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.

Optimization of blanking process using neural network simulation

International Nuclear Information System (INIS)

Hambli, R.

2005-01-01

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

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

Science.gov (United States)

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

2018-01-01

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

MATLAB Simulation of Gradient-Based Neural Network for Online Matrix Inversion

Science.gov (United States)

Zhang, Yunong; Chen, Ke; Ma, Weimu; Li, Xiao-Dong

This paper investigates the simulation of a gradient-based recurrent neural network for online solution of the matrix-inverse problem. Several important techniques are employed as follows to simulate such a neural system. 1) Kronecker product of matrices is introduced to transform a matrix-differential-equation (MDE) to a vector-differential-equation (VDE); i.e., finally, a standard ordinary-differential-equation (ODE) is obtained. 2) MATLAB routine "ode45" is introduced to solve the transformed initial-value ODE problem. 3) In addition to various implementation errors, different kinds of activation functions are simulated to show the characteristics of such a neural network. Simulation results substantiate the theoretical analysis and efficacy of the gradient-based neural network for online constant matrix inversion.

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

Science.gov (United States)

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

2017-11-01

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

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.

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

Science.gov (United States)

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

2018-04-01

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

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

Science.gov (United States)

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

2018-07-01

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

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

Science.gov (United States)

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

2017-08-01

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

PWR system simulation and parameter estimation with neural networks

Energy Technology Data Exchange (ETDEWEB)

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

2002-11-01

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

PWR system simulation and parameter estimation with neural networks

International Nuclear Information System (INIS)

Akkurt, Hatice; Colak, Uener

2002-01-01

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

Three-dimensional hydrogel cell culture systems for modeling neural tissue

Science.gov (United States)

Frampton, John

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

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.

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.

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.

DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation.

Science.gov (United States)

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

2018-01-01

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

Dynamic simulation of a steam generator by neural networks

International Nuclear Information System (INIS)

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

1999-01-01

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

Simulation of nonlinear random vibrations using artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

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

1997-02-01

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

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

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

International Nuclear Information System (INIS)

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

2009-01-01

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

Simulation of lung motions using an artificial neural network

International Nuclear Information System (INIS)

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

2011-01-01

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

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

Science.gov (United States)

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

2018-03-01

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

Hybrid neural network bushing model for vehicle dynamics simulation

International Nuclear Information System (INIS)

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

2008-01-01

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

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.

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

International Nuclear Information System (INIS)

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

1992-01-01

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

Electric field effects in hyperexcitable neural tissue: A review

International Nuclear Information System (INIS)

Durand, D.M.

2003-01-01

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

System Identification, Prediction, Simulation and Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1997-01-01

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

[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.

Computer simulation system of neural PID control on nuclear reactor

International Nuclear Information System (INIS)

Chen Yuzhong; Yang Kaijun; Shen Yongping

2001-01-01

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

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

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}.

Assessing the quality of force feedback in soft tissue simulation.

Science.gov (United States)

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

2011-01-01

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

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

International Nuclear Information System (INIS)

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

2002-01-01

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

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

Science.gov (United States)

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

2016-12-01

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

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

International Nuclear Information System (INIS)

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

1991-01-01

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

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

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

International Nuclear Information System (INIS)

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

1990-01-01

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

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.

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...

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.

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

Science.gov (United States)

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

2018-06-01

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

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

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.

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

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.

Accelerator and feedback control simulation using neural networks

International Nuclear Information System (INIS)

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

1991-05-01

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

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.

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

International Nuclear Information System (INIS)

Laurent, R.

2011-01-01

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

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

Science.gov (United States)

Tosi, Zachary; Yoshimi, Jeffrey

2016-11-01

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

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

Science.gov (United States)

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

2013-03-01

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

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

Science.gov (United States)

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

2018-06-01

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

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.

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

International Nuclear Information System (INIS)

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

2016-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-12-01

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

Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

Directory of Open Access Journals (Sweden)

Ayman Hamdy Kassem

2011-01-01

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

Neural Network Emulation of Reionization Simulations

Science.gov (United States)

Schmit, Claude J.; Pritchard, Jonathan R.

2018-05-01

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

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

Directory of Open Access Journals (Sweden)

Jan Hahne

2017-05-01

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

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.

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

International Nuclear Information System (INIS)

Ghenniwa, Fatma Suleiman

2008-01-01

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

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

Science.gov (United States)

Mishchenko, Yuriy

2009-01-30

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

Dissipative particle dynamics simulations for biological tissues: rheology and competition

International Nuclear Information System (INIS)

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

2011-01-01

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

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

Science.gov (United States)

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

2018-03-15

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

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

International Nuclear Information System (INIS)

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

2005-01-01

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

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

Directory of Open Access Journals (Sweden)

Philipp Weidel

2016-08-01

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

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

International Nuclear Information System (INIS)

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

2003-01-01

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

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

DEFF Research Database (Denmark)

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

1999-01-01

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

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.

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.

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.)

An artifical neural network for detection of simulated dental caries

International Nuclear Information System (INIS)

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

2006-01-01

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

The neural basis of event simulation: an FMRI study.

Directory of Open Access Journals (Sweden)

Yukihito Yomogida

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

Extraintestinal heterotopic gastric tissue simulating acute appendicitis

Institute of Scientific and Technical Information of China (English)

Elizabeth Bender; Steven P Schmidt

2008-01-01

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

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.

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.

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

Directory of Open Access Journals (Sweden)

S. Nikbakht Katouli

2016-06-01

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

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.

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

International Nuclear Information System (INIS)

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

2002-01-01

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

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

Directory of Open Access Journals (Sweden)

Ding Fang

2013-06-01

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

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.

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.

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.

Brian: a simulator for spiking neural networks in python.

Science.gov (United States)

Goodman, Dan; Brette, Romain

2008-01-01

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

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

Science.gov (United States)

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

2018-05-15

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

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

Science.gov (United States)

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

2017-02-01

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

Proposal of a model of mammalian neural induction

Science.gov (United States)

Levine, Ariel J.; Brivanlou, Ali H.

2009-01-01

How does the vertebrate embryo make a nervous system? This complex question has been at the center of developmental biology for many years. The earliest step in this process – the induction of neural tissue – is intimately linked to patterning of the entire early embryo, and the molecular and embryological basis these processes are beginning to emerge. Here, we analyze classic and cutting-edge findings on neural induction in the mouse. We find that data from genetics, tissue explants, tissue grafting, and molecular marker expression support a coherent framework for mammalian neural induction. In this model, the gastrula organizer of the mouse embryo inhibits BMP signaling to allow neural tissue to form as a default fate – in the absence of instructive signals. The first neural tissue induced is anterior and subsequent neural tissue is posteriorized to form the midbrain, hindbrain, and spinal cord. The anterior visceral endoderm protects the pre-specified anterior neural fate from similar posteriorization, allowing formation of forebrain. This model is very similar to the default model of neural induction in the frog, thus bridging the evolutionary gap between amphibians and mammals. PMID:17585896

Experimental evaluation of neural probe’s insertion induced injury based on digital image correlation method

Energy Technology Data Exchange (ETDEWEB)

Zhang, Wenguang, E-mail: zhwg@sjtu.edu.cn; Ma, Yakun; Li, Zhengwei [State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240 (China)

2016-01-15

Purpose: The application of neural probes in clinic has been challenged by probes’ short lifetime when implanted into brain tissue. The primary goal is to develop an evaluation system for testing brain tissue injury induced by neural probe’s insertion using microscope based digital image correlation method. Methods: A brain tissue phantom made of silicone rubber with speckle pattern on its surface was fabricated. To obtain the optimal speckle pattern, mean intensity gradient parameter was used for quality assessment. The designed testing system consists of three modules: (a) load module for simulating neural electrode implantation process; (b) data acquisition module to capture micrographs of speckle pattern and to obtain reactive forces during the insertion of the probe; (c) postprocessing module for extracting tissue deformation information from the captured speckle patterns. On the basis of the evaluation system, the effects of probe wedge angle, insertion speed, and probe streamline on insertion induced tissue injury were investigated. Results: The optimal quality speckle pattern can be attained by the following fabrication parameters: spin coating rate—1000 r/min, silicone rubber component A: silicone rubber component B: softener: graphite = 5 ml: 5 ml: 2 ml: 0.6 g. The probe wedge angle has a significant effect on tissue injury. Compared to wedge angle 40° and 20°, maximum principal strain of 60° wedge angle was increased by 40.3% and 87.5%, respectively; compared with a relatively higher speed (500 μm/s), the maximum principle strain within the tissue induced by slow insertion speed (100 μm/s) was increased by 14.3%; insertion force required by probe with convex streamline was smaller than the force of traditional probe. Based on the experimental results, a novel neural probe that has a rounded tip covered by a biodegradable silk protein coating with convex streamline was proposed, which has both lower insertion and micromotion induced tissue

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

Science.gov (United States)

Zenke, Friedemann; Gerstner, Wulfram

2014-01-01

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

Accurate lithography simulation model based on convolutional neural networks

Science.gov (United States)

Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

2017-07-01

Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

Neural tissue engineering options for peripheral nerve regeneration.

Science.gov (United States)

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

2014-08-01

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

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.

Program Helps Simulate Neural Networks

Science.gov (United States)

Villarreal, James; Mcintire, Gary

1993-01-01

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

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

Science.gov (United States)

Sellier, Jean Michel

2018-04-01

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

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

Science.gov (United States)

Bentil, Sarah A; Dupaix, Rebecca B

2014-02-01

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

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

Directory of Open Access Journals (Sweden)

Yan Liang

2017-01-01

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

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

Science.gov (United States)

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

2018-02-01

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

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

NARCIS (Netherlands)

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

1999-01-01

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

Optimization behavior of brainstem respiratory neurons. A cerebral neural network model.

Science.gov (United States)

Poon, C S

1991-01-01

A recent model of respiratory control suggested that the steady-state respiratory responses to CO2 and exercise may be governed by an optimal control law in the brainstem respiratory neurons. It was not certain, however, whether such complex optimization behavior could be accomplished by a realistic biological neural network. To test this hypothesis, we developed a hybrid computer-neural model in which the dynamics of the lung, brain and other tissue compartments were simulated on a digital computer. Mimicking the "controller" was a human subject who pedalled on a bicycle with varying speed (analog of ventilatory output) with a view to minimize an analog signal of the total cost of breathing (chemical and mechanical) which was computed interactively and displayed on an oscilloscope. In this manner, the visuomotor cortex served as a proxy (homolog) of the brainstem respiratory neurons in the model. Results in 4 subjects showed a linear steady-state ventilatory CO2 response to arterial PCO2 during simulated CO2 inhalation and a nearly isocapnic steady-state response during simulated exercise. Thus, neural optimization is a plausible mechanism for respiratory control during exercise and can be achieved by a neural network with cognitive computational ability without the need for an exercise stimulus.

A framework for plasticity implementation on the SpiNNaker neural architecture.

Science.gov (United States)

Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A; Furber, Steve B; Benosman, Ryad B

2014-01-01

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

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.

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

Science.gov (United States)

Vomero, Maria

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

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.

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.

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…

Differentiation between non-neural and neural contributors to ankle joint stiffness in cerebral palsy.

Science.gov (United States)

de Gooijer-van de Groep, Karin L; de Vlugt, Erwin; de Groot, Jurriaan H; van der Heijden-Maessen, Hélène C M; Wielheesen, Dennis H M; van Wijlen-Hempel, Rietje M S; Arendzen, J Hans; Meskers, Carel G M

2013-07-23

Spastic paresis in cerebral palsy (CP) is characterized by increased joint stiffness that may be of neural origin, i.e. improper muscle activation caused by e.g. hyperreflexia or non-neural origin, i.e. altered tissue viscoelastic properties (clinically: "spasticity" vs. "contracture"). Differentiation between these components is hard to achieve by common manual tests. We applied an assessment instrument to obtain quantitative measures of neural and non-neural contributions to ankle joint stiffness in CP. Twenty-three adolescents with CP and eleven healthy subjects were seated with their foot fixated to an electrically powered single axis footplate. Passive ramp-and-hold rotations were applied over full ankle range of motion (RoM) at low and high velocities. Subject specific tissue stiffness, viscosity and reflexive torque were estimated from ankle angle, torque and triceps surae EMG activity using a neuromuscular model. In CP, triceps surae reflexive torque was on average 5.7 times larger (p = .002) and tissue stiffness 2.1 times larger (p = .018) compared to controls. High tissue stiffness was associated with reduced RoM (p therapy.

Image reconstruction using Monte Carlo simulation and artificial neural networks

International Nuclear Information System (INIS)

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

1997-01-01

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

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.

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

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.

Studies in RF power communication, SAR, and temperature elevation in wireless implantable neural interfaces.

Directory of Open Access Journals (Sweden)

Yujuan Zhao

Full Text Available Implantable neural interfaces are designed to provide a high spatial and temporal precision control signal implementing high degree of freedom real-time prosthetic systems. The development of a Radio Frequency (RF wireless neural interface has the potential to expand the number of applications as well as extend the robustness and longevity compared to wired neural interfaces. However, it is well known that RF signal is absorbed by the body and can result in tissue heating. In this work, numerical studies with analytical validations are performed to provide an assessment of power, heating and specific absorption rate (SAR associated with the wireless RF transmitting within the human head. The receiving antenna on the neural interface is designed with different geometries and modeled at a range of implanted depths within the brain in order to estimate the maximum receiving power without violating SAR and tissue temperature elevation safety regulations. Based on the size of the designed antenna, sets of frequencies between 1 GHz to 4 GHz have been investigated. As expected the simulations demonstrate that longer receiving antennas (dipole and lower working frequencies result in greater power availability prior to violating SAR regulations. For a 15 mm dipole antenna operating at 1.24 GHz on the surface of the brain, 730 uW of power could be harvested at the Federal Communications Commission (FCC SAR violation limit. At approximately 5 cm inside the head, this same antenna would receive 190 uW of power prior to violating SAR regulations. Finally, the 3-D bio-heat simulation results show that for all evaluated antennas and frequency combinations we reach FCC SAR limits well before 1 °C. It is clear that powering neural interfaces via RF is possible, but ultra-low power circuit designs combined with advanced simulation will be required to develop a functional antenna that meets all system requirements.

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.

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.

Neural Networks Simulation of the Transport of Contaminants in Groundwater

Directory of Open Access Journals (Sweden)

Enrico Zio

2009-12-01

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

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.

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

International Nuclear Information System (INIS)

Subramanian, Swetha; Mast, T Douglas

2015-01-01

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

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

Science.gov (United States)

Subramanian, Swetha; Mast, T Douglas

2015-10-07

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

Neurophysiology and neural engineering: a review.

Science.gov (United States)

Prochazka, Arthur

2017-08-01

Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight. Copyright © 2017 the American Physiological Society.

Chondroitin sulfate effects on neural stem cell differentiation.

Science.gov (United States)

Canning, David R; Brelsford, Natalie R; Lovett, Neil W

2016-01-01

We have investigated the role chondroitin sulfate has on cell interactions during neural plate formation in the early chick embryo. Using tissue culture isolates from the prospective neural plate, we have measured neural gene expression profiles associated with neural stem cell differentiation. Removal of chondroitin sulfate from stage 4 neural plate tissue leads to altered associations of N-cadherin-positive neural progenitors and causes changes in the normal sequence of neural marker gene expression. Absence of chondroitin sulfate in the neural plate leads to reduced Sox2 expression and is accompanied by an increase in the expression of anterior markers of neural regionalization. Results obtained in this study suggest that the presence of chondroitin sulfate in the anterior chick embryo is instrumental in maintaining cells in the neural precursor state.

