepithelial connections and neuron-dependent contractility and relaxation in ENCC-HIO-TESI. RNA sequencing identified differentially expressed genes involved in neurogenesis, gliogenesis, gastrointestinal tract development, and differentiated epithelial cell types when ENS elements are restored during in vivo development of HIO-TESI. Our findings validate an effective approach to restoring hPSC-derived ENS components in HIO-TESI and may implicate their potential for the treatment of enteric neuropathies. Published by Elsevier Inc.

  17. Study on method to simulate light propagation on tissue with characteristics of radial-beam LED based on Monte-Carlo method.

    Science.gov (United States)

    Song, Sangha; Elgezua, Inko; Kobayashi, Yo; Fujie, Masakatsu G

    2013-01-01

    In biomedical, Monte-carlo simulation is commonly used for simulation of light diffusion in tissue. But, most of previous studies did not consider a radial beam LED as light source. Therefore, we considered characteristics of a radial beam LED and applied them on MC simulation as light source. In this paper, we consider 3 characteristics of radial beam LED. The first is an initial launch area of photons. The second is an incident angle of a photon at an initial photon launching area. The third is the refraction effect according to contact area between LED and a turbid medium. For the verification of the MC simulation, we compared simulation and experimental results. The average of the correlation coefficient between simulation and experimental results is 0.9954. Through this study, we show an effective method to simulate light diffusion on tissue with characteristics for radial beam LED based on MC simulation.

  18. Simulation of diffuse photon migration in tissue by a Monte Carlo method derived from the optical scattering of spheroids.

    Science.gov (United States)

    Hart, Vern P; Doyle, Timothy E

    2013-09-01

    A Monte Carlo method was derived from the optical scattering properties of spheroidal particles and used for modeling diffuse photon migration in biological tissue. The spheroidal scattering solution used a separation of variables approach and numerical calculation of the light intensity as a function of the scattering angle. A Monte Carlo algorithm was then developed which utilized the scattering solution to determine successive photon trajectories in a three-dimensional simulation of optical diffusion and resultant scattering intensities in virtual tissue. Monte Carlo simulations using isotropic randomization, Henyey-Greenstein phase functions, and spherical Mie scattering were additionally developed and used for comparison to the spheroidal method. Intensity profiles extracted from diffusion simulations showed that the four models differed significantly. The depth of scattering extinction varied widely among the four models, with the isotropic, spherical, spheroidal, and phase function models displaying total extinction at depths of 3.62, 2.83, 3.28, and 1.95 cm, respectively. The results suggest that advanced scattering simulations could be used as a diagnostic tool by distinguishing specific cellular structures in the diffused signal. For example, simulations could be used to detect large concentrations of deformed cell nuclei indicative of early stage cancer. The presented technique is proposed to be a more physical description of photon migration than existing phase function methods. This is attributed to the spheroidal structure of highly scattering mitochondria and elongation of the cell nucleus, which occurs in the initial phases of certain cancers. The potential applications of the model and its importance to diffusive imaging techniques are discussed.

  19. 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

    2017-05-03

    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.

  20. 3D-CFD simulation and neural network model for the j and f factors of the wavy fin-and-flat tube heat exchangers

    Directory of Open Access Journals (Sweden)

    M Khoshvaght Aliabadi

    2011-09-01

    Full Text Available A three dimensional (3D computational fluid dynamics (CFD simulation and a neural network model are presented to estimate the behaviors of the Colburn factor (j and the Fanning friction factor (f for wavy fin - and - flat tube (WFFT heat exchangers. Effects of the five geometrical factors of fin pitch, fin height, fin length, fin thickness, and wavy amplitude are investigated over a wide range of Reynolds number (600simulation results express that the geometrical parameters of wavy fins have significant effects on the j and f factors as a function of Reynolds number. The computational results have an adequate accuracy when compared to experimental data. The accuracy of the calculations of the j and f factors are evaluated by the values of the absolute average relative deviation (AARD, being respectively 3.8% and 8.2% for the CFD simulation and 1.3% and 1% for the neural network model. Finally, new correlations are proposed to estimate the values of the j and f factors with 3.22% and 3.68% AARD respectively.

  1. Overreliance on auditory feedback may lead to sound/syllable repetitions: simulations of stuttering and fluency-inducing conditions with a neural model of speech production

    Science.gov (United States)

    Civier, Oren; Tasko, Stephen M.; Guenther, Frank H.

    2010-01-01

    This paper investigates the hypothesis that stuttering may result in part from impaired readout of feedforward control of speech, which forces persons who stutter (PWS) to produce speech with a motor strategy that is weighted too much toward auditory feedback control. Over-reliance on feedback control leads to production errors which, if they grow large enough, can cause the motor system to “reset” and repeat the current syllable. This hypothesis is investigated using computer simulations of a “neurally impaired” version of the DIVA model, a neural network model of speech acquisition and production. The model’s outputs are compared to published acoustic data from PWS’ fluent speech, and to combined acoustic and articulatory movement data collected from the dysfluent speech of one PWS. The simulations mimic the errors observed in the PWS subject’s speech, as well as the repairs of these errors. Additional simulations were able to account for enhancements of fluency gained by slowed/prolonged speech and masking noise. Together these results support the hypothesis that many dysfluencies in stuttering are due to a bias away from feedforward control and toward feedback control. PMID:20831971

  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. Necrotic pulp tissue dissolution by passive ultrasonic irrigation in simulated accessory canals: impact of canal location and angulation.

    Science.gov (United States)

    Al-Jadaa, A; Paqué, F; Attin, T; Zehnder, M

    2009-01-01

    To evaluate whether passive ultrasonic irrigation (PUI) of 2.5% NaOCl would dissolve necrotic pulp tissue from simulated accessory root canals (SACs) better than passive placement of the irrigant, when temperature was equilibrated between the two treatments. Transparent root canal models (n = 6) were made from epoxy resin. SACs of 0.2 mm diameter were placed at defined angles and positions in the mid-canal and apical area. SACs were filled with necrotic bovine pulp tissue. PUI was performed five times for 1 min each with irrigant replenishment after every minute. Main canal temperature was measured after each minute, and a digital photograph was taken. In control experiments, mock treatments were performed with the same set-up without activation of the file using heated NaOCl to mimic the temperature created by PUI. Experiments were repeated five times. Digital photographs were analysed for the distance of dissolved tissue into the SACs in mm. Overall comparison (sum of dissolved tissue from all five accessory canals) between treatments was performed using paired t-test. Differences between SAC angulation and position after PUI were investigated using anova/Bonferroni (alpha temperature in the main canal to 53.5 +/- 2.7 degrees C after the fifth minute. PUI dissolved a total of 6.4 +/- 2.1 mm, mock treatment controlled for heat: 1.4 +/- 0.6 mm (P temperature.

  4. 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

  5. A model for Monte Carlo simulation of low angle photon scattering in biological tissues

    CERN Document Server

    Tartari, A; Bonifazzi, C

    2001-01-01

    In order to include the molecular interference effect, a simple procedure is proposed and demonstrated to be able to update the usual cross section database for photon coherent scattering modelling in Monte Carlo codes. This effect was evaluated by measurement of coherent scattering distributions and by means of a model based on four basic materials composing biological tissues.

  6. Accessory extensor digiti minimi muscle simulating a soft tissue mass during surgery: a case report.

    Science.gov (United States)

    Natsis, Konstantinos; Papathanasiou, Efthymia; Anastasopoulos, Nikolas

    2010-01-01

    During a wrist ganglion excision originating at the tendon sheath of the extensor carpi ulnaris muscle, a soft tissue mass was observed just radial and distal to the surgical field. Dissection of the mass revealed an accessory extensor digiti minimi muscle belly which joined the radial extensor digiti minimi tendon. The surgical impact is discussed.

  7. Simulation of fracture healing incorporating mechanoregulation of tissue differentiation and dispersal/proliferation of cells

    NARCIS (Netherlands)

    Andreykiv, A.; Van Keulen, F.; Prendergast, P.J.

    2007-01-01

    Modelling the course of healing of a long bone subjected to loading has been the subject of several investigations. These have succeeded in predicting the differentiation of tissues in the callus in response to a static mechanical load and the diffusion of biological factors. In this paper an

  8. The development of a material model and wheel-tissue interaction for simulating wheeled surgical robot mobility.

    Science.gov (United States)

    Rentschler, Mark E; Reid, John D

    2009-04-01

    In vivo surgical robot wheel and tissue interaction was studied using a nonlinear finite element model. A liver material model, derived from laboratory experiments, was implemented as a viscoelastic material. A finite element simulation of this laboratory test confirmed the accuracy of the liver material model. This material model was then used as the tissue model to study wheel performance. A helical wheel moving on the liver model was used to replicate laboratory experiments that included several different slip ratios and applied loads. The drawbar force produced in this model showed good agreement with the physical tests. These results have provided the baseline for studying how changes in wheel geometry, such as tread height, tread spacing and wheel diameter, affect drawbar force and ultimately wheel performance. These results will be used in future surgical robot wheel designs.

  9. Near-infrared fluorescence (NIRF) imaging in breast-conserving surgery: assessing intraoperative techniques in tissue-simulating breast phantoms.

