In this study, we combine heat diffusion equation and modified Hodgkin-Huxley axonal model to investigate how an action potential is generated during infrared neural stimulation. The effects of ...temporal and spatial distribution of heat induced by infrared pulsed lasers on variation of electrical membrane capacitance are investigated. These variations can lead to depolarize the membrane and generate an action potential. We estimate the threshold values of laser light parameters such as energy density, pulse duration, and repetition rate are needed to trigger an action potential. In order to do it, we present an analytic solution to heat diffusion equation. Then, the analytic results are verified by experimental results. Furthermore, the modified Hodgkin-Huxley axonal model is applied to simulate the generation of action potential during infrared neural stimulation by taking into account the temperature dependence of electrical membrane capacitance. Results show that the threshold temperature increase induced by a train infrared pulse laser can be smaller if repetition rate is higher. These results also indicate that temperature rise time and axon diameter influence on threshold temperature increase. To verify threshold values estimated by the presented method, we use a train infrared pulsed laser (
λ
= 1450 nm with repetition rate of 3.8 Hz, pulse duration of 18 ms and energy density of 5 J/cm
2
) to optically pace an adult rat heart, and we are able to successfully pace the rat heart during an open-heart surgery. The presented method can be used to estimate threshold values of laser parameters required for generating an action potential, and it can provide an insight to how the temperature changes lead to neural stimulation during INS.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This second paper adopts a more rigorous, in-depth approach to modeling the resulting dynamic-pressures in the human brain, following a transitory improvised explosive device (IED) shock-wave ...entering the head. Determining more complicated boundary conditions, a set of particular-solutions for both Burgers' and the Transport equations has been obtained to describe the highly damped neurological pressures, complete with respective graphical plots. Many of these two-dimensional solution-curves closely resemble the Friedlander curve 1–4, not only illustrating enormous over-pressures that result almost immediately after the initial impact, but under-pressures experimentally depicted in all cases, due to oscillatory motion. It appears, given experimental evidence, that most—if not all—of these models can be aptly described by damped sinusoidal functions, these facts being further corroborated by existing literature, referencing models expounded by Friedlander's seminal work 1–4. Using other advanced mathematical techniques, such as the Hopf-Cole Transformation, application of the Dirac-delta function and the Heat-Diffusion equation, expressions have been determined to model and predict the associated energies and temperatures within this paper.
Infrared neural stimulation techniques have potential applications in the diagnosis and treatment of numerous neurological and psychiatric disorders. There has been little progress in the ...computational modeling of these techniques and further improvement is needed in this area. In this paper, a comprehensive computational model is presented for simulating the complete mechanism of direct and plasmonic nanoparticle‐mediated infrared neural stimulation techniques in schematic samples of experimental setups. The simulation process involves three phases: 1) Simulating the light transmission and absorption in setups containing pure water or a gold nanorod solution using developed 3D, time‐independent, and time‐dependent Monte Carlo models, 2) calculating the spatiotemporal evolutions of temperature within the setup using the finite difference method and a presented novel method, and 3) simulating the thermally induced responses of lipid membranes using an improved method compared to existing theoretical models. The model is validated by comparing the computational results with existing experimental data. The effect of the laser pulse characteristics, nanofluid properties, and some other related parameters on the thermally induced membrane responses is investigated. The computational results help to optimize the parameters selection and maximize the overall efficiency of the infrared neural stimulation techniques.
In this paper, a comprehensive computational model is presented for simulating the complete mechanism of direct and plasmonic nanoparticle‐mediated infrared neural stimulation techniques. The model is validated by comparing the computational results with existing experimental data. The effect of the laser pulse characteristics, nanofluid properties, and some other related parameters on the thermally induced membrane responses is investigated.
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24.