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

International Nuclear Information System (INIS)

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

1987-01-01

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

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

Directory of Open Access Journals (Sweden)

Bravo S.

2004-01-01

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

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.

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

Energy Technology Data Exchange (ETDEWEB)

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

2011-04-15

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

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.

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.

Efficient Cancer Detection Using Multiple Neural Networks.

Science.gov (United States)

Shell, John; Gregory, William D

2017-01-01

The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

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

Science.gov (United States)

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

2015-11-07

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

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

International Nuclear Information System (INIS)

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

1996-01-01

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

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.

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

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

Science.gov (United States)

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

2000-01-01

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

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

Science.gov (United States)

Basafa, Ehsan; Farahmand, Farzam

2011-05-01

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

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

Directory of Open Access Journals (Sweden)

Krzysztof Gajowniczek

2018-04-01

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

Modelling organs, tissues, cells and devices using Matlab and Comsol multiphysics

CERN Document Server

Dokos, Socrates

2017-01-01

This book presents a theoretical and practical overview of computational modeling in bioengineering, focusing on a range of applications including electrical stimulation of neural and cardiac tissue, implantable drug delivery, cancer therapy, biomechanics, cardiovascular dynamics, as well as fluid-structure interaction for modelling of organs, tissues, cells and devices. It covers the basic principles of modeling and simulation with ordinary and partial differential equations using MATLAB and COMSOL Multiphysics numerical software. The target audience primarily comprises postgraduate students and researchers, but the book may also be beneficial for practitioners in the medical device industry.

Differentiation between non-neural and neural contributors to ankle joint stiffness in cerebral palsy

NARCIS (Netherlands)

De Gooijer-van de Groep, K.L.; De Vlugt, E.; De Groot, J.H.; Van der Heijden-Maessen, H.C.M.; Wielheesen, D.H.M.; Van Wijlen-Hempel, R.M.S.; Arendzen, J.H.; Meskers, C.G.M.

2013-01-01

Background Spastic paresis in cerebral palsy (CP) is characterized by increased joint stiffness that may be of neural origin, i.e. improper muscle activation caused by e.g. hyperreflexia or non-neural origin, i.e. altered tissue viscoelastic properties (clinically: “spasticity” vs. “contracture”).

Chaotic diagonal recurrent neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Yi

2012-01-01

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

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.

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

International Nuclear Information System (INIS)

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

2013-01-01

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

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

CSIR Research Space (South Africa)

Ngwangwa, HM

2010-04-01

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

Neural Networks: Implementations and Applications

OpenAIRE

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

1996-01-01

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

Characterization of the angular memory effect of scattered light in biological tissues.

Science.gov (United States)

Schott, Sam; Bertolotti, Jacopo; Léger, Jean-Francois; Bourdieu, Laurent; Gigan, Sylvain

2015-05-18

High resolution optical microscopy is essential in neuroscience but suffers from scattering in biological tissues and therefore grants access to superficial brain layers only. Recently developed techniques use scattered photons for imaging by exploiting angular correlations in transmitted light and could potentially increase imaging depths. But those correlations ('angular memory effect') are of a very short range and should theoretically be only present behind and not inside scattering media. From measurements on neural tissues and complementary simulations, we find that strong forward scattering in biological tissues can enhance the memory effect range and thus the possible field-of-view by more than an order of magnitude compared to isotropic scattering for ∼1 mm thick tissue layers.

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

International Nuclear Information System (INIS)

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

2007-01-01

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

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.

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

Science.gov (United States)

Shen, Lin; Yang, Weitao

2018-03-13

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

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.

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.

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

Science.gov (United States)

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

2017-02-01

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

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

International Nuclear Information System (INIS)

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

2013-01-01

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

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.

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

International Nuclear Information System (INIS)

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

2013-01-01

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

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

Science.gov (United States)

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

2014-02-01

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

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

Science.gov (United States)

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

2014-05-01

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

Neural control of magnetic suspension systems

Science.gov (United States)

Gray, W. Steven

1993-01-01

The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

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

International Nuclear Information System (INIS)

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

2011-01-01

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

CUDA accelerated simulation of needle insertions in deformable tissue

International Nuclear Information System (INIS)

Patriciu, Alexandru

2012-01-01

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

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

Czech Academy of Sciences Publication Activity Database

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

2004-01-01

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

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....

The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

Science.gov (United States)

Pla, Patrick; Monsoro-Burq, Anne H

2018-05-28

The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

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.

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.

Runoff Modelling in Urban Storm Drainage by Neural Networks

DEFF Research Database (Denmark)

Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld

1995-01-01

A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....

Development and Evaluation of Micro-Electrocorticography Arrays for Neural Interfacing Applications

Science.gov (United States)

Schendel, Amelia Ann

Neural interfaces have great promise for both electrophysiological research and therapeutic applications. Whether for the study of neural circuitry or for neural prosthetic or other therapeutic applications, micro-electrocorticography (micro-ECoG) arrays have proven extremely useful as neural interfacing devices. These devices strike a balance between invasiveness and signal resolution, an important step towards eventual human application. The objective of this research was to make design improvements to micro-ECoG devices to enhance both biocompatibility and device functionality. To best evaluate the effectiveness of these improvements, a cranial window imaging method for in vivo monitoring of the longitudinal tissue response post device implant was developed. Employment of this method provided valuable insight into the way tissue grows around micro-ECoG arrays after epidural implantation, spurring a study of the effects of substrate geometry on the meningeal tissue response. The results of the substrate footprint comparison suggest that a more open substrate geometry provides an easy path for the tissue to grow around to the top side of the device, whereas a solid device substrate encourages the tissue to thicken beneath the device, between the electrode sites and the brain. The formation of thick scar tissue between the recording electrode sites and the neural tissue is disadvantageous for long-term recorded signal quality, and thus future micro-ECoG device designs should incorporate open-architecture substrates for enhanced longitudinal in vivo function. In addition to investigating improvements for long-term device reliability, it was also desired to enhance the functionality of micro-ECoG devices for neural electrophysiology research applications. To achieve this goal, a completely transparent graphene-based device was fabricated for use with the cranial window imaging method and optogenetic techniques. The use of graphene as the conductive material provided

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

Science.gov (United States)

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

2012-01-01

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

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.

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

Science.gov (United States)

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

2016-03-01

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

Osteotomy simulation and soft tissue prediction using computer tomography scans

International Nuclear Information System (INIS)

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

1999-01-01

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

[Preparation of simulate craniocerebral models via three dimensional printing technique].

Science.gov (United States)

Lan, Q; Chen, A L; Zhang, T; Zhu, Q; Xu, T

2016-08-09

Three dimensional (3D) printing technique was used to prepare the simulate craniocerebral models, which were applied to preoperative planning and surgical simulation. The image data was collected from PACS system. Image data of skull bone, brain tissue and tumors, cerebral arteries and aneurysms, and functional regions and relative neural tracts of the brain were extracted from thin slice scan (slice thickness 0.5 mm) of computed tomography (CT), magnetic resonance imaging (MRI, slice thickness 1mm), computed tomography angiography (CTA), and functional magnetic resonance imaging (fMRI) data, respectively. MIMICS software was applied to reconstruct colored virtual models by identifying and differentiating tissues according to their gray scales. Then the colored virtual models were submitted to 3D printer which produced life-sized craniocerebral models for surgical planning and surgical simulation. 3D printing craniocerebral models allowed neurosurgeons to perform complex procedures in specific clinical cases though detailed surgical planning. It offered great convenience for evaluating the size of spatial fissure of sellar region before surgery, which helped to optimize surgical approach planning. These 3D models also provided detailed information about the location of aneurysms and their parent arteries, which helped surgeons to choose appropriate aneurismal clips, as well as perform surgical simulation. The models further gave clear indications of depth and extent of tumors and their relationship to eloquent cortical areas and adjacent neural tracts, which were able to avoid surgical damaging of important neural structures. As a novel and promising technique, the application of 3D printing craniocerebral models could improve the surgical planning by converting virtual visualization into real life-sized models.It also contributes to functional anatomy study.

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.

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

Science.gov (United States)

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

2018-04-15

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

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

International Nuclear Information System (INIS)

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

2009-01-01

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

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

International Nuclear Information System (INIS)

Wang Baoyong; Ding Zhihua

2011-01-01

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

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

Science.gov (United States)

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

2017-03-01

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

Adipose tissue-derived stem cells in neural regenerative medicine.

Science.gov (United States)

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

2015-01-01

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

Real-Time Million-Synapse Simulation of Rat Barrel Cortex

Directory of Open Access Journals (Sweden)

Thomas eSharp

2014-05-01

Full Text Available Simulations of neural circuits are bounded in scale and speed by available computing resources, and particularly by the differences in parallelism and communication patterns between the brain and high-performance computers. SpiNNaker is a computer architecture designed to address this problem by emulating the structure and function of neural tissue, using very many low-power processors and an interprocessor communication mechanism inspired by axonal arbors. Here we demonstrate that thousand-processor SpiNNaker prototypes can simulate models of the rodent barrel system comprising fifty thousand neurons and fifty million synapses. We use the PyNN library to specify models, and the intrinsic features of Python to control experimental procedures and analysis. The models reproduce known thalamocortical response transformations, exhibit known, balanced dynamics of excitation and inhibition, and show a spatiotemporal spread of activity though the superficial cortical layers. These demonstrations are a significant step towards tractable simulations of entire cortical areas on the million-processor SpiNNaker machines in development.

Real-time million-synapse simulation of rat barrel cortex.

Science.gov (United States)

Sharp, Thomas; Petersen, Rasmus; Furber, Steve

2014-01-01

Simulations of neural circuits are bounded in scale and speed by available computing resources, and particularly by the differences in parallelism and communication patterns between the brain and high-performance computers. SpiNNaker is a computer architecture designed to address this problem by emulating the structure and function of neural tissue, using very many low-power processors and an interprocessor communication mechanism inspired by axonal arbors. Here we demonstrate that thousand-processor SpiNNaker prototypes can simulate models of the rodent barrel system comprising 50,000 neurons and 50 million synapses. We use the PyNN library to specify models, and the intrinsic features of Python to control experimental procedures and analysis. The models reproduce known thalamocortical response transformations, exhibit known, balanced dynamics of excitation and inhibition, and show a spatiotemporal spread of activity though the superficial cortical layers. These demonstrations are a significant step toward tractable simulations of entire cortical areas on the million-processor SpiNNaker machines in development.

Pax7 lineage contributions to the mammalian neural crest.

Directory of Open Access Journals (Sweden)

Barbara Murdoch

Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

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

Science.gov (United States)

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

2017-10-01

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

Simulation of electrochemical processes in cardiac tissue based on cellular automaton

International Nuclear Information System (INIS)

Avdeev, S A; Bogatov, N M

2014-01-01

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

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.

Development of human nervous tissue upon differentiation of embryonic stem cells in three-dimensional culture.

Science.gov (United States)

Preynat-Seauve, Olivier; Suter, David M; Tirefort, Diderik; Turchi, Laurent; Virolle, Thierry; Chneiweiss, Herve; Foti, Michelangelo; Lobrinus, Johannes-Alexander; Stoppini, Luc; Feki, Anis; Dubois-Dauphin, Michel; Krause, Karl Heinz

2009-03-01

Researches on neural differentiation using embryonic stem cells (ESC) require analysis of neurogenesis in conditions mimicking physiological cellular interactions as closely as possible. In this study, we report an air-liquid interface-based culture of human ESC. This culture system allows three-dimensional cell expansion and neural differentiation in the absence of added growth factors. Over a 3-month period, a macroscopically visible, compact tissue developed. Histological coloration revealed a dense neural-like neural tissue including immature tubular structures. Electron microscopy, immunochemistry, and electrophysiological recordings demonstrated a dense network of neurons, astrocytes, and oligodendrocytes able to propagate signals. Within this tissue, tubular structures were niches of cells resembling germinal layers of human fetal brain. Indeed, the tissue contained abundant proliferating cells expressing markers of neural progenitors. Finally, the capacity to generate neural tissues on air-liquid interface differed for different ESC lines, confirming variations of their neurogenic potential. In conclusion, this study demonstrates in vitro engineering of a human neural-like tissue with an organization that bears resemblance to early developing brain. As opposed to previously described methods, this differentiation (a) allows three-dimensional organization, (b) yields dense interconnected neural tissue with structurally and functionally distinct areas, and (c) is spontaneously guided by endogenous developmental cues.

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

Science.gov (United States)

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

2018-05-01

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

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

International Nuclear Information System (INIS)

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

2007-01-01

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

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

Science.gov (United States)

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

2010-04-01

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

Tissue motion in blood velocity estimation and its simulation

DEFF Research Database (Denmark)

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

1998-01-01

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

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

Science.gov (United States)

Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

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

DEFF Research Database (Denmark)

Talebnia, Farid; Mighani, Moein; Rahimnejad, Mostafa

2015-01-01

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

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

Combined Monte Carlo and path-integral method for simulated library of time-resolved reflectance curves from layered tissue models

Science.gov (United States)

Wilson, Robert H.; Vishwanath, Karthik; Mycek, Mary-Ann

2009-02-01

Monte Carlo (MC) simulations are considered the "gold standard" for mathematical description of photon transport in tissue, but they can require large computation times. Therefore, it is important to develop simple and efficient methods for accelerating MC simulations, especially when a large "library" of related simulations is needed. A semi-analytical method involving MC simulations and a path-integral (PI) based scaling technique generated time-resolved reflectance curves from layered tissue models. First, a zero-absorption MC simulation was run for a tissue model with fixed scattering properties in each layer. Then, a closed-form expression for the average classical path of a photon in tissue was used to determine the percentage of time that the photon spent in each layer, to create a weighted Beer-Lambert factor to scale the time-resolved reflectance of the simulated zero-absorption tissue model. This method is a unique alternative to other scaling techniques in that it does not require the path length or number of collisions of each photon to be stored during the initial simulation. Effects of various layer thicknesses and absorption and scattering coefficients on the accuracy of the method will be discussed.

Sonar discrimination of cylinders from different angles using neural networks neural networks

DEFF Research Database (Denmark)

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

1999-01-01

This paper describes an underwater object discrimination system applied to recognize cylinders of various compositions from different angles. The system is based on a new combination of simulated dolphin clicks, simulated auditory filters and artificial neural networks. The model demonstrates its...

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

Directory of Open Access Journals (Sweden)

Sagar Singh

2016-03-01

Full Text Available 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 (<10% error. The ratio of the predicted buckling force-to-predicted insertion force, where a value greater than one would ideally be expected to result in successful insertion, was plotted against the actual success rate from experiments. A sigmoidal relationship was observed, with a ratio of 1.35 corresponding to equal probability of insertion and failure, and a ratio of 3.5 corresponding to a 100% success rate. This ratio was dubbed the “safety factor”, as it indicated the degree to which the coating

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

International Nuclear Information System (INIS)

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

2011-01-01

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

Neural network segmentation of magnetic resonance images

International Nuclear Information System (INIS)

Frederick, B.