    Science.gov (United States)

    Pleijhuis, R G; Langhout, G C; Helfrich, W; Themelis, G; Sarantopoulos, A; Crane, L M A; Harlaar, N J; de Jong, J S; Ntziachristos, V; van Dam, G M

    2011-01-01

    Breast-conserving surgery (BCS) results in tumour-positive surgical margins in up to 40% of the patients. Therefore, new imaging techniques are needed that support the surgeon with real-time feedback on tumour location and margin status. In this study, the potential of near-infrared fluorescence (NIRF) imaging in BCS for pre- and intraoperative tumour localization, margin status assessment and detection of residual disease was assessed in tissue-simulating breast phantoms. Breast-shaped phantoms were produced with optical properties that closely match those of normal breast tissue. Fluorescent tumour-like inclusions containing indocyanine green (ICG) were positioned at predefined locations in the phantoms to allow for simulation of (i) preoperative tumour localization, (ii) real-time NIRF-guided tumour resection, and (iii) intraoperative margin assessment. Optical imaging was performed using a custom-made clinical prototype NIRF intraoperative camera. Tumour-like inclusions in breast phantoms could be detected up to a depth of 21 mm using a NIRF intraoperative camera system. Real-time NIRF-guided resection of tumour-like inclusions proved feasible. Moreover, intraoperative NIRF imaging reliably detected residual disease in case of inadequate resection. We evaluated the potential of NIRF imaging applications for BCS. The clinical setting was simulated by exploiting tissue-like breast phantoms with fluorescent tumour-like agarose inclusions. From this evaluation, we conclude that intraoperative NIRF imaging is feasible and may improve BCS by providing the surgeon with imaging information on tumour location, margin status, and presence of residual disease in real-time. Clinical studies are needed to further validate these results. Copyright © 2010 Elsevier Ltd. All rights reserved.

  10. Evaluation Different Boundary Condition in Depth of Tissue for the Task of Mathematical Simulation of Heat Transfer in Human Skin

    Directory of Open Access Journals (Sweden)

    Korobkina Dariya V.

    2014-01-01

    Full Text Available The problem of an estimation of thermal defeats at influence of affected factors of fire has great value for various areas of technics, industry and medicine. Results of mathematical simulation of heat transfer in layered structure of human skin influenced by the radiant thermal flux of the set value are presented in the work. The three-layer system of skin is considered. Research according to the various boundary conditions exposed in human tissue is carried out. Temperature distribution on thickness of skin is presented.

  11. Catheter simulator software tool to generate electrograms of any multi-polar diagnostic catheter from 3D atrial tissue.

    Science.gov (United States)

    Shillieto, Kristina E; Ganesan, Prasanth; Salmin, Anthony J; Cherry, Elizabeth M; Pertsov, Arkady M; Ghoraani, Behnaz

    2016-08-01

    Simulations are excellent tools for assessing new therapeutic strategies and are often conducted before implementing new therapy options in a clinical practice. For patients suffering from a heart arrhythmia, the main source of information comes from an intracardiac catheter. One of the common catheters is a Lasso multi-pole diagnostic catheter, which is a catheter that has 20 electrodes in a circular pattern. In this paper, we developed algorithm and simulation software that allows the users to place a multi-pole catheter on the atrial endocardial surface and record electrograms. In 3D atrial tissue, the plane of principal curvature is determined using eigenvectors of catheter vertices, from where the normals are projected and registered to the surface using 3D geodesic distance. This tool provides a platform for performing customized virtual cardiac experiments.

  12. 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

  13. 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)

  14. Primary cutaneous actinomycosis of the foot simulating a soft tissue neoplasm: a case report; Actinomicose cutanea primaria do pe simulando neoplasia de partes moles: relato de caso

    Energy Technology Data Exchange (ETDEWEB)

    Vieira, Renata La Rocca; Meirelles, Gustavo de Souza Portes; Yamashita, Jane; Oliveira, Heverton Cesar de; Fernandes, Artur da Rocha Correa [Universidade Federal de Sao Paulo (UNIFESP), SP (Brazil). Escola Paulista de Medicina (EPM). Dept. de Diagnostico por Imagem; Turrini, Elizabete [Universidade Federal de Sao Paulo (UNIFESP), SP (Brazil). Escola Paulista de Medicina (EPM). Disciplina de Dermatologia]. E-mail: renatalarocca@bol.com.br

    2003-08-01

    We report a case of a patient with primary cutaneous actinomycosis of the foot simulating a soft tissue neoplasm. A literature review on the incidence, clinical features, pathology and imaging findings is also presented. The plain films and magnetic resonance imaging findings and the pathology results are presented. This paper reports a rare disease occurring in an atypical location, simulating a soft tissue neoplasm. (author)

  15. A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.

    Science.gov (United States)

    Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; Moore, Kathleen; Liu, Hong; Zheng, Bin

    2017-06-01

    Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estimate size of fat areas, this study aims to develop and test a computer-aided detection (CAD) scheme based on deep learning technique to automatically segment subcutaneous fat areas (SFA) and visceral fat areas (VFA) depicting on volumetric CT images. A retrospectively collected CT image dataset was divided into two independent training and testing groups. The proposed CAD framework consisted of two steps with two convolution neural networks (CNNs) namely, Selection-CNN and Segmentation-CNN. The first CNN was trained using 2,240 CT slices to select abdominal CT slices depicting SFA and VFA. The second CNN was trained with 84,000pixel patches and applied to the selected CT slices to identify fat-related pixels and assign them into SFA and VFA classes. Comparing to the manual CT slice selection and fat pixel segmentation results, the accuracy of CT slice selection using the Selection-CNN yielded 95.8%, while the accuracy of fat pixel segmentation using the Segmentation-CNN was 96.8%. This study demonstrated the feasibility of applying a new deep learning based CAD scheme to automatically recognize abdominal section of human body from CT scans and segment SFA and VFA from volumetric CT data with high accuracy or agreement with the manual segmentation results. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. The Virtual Liver Project: Simulating Tissue Injury Through Molecular and Cellular Processes

    Science.gov (United States)

    Efficiently and humanely testing the safety of thousands of environmental chemicals is a challenge. The US EPA Virtual Liver Project (v-Liver™) is aimed at simulating the effects of environmental chemicals computationally in order to estimate the risk of toxic outcomes in humans...

  17. Interval type-2 fuzzy neural network controller for a multivariable anesthesia system based on a hardware-in-the-loop simulation.

    Science.gov (United States)

    El-Nagar, Ahmad M; El-Bardini, Mohammad

    2014-05-01

    This manuscript describes the use of a hardware-in-the-loop simulation to simulate the control of a multivariable anesthesia system based on an interval type-2 fuzzy neural network (IT2FNN) controller. The IT2FNN controller consists of an interval type-2 fuzzy linguistic process as the antecedent part and an interval neural network as the consequent part. It has been proposed that the IT2FNN controller can be used for the control of a multivariable anesthesia system to minimize the effects of surgical stimulation and to overcome the uncertainty problem introduced by the large inter-individual variability of the patient parameters. The parameters of the IT2FNN controller were trained online using a back-propagation algorithm. Three experimental cases are presented. All of the experimental results show good performance for the proposed controller over a wide range of patient parameters. Additionally, the results show better performance than the type-1 fuzzy neural network (T1FNN) controller under the effect of surgical stimulation. The response of the proposed controller has a smaller settling time and a smaller overshoot compared with the T1FNN controller and the adaptive interval type-2 fuzzy logic controller (AIT2FLC). The values of the performance indices for the proposed controller are lower than those obtained for the T1FNN controller and the AIT2FLC. The IT2FNN controller is superior to the T1FNN controller for the handling of uncertain information due to the structure of type-2 fuzzy logic systems (FLSs), which are able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of the FLSs. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. The Use of Modular, Electronic Neuron Simulators for Neural Circuit Construction Produces Learning Gains in an Undergraduate Anatomy and Physiology Course.

    Science.gov (United States)

    Petto, Andrew; Fredin, Zachary; Burdo, Joseph

    2017-01-01

    During the spring of 2016 at the University of Wisconsin-Milwaukee, we implemented a novel educational technology designed to teach undergraduates about the nervous system while allowing them to physically construct their own neural circuits. Modular, electronic neuron simulators called NeuroBytes were used by the students in BIOSCI202 Anatomy and Physiology I, a four-credit course consisting of three hours per week each of lecture and laboratory time. 162 students participated in the laboratory sessions that covered reflexes; 83 in the experimental sections used the NeuroBytes to build a model of the patellar tendon reflex, while 79 in the control sections participated in alternate reflex curricula. To address the question of whether or not the NeuroBytes-based patellar tendon reflex simulation brought about learning gains, the control and experimental group students underwent pre/post testing before and after their laboratory sections. We found that for several of the neuroscience and physiology concepts assessed on the test, the experimental group students had significantly greater declarative learning gains between the pre- and post-test as compared to the control group students. While there are numerous virtual neuroscience education tools available to undergraduate educators, there are relatively few designed to engage students in the basics of electrophysiology and neural circuitry using physical manipulatives, and none to our knowledge that allow them to build circuits from functioning hand-held "neurons."

  19. 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.

  20. Characterization and standardization of tissue-simulating protoporphyrin IX optical phantoms.

    Science.gov (United States)

    Marois, Mikael; Bravo, Jaime; Davis, Scott C; Kanick, Stephen Chad

    2016-03-01

    Optical devices for measuring protoporphryin IX (PpIX) fluorescence in tissue are routinely validated by measurements in optical phantoms. Yet there exists limited data to form a consensus on the recipe for phantoms that both mimic the optical properties found in tissue and yield a reliable and stable relationship between PpIX concentration and the fluorescence remission intensity. This study characterizes the influence of multiple phantom components on PpIX fluorescence emission intensity, using Intralipid as the scattering source, bovine whole blood as the background absorber, and Tween as a surfactant to prevent PpIX aggregation. Optical measurements showed a linear proportionality (r > 0.99) between fluorescence intensity and PpIX concentration (0.1 to 10 μg/mL) over a range of Intralipid (1 to 2%) and whole blood (0.5 to 3%) for phantoms containing low surfactant (≤ 0.1%), with fluorescence intensities and scattering and absorption properties stable for 5 h after mixing. The role of surfactant in PpIX phantoms was found to be complex, as aggregation was evident in aqueous nonturbid phantoms with no surfactant (0% Tween), and avoided in phantoms containing Intralipid as the scattering source with no additional or low amounts of added surfactant (≤ 0.1% Tween). Conversely, phantoms containing higher surfactant content (>0.1% Tween) and whole blood showed interactions that distorted the fluorescence emissions.