1990-01-01

Neural networks are well adapted to the task of grouping input patterns into subsets which share some similarity. Moreover, once trained, they can generalize their classification rules to classify new data sets. Sets of pixel intensities from magnetic resonance (MR) images provide a natural input to a neural network; by varying imaging parameters, MR images can reflect various independent physical parameters of tissues in their pixel intensities. A neural net can then be trained to classify physically similar tissue types based on sets of pixel intensities resulting from different imaging studies on the same subject. This paper reports that a neural network classifier for image segmentation was implanted on a Sun 4/60, and was tested on the task of classifying tissues of canine head MR images. Four images of a transaxial slice with different imaging sequences were taken as input to the network (three spin-echo images and an inversion recovery image). The training set consisted of 691 representative samples of gray matter, white matter, cerebrospinal fluid, bone, and muscle preclassified by a neuroscientist. The network was trained using a fast backpropagation algorithm to derive the decision criteria to classify any location in the image by its pixel intensities, and the image was subsequently segmented by the classifier

Neural network to diagnose lining condition

Science.gov (United States)

Yemelyanov, V. A.; Yemelyanova, N. Y.; Nedelkin, A. A.; Zarudnaya, M. V.

2018-03-01

The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.

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

Energy Technology Data Exchange (ETDEWEB)

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

1985-04-01

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

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

Science.gov (United States)

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

2018-06-01

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

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

Science.gov (United States)

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

2017-03-01

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

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

Science.gov (United States)

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

2018-03-05

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

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

Science.gov (United States)

Kemp, Z. D. C.

2018-04-01

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

Inversion of a lateral log using neural networks

International Nuclear Information System (INIS)

Garcia, G.; Whitman, W.W.

1992-01-01

In this paper a technique using neural networks is demonstrated for the inversion of a lateral log. The lateral log is simulated by a finite difference method which in turn is used as an input to a backpropagation neural network. An initial guess earth model is generated from the neural network, which is then input to a Marquardt inversion. The neural network reacts to gross and subtle data features in actual logs and produces a response inferred from the knowledge stored in the network during a training process. The neural network inversion of lateral logs is tested on synthetic and field data. Tests using field data resulted in a final earth model whose simulated lateral is in good agreement with the actual log data

Genetic Algorithm Optimized Neural Networks Ensemble as ...

African Journals Online (AJOL)

NJD

Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous ... process by training a number of neural networks. .... Matlab® version 6.1 was employed for building principal component ... provide a fair simulation of calibration data set with some degree.

Antenna analysis using neural networks

Science.gov (United States)

Smith, William T.

1992-01-01

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

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

International Nuclear Information System (INIS)

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

2013-01-01

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

Stochastic sensitivity analysis and Langevin simulation for neural network learning

International Nuclear Information System (INIS)

Koda, Masato

1997-01-01

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

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...

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

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

International Nuclear Information System (INIS)

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

2016-01-01

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

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

International Nuclear Information System (INIS)

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

2004-01-01

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

Transient analysis for PWR reactor core using neural networks predictors

International Nuclear Information System (INIS)

Gueray, B.S.

2001-01-01

In this study, transient analysis for a Pressurized Water Reactor core has been performed. A lumped parameter approximation is preferred for that purpose, to describe the reactor core together with mechanism which play an important role in dynamic analysis. The dynamic behavior of the reactor core during transients is analyzed considering the transient initiating events, wich are an essential part of Safety Analysis Reports. several transients are simulated based on the employed core model. Simulation results are in accord the physical expectations. A neural network is developed to predict the future response of the reactor core, in advance. The neural network is trained using the simulation results of a number of representative transients. Structure of the neural network is optimized by proper selection of transfer functions for the neurons. Trained neural network is used to predict the future responses following an early observation of the changes in system variables. Estimated behaviour using the neural network is in good agreement with the simulation results for various for types of transients. Results of this study indicate that the designed neural network can be used as an estimator of the time dependent behavior of the reactor core under transient conditions

Nano-topography Enhances Communication in Neural Cells Networks

KAUST Repository

Onesto, V.

2017-08-23

Neural cells are the smallest building blocks of the central and peripheral nervous systems. Information in neural networks and cell-substrate interactions have been heretofore studied separately. Understanding whether surface nano-topography can direct nerve cells assembly into computational efficient networks may provide new tools and criteria for tissue engineering and regenerative medicine. In this work, we used information theory approaches and functional multi calcium imaging (fMCI) techniques to examine how information flows in neural networks cultured on surfaces with controlled topography. We found that substrate roughness Sa affects networks topology. In the low nano-meter range, S-a = 0-30 nm, information increases with Sa. Moreover, we found that energy density of a network of cells correlates to the topology of that network. This reinforces the view that information, energy and surface nano-topography are tightly inter-connected and should not be neglected when studying cell-cell interaction in neural tissue repair and regeneration.

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

Science.gov (United States)

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

2016-07-03

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

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

International Nuclear Information System (INIS)

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

2001-01-01

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

Neural networks within multi-core optic fibers.

Science.gov (United States)

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

2016-07-07

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

Tissue stiffening coordinates morphogenesis by triggering collective cell migration in vivo.

Science.gov (United States)

Barriga, Elias H; Franze, Kristian; Charras, Guillaume; Mayor, Roberto

2018-02-22

Collective cell migration is essential for morphogenesis, tissue remodelling and cancer invasion. In vivo, groups of cells move in an orchestrated way through tissues. This movement involves mechanical as well as molecular interactions between cells and their environment. While the role of molecular signals in collective cell migration is comparatively well understood, how tissue mechanics influence collective cell migration in vivo remains unknown. Here we investigated the importance of mechanical cues in the collective migration of the Xenopus laevis neural crest cells, an embryonic cell population whose migratory behaviour has been likened to cancer invasion. We found that, during morphogenesis, the head mesoderm underlying the cephalic neural crest stiffens. This stiffening initiates an epithelial-to-mesenchymal transition in neural crest cells and triggers their collective migration. To detect changes in their mechanical environment, neural crest cells use mechanosensation mediated by the integrin-vinculin-talin complex. By performing mechanical and molecular manipulations, we show that mesoderm stiffening is necessary and sufficient to trigger neural crest migration. Finally, we demonstrate that convergent extension of the mesoderm, which starts during gastrulation, leads to increased mesoderm stiffness by increasing the cell density underneath the neural crest. These results show that convergent extension of the mesoderm has a role as a mechanical coordinator of morphogenesis, and reveal a link between two apparently unconnected processes-gastrulation and neural crest migration-via changes in tissue mechanics. Overall, we demonstrate that changes in substrate stiffness can trigger collective cell migration by promoting epithelial-to-mesenchymal transition in vivo. More broadly, our results raise the idea that tissue mechanics combines with molecular effectors to coordinate morphogenesis.

Fuzzy neural network theory and application

CERN Document Server

Liu, Puyin

2004-01-01

This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he

Applications of Mesenchymal Stem Cells and Neural Crest Cells in Craniofacial Skeletal Research

Directory of Open Access Journals (Sweden)

Satoru Morikawa

2016-01-01

Full Text Available Craniofacial skeletal tissues are composed of tooth and bone, together with nerves and blood vessels. This composite material is mainly derived from neural crest cells (NCCs. The neural crest is transient embryonic tissue present during neural tube formation whose cells have high potential for migration and differentiation. Thus, NCCs are promising candidates for craniofacial tissue regeneration; however, the clinical application of NCCs is hindered by their limited accessibility. In contrast, mesenchymal stem cells (MSCs are easily accessible in adults, have similar potential for self-renewal, and can differentiate into skeletal tissues, including bones and cartilage. Therefore, MSCs may represent good sources of stem cells for clinical use. MSCs are classically identified under adherent culture conditions, leading to contamination with other cell lineages. Previous studies have identified mouse- and human-specific MSC subsets using cell surface markers. Additionally, some studies have shown that a subset of MSCs is closely related to neural crest derivatives and endothelial cells. These MSCs may be promising candidates for regeneration of craniofacial tissues from the perspective of developmental fate. Here, we review the fundamental biology of MSCs in craniofacial research.

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

Energy Technology Data Exchange (ETDEWEB)

Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Tour 45-55/Etage 4/Case 100, UPMC, Paris Cedex 05 (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

2007-02-15

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere-Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the &apos

Tailor-made conductive inks from cellulose nanofibrils for 3D printing of neural guidelines.

Science.gov (United States)

Kuzmenko, Volodymyr; Karabulut, Erdem; Pernevik, Elin; Enoksson, Peter; Gatenholm, Paul

2018-06-01

Neural tissue engineering (TE), an innovative biomedical method of brain study, is very dependent on scaffolds that support cell development into a functional tissue. Recently, 3D patterned scaffolds for neural TE have shown significant positive effects on cells by a more realistic mimicking of actual neural tissue. In this work, we present a conductive nanocellulose-based ink for 3D printing of neural TE scaffolds. It is demonstrated that by using cellulose nanofibrils and carbon nanotubes as ink constituents, it is possible to print guidelines with a diameter below 1 mm and electrical conductivity of 3.8 × 10 -1  S cm -1 . The cell culture studies reveal that neural cells prefer to attach, proliferate, and differentiate on the 3D printed conductive guidelines. To our knowledge, this is the first research effort devoted to using cost-effective cellulosic 3D printed structures in neural TE, and we suppose that much more will arise in the near future. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.)

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

Science.gov (United States)

Ginter, S

2000-07-01

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

The neural basis for simulated drawing and the semantic implications.

Science.gov (United States)

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

2009-03-01

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

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.

Region stability analysis and tracking control of memristive recurrent neural network.

Science.gov (United States)

Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Dynamics of neural cryptography

International Nuclear Information System (INIS)

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-01-01

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

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.

AKT signaling displays multifaceted functions in neural crest development.

Science.gov (United States)

Sittewelle, Méghane; Monsoro-Burq, Anne H

2018-05-31

AKT signaling is an essential intracellular pathway controlling cell homeostasis, cell proliferation and survival, as well as cell migration and differentiation in adults. Alterations impacting the AKT pathway are involved in many pathological conditions in human disease. Similarly, during development, multiple transmembrane molecules, such as FGF receptors, PDGF receptors or integrins, activate AKT to control embryonic cell proliferation, migration, differentiation, and also cell fate decisions. While many studies in mouse embryos have clearly implicated AKT signaling in the differentiation of several neural crest derivatives, information on AKT functions during the earliest steps of neural crest development had remained relatively scarce until recently. However, recent studies on known and novel regulators of AKT signaling demonstrate that this pathway plays critical roles throughout the development of neural crest progenitors. Non-mammalian models such as fish and frog embryos have been instrumental to our understanding of AKT functions in neural crest development, both in neural crest progenitors and in the neighboring tissues. This review combines current knowledge acquired from all these different vertebrate animal models to describe the various roles of AKT signaling related to neural crest development in vivo. We first describe the importance of AKT signaling in patterning the tissues involved in neural crest induction, namely the dorsal mesoderm and the ectoderm. We then focus on AKT signaling functions in neural crest migration and differentiation. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

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

Science.gov (United States)

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

2017-07-01

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

Realistic soft tissue deformation strategies for real time surgery simulation.

Science.gov (United States)

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

2008-01-01

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

High-Density Stretchable Electrode Grids for Chronic Neural Recording.

Science.gov (United States)

Tybrandt, Klas; Khodagholy, Dion; Dielacher, Bernd; Stauffer, Flurin; Renz, Aline F; Buzsáki, György; Vörös, János

2018-04-01

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications. © 2018 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Requirement for Foxd3 in the maintenance of neural crest progenitors.

Science.gov (United States)

Teng, Lu; Mundell, Nathan A; Frist, Audrey Y; Wang, Qiaohong; Labosky, Patricia A

2008-05-01

Understanding the molecular mechanisms of stem cell maintenance is crucial for the ultimate goal of manipulating stem cells for the treatment of disease. Foxd3 is required early in mouse embryogenesis; Foxd3(-/-) embryos fail around the time of implantation, cells of the inner cell mass cannot be maintained in vitro, and blastocyst-derived stem cell lines cannot be established. Here, we report that Foxd3 is required for maintenance of the multipotent mammalian neural crest. Using tissue-specific deletion of Foxd3 in the neural crest, we show that Foxd3(flox/-); Wnt1-Cre mice die perinatally with a catastrophic loss of neural crest-derived structures. Cranial neural crest tissues are either missing or severely reduced in size, the peripheral nervous system consists of reduced dorsal root ganglia and cranial nerves, and the entire gastrointestinal tract is devoid of neural crest derivatives. These results demonstrate a global role for this transcriptional repressor in all aspects of neural crest maintenance along the anterior-posterior axis, and establish an unprecedented molecular link between multiple divergent progenitor lineages of the mammalian embryo.

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

Science.gov (United States)

Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

2017-01-01

Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

Simulating neural systems with Xyce.

Energy Technology Data Exchange (ETDEWEB)

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

2012-12-01

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

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

Science.gov (United States)

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

2010-01-01

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

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan; Naous, Rawan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

2015-01-01

. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards

Integrated Neural Flight and Propulsion Control System

Science.gov (United States)

Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

2001-01-01

This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

Science.gov (United States)

Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

2016-12-01

Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

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

Science.gov (United States)

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

2015-08-01

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

Study of functional viability of SU-8-based microneedles for neural applications

International Nuclear Information System (INIS)

Fernández, Luis J; Altuna, Ane; Tijero, Maria; Vilares, Roman; Berganzo, Javier; Blanco, F J; Gabriel, Gemma; Villa, Rosa; Rodríguez, Manuel J; Batlle, Montse

2009-01-01

This paper presents the design, fabrication, packaging and first test results of SU-8-based microneedles for neural applications. By the use of photolithography, sputtering and bonding techniques, polymer needles with integrated microchannels and electrodes have been successfully fabricated. The use of photolithography for the patterning of the fluidic channel integrated in the needle allows the design of multiple outlet ports at the needle tip, minimizing the possibility of being blocked by the tissue. Furthermore, the flexibility of the polymer reduces the risk of fracture and tissue damage once the needle is inserted, while it is still rigid enough to allow a perfect insertion into the neural tissue. Fluidic and electric characterization of the microneedles has shown their viability for drug delivery and monitoring in neural applications. First drug delivery tests in ex vivo tissue demonstrated the functional viability of the needle to deliver drugs to precise points. Furthermore, in vivo experiments have demonstrated lower associated damages during insertion than those by stereotaxic standard needles

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

International Nuclear Information System (INIS)

Otero, F

1998-01-01

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

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 ...

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

Science.gov (United States)

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

2013-11-01

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

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

Science.gov (United States)

Chon, K. H.; Cohen, R. J.