  1. Analysis of nanoparticles optical propagation influence in biological tissue simulating phantoms

    Science.gov (United States)

    Rodríguez-Colmenares, Miguel A.; Fanjul-Vélez, Félix; Arévalo-Díaz, Laura; Arce-Diego, José L.

    2017-02-01

    The applications of nanoparticles in optical techniques of diagnosis and treatment of biological tissues are increasing. Image contrast can be improved in diagnostic approaches such as fluorescence, spectroscopy or optical coherence tomography. The therapeutic effect can be increased if nanoparticles are previously incorporated in the biological tissue. This is the case in thermotherapy, or in Photodynamic Therapy. All these applications take advantage of specific properties of the nanoparticles involved, either optical up- or down-conversion, thermal confinement or the ability to act as a drug-carrier. Although many biomedical applications that involve nanoparticles are being proposed and tested, there is a need to take into account the influence of those nanoparticles on optical radiation propagation. The previously mentioned optical treatment and diagnosis techniques assume a particular optical propagation pattern, which is altered by the addition of nanoparticles. This change depends on the nanoparticle material, shape, size and concentration, among other parameters. In order to try to quantify these changes, in this work several phantoms that include different nanoparticles are analyzed, in order to estimate the influence of nanoparticles in optical propagation. A theoretical model of optical propagation, which takes into account the absorption and scattering changes in the medium, is also considered. Nanoparticles of different sizes from 40 nm to 1 μm are analyzed. Nanoparticle materials of interest in biomedical applications are employed. The results are relevant in diagnosis interpretation of images and treatment outcome evaluation when nanoparticles are present.

  2. 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.

  3. Use of Artificial Neural Network for the Simulation of Radon Emission Concentration of Granulated Blast Furnace Slag Mortar.

    Science.gov (United States)

    Jang, Hong-Seok; Xing, Shuli; Lee, Malrey; Lee, Young-Keun; So, Seung-Young

    2016-05-01

    In this study, an artificial neural networks study was carried out to predict the quantity of radon of Granulated Blast Furnace Slag (GBFS) cement mortar. A data set of a laboratory work, in which a total of 3 mortars were produced, was utilized in the Artificial Neural Networks (ANNs) study. The mortar mixture parameters were three different GBFS ratios (0%, 20%, 40%). Measurement radon of moist cured specimens was measured at 3, 10, 30, 100, 365 days by sensing technology for continuous monitoring of indoor air quality (IAQ). ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of two input parameters that cover the cement, GBFS and age of samples and, an output parameter which is concentrations of radon emission of mortar. The results showed that ANN can be an alternative approach for the predicting the radon concentration of GBFS mortar using mortar ingredients as input parameters.

  4. Neural Network Based Simulation of Micro Creeping Fibrous Composites SiC/Al6061 for Plastic Behaviour

    Science.gov (United States)

    Monfared, Vahid

    2017-03-01

    The present work presents a new approach based on neural network prediction for simple and fast estimation of the creep plastic behaviour of the short fiber composites. Also, this approach is proposed to reduce the solution procedure. Moreover, as a significant application of the method, shuttles and spaceships, turbine blades and discs are generally subjected to the creep effects. Consequently, analysis of the creep phenomenon is required and vital in different industries. Analysis of the creep behaviour is required for failure, fracture, fatigue, and creep resistance of the optoelectronic/photonic composites, and sensors. One of the main applications of the present work is in designing the composites with optical fibers and devices. At last, a good agreement is seen among the present prediction by neural network approach, finite element method (FEM), and the experimental results.

  5. 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

    2017-01-20

    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.

  6. Wave Propagation of Junctional Remodeling in Collective Cell Movement of Epithelial Tissue: Numerical Simulation Study

    Directory of Open Access Journals (Sweden)

    Tetsuya Hiraiwa

    2017-07-01

    Full Text Available During animal development, epithelial cells forming a monolayer sheet move collectively to achieve the morphogenesis of epithelial tissues. One driving mechanism of such collective cell movement is junctional remodeling, which is found in the process of clockwise rotation of Drosophila male terminalia during metamorphosis. However, it still remains unknown how the motions of cells are spatiotemporally organized for collective movement by this mechanism. Since these moving cells undergo elastic deformations, the influence of junctional remodeling may mechanically propagate among them, leading to spatiotemporal pattern formations. Here, using a numerical cellular vertex model, we found that the junctional remodeling in collective cell movement exhibits spatiotemporal self-organization without requiring spatial patterns of molecular signaling activity. The junctional remodeling propagates as a wave in a specific direction with a much faster speed than that of cell movement. Such propagation occurs in both the absence and presence of fluctuations in the contraction of cell boundaries.

  7. 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...

  8. FLUKA and PENELOPE simulations of 10 keV to 10 MeV photons in LYSO and soft tissue

    Science.gov (United States)

    Chin, M. P. W.; Böhlen, T. T.; Fassò, A.; Ferrari, A.; Ortega, P. G.; Sala, P. R.

    2014-02-01

    Monte Carlo simulations of electromagnetic particle interactions and transport by FLUKA and PENELOPE were compared. 10 keV 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 over-estimate 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 keV sharp line, whereas FLUKA produced visible acolinearity, a feature recently implemented to account for the momentum of shell electrons.

  9. 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.

  10. Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture and Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Water

    National Research Council Canada - National Science Library

    Partin, Alan

    2000-01-01

    Purpose: To combine clinical, serum, pathologic and computer derived information into an artificial neural network to develop/validate a model to predict prostate cancer tumor aggressiveness in both a...

  11. Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture & Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Material

    National Research Council Canada - National Science Library

    Partin, Alan

    1999-01-01

    the purpose of this report is to combine clinical, serum, pathological and computer derived information into an artificial neural network to develop/validate a model to predict prostate cancer tumor...

  12. Fast porous visco-hyperelastic soft tissue model for surgery simulation: application to liver surgery.

    Science.gov (United States)

    Marchesseau, Stéphanie; Heimann, Tobias; Chatelin, Simon; Willinger, Rémy; Delingette, Hervé

    2010-12-01

    Understanding and modeling liver biomechanics represents a significant challenge due to its complex nature. In this paper, we tackle this issue in the context of real-time surgery simulation where a compromise between biomechanical accuracy and computational efficiency must be found. We describe a realistic liver model including hyperelasticity, porosity and viscosity that is implemented within an implicit time integration scheme. To optimize its computation, we introduce the Multiplicative Jacobian Energy Decomposition (MJED) method for discretizing hyperelastic materials on linear tetrahedral meshes which leads to faster matrix assembly than the standard Finite Element Method. Visco-hyperelasticity is modeled by Prony series while the mechanical effect of liver perfusion is represented with a linear Darcy law. Dynamic mechanical analysis has been performed on 60 porcine liver samples in order to identify some viscoelastic parameters. Finally, we show that liver deformation can be simulated in real-time on a coarse mesh and study the relative effects of the hyperelastic, viscous and porous components on the liver biomechanics. Copyright © 2010 Elsevier Ltd. All rights reserved.

  13. The role of cue information in the outcome-density effect: evidence from neural network simulations and a causal learning experiment

    Science.gov (United States)

    Musca, Serban C.; Vadillo, Miguel A.; Blanco, Fernando; Matute, Helena

    2010-06-01

    Although normatively irrelevant to the relationship between a cue and an outcome, outcome density (i.e. its base-rate probability) affects people's estimation of causality. By what process causality is incorrectly estimated is of importance to an integrative theory of causal learning. A potential explanation may be that this happens because outcome density induces a judgement bias. An alternative explanation is explored here, following which the incorrect estimation of causality is grounded in the processing of cue-outcome information during learning. A first neural network simulation shows that, in the absence of a deep processing of cue information, cue-outcome relationships are acquired but causality is correctly estimated. The second simulation shows how an incorrect estimation of causality may emerge from the active processing of both cue and outcome information. In an experiment inspired by the simulations, the role of a deep processing of cue information was put to test. In addition to an outcome density manipulation, a shallow cue manipulation was introduced: cue information was either still displayed (concurrent) or no longer displayed (delayed) when outcome information was given. Behavioural and simulation results agree: the outcome-density effect was maximal in the concurrent condition. The results are discussed with respect to the extant explanations of the outcome-density effect within the causal learning framework.

  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. 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.

  16. Computational simulation: astrocyte-induced depolarization of neighboring neurons mediates synchronous UP states in a neural network.

    Science.gov (United States)

    Kuriu, Takayuki; Kakimoto, Yuta; Araki, Osamu

    2015-09-01

    Although recent reports have suggested that synchronous neuronal UP states are mediated by astrocytic activity, the mechanism responsible for this remains unknown. Astrocytic glutamate release synchronously depolarizes adjacent neurons, while synaptic transmissions are blocked. The purpose of this study was to confirm that astrocytic depolarization, propagated through synaptic connections, can lead to synchronous neuronal UP states. We applied astrocytic currents to local neurons in a neural network consisting of model cortical neurons. Our results show that astrocytic depolarization may generate synchronous UP states for hundreds of milliseconds in neurons even if they do not directly receive glutamate release from the activated astrocyte.

  17. 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.

  18. Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

    Science.gov (United States)

    Behler, Jörg; Martoňák, Roman; Donadio, Davide; Parrinello, Michele

    2008-05-01

    We study in a systematic way the complex sequence of the high-pressure phases of silicon obtained upon compression by combining an accurate high-dimensional neural network representation of the density-functional theory potential-energy surface with the metadynamics scheme. Starting from the thermodynamically stable diamond structure at ambient conditions we are able to identify all structural phase transitions up to the highest-pressure fcc phase at about 100 GPa. The results are in excellent agreement with experiment. The method developed promises to be of great value in the study of inorganic solids, including those having metallic phases.

  19. Isolation of dentin tissue by usinga new liner biodentine at management of simulated experimental caries.

    Science.gov (United States)

    Ustiashvili, M; Mamaladze, M; Sanodze, L; Labuchidze, G

    2014-06-01

    The aim of our study was the use of different types of isolation systems in the treatment of experimentally simulated dental caries that will allow to present additional comparative characteristic for morphological responses of the pulp.For realization of this purpose, 3 systems have been selected: UltraBlend (Ultradent), Biodentine (Septodont) and adhesive system Prime&Bond NT (Dentsply). The study was conducted at the laboratory of Alexander Natishvili Institute of Morphology. For this experiment, 12 male, 6 months of age rabbits were selected. There were created 3 experimental groups, each of which included 4 rabbits. Restoration of the teeth in experimental rabbits with Biodentine revealed sufficient physical properties enabling the operator most comfortably conduct his/her clinical activities: kneading, bringing into caries cavity, condensing and filling dental defect. Isolation of dentin by Biodentine doesn't contradict and/or reduce application of adhesive systems, which is also important for teeth restorations. Biodentine has optimal working time (final curing 10-12 minutes) enabling the operator to conduct maximal formation of material at the bottom of caries cavity.

  20. 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.

  1. Growth of plant tissue cultures in simulated lunar soil: Implications for a lunar base CELSS (Controlled Ecological Life Support System)

    Science.gov (United States)

    Venketeswaran, S.

    1988-01-01

    Experiments were carried out on plant tissue cultures, seed germination, seedling development and plants grown on Simulated Lunar Soil to evaluate the potential of future development of lunar based agriculture. The studies done to determine the effect of the placement of SLS on tissue cultures showed no adverse effect of SLS on tissue cultures. Although statistically insignificant, SLS in suspension showed a comparatively higher growth rate. Observations indicate the SLS, itself cannot support calli growth but was able to show a positive effect on growth rate of calli when supplemented with MS salts. This positive effect related to nutritive value of the SLS was found to have improved at high pH levels, than at the recommended low pH levels for standard media. Results from seed germination indicated that there is neither inhibitory, toxicity nor stimulatory effect of SLS, even though SLS contains high amounts of aluminum compounds compared to earth soil. Analysis of seeding development and growth data showed significant reduction in growth rate indicating that, SLS was a poor growth medium for plant life. This was confirmed by the studies done with embryos and direct plant growth on SLS. Further observations attributed this poor quality of SLS is due to it's lack of essential mineral elements needed for plant growth. By changing the pH of the soil, to more basic conditions, the quality of SLS for plant growth could be improved up to a significant level. Also it was found that the quality of SLS could be improved by almost twice, by external supply of major mineral elements, directly to SLS.

  2. Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks.

    Science.gov (United States)

    Lashkari, Negin; Poshtan, Javad; Azgomi, Hamid Fekri

    2015-11-01

    The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contributing factors that inevitably affect stator currents and therefore the three-phase shift. Thus, it is necessary to propose a method that is able to identify whether the unbalance of three currents is caused by ITSC or supply voltage fault. This paper presents a feedforward multilayer-perceptron Neural Network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift. The data which are required for training and test NN are generated using simulated model of stator. The experimental results are presented to verify the superior accuracy of the proposed method. Copyright © 2015. Published by Elsevier Ltd.

  3. Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model

    Directory of Open Access Journals (Sweden)

    Vladimir M. Krasnopolsky

    2013-01-01

    Full Text Available A novel approach based on the neural network (NN ensemble technique is formulated and used for development of a NN stochastic convection parameterization for climate and numerical weather prediction (NWP models. This fast parameterization is built based on learning from data simulated by a cloud-resolving model (CRM initialized with and forced by the observed meteorological data available for 4-month boreal winter from November 1992 to February 1993. CRM-simulated data were averaged and processed to implicitly define a stochastic convection parameterization. This parameterization is learned from the data using an ensemble of NNs. The NN ensemble members are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived following this approach is estimated. The newly developed NN convection parameterization has been tested in National Center of Atmospheric Research (NCAR Community Atmospheric Model (CAM. It produced reasonable and promising decadal climate simulations for a large tropical Pacific region. The extent of the adaptive ability of the developed NN parameterization to the changes in the model environment is briefly discussed. This paper is devoted to a proof of concept and discusses methodology, initial results, and the major challenges of using the NN technique for developing convection parameterizations for climate and NWP models.

  4. voltage compensation using artificial neural network

    African Journals Online (AJOL)

    Offor Theophilos

    VOLTAGE COMPENSATION USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY OF. RUMUOLA ... using artificial neural network (ANN) controller based dynamic voltage restorer (DVR). ... substation by simulating with sample of average voltage for Omerelu, Waterlines, Rumuola, Shell Industrial and Barracks.

  5. 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.

  6. 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.

  7. FNS: an event-driven spiking neural network framework for efficient simulations of large-scale brain models

    OpenAIRE

    Susi, Gianluca; Garces, Pilar; Cristini, Alessandro; Paracone, Emanuele; Salerno, Mario; Maestu, Fernando; Pereda, Ernesto

    2018-01-01

    Limitations in processing capabilities and memory of today's computers make spiking neuron-based (human) whole-brain simulations inevitably characterized by a compromise between bio-plausibility and computational cost. It translates into brain models composed of a reduced number of neurons and a simplified neuron's mathematical model. Taking advantage of the sparse character of brain-like computation, eventdriven technique allows us to carry out efficient simulation of large-scale Spiking Neu...

  8. Neural simulation of actions: effector- versus action-specific motor maps within the human premotor and posterior parietal area?

    Science.gov (United States)

    Lorey, Britta; Naumann, Tim; Pilgramm, Sebastian; Petermann, Carmen; Bischoff, Matthias; Zentgraf, Karen; Stark, Rudolf; Vaitl, Dieter; Munzert, Jörn

    2014-04-01

    This study addresses the controversy over how motor maps are organized during action simulation by examining whether action simulation states, that is, motor imagery and action observation, run on either effector-specific and/or action-specific motor maps. Subjects had to observe or imagine three types of movements effected by the right hand or the right foot with different action goals. The functional magnetic resonance imaging results showed an action-specific organization within premotor and posterior parietal areas of both hemispheres during action simulation, especially during action observation. There were also less pronounced effector-specific activation sites during both simulation processes. It is concluded that the premotor and parietal areas contain multiple motor maps rather than a single, continuous map of the body. The forms of simulation (observation, imagery), the task contexts (movements related to an object, with usual/unusual effector), and the underlying reason for performing the simulation (rate your subjective success afterwards) lead to the specific use of different representational motor maps within both regions. In our experimental setting, action-specific maps are dominant especially, during action observation, whereas effector-specific maps are recruited to only a lesser degree. Copyright © 2013 Wiley Periodicals, Inc.

  9. General regression neural network and Monte Carlo simulation model for survival and growth of Salmonella on raw chicken skin as a function of serotype, temperature and time for use in risk assessment

    Science.gov (United States)

    A general regression neural network and Monte Carlo simulation model for predicting survival and growth of Salmonella on raw chicken skin as a function of serotype (Typhimurium, Kentucky, Hadar), temperature (5 to 50C) and time (0 to 8 h) was developed. Poultry isolates of Salmonella with natural r...

  10. 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.

  11. Two-stage ionoacoustic range verification leveraging Monte Carlo and acoustic simulations to stably account for tissue inhomogeneity and accelerator-specific time structure - A simulation study.

    Science.gov (United States)

    Patch, Sarah K; Hoff, Daniel E M; Webb, Tyler B; Sobotka, Lee G; Zhao, Tianyu

    2018-02-01

    Range errors constrain treatment planning by limiting choice of ion beam angles and requiring large margins. Ionoacoustic range verification requires recovering the location of an acoustic source from low frequency signals. A priori information is applied to stably overcome resolution limits of inverse acoustic source imaging in this simulation study. In particular, the accuracy and robustness of ionoacoustic range verification for lateral and oblique delivery of high-energy protons to the prostate is examined. Dose maps were computed using GEANT4 Monte Carlo simulations via the TOPAS user interface. Thermoacoustic pulses were propagated using k-Wave software, with initial pressures corresponding to instantaneous dose deposition and piecewise constant maps of tissue properties derived from the planning CT. A database of dose maps with corresponding thermoacoustic emissions and Bragg peak locations, referred to as "control points," were precomputed. Corresponding thermoacoustic emissions were also precomputed. Pulses were recorded at four coplanar locations corresponding to the outer surface of a virtual transrectal array. To model experimental beam delivery, k-Wave results were convolved in time with a Gaussian envelope to account for noninstantaneous proton delivery by a synchrocyclotron. Thermoacoustic pulses were bandlimited below 150 kHz, and amplitudes were directly proportional to charge delivered. To test robustness of our method, white noise was added. Range was estimated in a two-step process. The first step obtained a preliminary range estimate by one-way beamforming. The second step was taken using data corresponding to the "control point" nearest to the preliminary range estimate. For each receiver, the time of flight difference, ∆t, between the measured and control thermoacoustic signals were accurately estimated by applying the Fourier shift theorem. Receiver-Bragg peak distance was then estimated by adding vs ∆t to the known distance of the

  12. Modelling personal exposure to particulate air pollution: an assessment of time-integrated activity modelling, Monte Carlo simulation & artificial neural network approaches.