1997-01-01

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

Enteric neurospheres are not specific to neural crest cultures : Implications for neural stem cell therapies

NARCIS (Netherlands)

Binder, E. (Ellen); D. Natarajan (Dipa); J.E. Cooper (Julie E.); Kronfli, R. (Rania); Cananzi, M. (Mara); J.-M. Delalande (Jean-Marie); C. Mccann; A.J. Burns (Alan); N. Thapar (Nikhil)

2015-01-01

textabstractObjectives Enteric neural stem cells provide hope of curative treatment for enteric neuropathies. Current protocols for their harvesting from humans focus on the generation of 'neurospheres' from cultures of dissociated gut tissue. The study aims to better understand the derivation,

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

Science.gov (United States)

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

2007-08-01

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

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

Science.gov (United States)

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

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

Braided Multi-Electrode Probes (BMEPs) for Neural Interfaces

Science.gov (United States)

Kim, Tae Gyo

Although clinical use of invasive neural interfaces is very limited, due to safety and reliability concerns, the potential benefits of their use in brain machine interfaces (BMIs) seem promising and so they have been widely used in the research field. Microelectrodes as invasive neural interfaces are the core tool to record neural activities and their failure is a critical issue for BMI systems. Possible sources of this failure are neural tissue motions and their interactions with stiff electrode arrays or probes fixed to the skull. To overcome these tissue motion problems, we have developed novel braided multi-electrode probes (BMEPs). By interweaving ultra-fine wires into a tubular braid structure, we obtained a highly flexible multi-electrode probe. In this thesis we described BMEP designs and how to fabricate BMEPs, and explore experiments to show the advantages of BMEPs through a mechanical compliance comparison and a chronic immunohistological comparison with single 50microm nichrome wires used as a reference electrode type. Results from the mechanical compliance test showed that the bodies of BMEPs have 4 to 21 times higher compliance than the single 50microm wire and the tethers of BMEPs were 6 to 96 times higher compliance, depending on combinations of the wire size (9.6microm or 12.7microm), the wire numbers (12 or 24), and the length of tether (3, 5 or 10 mm). Results from the immunohistological comparison showed that both BMEPs and 50microm wires anchored to the skull caused stronger tissue reactions than unanchored BMEPs and 50microm wires, and 50microm wires caused stronger tissue reactions than BMEPs. In in-vivo tests with BMEPs, we succeeded in chronic recordings from the spinal cord of freely jumping frogs and in acute recordings from the spinal cord of decerebrate rats during air stepping which was evoked by mesencephalic locomotor region (MLR) stimulation. This technology may provide a stable and reliable neural interface to spinal cord

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

Science.gov (United States)

Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

2018-01-01

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

Small leucine rich proteoglycan family regulates multiple signalling pathways in neural development and maintenance.

Science.gov (United States)

Dellett, Margaret; Hu, Wanzhou; Papadaki, Vasiliki; Ohnuma, Shin-ichi

2012-04-01

The small leucine-rich repeat proteoglycan (SLRPs) family of proteins currently consists of five classes, based on their structural composition and chromosomal location. As biologically active components of the extracellular matrix (ECM), SLRPs were known to bind to various collagens, having a role in regulating fibril assembly, organization and degradation. More recently, as a function of their diverse proteins cores and glycosaminoglycan side chains, SLRPs have been shown to be able to bind various cell surface receptors, growth factors, cytokines and other ECM components resulting in the ability to influence various cellular functions. Their involvement in several signaling pathways such as Wnt, transforming growth factor-β and epidermal growth factor receptor also highlights their role as matricellular proteins. SLRP family members are expressed during neural development and in adult neural tissues, including ocular tissues. This review focuses on describing SLRP family members involvement in neural development with a brief summary of their role in non-neural ocular tissues and in response to neural injury. © 2012 The Authors Development, Growth & Differentiation © 2012 Japanese Society of Developmental Biologists.

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.

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

Science.gov (United States)

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

1997-09-01

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

Neural Network to Solve Concave Games

OpenAIRE

Liu, Zixin; Wang, Nengfa

2014-01-01

The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given to illustrate the theoretical results.

Gelatin methacrylamide hydrogel with graphene nanoplatelets for neural cell-laden 3D bioprinting.

Science.gov (United States)

Wei Zhu; Harris, Brent T; Zhang, Lijie Grace

2016-08-01

Nervous system is extremely complex which leads to rare regrowth of nerves once injury or disease occurs. Advanced 3D bioprinting strategy, which could simultaneously deposit biocompatible materials, cells and supporting components in a layer-by-layer manner, may be a promising solution to address neural damages. Here we presented a printable nano-bioink composed of gelatin methacrylamide (GelMA), neural stem cells, and bioactive graphene nanoplatelets to target nerve tissue regeneration in the assist of stereolithography based 3D bioprinting technique. We found the resultant GelMA hydrogel has a higher compressive modulus with an increase of GelMA concentration. The porous GelMA hydrogel can provide a biocompatible microenvironment for the survival and growth of neural stem cells. The cells encapsulated in the hydrogel presented good cell viability at the low GelMA concentration. Printed neural construct exhibited well-defined architecture and homogenous cell distribution. In addition, neural stem cells showed neuron differentiation and neurites elongation within the printed construct after two weeks of culture. These findings indicate the 3D bioprinted neural construct has great potential for neural tissue regeneration.

Parameter estimation of brain tumors using intraoperative thermal imaging based on artificial tactile sensing in conjunction with artificial neural network

International Nuclear Information System (INIS)

Sadeghi-Goughari, M; Mojra, A; Sadeghi, S

2016-01-01

Intraoperative Thermal Imaging (ITI) is a new minimally invasive diagnosis technique that can potentially locate margins of brain tumor in order to achieve maximum tumor resection with least morbidity. This study introduces a new approach to ITI based on artificial tactile sensing (ATS) technology in conjunction with artificial neural networks (ANN) and feasibility and applicability of this method in diagnosis and localization of brain tumors is investigated. In order to analyze validity and reliability of the proposed method, two simulations were performed. (i) An in vitro experimental setup was designed and fabricated using a resistance heater embedded in agar tissue phantom in order to simulate heat generation by a tumor in the brain tissue; and (ii) A case report patient with parafalcine meningioma was presented to simulate ITI in the neurosurgical procedure. In the case report, both brain and tumor geometries were constructed from MRI data and tumor temperature and depth of location were estimated. For experimental tests, a novel assisted surgery robot was developed to palpate the tissue phantom surface to measure temperature variations and ANN was trained to estimate the simulated tumor’s power and depth. Results affirm that ITI based ATS is a non-invasive method which can be useful to detect, localize and characterize brain tumors. (paper)

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

Science.gov (United States)

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

2018-04-01

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

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.

Neural network based multiscale image restoration approach

Science.gov (United States)

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

2007-02-01

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

Electrode impedance analysis of chronic tungsten microwire neural implants: understanding abiotic vs. biotic contributions

Directory of Open Access Journals (Sweden)

Viswanath eSankar

2014-05-01

Full Text Available Changes in biotic and abiotic factors can be reflected in the complex impedance spectrum of the microelectrodes chronically implanted into the neural tissue. The recording surface of the tungsten electrode in vivo undergoes abiotic changes due to recording site corrosion and insulation delamination as well as biotic changes due to tissue encapsulation as a result of the foreign body immune response. We reported earlier that large changes in electrode impedance measured at 1 kHz were correlated with poor electrode functional performance, quantified through electrophysiological recordings during the chronic lifetime of the electrode. There is a need to identity the factors that contribute to the chronic impedance variation. In this work, we use numerical simulation and regression to equivalent circuit models to evaluate both the abiotic and biotic contributions to the impedance response over chronic implant duration. COMSOL® simulation of abiotic electrode morphology changes provide a possible explanation for the decrease in the electrode impedance at long implant duration while biotic changes play an important role in the large increase in impedance observed initially.

Battery Performance Modelling ad Simulation: a Neural Network Based Approach

Science.gov (United States)

Ottavianelli, Giuseppe; Donati, Alessandro

2002-01-01

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

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

KAUST Repository

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

2018-01-01

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

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

KAUST Repository

Limongi, Tania

2018-04-04

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

A study of reactor monitoring method with neural network

Energy Technology Data Exchange (ETDEWEB)

Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2001-03-01

The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)

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

Science.gov (United States)

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

2014-09-01

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

Different propagation speeds of recalled sequences in plastic spiking neural networks

Science.gov (United States)

Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

2015-03-01

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in

Dynamic training algorithm for dynamic neural networks

International Nuclear Information System (INIS)

Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

1996-01-01

The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

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

Science.gov (United States)

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

2017-11-03

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

Simulating large-scale spiking neuronal networks with NEST

OpenAIRE

Schücker, Jannis; Eppler, Jochen Martin

2014-01-01

The Neural Simulation Tool NEST [1, www.nest-simulator.org] is the simulator for spiking neural networkmodels of the HBP that focuses on the dynamics, size and structure of neural systems rather than on theexact morphology of individual neurons. Its simulation kernel is written in C++ and it runs on computinghardware ranging from simple laptops to clusters and supercomputers with thousands of processor cores.The development of NEST is coordinated by the NEST Initiative [www.nest-initiative.or...

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

International Nuclear Information System (INIS)

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

1987-01-01

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

Pulsed neural networks consisting of single-flux-quantum spiking neurons

International Nuclear Information System (INIS)

Hirose, T.; Asai, T.; Amemiya, Y.

2007-01-01

An inhibitory pulsed neural network was developed for brain-like information processing, by using single-flux-quantum (SFQ) circuits. It consists of spiking neuron devices that are coupled to each other through all-to-all inhibitory connections. The network selects neural activity. The operation of the neural network was confirmed by computer simulation. SFQ neuron devices can imitate the operation of the inhibition phenomenon of neural networks

Transcriptional profiling of adult neural stem-like cells from the human brain.

Directory of Open Access Journals (Sweden)

Cecilie Jonsgar Sandberg

Full Text Available There is a great potential for the development of new cell replacement strategies based on adult human neural stem-like cells. However, little is known about the hierarchy of cells and the unique molecular properties of stem- and progenitor cells of the nervous system. Stem cells from the adult human brain can be propagated and expanded in vitro as free floating neurospheres that are capable of self-renewal and differentiation into all three cell types of the central nervous system. Here we report the first global gene expression study of adult human neural stem-like cells originating from five human subventricular zone biopsies (mean age 42, range 33-60. Compared to adult human brain tissue, we identified 1,189 genes that were significantly up- and down-regulated in adult human neural stem-like cells (1% false discovery rate. We found that adult human neural stem-like cells express stem cell markers and have reduced levels of markers that are typical of the mature cells in the nervous system. We report that the genes being highly expressed in adult human neural stem-like cells are associated with developmental processes and the extracellular region of the cell. The calcium signaling pathway and neuroactive ligand-receptor interactions are enriched among the most differentially regulated genes between adult human neural stem-like cells and adult human brain tissue. We confirmed the expression of 10 of the most up-regulated genes in adult human neural stem-like cells in an additional sample set that included adult human neural stem-like cells (n = 6, foetal human neural stem cells (n = 1 and human brain tissues (n = 12. The NGFR, SLITRK6 and KCNS3 receptors were further investigated by immunofluorescence and shown to be heterogeneously expressed in spheres. These receptors could potentially serve as new markers for the identification and characterisation of neural stem- and progenitor cells or as targets for manipulation of cellular

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

Dynamic decomposition of spatiotemporal neural signals.

Directory of Open Access Journals (Sweden)

Luca Ambrogioni

2017-05-01

Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

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

Science.gov (United States)

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

2018-01-04

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

Optimal Brain Surgeon on Artificial Neural Networks in

DEFF Research Database (Denmark)

Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine

2012-01-01

It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

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.

Multimodality instrument for tissue characterization

Science.gov (United States)

Mah, Robert W. (Inventor); Andrews, Russell J. (Inventor)

2004-01-01

A system with multimodality instrument for tissue identification includes a computer-controlled motor driven heuristic probe with a multisensory tip. For neurosurgical applications, the instrument is mounted on a stereotactic frame for the probe to penetrate the brain in a precisely controlled fashion. The resistance of the brain tissue being penetrated is continually monitored by a miniaturized strain gauge attached to the probe tip. Other modality sensors may be mounted near the probe tip to provide real-time tissue characterizations and the ability to detect the proximity of blood vessels, thus eliminating errors normally associated with registration of pre-operative scans, tissue swelling, elastic tissue deformation, human judgement, etc., and rendering surgical procedures safer, more accurate, and efficient. A neural network program adaptively learns the information on resistance and other characteristic features of normal brain tissue during the surgery and provides near real-time modeling. A fuzzy logic interface to the neural network program incorporates expert medical knowledge in the learning process. Identification of abnormal brain tissue is determined by the detection of change and comparison with previously learned models of abnormal brain tissues. The operation of the instrument is controlled through a user friendly graphical interface. Patient data is presented in a 3D stereographics display. Acoustic feedback of selected information may optionally be provided. Upon detection of the close proximity to blood vessels or abnormal brain tissue, the computer-controlled motor immediately stops probe penetration. The use of this system will make surgical procedures safer, more accurate, and more efficient. Other applications of this system include the detection, prognosis and treatment of breast cancer, prostate cancer, spinal diseases, and use in general exploratory surgery.

Neural-fuzzy control of adept one SCARA

International Nuclear Information System (INIS)

Er, M.J.; Toh, B.H.; Toh, B.Y.

1998-01-01

This paper presents an Intelligent Control Strategy for the Adept One SCARA (Selective Compliance Assembly Robot Arm). It covers the design and simulation study of a Neural-Fuzzy Controller (NFC) for the SCARA with a view of tracking a predetermined trajectory of motion in the joint space. The SCARA was simulated as a three-axis manipulator with the dynamics of the tool (fourth link) neglected and the mass of the load incorporated into the mass of the third link. The overall performance of the control system under different conditions, namely variation in playload, variations in coefficients of static, dynamic and viscous friction and different trajectories were studied and comparison made with an existing Neural Network Controller and two Computed Torque Controllers. The NFC was shown to be robust and is able to overcome the drawback of the existing Neural Network Controller

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

Science.gov (United States)

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

2010-01-01

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

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

Science.gov (United States)

Duadi, Hamootal; Fixler, Dror; Popovtzer, Rachela

2013-11-01

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

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

Science.gov (United States)

Xu, Lang; Lu, Yuhua; Liu, Qian

2018-02-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2010-07-21

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

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

International Nuclear Information System (INIS)

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

2008-01-01

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

Prediction based chaos control via a new neural network

International Nuclear Information System (INIS)

Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

2008-01-01

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

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.)

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

International Nuclear Information System (INIS)

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

2003-01-01

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

Identification of generalized state transfer matrix using neural networks

International Nuclear Information System (INIS)

Zhu Changchun

2001-01-01

The research is introduced on identification of generalized state transfer matrix of linear time-invariant (LTI) system by use of neural networks based on LM (Levenberg-Marquart) algorithm. Firstly, the generalized state transfer matrix is defined. The relationship between the identification of state transfer matrix of structural dynamics and the identification of the weight matrix of neural networks has been established in theory. A singular layer neural network is adopted to obtain the structural parameters as a powerful tool that has parallel distributed processing ability and the property of adaptation or learning. The constraint condition of weight matrix of the neural network is deduced so that the learning and training of the designed network can be more effective. The identified neural network can be used to simulate the structural response excited by any other signals. In order to cope with its further application in practical problems, some noise (5% and 10%) is expected to be present in the response measurements. Results from computer simulation studies show that this method is valid and feasible

Tracking and vertex finding with drift chambers and neural networks

International Nuclear Information System (INIS)

Lindsey, C.

1991-09-01

Finding tracks, track vertices and event vertices with neural networks from drift chamber signals is discussed. Simulated feed-forward neural networks have been trained with back-propagation to give track parameters using Monte Carlo simulated tracks in one case and actual experimental data in another. Effects on network performance of limited weight resolution, noise and drift chamber resolution are given. Possible implementations in hardware are discussed. 7 refs., 10 figs

Radioactive fallout and neural tube defects

African Journals Online (AJOL)

Nejat Akar

2015-07-10

Jul 10, 2015 ... It is a prenatal failure of the embryonic neural tube to close over the ... and the ability of radioisotopes to attach to cells, tissues, and ... The Egyptian Journal of Medical Human Genetics .... Stem Cells 1997;15(Suppl 2):255–60.