    Science.gov (United States)

    McCreddin, A; Alam, M S; McNabola, A

    2015-01-01

    An experimental assessment of personal exposure to PM10 in 59 office workers was carried out in Dublin, Ireland. 255 samples of 24-h personal exposure were collected in real time over a 28 month period. A series of modelling techniques were subsequently assessed for their ability to predict 24-h personal exposure to PM10. Artificial neural network modelling, Monte Carlo simulation and time-activity based models were developed and compared. The results of the investigation showed that using the Monte Carlo technique to randomly select concentrations from statistical distributions of exposure concentrations in typical microenvironments encountered by office workers produced the most accurate results, based on 3 statistical measures of model performance. The Monte Carlo simulation technique was also shown to have the greatest potential utility over the other techniques, in terms of predicting personal exposure without the need for further monitoring data. Over the 28 month period only a very weak correlation was found between background air quality and personal exposure measurements, highlighting the need for accurate models of personal exposure in epidemiological studies. Copyright © 2014 Elsevier GmbH. All rights reserved.

  13. Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks.

    Science.gov (United States)

    Skovsen, Søren; Dyrmann, Mads; Mortensen, Anders Krogh; Steen, Kim Arild; Green, Ole; Eriksen, Jørgen; Gislum, René; Jørgensen, Rasmus Nyholm; Karstoft, Henrik

    2017-12-17

    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%.

  14. Optimization of a polymer composite employing molecular mechanic simulations and artificial neural networks for a novel intravaginal bioadhesive drug delivery device.

    Science.gov (United States)

    Ndesendo, Valence M K; Pillay, Viness; Choonara, Yahya E; du Toit, Lisa C; Kumar, Pradeep; Buchmann, Eckhart; Meyer, Leith C R; Khan, Riaz A

    2012-01-01

    This study aimed at elucidating an optimal synergistic polymer composite for achieving a desirable molecular bioadhesivity and Matrix Erosion of a bioactive-loaded Intravaginal Bioadhesive Polymeric Device (IBPD) employing Molecular Mechanic Simulations and Artificial Neural Networks (ANN). Fifteen lead caplet-shaped devices were formulated by direct compression with the model bioactives zidovudine and polystyrene sulfonate. The Matrix Erosion was analyzed in simulated vaginal fluid to assess the critical integrity. Blueprinting the molecular mechanics of bioadhesion between vaginal epithelial glycoprotein (EGP), mucin (MUC) and the IBPD were performed on HyperChem 8.0.8 software (MM+ and AMBER force fields) for the quantification and characterization of correlative molecular interactions during molecular bioadhesion. Results proved that the IBPD bioadhesivity was pivoted on the conformation, orientation, and poly(acrylic acid) (PAA) composition that interacted with EGP and MUC present on the vaginal epithelium due to heterogeneous surface residue distributions (free energy= -46.33 kcalmol(-1)). ANN sensitivity testing as a connectionist model enabled strategic polymer selection for developing an IBPD with an optimally prolonged Matrix Erosion and superior molecular bioadhesivity (ME = 1.21-7.68%; BHN = 2.687-4.981 N/mm(2)). Molecular modeling aptly supported the EGP-MUC-PAA molecular interaction at the vaginal epithelium confirming the role of PAA in bioadhesion of the IBPD once inserted into the posterior fornix of the vagina.

  15. 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.

  16. Development of Temperature Distribution and Light Propagation Model in Biological Tissue Irradiated by 980 nm Laser Diode and Using COMSOL Simulation.

    Science.gov (United States)

    Shurrab, Kawthar; Kochaji, Nabil; Bachir, Wesam

    2017-01-01

    Introduction: The purpose of this project is to develop a mathematical model to investigate light distribution and study effective parameters such as laser power and irradiated time to get the optimal laser dosage to control hyperthermia. This study is expected to have a positive impact and a better simulation on laser treatment planning of biological tissues. Moreover, it may enable us to replace animal tests with the results of a COMSOL predictive model. Methods: We used in this work COMSOL5 model to simulate the light diffusion and bio-heat equation of the mouse tissue when irradiated by 980 nm laser diode and the effect of different parameters (laser power, and irradiated time) on the surrounding tissue of the tumor treatment in order to prevent damage from excess heat Results: The model was applied to study light propagation and several parameters (laser power, irradiated time) and their impact on light-heat distribution within the tumor in the mouse back tissue The best result is at laser power 0.5 W and time irradiation 0.5 seconds in order to get the maximum temperature hyperthermia at 52°C. Conclusion: The goal of this study is to simulate a mouse model to control excess heating of tissue and reduce the number of animals in experimental research to get the best laser parameters that was safe for use in living animals and in human subjects.

  17. The biomechanical determinants of concussion: finite element simulations to investigate brain tissue deformations during sporting impacts to the unprotected head.

    Science.gov (United States)

    Patton, Declan A; McIntosh, Andrew S; Kleiven, Svein

    2013-12-01

    Concussion is an injury of specific interest in collision and contact sports, resulting in a need to develop effective preventive strategies. A detailed finite element model of the human head was used to approximate the regional distribution of tissue deformations in the brain by simulating reconstructions of unhelmeted concussion and no-injury head impacts. The results were evaluated using logistic regression analysis and it was found that angular kinematics, in the coronal plane, and maximum principal strains, in all regions of the brain, were significantly associated with concussion. The results suggested that impacts to the temporal region of the head cause coronal rotations, which render injurious strain levels in the brain. Tentative strain tolerance levels of 0.13, 0.15, and 0.26 in the thalamus, corpus callosum, and white matter, respectively, for a 50% likelihood of concussion were determined by logistic regression. The tentative strain tolerance levels compared well with previously reported results from reconstruction studies of American football and single axon, optic nerve, and brain slice culture model studies. The methods used in the current study provide an opportunity to collect unique kinematic data of sporting impacts to the unprotected head, which can be employed in various ways to broaden the understanding of concussion.

  18. Three-dimensional finite element stress analysis: the technique and methodology of non-linear property simulation and soft tissue loading behavior for different partial denture designs.

    Science.gov (United States)

    Kanbara, Ryo; Nakamura, Yoshinori; Ochiai, Kent T; Kawai, Tatsushi; Tanaka, Yoshinobu

    2012-01-01

    The purpose of this study was to develop and report upon a methodology for a non-linear capacity 3D modeling finite element analysis evaluating the loading behavior of different partial denture designs. A 3D finite element model using human CT data was constructed. An original material constant conversion program was implemented in the data simulation of non-linear tissue behavior. The finite element method material properties of residual ridge mucosa were found to have seven material constants and six conversion points of stress values. Periodontal tissues were found to have three constants, and two conversion points. Three magnetic attachment partial denture designs with different bracing elements were evaluated. Technical procedures for finite element model simulation of nonlinear tissue behavior properties evaluating the oral behavior of prosthetic device designs are reported for prosthodontic testing. The use of horizontal cross-arch bracing positively impacts upon the comparative stability of the partial denture designs tested.

  19. Rod-Shaped Neural Units for Aligned 3D Neural Network Connection.

    Science.gov (United States)

    Kato-Negishi, Midori; Onoe, Hiroaki; Ito, Akane; Takeuchi, Shoji

    2017-08-01

    This paper proposes neural tissue units with aligned nerve fibers (called rod-shaped neural units) that connect neural networks with aligned neurons. To make the proposed units, 3D fiber-shaped neural tissues covered with a calcium alginate hydrogel layer are prepared with a microfluidic system and are cut in an accurate and reproducible manner. These units have aligned nerve fibers inside the hydrogel layer and connectable points on both ends. By connecting the units with a poly(dimethylsiloxane) guide, 3D neural tissues can be constructed and maintained for more than two weeks of culture. In addition, neural networks can be formed between the different neural units via synaptic connections. Experimental results indicate that the proposed rod-shaped neural units are effective tools for the construction of spatially complex connections with aligned nerve fibers in vitro. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. Performance and complementarity of two systemic models (reservoir and neural networks) used to simulate spring discharge and piezometry for a karst aquifer

    Science.gov (United States)

    Kong-A-Siou, Line; Fleury, Perrine; Johannet, Anne; Borrell Estupina, Valérie; Pistre, Séverin; Dörfliger, Nathalie

    2014-11-01

    Karst aquifers can provide previously untapped freshwater resources and have thus generated considerable interest among stakeholders involved in the water supply sector. Here we compare the capacity of two systemic models to simulate the discharge and piezometry of a karst aquifer. Systemic models have the advantage of allowing the study of heterogeneous, complex karst systems without relying on extensive geographical and meteorological datasets. The effectiveness and complementarity of the two models are evaluated for a range of hydrologic conditions and for three methods to estimate evapotranspiration (Monteith, a priori ET, and effective rainfall). The first model is a reservoir model (referred to as VENSIM, after the software used), which is designed with just one reservoir so as to be as parsimonious as possible. The second model is a neural network (NN) model. The models are designed to simulate the rainfall-runoff and rainfall-water level relations in a karst conduit. The Lez aquifer, a karst aquifer located near the city of Montpellier in southern France and a critical water resource, was chosen to compare the two models. Simulated discharge and water level were compared after completing model design and calibration. The results suggest that the NN model is more effective at incorporating the nonlinearity of the karst spring for extreme events (extreme low and high water levels), whereas VENSIM provides a better representation of intermediate-amplitude water level fluctuations. VENSIM is sensitive to the method used to estimate evapotranspiration, whereas the NN model is not. Given that the NN model performs better for extreme events, it is better for operational applications (predicting floods or determining water pumping height). VENSIM, on the other hand, seems more appropriate for representing the hydrologic state of the basin during intermediate periods, when several effects are at work: rain, evapotranspiration, development of vegetation, etc. A

  1. 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.

  2. Radio-frequency lesioning in brain tissue with coagulation-dependent thermal conductivity: modelling, simulation and analysis of parameter influence and interaction.

    Science.gov (United States)

    Johansson, Johannes D; Eriksson, Ola; Wren, Joakim; Loyd, Dan; Wårdell, Karin

    2006-09-01

    Radio-frequency brain lesioning is a method for reducing e.g. symptoms of movement disorders. A small electrode is used to thermally coagulate malfunctioning tissue. Influence on lesion size from thermal and electric conductivity of the tissue, microvascular perfusion and preset electrode temperature was investigated using a finite-element model. Perfusion was modelled as an increased thermal conductivity in non-coagulated tissue. The parameters were analysed using a 2(4)-factorial design (n=16) and quadratic regression analysis (n=47). Increased thermal conductivity of the tissue increased lesion volume, while increased perfusion decreased it since coagulation creates a thermally insulating layer due to the cessation of blood perfusion. These effects were strengthened with increased preset temperature. The electric conductivity had negligible effect. Simulations were found realistic compared to in vivo experimental lesions.

  3. 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.

  4. [Artificial neural networks in Neurosciences].

    Science.gov (United States)

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  5. 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.

    2017-06-01

    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.

  6. 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.

  7. Neural-based pile-up correction and ballistic deficit correction of X-ray semiconductor detectors using the Monte Carlo simulation and the Ramo theorem

    Science.gov (United States)

    Kafaee, Mahdi; Moussavi Zarandi, Ali; Taheri, Ali

    2016-03-01

    Pile-up distortion is a common problem in many nuclear radiation detection systems, especially in high count rates. It can be solved by hardware-based pile-up rejections, but there is no complete pile-up elimination in this way. Additionally, the methods can lead to poor quantitative results. Generally, time characteristics of semiconductor detector pulses are different from Scintillator detector pulses due to ballistic deficit. Hence, pulse processing-based pile-up correction in the detectors should consider this specification. In this paper, the artificial neural network pile-up correction method is applied for silicon detector piled-up pulses. For this purpose, the interaction of photons with a silicon detector is simulated by the MCNP4c code and the pulse current is calculated by Ramo's theorem. In this approach, we use a sub-Nyquist frequency sampling. The results show that the proposed method is reliable for pile-up correction and ballistic deficit in semiconductor detectors. The technique is remarkable for commercial considerations and high-speed, real-time calculations.

  8. Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure

    Energy Technology Data Exchange (ETDEWEB)

    Mellit, A. [University Centre of Medea, Institute of Science Engineering, Medea (Algeria). Department of Electronics; Benghanem, M. [University of Sciences and Technologies Houari Boumadiene, Algiers (Algeria). Faculty of Electrical Engineering; Kalogirou, S.A. [Higher Technical Institute, Nicosia (Cyprus). Department of Mechanical Engineering

    2007-02-15

    This paper presents an adaptive artificial neural network (ANN) for modeling and simulation of a Stand-Alone photovoltaic (SAPV) system operating under variable climatic conditions. The ANN combines the Levenberg-Marquardt algorithm (LM) with an infinite impulse response (IIR) filter in order to accelerate the convergence of the network. SAPV systems are widely used in renewable energy source (RES) applications and it is important to be able to evaluate the performance of installed systems. The modeling of the complete SAPV system is achieved by combining the models of the different components of the system (PV-generator, battery and regulator). A global model can identify the SAPV characteristics by knowing only the climatological conditions. In addition, a new procedure proposed for SAPV system sizing is presented in this work. Different measured signals of solar radiation sequences and electrical parameters (photovoltaic voltage and current) from a SAPV system installed at the south of Algeria have been recorded during a period of 5-years. These signals have been used for the training and testing the developed models, one for each component of the system and a global model of the complete system. The ANN model predictions allow the users of SAPV systems to predict the different signals for each model and identify the output current of the system for different climatological conditions. The comparison between simulated and experimental signals of the SAPV gave good results. The correlation coefficient obtained varies from 90% to 96% for each estimated signals, which is considered satisfactory. A comparison between multilayer perceptron (MLP), radial basis function (RBF) network and the proposed LM-IIR model is presented in order to confirm the advantage of this model. (author)

  9. Training a Neural Network Via Large-Eddy Simulation for Autonomous Location and Quantification of CH4 Leaks at Natural Gas Facilities

    Science.gov (United States)

    Sauer, J.; Travis, B. J.; Munoz-Esparza, D.; Dubey, M. K.

    2015-12-01

    Fugitive methane (CH4) leaks from oil and gas production fields are a potential significant source of atmospheric methane. US DOE's ARPA-E MONITOR program is supporting research to locate and quantify fugitive methane leaks at natural gas facilities in order to achieve a 90% reduction in CH4 emissions. LANL, Aeris and Rice University are developing an LDS (leak detection system) that employs a compact laser absorption methane sensor and sonic anemometer coupled to an artificial neural network (ANN)-based source attribution algorithm. LANL's large-eddy simulation model, HIGRAD, provides high-fidelity simulated wind fields and turbulent CH4 plume dispersion data for various scenarios used in training the ANN. Numerous inverse solution methodologies have been applied over the last decade to assessment of greenhouse gas emissions. ANN learning 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 site. ANNs have been shown to achieve higher accuracy with more efficiency than other inverse modeling approaches in studies at larger scales, in urban environments, over short time scales, and even at small spatial scales for efficient source localization of indoor airborne contaminants. Our ANN is intended to characterize fugitive leaks rapidly, given site-specific, real-time, wind and CH4 concentration time-series data at multiple sensor locations, leading to a minimum time-to-detection and providing a first order improvement with respect to overall minimization of methane loss. Initial studies with the ANN on a variety of source location, sensor location, and meteorological condition scenarios are presented and discussed.

  10. Modeling the Insertion Mechanics of Flexible Neural Probes Coated with Sacrificial Polymers for Optimizing Probe Design

    Science.gov (United States)

    Singh, Sagar; Lo, Meng-Chen; Damodaran, Vinod B.; Kaplan, Hilton M.; Kohn, Joachim; Zahn, Jeffrey D.; Shreiber, David I.

    2016-01-01

    Single-unit recording neural probes have significant advantages towards improving signal-to-noise ratio and specificity for signal acquisition in brain-to-computer interface devices. Long-term effectiveness is unfortunately limited by the chronic injury response, which has been linked to the mechanical mismatch between rigid probes and compliant brain tissue. Small, flexible microelectrodes may overcome this limitation, but insertion of these probes without buckling requires supporting elements such as a stiff coating with a biodegradable polymer. For these coated probes, there is a design trade-off between the potential for successful insertion into brain tissue and the degree of trauma generated by the insertion. The objective of this study was to develop and validate a finite element model (FEM) to simulate insertion of coated neural probes of varying dimensions and material properties into brain tissue. Simulations were performed to predict the buckling and insertion forces during insertion of coated probes into a tissue phantom with material properties of brain. The simulations were validated with parallel experimental studies where probes were inserted into agarose tissue phantom, ex vivo chick embryonic brain tissue, and ex vivo rat brain tissue. Experiments were performed with uncoated copper wire and both uncoated and coated SU-8 photoresist and Parylene C probes. Model predictions were found to strongly agree with experimental results (probe length were the most important features in influencing insertion potential. The model also revealed the effects of manufacturing flaws on insertion potential. PMID:26959021

  11. 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.

  12. The interaction of force and repetition on musculoskeletal and neural tissue responses and sensorimotor behavior in a rat model of work-related musculoskeletal disorders

    Science.gov (United States)

    2013-01-01

    Background We examined the relationship of musculoskeletal risk factors underlying force and repetition on tissue responses in an operant rat model of repetitive reaching and pulling, and if force x repetition interactions were present, indicative of a fatigue failure process. We examined exposure-dependent changes in biochemical, morphological and sensorimotor responses occurring with repeated performance of a handle-pulling task for 12 weeks at one of four repetition and force levels: 1) low repetition with low force, 2) high repetition with low force, 3) low repetition with high force, and 4) high repetition with high force (HRHF). Methods Rats underwent initial training for 4–6 weeks, and then performed one of the tasks for 12 weeks, 2 hours/day, 3 days/week. Reflexive grip strength and sensitivity to touch were assayed as functional outcomes. Flexor digitorum muscles and tendons, forelimb bones, and serum were assayed using ELISA for indicators of inflammation, tissue stress and repair, and bone turnover. Histomorphometry was used to assay macrophage infiltration of tissues, spinal cord substance P changes, and tissue adaptative or degradative changes. MicroCT was used to assay bones for changes in bone quality. Results Several force x repetition interactions were observed for: muscle IL-1alpha and bone IL-1beta; serum TNFalpha, IL-1alpha, and IL-1beta; muscle HSP72, a tissue stress and repair protein; histomorphological evidence of tendon and cartilage degradation; serum biomarkers of bone degradation (CTXI) and bone formation (osteocalcin); and morphological evidence of bone adaptation versus resorption. In most cases, performance of the HRHF task induced the greatest tissue degenerative changes, while performance of moderate level tasks induced bone adaptation and a suggestion of muscle adaptation. Both high force tasks induced median nerve macrophage infiltration, spinal cord sensitization (increased substance P), grip strength declines and forepaw

  13. 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.

  14. Microscopic and macroscopic volume conduction in skeletal muscle tissue, applied to simulation of single-fibre action potentials

    NARCIS (Netherlands)

    Alberts, B.A.; Rutten, Wim; Wallinga, W.; Boom, H.B.K.

    1988-01-01

    Extracellular action potentials of a single active muscle fibre in a surrounding of passive muscle tissue were calculated, using a microscopic volume conductor model which accounts for the travelling aspect of the source, the structure of skeletal muscle tissue and the electrical properties at the

  15. 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...