Epidural anaesthesia with levobupivacaine and ropivacaine : effects of age on the pharmacokinetics, neural blockade and haemodynamics

NARCIS (Netherlands)

Simon, Mischa J.G.

2006-01-01

Epidural neural blockade results from processes after the administration of a local anaesthetic in the epidural space until the uptake in neural tissue. The pharmacokinetics, neural blockade and haemodynamics after epidural anaesthesia may be influenced by several factors, with age as the most

Graphene and carbon nanocompounds: biofunctionalization and applications in tissue engineering

International Nuclear Information System (INIS)

Jesion, Iwona; Skibniewska, Ewa; Skibniewski, Michał; Pasternak, Iwona; Strupiński, Włodzimierz; Krajewska, Aleksandra; Szulc-Dąbrowska, Lidia; Kowalczyk, Paweł; Pińkowski, Roman

2015-01-01

In tissue engineering, the possibility of a comprehensive restoration of the tissue, structure or a portion of the organ is largely determined by the type of material used. A wide range of materials such as graphene and other carbon nanocompounds which have different physical and chemical properties can be expected to react differently upon contact with biomolecules, cells and tissues. This mini-review describes the current knowledge on biocompatibility of graphene and its derivatives with a variety of mammalian cells, such as osteoblasts, neuroendocrine cells, fibroblasts NIH/3T3 line, PMEFs (primary mouse embryonic fibroblasts), stem cells and neurons. The results from different studies give hope for the possibility of graphene to be used in the regeneration of almost all tissues, including neural tissue implants or in the form of neural chips, which may allow in the future treatment of degenerative diseases and injuries of the central nervous system. Keywords: nanotechnology; mammalian cells; tissue regeneration; biocompatibility; cytotoxicity

Dynamic skin deformation simulation using musculoskeletal model and soft tissue dynamics

Institute of Scientific and Technical Information of China (English)

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

2017-01-01

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

Dynamic skin deformation simulation using musculoskeletal model and soft tissue dynamics

Institute of Scientific and Technical Information of China (English)

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

2017-01-01

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

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

Science.gov (United States)

Dornay, M; Sanger, T D

1993-01-01

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

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%.

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

International Nuclear Information System (INIS)

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

2015-01-01

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

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

Science.gov (United States)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic

SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.

Science.gov (United States)

Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan

2013-04-30

The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.

Artificial neural networks based estimation of optical parameters by diffuse reflectance imaging under in vitro conditions

Directory of Open Access Journals (Sweden)

Mahmut Ozan Gökkan

2017-01-01

Full Text Available Optical parameters (properties of tissue-mimicking phantoms are determined through noninvasive optical imaging. Objective of this study is to decompose obtained diffuse reflectance into these optical properties such as absorption and scattering coefficients. To do so, transmission spectroscopy is firstly used to measure the coefficients via an experimental setup. Next, the optical properties of each characterized phantom are input for Monte Carlo (MC simulations to get diffuse reflectance. Also, a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 5∘ angle to the phantoms. For the illumination of light, a laser light source at 633nm wavelength is preferred, because optical properties of different components in a biological tissue on that wavelength are nonoverlapped. During in vitro measurements, we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion (CILE and evans blue (EB dye into a distilled water. Finally, all obtained diffuse reflectance values are used to estimate the optical coefficients by artificial neural networks (ANNs in inverse modeling. For a biological tissue it is found that the simulated and measured values in our results are in good agreement.

Connecting Neural Coding to Number Cognition: A Computational Account

Science.gov (United States)

Prather, Richard W.

2012-01-01

The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has…

Application of Artificial Neural Network Models in Segmentation and Classification of Nodules in Breast Ultrasound Digital Images

Directory of Open Access Journals (Sweden)

Karem D. Marcomini

2016-01-01

Full Text Available This research presents a methodology for the automatic detection and characterization of breast sonographic findings. We performed the tests in ultrasound images obtained from breast phantoms made of tissue mimicking material. When the results were considerable, we applied the same techniques to clinical examinations. The process was started employing preprocessing (Wiener filter, equalization, and median filter to minimize noise. Then, five segmentation techniques were investigated to determine the most concise representation of the lesion contour, enabling us to consider the neural network SOM as the most relevant. After the delimitation of the object, the most expressive features were defined to the morphological description of the finding, generating the input data to the neural Multilayer Perceptron (MLP classifier. The accuracy achieved during training with simulated images was 94.2%, producing an AUC of 0.92. To evaluating the data generalization, the classification was performed with a group of unknown images to the system, both to simulators and to clinical trials, resulting in an accuracy of 90% and 81%, respectively. The proposed classifier proved to be an important tool for the diagnosis in breast ultrasound.

Neural network modeling for near wall turbulent flow

International Nuclear Information System (INIS)

Milano, Michele; Koumoutsakos, Petros

2002-01-01

A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control

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

Science.gov (United States)

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

2007-04-20

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

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

Science.gov (United States)

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

2016-06-01

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

Coating flexible probes with an ultra fast degrading polymer to aid in tissue insertion.

Science.gov (United States)

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

2015-04-01

We report a fabrication process for coating neural probes with an ultrafast degrading polymer to create consistent and reproducible devices for neural tissue insertion. The rigid polymer coating acts as a probe insertion aid, but resorbs within hours post-implantation. Despite the feasibility for short term neural recordings from currently available neural prosthetic devices, most of these devices suffer from long term gliosis, which isolates the probes from adjacent neurons, increasing the recording impedance and stimulation threshold. The size and stiffness of implanted probes have been identified as critical factors that lead to this long term gliosis. Smaller, more flexible probes that match the mechanical properties of brain tissue could allow better long term integration by limiting the mechanical disruption of the surrounding tissue during and after probe insertion, while being flexible enough to deform with the tissue during brain movement. However, these small flexible probes inherently lack the mechanical strength to penetrate the brain on their own. In this work, we have developed a micromolding method for coating a non-functional miniaturized SU-8 probe with an ultrafast degrading tyrosine-derived polycarbonate (E5005(2K)). Coated, non-functionalized probes of varying dimensions were reproducibly fabricated with high yields. The polymer erosion/degradation profiles of the probes were characterized in vitro. The probes were also mechanically characterized in ex vivo brain tissue models by measuring buckling and insertion forces during probe insertion. The results demonstrate the ability to produce polymer coated probes of consistent quality for future in vivo use, for example to study the effects of different design parameters that may affect tissue response during long term chronic intra-cortical microelectrode neural recordings.

Artificial Neural Networks For Hadron Hadron Cross-sections

International Nuclear Information System (INIS)

ELMashad, M.; ELBakry, M.Y.; Tantawy, M.; Habashy, D.M.

2011-01-01

In recent years artificial neural networks (ANN ) have emerged as a mature and viable framework with many applications in various areas. Artificial neural networks theory is sometimes used to refer to a branch of computational science that uses neural networks as models to either simulate or analyze complex phenomena and/or study the principles of operation of neural networks analytically. In this work a model of hadron- hadron collision using the ANN technique is present, the hadron- hadron based ANN model calculates the cross sections of hadron- hadron collision. The results amply demonstrate the feasibility of such new technique in extracting the collision features and prove its effectiveness

Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control

Directory of Open Access Journals (Sweden)

Mehmet eKocaturk

2015-08-01

Full Text Available In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE as a practical platform for the development of novel brain machine interface (BMI controllers which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two target reaching task in one dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN simulations with powerful online data visualization tools and is a low-cost, PC-based and all-in-one solution for developing neurally-inspired BMI controllers. We believe the BNDE is the first implementation which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.

Simulation of induced electric field distribution based on five-sphere model used in rTMS.

Science.gov (United States)

Pu, Lina; Liu, Zhipeng; Yin, Tao; An, Hao; Li, Song

2010-01-01

Repetitive Transcranial magnetic stimulation (TMS) is a relatively new technique, which is non-invasive and painless used to stimulate the central and peripheral neural tissues. The principle is generating time-varying magnetic fields to stimulate the cerebral cortex neuron and inducing eddy current inside the tissues. Many researches study on the distributing of magnetic field and electric field induced inside the human brain, whereas the static electric field was neglected roughly in many studies. In this paper, a five-sphere model is established to simulate the human head used in rTMS. According to the different dielectric properties of the head tissues, the Laplace equation of static electric field is deduced by both of Gauss theorem and current's continuity principle. Boundary conditions used in different interface between two adjacent layers in the five-sphere model is proposed in this paper. Simulating study is conducted to calculate the distribution of the electric field in the model. Simulating results suggest that the model is useful to get the parameters of the most focus coil. Therefore this study could be potential to promote the development of rTMS stimulator.

Intra-uterine tissue engineering of full-thickness skin defects in a fetal sheep model

NARCIS (Netherlands)

Hosper, Nynke A.; Eggink, Alex J.; Roelofs, Luc A. J.; Wijnen, Rene M. H.; van Luyn, Marja J. A.; Bank, Ruud A.; Harmsen, Martin C.; Geutjes, Paul J.; Daamen, Willeke F.; van Kuppevelt, Toin H.; Tiemessen, Dorien M.; Oosterwijk, Egbert; Crevels, Jane J.; Blokx, Willeke A. M.; Lotgering, Fred K.; van den Berg, Paul P.; Feitz, Wout F. J.

In spina bifida the neural tube fails to close during the embryonic period and it is thought that prolonged exposure of the unprotected spinal cord to the amniotic fluid during pregnancy causes additional neural damage. Intra-uterine repair might protect the neural tissue from exposure to amniotic

Nonlinear programming with feedforward neural networks.

Energy Technology Data Exchange (ETDEWEB)

Reifman, J.

1999-06-02

We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.

Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

Directory of Open Access Journals (Sweden)

Wei Liu

2013-01-01

Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

Neural Networks in Mobile Robot Motion

Directory of Open Access Journals (Sweden)

Danica Janglová

2004-03-01

Full Text Available This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.

Optimization of operation schemes in boiling water reactors using neural networks

International Nuclear Information System (INIS)

Ortiz S, J. J.; Castillo M, A.; Pelta, D. A.

2012-10-01

In previous works were presented the results of a recurrent neural network to find the best combination of several groups of fuel cells, fuel load and control bars patterns. These solution groups to each problem of Fuel Management were previously optimized by diverse optimization techniques. The neural network chooses the partial solutions so the combination of them, correspond to a good configuration of the reactor according to a function objective. The values of the involved variables in this objective function are obtained through the simulation of the combination of partial solutions by means of Simulate-3. In the present work, a multilayer neural network that learned how to predict some results of Simulate-3 was used so was possible to substitute it in the objective function for the neural network and to accelerate the response time of the whole system of this way. The preliminary results shown in this work are encouraging to continue carrying out efforts in this sense and to improve the response quality of the system. (Author)

Bio-inspired spiking neural network for nonlinear systems control.

Science.gov (United States)

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

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....

A fully implantable rodent neural stimulator

Science.gov (United States)

Perry, D. W. J.; Grayden, D. B.; Shepherd, R. K.; Fallon, J. B.

2012-02-01

The ability to electrically stimulate neural and other excitable tissues in behaving experimental animals is invaluable for both the development of neural prostheses and basic neurological research. We developed a fully implantable neural stimulator that is able to deliver two channels of intra-cochlear electrical stimulation in the rat. It is powered via a novel omni-directional inductive link and includes an on-board microcontroller with integrated radio link, programmable current sources and switching circuitry to generate charge-balanced biphasic stimulation. We tested the implant in vivo and were able to elicit both neural and behavioural responses. The implants continued to function for up to five months in vivo. While targeted to cochlear stimulation, with appropriate electrode arrays the stimulator is well suited to stimulating other neurons within the peripheral or central nervous systems. Moreover, it includes significant on-board data acquisition and processing capabilities, which could potentially make it a useful platform for telemetry applications, where there is a need to chronically monitor physiological variables in unrestrained animals.

Implantable liquid metal-based flexible neural microelectrode array and its application in recovering animal locomotion functions

Science.gov (United States)

Guo, Rui; Liu, Jing

2017-10-01

With significant advantages in rapidly restoring the nerve function, electrical stimulation of nervous tissue is a crucial treatment of peripheral nerve injuries leading to common movement disorder. However, the currently available stimulating electrodes generally based on rigid conductive materials would cause a potential mechanical mismatch with soft neural tissues which thus reduces long-term effects of electrical stimulation. Here, we proposed and fabricated a flexible neural microelectrode array system based on the liquid metal GaIn alloy (75.5% Ga and 24.5% In by weight) and via printing approach. Such an alloy with a unique low melting point (10.35 °C) owns excellent electrical conductivity and high compliance, which are beneficial to serve as implantable flexible neural electrodes. The flexible neural microelectrode array embeds four liquid metal electrodes and stretchable interconnects in a PDMS membrane (500 µm in thickness) that possess a lower elastic modulus (1.055 MPa), which is similar to neural tissues with elastic moduli in the 0.1-1.5 MPa range. The electrical experiments indicate that the liquid metal interconnects could sustain over 7000 mechanical stretch cycles with resistance approximately staying at 4 Ω. Over the conceptual experiments on animal sciatic nerve electrical stimulation, the dead bullfrog implanted with flexible neural microelectrode array could even rhythmically contract and move its lower limbs under the electrical stimulations from the implant. This demonstrates a highly efficient way for quickly recovering biological nerve functions. Further, the good biocompatibility of the liquid metal material was justified via a series of biological experiments. This liquid metal modality for neural stimulation is expected to play important roles as biologic electrodes to overcome the fundamental mismatch in mechanics between biological tissues and electronic devices in the coming time.

Implantable liquid metal-based flexible neural microelectrode array and its application in recovering animal locomotion functions

International Nuclear Information System (INIS)

Guo, Rui; Liu, Jing

2017-01-01

With significant advantages in rapidly restoring the nerve function, electrical stimulation of nervous tissue is a crucial treatment of peripheral nerve injuries leading to common movement disorder. However, the currently available stimulating electrodes generally based on rigid conductive materials would cause a potential mechanical mismatch with soft neural tissues which thus reduces long-term effects of electrical stimulation. Here, we proposed and fabricated a flexible neural microelectrode array system based on the liquid metal GaIn alloy (75.5% Ga and 24.5% In by weight) and via printing approach. Such an alloy with a unique low melting point (10.35 °C) owns excellent electrical conductivity and high compliance, which are beneficial to serve as implantable flexible neural electrodes. The flexible neural microelectrode array embeds four liquid metal electrodes and stretchable interconnects in a PDMS membrane (500 µ m in thickness) that possess a lower elastic modulus (1.055 MPa), which is similar to neural tissues with elastic moduli in the 0.1–1.5 MPa range. The electrical experiments indicate that the liquid metal interconnects could sustain over 7000 mechanical stretch cycles with resistance approximately staying at 4 Ω. Over the conceptual experiments on animal sciatic nerve electrical stimulation, the dead bullfrog implanted with flexible neural microelectrode array could even rhythmically contract and move its lower limbs under the electrical stimulations from the implant. This demonstrates a highly efficient way for quickly recovering biological nerve functions. Further, the good biocompatibility of the liquid metal material was justified via a series of biological experiments. This liquid metal modality for neural stimulation is expected to play important roles as biologic electrodes to overcome the fundamental mismatch in mechanics between biological tissues and electronic devices in the coming time. (paper)

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

Science.gov (United States)

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

2009-08-01

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

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

International Nuclear Information System (INIS)

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

2009-01-01

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

iSpike: a spiking neural interface for the iCub robot

International Nuclear Information System (INIS)

Gamez, D; Fidjeland, A K; Lazdins, E

2012-01-01

This paper presents iSpike: a C++ library that interfaces between spiking neural network simulators and the iCub humanoid robot. It uses a biologically inspired approach to convert the robot’s sensory information into spikes that are passed to the neural network simulator, and it decodes output spikes from the network into motor signals that are sent to control the robot. Applications of iSpike range from embodied models of the brain to the development of intelligent robots using biologically inspired spiking neural networks. iSpike is an open source library that is available for free download under the terms of the GPL. (paper)

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

Directory of Open Access Journals (Sweden)

Miriam eZacksenhouse

2015-05-01

Full Text Available Recent experiments with brain-machine-interfaces (BMIs indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

Science.gov (United States)

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

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

Directory of Open Access Journals (Sweden)

Hossein Rezvantalab

2011-01-01

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

Neural networks, D0, and the SSC

International Nuclear Information System (INIS)

Barter, C.; Cutts, D.; Hoftun, J.S.; Partridge, R.A.; Sornborger, A.T.; Johnson, C.T.; Zeller, R.T.