  16. 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)

  17. Neural repair in the adult brain

    Science.gov (United States)

    Jessberger, Sebastian

    2016-01-01

    Acute or chronic injury to the adult brain often results in substantial loss of neural tissue and subsequent permanent functional impairment. Over the last two decades, a number of approaches have been developed to harness the regenerative potential of neural stem cells and the existing fate plasticity of neural cells in the nervous system to prevent tissue loss or to enhance structural and functional regeneration upon injury. Here, we review recent advances of stem cell-associated neural repair in the adult brain, discuss current challenges and limitations, and suggest potential directions to foster the translation of experimental stem cell therapies into the clinic. PMID:26918167

  18. 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....

  19. A comparison of the techniques of secondary ion mass spectrometry and resonance ionization mass spectrometry for the analysis of potentially toxic element accumulation in neural tissue.

    Science.gov (United States)

    Jones, O R; Perks, R M; Abraham, C J; Telle, H H; Oakley, A E

    1997-01-01

    A comparison is made of the techniques of secondary ion mass spectrometry (SIMS) and resonance ionization mass spectrometry (RIMS) for the detection of the neuro-toxic element aluminium in cortical tissue. Experiments were performed using a reflectron-type time-of-flight mass spectrometer (TOFMS) in conjunction with an Ar+ source for target sputtering and a pulsed tuneable dye laser system for resonance ionization. It is shown how isobaric interference of species such as CNH and C2H3 in the case of aluminium greatly affect the quantitative accuracy and the detection limit of aluminium in biological samples when analysed using SIMS. In contrast the use of RIMS virtually eliminates this problem, so allowing easier quantification and much lower detection limits to be achieved. Detection limits of approximately 3 ppm for aluminium in brain tissue homogenates were achieved using RIMS, with a spatial resolution of less than 100 microns.

  20. Concise Review: Reprogramming, Behind the Scenes: Noncanonical Neural Stem Cell Signaling Pathways Reveal New, Unseen Regulators of Tissue Plasticity With Therapeutic Implications.

    Science.gov (United States)

    Poser, Steven W; Chenoweth, Josh G; Colantuoni, Carlo; Masjkur, Jimmy; Chrousos, George; Bornstein, Stefan R; McKay, Ronald D; Androutsellis-Theotokis, Andreas

    2015-11-01

    Interest is great in the new molecular concepts that explain, at the level of signal transduction, the process of reprogramming. Usually, transcription factors with developmental importance are used, but these approaches give limited information on the signaling networks involved, which could reveal new therapeutic opportunities. Recent findings involving reprogramming by genetic means and soluble factors with well-studied downstream signaling mechanisms, including signal transducer and activator of transcription 3 (STAT3) and hairy and enhancer of split 3 (Hes3), shed new light into the molecular mechanisms that might be involved. We examine the appropriateness of common culture systems and their ability to reveal unusual (noncanonical) signal transduction pathways that actually operate in vivo. We then discuss such novel pathways and their importance in various plastic cell types, culminating in their emerging roles in reprogramming mechanisms. We also discuss a number of reprogramming paradigms (mouse induced pluripotent stem cells, direct conversion to neural stem cells, and in vivo conversion of acinar cells to β-like cells). Specifically for acinar-to-β-cell reprogramming paradigms, we discuss the common view of the underlying mechanism (involving the Janus kinase-STAT pathway that leads to STAT3-tyrosine phosphorylation) and present alternative interpretations that implicate STAT3-serine phosphorylation alone or serine and tyrosine phosphorylation occurring in sequential order. The implications for drug design and therapy are important given that different phosphorylation sites on STAT3 intercept different signaling pathways. We introduce a new molecular perspective in the field of reprogramming with broad implications in basic, biotechnological, and translational research. Reprogramming is a powerful approach to change cell identity, with implications in both basic and applied biology. Most efforts involve the forced expression of key transcription

  1. Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine

    Science.gov (United States)

    Bahrami, Saeed; Doulati Ardejani, Faramarz; Baafi, Ernest

    2016-05-01

    In this study, hybrid models are designed to predict groundwater inflow to an advancing open pit mine and the hydraulic head (HH) in observation wells at different distances from the centre of the pit during its advance. Hybrid methods coupling artificial neural network (ANN) with genetic algorithm (GA) methods (ANN-GA), and simulated annealing (SA) methods (ANN-SA), were utilised. Ratios of depth of pit penetration in aquifer to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the HH in the observation wells to the distance of observation wells from the centre of the pit were used as inputs to the networks. To achieve the objective two hybrid models consisting of ANN-GA and ANN-SA with 4-5-3-1 arrangement were designed. In addition, by switching the last argument of the input layer with the argument of the output layer of two earlier models, two new models were developed to predict the HH in the observation wells for the period of the mining process. The accuracy and reliability of models are verified by field data, results of a numerical finite element model using SEEP/W, outputs of simple ANNs and some well-known analytical solutions. Predicted results obtained by the hybrid methods are closer to the field data compared to the outputs of analytical and simple ANN models. Results show that despite the use of fewer and simpler parameters by the hybrid models, the ANN-GA and to some extent the ANN-SA have the ability to compete with the numerical models.

  2. Tissue segmentation-assisted analysis of fMRI for human motor response: an approach combining artificial neural network and fuzzy C means

    OpenAIRE

    Chiu, MJ; Lin, CC; Chuang, KH; Chen, JH; Huang, KM

    2001-01-01

    The authors have developed an automated algorithm for segmentation of magnetic resonance images (MRI) of the human brain. They investigated the quantitative analysis of tissue-specific human motor response through an approach combining gradient echo functional MRI and automated segmentation analysis. Fifteen healthy volunteers, placed in a 1.5 T clinical MR imager, performed a self-paced finger opposition throughout the activation periods. T1-weighted images (WI), T2WI, and proton density WI ...

  3. Tissue segmentation-assisted analysis of fMRI for human motor response: an approach combining artificial neural network and fuzzy C means.

    Science.gov (United States)

    Chiu, M J; Lin, C C; Chuang, K H; Chen, J H; Huang, K M

    2001-03-01

    The authors have developed an automated algorithm for segmentation of magnetic resonance images (MRI) of the human brain. They investigated the quantitative analysis of tissue-specific human motor response through an approach combining gradient echo functional MRI and automated segmentation analysis. Fifteen healthy volunteers, placed in a 1.5 T clinical MR imager, performed a self-paced finger opposition throughout the activation periods. T1-weighted images (WI), T2WI, and proton density WI were acquired for segmentation analysis. Single-slice axial T2* fast low-angle shot (FLASH) images were obtained during the functional study. Pixelwise cross-correlation analysis was performed to obtain an activation map. A cascaded algorithm, combining Kohonen feature maps and fuzzy C means, was applied for segmentation. After processing, masks for gray matter, white matter, small vessels, and large vessels were generated. Tissue-specific analysis showed a signal change rate of 4.53% in gray matter, 2.98% in white matter, 5.79% in small vessels, and 7.24% in large vessels. Different temporal patterns as well as different levels of activation were identified in the functional response from various types of tissue. High correlation exists between cortical gray matter and subcortical white matter (r = 0.957), while the vessel behaves somewhat different temporally. The cortical gray matter fits best to the assumed input function (r = 0.957) followed by subcortical white matter (r = 0.829) and vessels (r = 0.726). The automated algorithm of tissue-specific analysis thus can assist functional MRI studies with different modalities of response in different brain regions.

  4. Review of tissue simulating phantoms with controllable optical, mechanical and structural properties for use in optical coherence tomography

    Science.gov (United States)

    Lamouche, Guy; Kennedy, Brendan F.; Kennedy, Kelsey M.; Bisaillon, Charles-Etienne; Curatolo, Andrea; Campbell, Gord; Pazos, Valérie; Sampson, David D.

    2012-01-01

    We review the development of phantoms for optical coherence tomography (OCT) designed to replicate the optical, mechanical and structural properties of a range of tissues. Such phantoms are a key requirement for the continued development of OCT techniques and applications. We focus on phantoms based on silicone, fibrin and poly(vinyl alcohol) cryogels (PVA-C), as we believe these materials hold the most promise for durable and accurate replication of tissue properties. PMID:22741083

  5. 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

    2017-10-12

    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

  6. 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...... 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....

  7. 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

  8. Review on different experimental techniques developed for recording force-deformation behaviour of soft tissues; with a view to surgery simulation applications.

    Science.gov (United States)

    Afshari, Elnaz; Rostami, Mostafa; Farahmand, Farzam

    2017-05-01

    Different experimental techniques which have been developed to obtain data related to force-deformation behaviour of soft tissues play an important role in realistically simulating surgery processes as well as medical diagnoses and minimally invasive procedures. Indeed, an adequate quantitative description of soft-tissue-mechanical-behaviour requires high-quality experimental data to be obtained and analysed. In this review article we will first scan the motivations and basic technical issues on surgery simulation. Then, we will concentrate on different experimental techniques developed for recording force-deformation (stress-strain) behaviour of soft tissues with focussing on the in-vivo experimental setups. We will thoroughly review the available techniques by classifying them to four groups; elastography, indentation, aspiration and grasping techniques. The evolutions, advantages and limitations of each technique will be presented by a historical review. At the end, a discussion is given with the aim of summarising the proposed points and predicting the future of techniques utilised in extracting data related to force-deformation behaviour.

  9. Characterization of thin poly(dimethylsiloxane)-based tissue-simulating phantoms with tunable reduced scattering and absorption coefficients at visible and near-infrared wavelengths.