1989-01-01

We outline several exploratory studies involving neural network simulations applied to pattern recognition in high energy physics. We describe the D0 data acquisition system and a natual means by which algorithms derived from neural networks techniques may be incorporated into recently developed hardware associated with the D0 MicroVAX farm nodes. Such applications to the event filtering needed by SSC detectors look interesting. 10 refs., 11 figs

A three dimensional in vitro glial scar model to investigate the local strain effects from micromotion around neural implants.

Science.gov (United States)

Spencer, Kevin C; Sy, Jay C; Falcón-Banchs, Roberto; Cima, Michael J

2017-02-28

Glial scar formation remains a significant barrier to the long term success of neural probes. Micromotion coupled with mechanical mismatch between the probe and tissue is believed to be a key driver of the inflammatory response. In vitro glial scar models present an intermediate step prior to conventional in vivo histology experiments as they enable cell-device interactions to be tested on a shorter timescale, with the ability to conduct broader biochemical assays. No established in vitro models have incorporated methods to assess device performance with respect to mechanical factors. In this study, we describe an in vitro glial scar model that combines high-precision linear actuators to simulate axial micromotion around neural implants with a 3D primary neural cell culture in a collagen gel. Strain field measurements were conducted to visualize the local displacement within the gel in response to micromotion. Primary brain cell cultures were found to be mechanically responsive to micromotion after one week in culture. Astrocytes, as determined by immunohistochemical staining, were found to have significantly increased in cell areas and perimeters in response to micromotion compared to static control wells. These results demonstrate the importance of micromotion when considering the chronic response to neural implants. Going forward, this model provides advantages over existing in vitro models as it will enable critical mechanical design factors of neural implants to be evaluated prior to in vivo testing.

Radial basis function neural network in fault detection of automotive ...

African Journals Online (AJOL)

Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection

Neural networks for event filtering at D/O/

International Nuclear Information System (INIS)

Cutts, D.; Hoftun, J.S.; Sornborger, A.; Johnson, C.R.; Zeller, R.T.

1989-01-01

Neural networks may provide important tools for pattern recognition in high energy physics. We discuss an initial exploration of these techniques, presenting the result of network simulations of several filter algorithms. The D0 data acquisition system, a MicroVAX farm, will perform critical event selection; we describe a possible implementation of neural network algorithms in this system. 7 refs., 4 figs

Self-assembly of tissue spheroids on polymeric membranes.

Science.gov (United States)

Messina, Antonietta; Morelli, Sabrina; Forgacs, Gabor; Barbieri, Giuseppe; Drioli, Enrico; De Bartolo, Loredana

2017-07-01

In this study, multicellular tissue spheroids were fabricated on polymeric membranes in order to accelerate the fusion process and tissue formation. To this purpose, tissue spheroids composed of three different cell types, myoblasts, fibroblasts and neural cells, were formed and cultured on agarose and membranes of polycaprolactone (PCL) and chitosan (CHT). Membranes prepared by a phase-inversion technique display different physicochemical, mechanical and transport properties, which can affect the fusion process. The membranes accelerated the fusion process of a pair of spheroids with respect to the inert substrate. In this process, a critical role is played by the membrane properties, especially by their mechanical characteristics and oxygen and carbon dioxide mass transfer. The rate of fusion was quantified and found to be similar for fibroblast, myoblast and neural tissue spheroids on membranes, which completed the fusion within 3 days. These spheroids underwent faster fusion and maturation on PCL membrane than on agarose, the rate of fusion being proportional to the value of oxygen and carbon dioxide permeances and elastic characteristics. Consequently, tissue spheroids on the membranes expressed high biological activity in terms of oxygen uptake, making them more suitable as building blocks in the fabrication of tissues and organs. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan

2015-04-01

Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

Bumps, breathers, and waves in a neural network with spike frequency adaptation

International Nuclear Information System (INIS)

Coombes, S.; Owen, M.R.

2005-01-01

We introduce a continuum model of neural tissue that includes the effects of spike frequency adaptation (SFA). The basic model is an integral equation for synaptic activity that depends upon nonlocal network connectivity, synaptic response, and the firing rate of a single neuron. We consider a phenomenological model of SFA via a simple state-dependent threshold firing rate function. As without SFA, Mexican-hat connectivity allows for the existence of spatially localized states (bumps). Importantly recent Evans function techniques are used to show that bumps may destabilize leading to the emergence of breathers and traveling waves. Moreover, a similar analysis for traveling pulses leads to the conditions necessary to observe a stable traveling breather. Simulations confirm our theoretical predictions and illustrate the rich behavior of this model

Adult Mammalian Neural Stem Cells and Neurogenesis: Five Decades Later

Science.gov (United States)

Bond, Allison M.; Ming, Guo-li; Song, Hongjun

2015-01-01

Summary Adult somatic stem cells in various organs maintain homeostatic tissue regeneration and enhance plasticity. Since its initial discovery five decades ago, investigations of adult neurogenesis and neural stem cells have led to an established and expanding field that has significantly influenced many facets of neuroscience, developmental biology and regenerative medicine. Here we review recent progress and focus on questions related to adult mammalian neural stem cells that also apply to other somatic stem cells. We further discuss emerging topics that are guiding the field toward better understanding adult neural stem cells and ultimately applying these principles to improve human health. PMID:26431181

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

In vitro differentiation of adipose-tissue-derived mesenchymal stem cells into neural retinal cells through expression of human PAX6 (5a) gene.

Science.gov (United States)

Rezanejad, Habib; Soheili, Zahra-Soheila; Haddad, Farhang; Matin, Maryam M; Samiei, Shahram; Manafi, Ali; Ahmadieh, Hamid

2014-04-01

The neural retina is subjected to various degenerative conditions. Regenerative stem-cell-based therapy holds great promise for treating severe retinal degeneration diseases, although many drawbacks remain to be overcome. One important problem is to gain authentically differentiated cells for replacement. Paired box 6 protein (5a) (PAX6 (5a)) is a highly conserved master control gene that has an essential role in the development of the vertebrate visual system. Human adipose-tissue-derived stem cell (hADSC) isolation was performed by using fat tissues and was confirmed by the differentiation potential of the cells into adipocytes and osteocytes and by their surface marker profile. The coding region of the human PAX6 (5a) gene isoform was cloned and lentiviral particles were propagated in HEK293T. The differentiation of hADSCs into retinal cells was characterized by morphological characteristics, quantitative real-time reverse transcription plus the polymerase chain reaction (qPCR) and immunocytochemistry (ICC) for some retinal cell-specific and retinal pigmented epithelial (RPE) cell-specific markers. hADSCs were successfully isolated. Flow cytometric analysis of surface markers indicated the high purity (~97 %) of isolated hADSCs. After 30 h of post-transduction, cells gradually showed the characteristic morphology of neuronal cells and small axon-like processes emerged. qPCR and ICC confirmed the differentiation of some neural retinal cells and RPE cells. Thus, PAX6 (5a) transcription factor expression, together with medium supplemented with fibronectin, is able to induce the differentiation of hADSCs into retinal progenitors, RPE cells and photoreceptors.

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

Science.gov (United States)

Liu, Qingshan; Cao, Jinde

2010-06-01

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

Neural Control of Hemorrhage-Induced Tissue Cytokine Production

National Research Council Canada - National Science Library

Molina, Patrica E

2007-01-01

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

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

Science.gov (United States)

Yao, Weigang; Liou, Meng-Sing

2012-01-01

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

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.

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.

A wirelessly powered microspectrometer for neural probe-pin device

Science.gov (United States)

Choi, Sang H.; Kim, Min H.; Song, Kyo D.; Yoon, Hargsoon; Lee, Uhn

2015-12-01

Treatment of neurological anomalies, whether done invasively or not, places stringent demands on device functionality and size. We have developed a micro-spectrometer for use as an implantable neural probe to monitor neuro-chemistry in synapses. The micro-spectrometer, based on a NASA-invented miniature Fresnel grating, is capable of differentiating the emission spectra from various brain tissues. The micro-spectrometer meets the size requirements, and is able to probe the neuro-chemistry and suppression voltage typically associated with a neural anomaly. This neural probe-pin device (PPD) is equipped with wireless power technology (WPT) to enable operation in a continuous manner without requiring an implanted battery. The implanted neural PPD, together with a neural electronics interface and WPT, enable real-time measurement and control/feedback for remediation of neural anomalies. The design and performance of the combined PPD/WPT device for monitoring dopamine in a rat brain will be presented to demonstrate the current level of development. Future work on this device will involve the addition of an embedded expert system capable of performing semi-autonomous management of neural functions through a routine of sensing, processing, and control.

Methodology for neural networks prototyping. Application to traffic control

Energy Technology Data Exchange (ETDEWEB)

Belegan, I.C.

1998-07-01

The work described in this report was carried out in the context of the European project ASTORIA (Advanced Simulation Toolbox for Real-World Industrial Application in Passenger Management and Adaptive Control), and concerns the development of an advanced toolbox for complex transportation systems. Our work was focused on the methodology for prototyping a set of neural networks corresponding to specific strategies for traffic control and congestion management. The tool used for prototyping is SNNS (Stuggart Neural Network Simulator), developed at the University of Stuggart, Institute for Parallel and Distributed High Performance Systems, and the real data from the field were provided by ZELT. This report is structured into six parts. The introduction gives some insights about traffic control and its approaches. The second chapter discusses the various control strategies existing. The third chapter is an introduction to the field of neural networks. The data analysis and pre-processing is described in the fourth chapter. In the fifth chapter, the methodology for prototyping the neural networks is presented. Finally, conclusions and further work are presented. (author) 14 refs.

Xenopus reduced folate carrier regulates neural crest development epigenetically.

Directory of Open Access Journals (Sweden)

Jiejing Li

Full Text Available Folic acid deficiency during pregnancy causes birth neurocristopathic malformations resulting from aberrant development of neural crest cells. The Reduced folate carrier (RFC is a membrane-bound receptor for facilitating transfer of reduced folate into the cells. RFC knockout mice are embryonic lethal and develop multiple malformations, including neurocristopathies. Here we show that XRFC is specifically expressed in neural crest tissues in Xenopus embryos and knockdown of XRFC by specific morpholino results in severe neurocristopathies. Inhibition of RFC blocked the expression of a series of neural crest marker genes while overexpression of RFC or injection of 5-methyltetrahydrofolate expanded the neural crest territories. In animal cap assays, knockdown of RFC dramatically reduced the mono- and trimethyl-Histone3-K4 levels and co-injection of the lysine methyltransferase hMLL1 largely rescued the XRFC morpholino phenotype. Our data revealed that the RFC mediated folate metabolic pathway likely potentiates neural crest gene expression through epigenetic modifications.

Fault detection and classification in electrical power transmission system using artificial neural network.

Science.gov (United States)

Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

2015-01-01

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Neural network-based model reference adaptive control system.

Science.gov (United States)

Patino, H D; Liu, D

2000-01-01

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

Enteric Neural Cells From Hirschsprung Disease Patients Form Ganglia in Autologous Aneuronal ColonSummary

Directory of Open Access Journals (Sweden)

Benjamin N. Rollo

2016-01-01

Full Text Available Background & Aims: Hirschsprung disease (HSCR is caused by failure of cells derived from the neural crest (NC to colonize the distal bowel in early embryogenesis, resulting in absence of the enteric nervous system (ENS and failure of intestinal transit postnatally. Treatment is by distal bowel resection, but neural cell replacement may be an alternative. We tested whether aneuronal (aganglionic colon tissue from patients may be colonized by autologous ENS-derived cells. Methods: Cells were obtained and cryopreserved from 31 HSCR patients from the proximal resection margin of colon, and ENS cells were isolated using flow cytometry for the NC marker p75 (nine patients. Aneuronal colon tissue was obtained from the distal resection margin (23 patients. ENS cells were assessed for NC markers immunohistologically and by quantitative reverse-transcription polymerase chain reaction, and mitosis was detected by ethynyl-2′-deoxyuridine labeling. The ability of human HSCR postnatal ENS-derived cells to colonize the embryonic intestine was demonstrated by organ coculture with avian embryo gut, and the ability of human postnatal HSCR aneuronal colon muscle to support ENS formation was tested by organ coculture with embryonic mouse ENS cells. Finally, the ability of HSCR patient ENS cells to colonize autologous aneuronal colon muscle tissue was assessed. Results: ENS-derived p75-sorted cells from patients expressed multiple NC progenitor and differentiation markers and proliferated in culture under conditions simulating Wnt signaling. In organ culture, patient ENS cells migrated appropriately in aneural quail embryo gut, and mouse embryo ENS cells rapidly spread, differentiated, and extended axons in patient aneuronal colon muscle tissue. Postnatal ENS cells derived from HSCR patients colonized autologous aneuronal colon tissue in cocultures, proliferating and differentiating as neurons and glia. Conclusions: NC-lineage cells can be obtained from HSCR�

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

DEFF Research Database (Denmark)

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

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

Versatile electrochemial sensor for tissue culturing and sample handling

DEFF Research Database (Denmark)

Bakmand, Tanya; Kwasny, Dorota; Al Atraktchi, Fatima Al-Zahraa

2014-01-01

Culturing of organtypic brain tissues is a routine procedure in neural research. The visual inspection of the medium is the only way of determining the state of the tissue. At the end of culturing, post-processing techniques such as HPLC can be used to measure the concentration of the secreted...

Introduction to neural networks with electric power applications

International Nuclear Information System (INIS)

Wildberger, A.M.; Hickok, K.A.

1990-01-01

This is an introduction to the general field of neural networks with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in neural networks and to recognize those which might impact on electric power engineering. Beginning with a brief discussion of natural and artificial neurons, the characteristics of neural networks in general and how they learn, neural networks are compared with other modeling tools such as simulation and expert systems in order to provide guidance in selecting appropriate applications. In the power industry, possible applications include plant control, dispatching, and maintenance scheduling. In particular, neural networks are currently being investigated for enhancements to the Thermal Performance Advisor (TPA) which General Physics Corporation (GP) has developed to improve the efficiency of electric power generation

neural network based model o work based model of an industrial oil

African Journals Online (AJOL)

eobe

technique. g, Neural Network Model, Regression, Mean Square Error, PID controller. ... during the training processes. An additio ... used to carry out simulation studies of the mode .... A two-layer feed-forward neural network with Matlab.