    Science.gov (United States)

    Greening, Gage J; Istfan, Raeef; Higgins, Laura M; Balachandran, Kartik; Roblyer, Darren; Pierce, Mark C; Muldoon, Timothy J

    2014-01-01

    Optical phantoms are used in the development of various imaging systems. For certain applications, the development of thin phantoms that simulate the physical size and optical properties of tissue is important. Here, we demonstrate a method for producing thin phantom layers with tunable optical properties using poly(dimethylsiloxane) (PDMS) as a substrate material. The thickness of each layer (between 115 and 880 μm) was controlled using a spin coater. The reduced scattering and absorption coefficients were controlled using titanium dioxide and alcohol-soluble nigrosin, respectively. These optical coefficients were quantified at six discrete wavelengths (591, 631, 659, 691, 731, and 851 nm) at varying concentrations of titanium dioxide and nigrosin using spatial frequency domain imaging. From the presented data, we provide lookup tables to determine the appropriate concentrations of scattering and absorbing agents to be used in the design of PDMS-based phantoms with specific optical coefficients. In addition, heterogeneous phantoms mimicking the layered features of certain tissue types may be fabricated from multiple stacked layers, each with custom optical properties. These thin, tunable PDMS optical phantoms can simulate many tissue types and have broad imaging calibration applications in endoscopy, diffuse optical spectroscopic imaging, and optical coherence tomography, etc.

  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. 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 ...

  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

    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.

  13. 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.

  14. Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

    OpenAIRE

    Kale, S. N.; Dudul, S. V.

    2009-01-01

    Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN) can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors d...

  15. 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.

  16. 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.|info:eu-repo/dai/nl/119949997

    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

  17. 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

  18. 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.

  19. 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.

  20. Numerical Simulation of Mass Transfer and Three-Dimensional Fabrication of Tissue-Engineered Cartilages Based on Chitosan/Gelatin Hybrid Hydrogel Scaffold in a Rotating Bioreactor.

    Science.gov (United States)

    Zhu, Yanxia; Song, Kedong; Jiang, Siyu; Chen, Jinglian; Tang, Lingzhi; Li, Siyuan; Fan, Jiangli; Wang, Yiwei; Zhao, Jiaquan; Liu, Tianqing

    2017-01-01

    Cartilage tissue engineering is believed to provide effective cartilage repair post-injuries or diseases. Biomedical materials play a key role in achieving successful culture and fabrication of cartilage. The physical properties of a chitosan/gelatin hybrid hydrogel scaffold make it an ideal cartilage biomimetic material. In this study, a chitosan/gelatin hybrid hydrogel was chosen to fabricate a tissue-engineered cartilage in vitro by inoculating human adipose-derived stem cells (ADSCs) at both dynamic and traditional static culture conditions. A bioreactor that provides a dynamic culture condition has received greater applications in tissue engineering due to its optimal mass transfer efficiency and its ability to simulate an equivalent physical environment compared to human body. In this study, prior to cell-scaffold fabrication experiment, mathematical simulations were confirmed with a mass transfer of glucose and TGF-β2 both in rotating wall vessel bioreactor (RWVB) and static culture conditions in early stage of culture via computational fluid dynamic (CFD) method. To further investigate the feasibility of the mass transfer efficiency of the bioreactor, this RWVB was adopted to fabricate three-dimensional cell-hydrogel cartilage constructs in a dynamic environment. The results showed that the mass transfer efficiency of RWVB was faster in achieving a final equilibrium compared to culture in static culture conditions. ADSCs culturing in RWVB expanded three times more compared to that in static condition over 10 days. Induced cell cultivation in a dynamic RWVB showed extensive expression of extracellular matrix, while the cell distribution was found much more uniformly distributing with full infiltration of extracellular matrix inside the porous scaffold. The increased mass transfer efficiency of glucose and TGF-β2 from RWVB promoted cellular proliferation and chondrogenic differentiation of ADSCs inside chitosan/gelatin hybrid hydrogel scaffolds. The

  1. Neural networks for triggering

    Energy Technology Data Exchange (ETDEWEB)

    Denby, B. (Fermi National Accelerator Lab., Batavia, IL (USA)); Campbell, M. (Michigan Univ., Ann Arbor, MI (USA)); Bedeschi, F. (Istituto Nazionale di Fisica Nucleare, Pisa (Italy)); Chriss, N.; Bowers, C. (Chicago Univ., IL (USA)); Nesti, F. (Scuola Normale Superiore, Pisa (Italy))

    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.

  2. 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.

  3. Improved diagnostics using polarization imaging and artificial neural networks

    Science.gov (United States)

    Klimach, Uwe; Zhao, Hongzhi; Chen, Qiushui; Zou, Yingyin Kevin; Wang, Yue; Xuan, Jianhua

    2006-03-01

    In recent years there has been an increasing interest in studying the propagation of polarized light in randomly scattering media. This paper presents a novel approach for cell and tissue imaging by using full Stokes imaging and for its improved diagnostics by using artificial neural networks (ANNs). Phantom experiments have been conducted using a prototyped Stokes polarization imaging device. Several types of phantoms, consisting of polystyrene latex spheres in various diameters, were prepared to simulate different conditions of epidermal layer of skin. Several sets of four images that contain not only the intensity, but also the polarization information were taken for analysis. Wavelet transforms are first applied to the Stokes components for initial feature analysis and extraction. Artificial neural networks (ANNs) are then used to extract diagnostic features for improved classification and prediction. The experimental results show that the classification performance using Stokes images is significantly improved over that using the intensity image only.

  4. Monte Carlo simulation of radiation transfer in human skin with geometrically correct treatment of boundaries between different tissues

    Science.gov (United States)

    Premru, Jan; Milanič, Matija; Majaron, Boris

    2013-02-01

    In customary implementation of three-dimensional (3D) Monte Carlo (MC) numerical model of light transport in heterogeneous biological structures, the volume of interest is divided into voxels by a rectangular spatial grid. Each voxel is assumed to have homogeneous optical properties and curved boundaries between neighboring tissues inevitably become serrated. This raises some concerns over realism of the modeling results, especially with regard to reflection and refraction on such boundaries. In order to investigate the above concern, we have implemented an augmented 3D MC code, where tissue boundaries (e.g., blood vessel walls) are defined by analytical functions and thus maintain their shape regardless of grid discretization. Results of the customary and augmented model are compared for a few characteristic test geometries, mimicking a cutaneous blood vessel irradiated with a 532 nm laser beam of finite diameter. Our analysis shows that at specific locations inside the vessel, the amount of deposited laser energy can vary between the two models by up to 10%. Even physically relevant integral quantities, such as linear density of the energy absorbed by the vessel, can differ by as much as 30%. Moreover, the values obtained with the customary model vary strongly with discretization step and don't disappear with ever finer discretization. Meanwhile, our augmented model shows no such behavior, indicating that the customary approach suffers from inherent inaccuracies arising from physically flawed treatment of tissue boundaries.

  5. Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method

    CERN Document Server

    Barkaoui, Abdelwahed; Tarek, Merzouki; Hambli, Ridha; Ali, Mkaddem

    2014-01-01

    The complexity and heterogeneity of bone tissue require a multiscale modelling to understand its mechanical behaviour and its remodelling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network computation and homogenisation equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained neural network simulation. Finite element (FE) calculation is performed at nanoscopic levels to provide a database to train an in-house neural network program; (iii) in steps 2 to 10 from fibril to continuum cortical bone tissue, homogenisation equations are used to perform the computation at the higher s...

  6. The study of simulated microgravity effects on cardiac myocytes using a 3D heart tissue-equivalent model encapsulated in alginate microbeads

    Science.gov (United States)

    Li, Yu; Tian, Weiming; Zheng, Hongxia; Yu, Lei; Zhang, Yao; Han, Fengtong

    Long duration spaceflight may increase the risk and occurrence of potentially life-threatening heart rhythm disturbances associated with alterations of cardiac myocytes, myocyte connec-tivity, and extracellular matrix resulting from prolonged exposure to zero-or low-gravity. For understanding of the effects of microgravity, either traditional 2-dimensional (2D) cell cultures of adherent cell populations or animal models were typically used. The 2D in vitro systems do not allow assessment of the dynamic effects of intercellular interactions within tissues, whereas potentially confounding factors tend to be overlooked in animal models. Therefore novel cell culture model representative of the cellular interactions and with extracellular matrix present in tissues needs to be used. In this study, 3D multi-cellular heart tissue-equivalent model was constructed by culturing neonatal rat myocardial cells in alginate microbeads for one week. With this model we studied the simulated microgravity effects on myocardiocytes by incubat-ing the microbeads in NASA rotary cell culture system with a rate of 15rpm. Cytoskeletal changes, mitochondrial membrane potential and reactive oxygen production were studied after incubating for 24h, 48h and 72h respectively. Compared with 3D ground-culture group, sig-nificant cytoskeleton depolymerization characterized by pseudo-feet disappearance, significant increase of mitochondrial membrane potential, and greater reactive oxygen production were observed in after incubating 24h, 48h, and 72h, in NASA system. The beating rate of 3D heart tissue-equivalent decreased significantly at 24h, and all the samples stopped beating after 48h incubation while the beating rate of control group did not change. This study indicated that mi-crogravity affects both the structure and function of myocardial cells. Our results suggest that a 3D heart tissue-equivalent model maybe better for attempting to elucidate the microgravity effects on myocardiocytes in

  7. 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].

  8. CHARGEd with neural crest defects.

    Science.gov (United States)

    Pauli, Silke; Bajpai, Ruchi; Borchers, Annette

    2017-10-30

    Neural crest cells are highly migratory pluripotent cells that give rise to diverse derivatives including cartilage, bone, smooth muscle, pigment, and endocrine cells as well as neurons and glia. Abnormalities in neural crest-derived tissues contribute to the etiology of CHARGE syndrome, a complex malformation disorder that encompasses clinical symptoms like coloboma, heart defects, atresia of the choanae, retarded growth and development, genital hypoplasia, ear anomalies, and deafness. Mutations in the chromodomain helicase DNA-binding protein 7 (CHD7) gene are causative of CHARGE syndrome and loss-of-function data in different model systems have firmly established a role of CHD7 in neural crest development.