Fibrous dysplasia of the cranial vault: quantitative analysis based on neural networks

International Nuclear Information System (INIS)

Arana, E.; Marti-Bonmati, L.; Paredes, R.; Molla, E.

1998-01-01

To assess the utility of statistical analysis and neural networks in the quantitative analysis of fibrous dysplasia of the cranial vault. Ten patients with fibrous dysplasia (six women and four men with a mean age of 23.60±17.85 years) were selected from a series of 167 patients with lesions of the cranial vault evaluated by plain radiography and computed tomography (CT). Nineteen variables were taken from their medical records and radiological study. Their characterization was based on statistical analysis and neural network, and was validated by means of the leave-one-out method. The performance of the neural network was estimated by means of receiver operating characteristics (ROC) curves, using as a parameter the area under the curve A z . Bivariate analysis identified age, duration of symptoms, lytic and sclerotic patterns, sclerotic margin, ovoid shape, soft-tissue mas and periosteal reaction as significant variables. The area under the neural network curve was 0.9601±0.0435. The network selected the matrix and soft-tissue mass a variables that were indispensable for diagnosis. The neural network presents a high performance in the characterization of fibrous dysplasia of the cranial vault, disclosing occult interactions among the variables. (Author) 24 refs

Neural substrate expansion for the restoration of brain function

Directory of Open Access Journals (Sweden)

Han-Chiao Isaac Chen

2016-01-01

Full Text Available Restoring neurological and cognitive function in individuals who have suffered brain damage is one of the principal objectives of modern translational neuroscience. Electrical stimulation approaches, such as deep-brain stimulation, have achieved the most clinical success, but they ultimately may be limited by the computational capacity of the residual cerebral circuitry. An alternative strategy is brain substrate expansion, in which the computational capacity of the brain is augmented through the addition of new processing units and the reconstitution of network connectivity. This latter approach has been explored to some degree using both biological and electronic means but thus far has not demonstrated the ability to reestablish the function of large-scale neuronal networks. In this review, we contend that fulfilling the potential of brain substrate expansion will require a significant shift from current methods that emphasize direct manipulations of the brain (e.g., injections of cellular suspensions and the implantation of multi-electrode arrays to the generation of more sophisticated neural tissues and neural-electric hybrids in vitro that are subsequently transplanted into the brain. Drawing from neural tissue engineering, stem cell biology, and neural interface technologies, this strategy makes greater use of the manifold techniques available in the laboratory to create biocompatible constructs that recapitulate brain architecture and thus are more easily recognized and utilized by brain networks.

Photosensitive-polyimide based method for fabricating various neural electrode architectures

Directory of Open Access Journals (Sweden)

Yasuhiro X Kato

2012-06-01

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

Biomaterial applications in neural therapy and repair

Institute of Scientific and Technical Information of China (English)

Harmanvir Ghuman; Michel Modo

2017-01-01

The use of biomaterials,such as hydrogels,as a scaffold to deliver cells and drugs is becoming increasingly common to treat neurological conditions,including stroke.With a limited intrinsic ability to regenerate after injury,innovative tissue engineering strategies have shown the potential of biomaterials in facilitating neural tissue regeneration and functional recovery.Using biomaterials can not only promote the survival and integration of transplanted cells in the existing circuitry,but also support controlled site specific delivery of therapeutic drugs.This review aims to provide the reader an understanding of the brain tissue microenvironment after injury,biomaterial criteria that support tissue repair,commonly used natural and synthetic biomaterials,benefits of incorporating cells and neurotrophic factors,as well as the potential of endogenous neurogenesis in repairing the injured brain.

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

Directory of Open Access Journals (Sweden)

M. Bazazzadeh

2011-01-01

Full Text Available This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc. are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.

Chaotic Hopfield Neural Network Swarm Optimization and Its Application

Directory of Open Access Journals (Sweden)

Yanxia Sun

2013-01-01

Full Text Available A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.

The effect of micro-ECoG substrate footprint on the meningeal tissue response

Science.gov (United States)

Schendel, Amelia A.; Nonte, Michael W.; Vokoun, Corinne; Richner, Thomas J.; Brodnick, Sarah K.; Atry, Farid; Frye, Seth; Bostrom, Paige; Pashaie, Ramin; Thongpang, Sanitta; Eliceiri, Kevin W.; Williams, Justin C.

2014-08-01

Objective. There is great interest in designing implantable neural electrode arrays that maximize function while minimizing tissue effects and damage. Although it has been shown that substrate geometry plays a key role in the tissue response to intracortically implanted, penetrating neural interfaces, there has been minimal investigation into the effect of substrate footprint on the tissue response to surface electrode arrays. This study investigates the effect of micro-electrocorticography (micro-ECoG) device geometry on the longitudinal tissue response. Approach. The meningeal tissue response to two micro-ECoG devices with differing geometries was evaluated. The first device had each electrode site and trace individually insulated, with open regions in between, while the second device had a solid substrate, in which all 16 electrode sites were embedded in a continuous insulating sheet. These devices were implanted bilaterally in rats, beneath cranial windows, through which the meningeal tissue response was monitored for one month after implantation. Electrode site impedance spectra were also monitored during the implantation period. Main results. It was observed that collagenous scar tissue formed around both types of devices. However, the distribution of the tissue growth was different between the two array designs. The mesh devices experienced thick tissue growth between the device and the cranial window, and minimal tissue growth between the device and the brain, while the solid device showed the opposite effect, with thick tissue forming between the brain and the electrode sites. Significance. These data suggest that an open architecture device would be more ideal for neural recording applications, in which a low impedance path from the brain to the electrode sites is critical for maximum recording quality.

Active Engine Mounting Control Algorithm Using Neural Network

Directory of Open Access Journals (Sweden)

Fadly Jashi Darsivan

2009-01-01

Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

msh/Msx gene family in neural development.

Science.gov (United States)

Ramos, Casto; Robert, Benoît

2005-11-01

The involvement of Msx homeobox genes in skull and tooth formation has received a great deal of attention. Recent studies also indicate a role for the msh/Msx gene family in development of the nervous system. In this article, we discuss the functions of these transcription factors in neural-tissue organogenesis. We will deal mainly with the interactions of the Drosophila muscle segment homeobox (msh) gene with other homeobox genes and the repressive cascade that leads to neuroectoderm patterning; the role of Msx genes in neural-crest induction, focusing especially on the differences between lower and higher vertebrates; their implication in patterning of the vertebrate neural tube, particularly in diencephalon midline formation. Finally, we will examine the distinct activities of Msx1, Msx2 and Msx3 genes during neurogenesis, taking into account their relationships with signalling molecules such as BMP.

Using deep neural networks to augment NIF post-shot analysis

Science.gov (United States)

Humbird, Kelli; Peterson, Luc; McClarren, Ryan; Field, John; Gaffney, Jim; Kruse, Michael; Nora, Ryan; Spears, Brian

2017-10-01

Post-shot analysis of National Ignition Facility (NIF) experiments is the process of determining which simulation inputs yield results consistent with experimental observations. This analysis is typically accomplished by running suites of manually adjusted simulations, or Monte Carlo sampling surrogate models that approximate the response surfaces of the physics code. These approaches are expensive and often find simulations that match only a small subset of observables simultaneously. We demonstrate an alternative method for performing post-shot analysis using inverse models, which map directly from experimental observables to simulation inputs with quantified uncertainties. The models are created using a novel machine learning algorithm which automates the construction and initialization of deep neural networks to optimize predictive accuracy. We show how these neural networks, trained on large databases of post-shot simulations, can rigorously quantify the agreement between simulation and experiment. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Hardware Acceleration of Adaptive Neural Algorithms.

Energy Technology Data Exchange (ETDEWEB)

James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

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

Science.gov (United States)

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

2017-12-01

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

Prototype-Incorporated Emotional Neural Network.

Science.gov (United States)

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

Differentiation of insulin-producing cells from human neural progenitor cells.

Directory of Open Access Journals (Sweden)

Yuichi Hori

2005-04-01

Full Text Available BACKGROUND: Success in islet-transplantation-based therapies for type 1 diabetes, coupled with a worldwide shortage of transplant-ready islets, has motivated efforts to develop renewable sources of islet-replacement tissue. Islets and neurons share features, including common developmental programs, and in some species brain neurons are the principal source of systemic insulin. METHODS AND FINDINGS: Here we show that brain-derived human neural progenitor cells, exposed to a series of signals that regulate in vivo pancreatic islet development, form clusters of glucose-responsive insulin-producing cells (IPCs. During in vitro differentiation of neural progenitor cells with this novel method, genes encoding essential known in vivo regulators of pancreatic islet development were expressed. Following transplantation into immunocompromised mice, IPCs released insulin C-peptide upon glucose challenge, remained differentiated, and did not form detectable tumors. CONCLUSION: Production of IPCs solely through extracellular factor modulation in the absence of genetic manipulations may promote strategies to derive transplantable islet-replacement tissues from human neural progenitor cells and other types of multipotent human stem cells.

Three-Dimensional-Bioprinted Dopamine-Based Matrix for Promoting Neural Regeneration.

Science.gov (United States)

Zhou, Xuan; Cui, Haitao; Nowicki, Margaret; Miao, Shida; Lee, Se-Jun; Masood, Fahed; Harris, Brent T; Zhang, Lijie Grace

2018-03-14

Central nerve repair and regeneration remain challenging problems worldwide, largely because of the extremely weak inherent regenerative capacity and accompanying fibrosis of native nerves. Inadequate solutions to the unmet needs for clinical therapeutics encourage the development of novel strategies to promote nerve regeneration. Recently, 3D bioprinting techniques, as one of a set of valuable tissue engineering technologies, have shown great promise toward fabricating complex and customizable artificial tissue scaffolds. Gelatin methacrylate (GelMA) possesses excellent biocompatible and biodegradable properties because it contains many arginine-glycine-aspartic acids (RGD) and matrix metalloproteinase sequences. Dopamine (DA), as an essential neurotransmitter, has proven effective in regulating neuronal development and enhancing neurite outgrowth. In this study, GelMA-DA neural scaffolds with hierarchical structures were 3D-fabricated using our custom-designed stereolithography-based printer. DA was functionalized on GelMA to synthesize a biocompatible printable ink (GelMA-DA) for improving neural differentiation. Additionally, neural stem cells (NSCs) were employed as the primary cell source for these scaffolds because of their ability to terminally differentiate into a variety of cell types including neurons, astrocytes, and oligodendrocytes. The resultant GelMA-DA scaffolds exhibited a highly porous and interconnected 3D environment, which is favorable for supporting NSC growth. Confocal microscopy analysis of neural differentiation demonstrated that a distinct neural network was formed on the GelMA-DA scaffolds. In particular, the most significant improvements were the enhanced neuron gene expression of TUJ1 and MAP2. Overall, our results demonstrated that 3D-printed customizable GelMA-DA scaffolds have a positive role in promoting neural differentiation, which is promising for advancing nerve repair and regeneration in the future.

Phase Diagram of Spiking Neural Networks

Directory of Open Access Journals (Sweden)

Hamed eSeyed-Allaei

2015-03-01

Full Text Available In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probablilty of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations. but here, I take a different perspective, inspired by evolution. I simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable by nature. Networks which are configured according to the common values, have the best dynamic range in response to an impulse and their dynamic range is more robust in respect to synaptic weights. In fact, evolution has favored networks of best dynamic range. I present a phase diagram that shows the dynamic ranges of different networks of different parameteres. This phase diagram gives an insight into the space of parameters -- excitatory to inhibitory ratio, sparseness of connections and synaptic weights. It may serve as a guideline to decide about the values of parameters in a simulation of spiking neural network.

The existence and global attractivity of almost periodic sequence solution of discrete-time neural networks

International Nuclear Information System (INIS)

Huang Zhenkun; Wang Xinghua; Gao Feng

2006-01-01

In this Letter, we discuss discrete-time analogue of a continuous-time cellular neural network. Sufficient conditions are obtained for the existence of a unique almost periodic sequence solution which is globally attractive. Our results demonstrate dynamics of the formulated discrete-time analogue as mathematical models for the continuous-time cellular neural network in almost periodic case. Finally, a computer simulation illustrates the suitability of our discrete-time analogue as numerical algorithms in simulating the continuous-time cellular neural network conveniently

Central neural pathways for thermoregulation

Science.gov (United States)

Morrison, Shaun F.; Nakamura, Kazuhiro

2010-01-01

Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

Neural responses to exclusion predict susceptibility to social influence.

Science.gov (United States)

Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G

2014-05-01

Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

3D silicon neural probe with integrated optical fibers for optogenetic modulation.

Science.gov (United States)

Kim, Eric G R; Tu, Hongen; Luo, Hao; Liu, Bin; Bao, Shaowen; Zhang, Jinsheng; Xu, Yong

2015-07-21

Optogenetics is a powerful modality for neural modulation that can be useful for a wide array of biomedical studies. Penetrating microelectrode arrays provide a means of recording neural signals with high spatial resolution. It is highly desirable to integrate optics with neural probes to allow for functional study of neural tissue by optogenetics. In this paper, we report the development of a novel 3D neural probe coupled simply and robustly to optical fibers using a hollow parylene tube structure. The device shanks are hollow tubes with rigid silicon tips, allowing the insertion and encasement of optical fibers within the shanks. The position of the fiber tip can be precisely controlled relative to the electrodes on the shank by inherent design features. Preliminary in vivo rat studies indicate that these devices are capable of optogenetic modulation simultaneously with 3D neural signal recording.

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.

Expression of cardiac neural crest and heart genes isolated by modified differential display.

Science.gov (United States)

Martinsen, Brad J; Groebner, Nathan J; Frasier, Allison J; Lohr, Jamie L

2003-08-01

The invasion of the cardiac neural crest (CNC) into the outflow tract (OFT) and subsequent outflow tract septation are critical events during vertebrate heart development. We have performed four modified differential display screens in the chick embryo to identify genes that may be involved in CNC, OFT, secondary heart field, and heart development. The screens included differential display of RNA isolated from three different axial segments containing premigratory cranial neural crest cells; of RNA from distal outflow tract, proximal outflow tract, and atrioventricular tissue of embryonic chick hearts; and of RNA isolated from left and right cranial tissues, including the early heart fields. These screens have resulted in the identification of the five cDNA clones presented here, which are expressed in the cardiac neural crest, outflow tract and developing heart in patterns that are unique in heart development.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

Science.gov (United States)

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

Dlx proteins position the neural plate border and determine adjacent cell fates.

Science.gov (United States)

Woda, Juliana M; Pastagia, Julie; Mercola, Mark; Artinger, Kristin Bruk

2003-01-01

The lateral border of the neural plate is a major source of signals that induce primary neurons, neural crest cells and cranial placodes as well as provide patterning cues to mesodermal structures such as somites and heart. Whereas secreted BMP, FGF and Wnt proteins influence the differentiation of neural and non-neural ectoderm, we show here that members of the Dlx family of transcription factors position the border between neural and non-neural ectoderm and are required for the specification of adjacent cell fates. Inhibition of endogenous Dlx activity in Xenopus embryos with an EnR-Dlx homeodomain fusion protein expands the neural plate into non-neural ectoderm tissue whereas ectopic activation of Dlx target genes inhibits neural plate differentiation. Importantly, the stereotypic pattern of border cell fates in the adjacent ectoderm is re-established only under conditions where the expanded neural plate abuts Dlx-positive non-neural ectoderm. Experiments in which presumptive neural plate was grafted to ventral ectoderm reiterate induction of neural crest and placodal lineages and also demonstrate that Dlx activity is required in non-neural ectoderm for the production of signals needed for induction of these cells. We propose that Dlx proteins regulate intercellular signaling across the interface between neural and non-neural ectoderm that is critical for inducing and patterning adjacent cell fates.

See-Through Technology for Biological Tissue: 3-Dimensional Visualization of Macromolecules

Directory of Open Access Journals (Sweden)

Eunsoo Lee

2016-05-01

Full Text Available Tissue clearing technology is currently one of the fastest growing fields in biomedical sciences. Tissue clearing techniques have become a powerful approach to understand further the structural information of intact biological tissues. Moreover, technological improvements in tissue clearing and optics allowed the visualization of neural network in the whole brain tissue with subcellular resolution. Here, we described an overview of various tissue-clearing techniques, with focus on the tissue-hydrogel mediated clearing methods, and discussed the main advantages and limitations of transparent tissue for clinical diagnosis.

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.

Enhanced biocompatibility of neural probes by integrating microstructures and delivering anti-inflammatory agents via microfluidic channels

Science.gov (United States)

Liu, Bin; Kim, Eric; Meggo, Anika; Gandhi, Sachin; Luo, Hao; Kallakuri, Srinivas; Xu, Yong; Zhang, Jinsheng

2017-04-01

Objective. Biocompatibility is a major issue for chronic neural implants, involving inflammatory and wound healing responses of neurons and glial cells. To enhance biocompatibility, we developed silicon-parylene hybrid neural probes with open architecture electrodes, microfluidic channels and a reservoir for drug delivery to suppress tissue responses. Approach. We chronically implanted our neural probes in the rat auditory cortex and investigated (1) whether open architecture electrode reduces inflammatory reaction by measuring glial responses; and (2) whether delivery of antibiotic minocycline reduces inflammatory and tissue reaction. Four weeks after implantation, immunostaining for glial fibrillary acid protein (astrocyte marker) and ionizing calcium-binding adaptor molecule 1 (macrophages/microglia cell marker) were conducted to identify immunoreactive astrocyte and microglial cells, and to determine the extent of astrocytes and microglial cell reaction/activation. A comparison was made between using traditional solid-surface electrodes and newly-designed electrodes with open architecture, as well as between deliveries of minocycline and artificial cerebral-spinal fluid diffused through microfluidic channels. Main results. The new probes with integrated micro-structures induced minimal tissue reaction compared to traditional electrodes at 4 weeks after implantation. Microcycline delivered through integrated microfluidic channels reduced tissue response as indicated by decreased microglial reaction around the neural probes implanted. Significance. The new design will help enhance the long-term stability of the implantable devices.

Neural network for solving convex quadratic bilevel programming problems.

Science.gov (United States)

He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie

2014-03-01

In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

Functional Stem Cell Integration into Neural Networks Assessed by Organotypic Slice Cultures.

Science.gov (United States)

Forsberg, David; Thonabulsombat, Charoensri; Jäderstad, Johan; Jäderstad, Linda Maria; Olivius, Petri; Herlenius, Eric

2017-08-14

Re-formation or preservation of functional, electrically active neural networks has been proffered as one of the goals of stem cell-mediated neural therapeutics. A primary issue for a cell therapy approach is the formation of functional contacts between the implanted cells and the host tissue. Therefore, it is of fundamental interest to establish protocols that allow us to delineate a detailed time course of grafted stem cell survival, migration, differentiation, integration, and functional interaction with the host. One option for in vitro studies is to examine the integration of exogenous stem cells into an existing active neural network in ex vivo organotypic cultures. Organotypic cultures leave the structural integrity essentially intact while still allowing the microenvironment to be carefully controlled. This allows detailed studies over time of cellular responses and cell-cell interactions, which are not readily performed in vivo. This unit describes procedures for using organotypic slice cultures as ex vivo model systems for studying neural stem cell and embryonic stem cell engraftment and communication with CNS host tissue. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

Development and Tissue Origins of the Mammalian Cranial Base

Science.gov (United States)

Iseki, S.; Bamforth, S. D.; Olsen, B. R.; Morriss-Kay, G. M.

2008-01-01

The vertebrate cranial base is a complex structure composed of bone, cartilage and other connective tissues underlying the brain; it is intimately connected with development of the face and cranial vault. Despite its central importance in craniofacial development, morphogenesis and tissue origins of the cranial base have not been studied in detail in the mouse, an important model organism. We describe here the location and time of appearance of the cartilages of the chondrocranium. We also examine the tissue origins of the mouse cranial base using a neural crest cell lineage cell marker, Wnt1-Cre/R26R, and a mesoderm lineage cell marker, Mesp1-Cre/R26R. The chondrocranium develops between E11 and E16 in the mouse, beginning with development of the caudal (occipital) chondrocranium, followed by chondrogenesis rostrally to form the nasal capsule, and finally fusion of these two parts via the midline central stem and the lateral struts of the vault cartilages. X-Gal staining of transgenic mice from E8.0 to 10 days post-natal showed that neural crest cells contribute to all of the cartilages that form the ethmoid, presphenoid, and basisphenoid bones with the exception of the hypochiasmatic cartilages. The basioccipital bone and non-squamous parts of the temporal bones are mesoderm derived. Therefore the prechordal head is mostly composed of neural crest-derived tissues, as predicted by the New Head Hypothesis. However, the anterior location of the mesoderm-derived hypochiasmatic cartilages, which are closely linked with the extra-ocular muscles, suggests that some tissues associated with the visual apparatus may have evolved independently of the rest of the “New Head”. PMID:18680740

Identification and control of plasma vertical position using neural network in Damavand tokamak

International Nuclear Information System (INIS)

Rasouli, H.; Rasouli, C.; Koohi, A.

2013-01-01

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg–Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Identification and control of plasma vertical position using neural network in Damavand tokamak

Energy Technology Data Exchange (ETDEWEB)

Rasouli, H. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of); Advanced Process Automation and Control (APAC) Research Group, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran (Iran, Islamic Republic of); Rasouli, C.; Koohi, A. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of)

2013-02-15

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

Directory of Open Access Journals (Sweden)

Ruliang Wang

2012-01-01

Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

A one-layer recurrent neural network for constrained nonsmooth optimization.

Science.gov (United States)

Liu, Qingshan; Wang, Jun

2011-10-01

This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as the number of decision variables of optimization problems. Compared with existing neural networks for nonsmooth optimization problems, the global convexity condition on the objective functions and constraints is relaxed, which allows the objective functions and constraints to be nonconvex. It is proven that the state variables of the proposed neural network are convergent to optimal solutions if a single design parameter in the model is larger than a derived lower bound. Numerical examples with simulation results substantiate the effectiveness and illustrate the characteristics of the proposed neural network.

Development of Self-Assembled Nanoribbon Bound Peptide-Polyaniline Composite Scaffolds and Their Interactions with Neural Cortical Cells

Directory of Open Access Journals (Sweden)

Andrew M. Smith

2018-01-01

Full Text Available Degenerative neurological disorders and traumatic brain injuries cause significant damage to quality of life and often impact survival. As a result, novel treatments are necessary that can allow for the regeneration of neural tissue. In this work, a new biomimetic scaffold was designed with potential for applications in neural tissue regeneration. To develop the scaffold, we first prepared a new bolaamphiphile that was capable of undergoing self-assembly into nanoribbons at pH 7. Those nanoribbons were then utilized as templates for conjugation with specific proteins known to play a critical role in neural tissue growth. The template (Ile-TMG-Ile was prepared by conjugating tetramethyleneglutaric acid with isoleucine and the ability of the bolaamphiphile to self-assemble was probed at a pH range of 4 through 9. The nanoribbons formed under neutral conditions were then functionalized step-wise with the basement membrane protein laminin, the neurotropic factor artemin and Type IV collagen. The conductive polymer polyaniline (PANI was then incorporated through electrostatic and π–π stacking interactions to the scaffold to impart electrical properties. Distinct morphology changes were observed upon conjugation with each layer, which was also accompanied by an increase in Young’s Modulus as well as surface roughness. The Young’s Modulus of the dried PANI-bound biocomposite scaffolds was found to be 5.5 GPa, indicating the mechanical strength of the scaffold. Thermal phase changes studied indicated broad endothermic peaks upon incorporation of the proteins which were diminished upon binding with PANI. The scaffolds also exhibited in vitro biodegradable behavior over a period of three weeks. Furthermore, we observed cell proliferation and short neurite outgrowths in the presence of rat neural cortical cells, confirming that the scaffolds may be applicable in neural tissue regeneration. The electrochemical properties of the scaffolds were also

Development of Self-Assembled Nanoribbon Bound Peptide-Polyaniline Composite Scaffolds and Their Interactions with Neural Cortical Cells

Science.gov (United States)

Smith, Andrew M.; Pajovich, Harrison T.; Banerjee, Ipsita A.

2018-01-01

Degenerative neurological disorders and traumatic brain injuries cause significant damage to quality of life and often impact survival. As a result, novel treatments are necessary that can allow for the regeneration of neural tissue. In this work, a new biomimetic scaffold was designed with potential for applications in neural tissue regeneration. To develop the scaffold, we first prepared a new bolaamphiphile that was capable of undergoing self-assembly into nanoribbons at pH 7. Those nanoribbons were then utilized as templates for conjugation with specific proteins known to play a critical role in neural tissue growth. The template (Ile-TMG-Ile) was prepared by conjugating tetramethyleneglutaric acid with isoleucine and the ability of the bolaamphiphile to self-assemble was probed at a pH range of 4 through 9. The nanoribbons formed under neutral conditions were then functionalized step-wise with the basement membrane protein laminin, the neurotropic factor artemin and Type IV collagen. The conductive polymer polyaniline (PANI) was then incorporated through electrostatic and π–π stacking interactions to the scaffold to impart electrical properties. Distinct morphology changes were observed upon conjugation with each layer, which was also accompanied by an increase in Young’s Modulus as well as surface roughness. The Young’s Modulus of the dried PANI-bound biocomposite scaffolds was found to be 5.5 GPa, indicating the mechanical strength of the scaffold. Thermal phase changes studied indicated broad endothermic peaks upon incorporation of the proteins which were diminished upon binding with PANI. The scaffolds also exhibited in vitro biodegradable behavior over a period of three weeks. Furthermore, we observed cell proliferation and short neurite outgrowths in the presence of rat neural cortical cells, confirming that the scaffolds may be applicable in neural tissue regeneration. The electrochemical properties of the scaffolds were also studied by

Novel paths towards neural cellular products for neurological disorders.

Science.gov (United States)

Daadi, Marcel M

2011-11-01

The prospect of using neural cells derived from stem cells or from reprogrammed adult somatic cells provides a unique opportunity in cell therapy and drug discovery for developing novel strategies for brain repair. Cell-based therapeutic approaches for treating CNS afflictions caused by disease or injury aim to promote structural repair of the injured or diseased neural tissue, an outcome currently not achieved by drug therapy. Preclinical research in animal models of various diseases or injuries report that grafts of neural cells enhance endogenous repair, provide neurotrophic support to neurons undergoing degeneration and replace lost neural cells. In recent years, the sources of neural cells for treating neurological disorders have been rapidly expanding and in addition to offering therapeutic potential, neural cell products hold promise for disease modeling and drug discovery use. Specific neural cell types have been derived from adult or fetal brain, from human embryonic stem cells, from induced pluripotent stem cells and directly transdifferentiated from adult somatic cells, such as skin cells. It is yet to be determined if the latter approach will evolve into a paradigm shift in the fields of stem cell research and regenerative medicine. These multiple sources of neural cells cover a wide spectrum of safety that needs to be balanced with efficacy to determine the viability of the cellular product. In this article, we will review novel sources of neural cells and discuss current obstacles to developing them into viable cellular products for treating neurological disorders.

Using neural networks to describe tracer correlations

Directory of Open Access Journals (Sweden)

D. J. Lary

2004-01-01

Full Text Available Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and methane volume mixing ratio (v.m.r.. In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE which has continuously observed CH4  (but not N2O from 1991 till the present. The neural network Fortran code used is available for download.

ICG-assisted blood vessel detection during stereotactic neurosurgery: simulation study on excitation power limitations due to thermal effects in human brain tissue.

Science.gov (United States)

Rühm, Adrian; Göbel, Werner; Sroka, Ronald; Stepp, Herbert

2014-09-01

Intraoperative blood vessel detection based on intraluminal indocyanin-green (ICG) would allow to minimize the risk of blood vessel perforation during stereotactic brain tumor biopsy. For a fiber-based approach compatible with clinical conditions, the maximum tolerable excitation light power was derived from simulations of the thermal heat load on the tissue. Using the simulation software LITCIT, the temperature distribution in human brain tissue was calculated as a function of time for realistic single-fiber probes (0.72mm active diameter, numerical aperture 0.35, optional focusing to 0.29mm diameter) and for the optimum ICG excitation wavelength of 785nm. The asymptotic maximum temperature in the simulated tissue region was derived for different radiant fluxes at the distal fiber end. Worst case values were assumed for all other parameters. In addition to homogeneous (normal and tumor) brain tissue with homogeneous blood perfusion, models with localized extra blood vessels incorporated ahead of the distal fiber end were investigated. If one demands that destruction of normal brain tissue must be excluded by limiting the tissue heating to 42°C, then the radiant flux at the distal fiber end must be limited to 33mW with and 43mW without focusing. When considering extra blood vessels of 0.1mm diameter incorporated into homogeneously perfused brain tissue, the tolerable radiant flux is reduced to 22mW with and 32mW without focusing. The threshold value according to legal laser safety regulations for human skin tissue is 28.5mW. For the envisaged modality of blood vessel detection, light power limits for an application-relevant fiber configuration were determined and found to be roughly consistent with present legal regulations for skin tissue. Copyright © 2014 Elsevier B.V. All rights reserved.

Accident scenario diagnostics with neural networks

International Nuclear Information System (INIS)

Guo, Z.

1992-01-01

Nuclear power plants are very complex systems. The diagnoses of transients or accident conditions is very difficult because a large amount of information, which is often noisy, or intermittent, or even incomplete, need to be processed in real time. To demonstrate their potential application to nuclear power plants, neural networks axe used to monitor the accident scenarios simulated by the training simulator of TVA's Watts Bar Nuclear Power Plant. A self-organization network is used to compress original data to reduce the total number of training patterns. Different accident scenarios are closely related to different key parameters which distinguish one accident scenario from another. Therefore, the accident scenarios can be monitored by a set of small size neural networks, called modular networks, each one of which monitors only one assigned accident scenario, to obtain fast training and recall. Sensitivity analysis is applied to select proper input variables for modular networks

Review: the role of neural crest cells in the endocrine system.

Science.gov (United States)

Adams, Meghan Sara; Bronner-Fraser, Marianne

2009-01-01

The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.