How far and fast does heat propagate? Mendioroz Astigarraga, María Aránzazu; Salazar Hernández, Agustín; Oleaga Páramo, Alberto
Latin-American journal of physics education,
2019, Volume:
13, Issue:
2
Journal Article
Peer reviewed
Open access
Unlike wave motion, where the propagation speed is well defined, in diffusive processes this quantity is not clearly
established. In this way, any physicists can rapidly estimate the distance ...travelled by the light in vacuum or by the
sound in air in a given time interval. However, few of them would be able to answer to the question of how far heat
propagates (by conduction) inside a material in a given time. In this work, we use the concept of thermal diffusion
length and we calculate it analytically for three situations of common life: when the sample surface of a material is put
in contact with a thermal reservoir at a fixed temperature, when the surface is illuminated by a brief flash lighting and
when the surface is illuminated by a continuous light beam. An easy to remember formula allows us to estimate the
distance travelled by heat inside a material, which depends on its thermal diffusivity
A diferencia del movimiento ondulatorio, donde la velocidad de propagación está bien definida, en los procesos de
difusión esta cantidad no está claramente establecida. De esta manera, cualquier físico puede estimar rápidamente la
distancia recorrida por la luz en el vacío o por el sonido en el aire en un intervalo de tiempo determinado. Sin embargo,
pocos de ellos podrían responder a la pregunta de hasta qué punto se propaga el calor (por conducción) dentro de un
material en un momento dado. En este trabajo, utilizamos el concepto de longitud de difusión térmica y lo calculamos
analíticamente para tres situaciones de la vida común: cuando la superficie de la muestra de un material se pone en
contacto con un depósito térmico a una temperatura fija, cuando la superficie se ilumina por un breve destello de luz y
cuando la superficie está iluminada por un haz de luz continuo. Una fórmula fácil de recordar nos permite estimar la
distancia recorrida por el calor dentro de un material, que depende de su difusividad térmica.
Machine learning is expected to improve low throughput and high assay cost in cell-based phenotypic screening. However, it is still a challenge to apply machine learning to achieving sufficiently ...complex phenotypic screening due to imbalanced datasets, non-linear prediction, and unpredictability of new chemotypes. Here, we developed a prediction model based on the heat-diffusion equation (PM-HDE) to address this issue. The algorithm was verified as feasible for virtual compound screening using biotest data of 946 assay systems registered with PubChem. PM-HDE was then applied to actual screening. Based on supervised learning of the data of about 50,000 compounds from biological phenotypic screening with motor neurons derived from ALS-patient-induced pluripotent stem cells, virtual screening of >1.6 million compounds was implemented. We confirmed that PM-HDE enriched the hit compounds and identified new chemotypes. This prediction model could overcome the inflexibility in machine learning, and our approach could provide a novel platform for drug discovery.
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•Prediction model based on heat-diffusion equation (PM-HDE) was constructed•PM-HDE succeeded in increasing the hit ratio and identifying potent compounds•PM-HDE discovered new chemotypes in compound evaluation with an ALS-patient iPSC panel•PM-HDE could represent an algorithm for future drug discovery with AI
There remain many intractable diseases with no treatment available, including amyotrophic lateral sclerosis (ALS), for which the development of a cure is crucial. However, compound screening for drug development demands time, energy, and cost, and therefore artificial intelligence (AI) is expected to improve the efficiency of drug discovery. We built a novel machine-learning algorithm to predict hit compounds in compound screening using the heat-diffusion equation (HDE). This prediction model harbors the potential to solve issues that have been challenging for conventional machine learning and to exhibit accurate performance leading to the discovery of new drugs. In fact, the HDE model predicted hits with new chemotypes among millions of compounds for ALS therapeutics using a panel of large numbers of ALS patient-derived induced pluripotent stem cell models (ALS-patient iPSC panel). This algorithm could contribute to the acceleration and development of future drug discoveries using AI.
Compound screening is a useful tool for discovering new candidate drugs. However, it is still a major challenge, as it is extremely time-consuming and very costly to evaluate millions of compounds. We established a prediction model based on the heat-diffusion equation (PM-HDE) to predict hits in compound screening. PM-HDE succeeded in increasing the hit ratio, identifying compounds with potent broad-spectrum efficacy, and discovering new chemotypes in actual compound evaluation in phenotypic screening.
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When a Cl ion with energy of the order of megaelectronvolts collides with SiO
2 glass, it penetrates the glass along a straight line. The region through which the ion passes and its vicinity, called ...the latent track, can be easily etched by hydrofluoric acid, resulting in the formation of a nanopore. With increasing ion energy, the nanopore radius first increases, reaches a maximum, and then decreases. In order to analyze this strange phenomenon, we investigated the radius of the region that melted upon ion irradiation, as one of the possible approaches. We calculated its radius using heat diffusion equations and compared it with the radius of nanopores. We found that both the radii depend on the ion energy in a similar manner.
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With the aim of characterizing the thermal conductivity for nanometer-scale thermoelectric materials, we have constructed a new measurement system based on ac calorimetry. Analysis of the obtained ...data requires time-evolution of temperature distribution in nanometer-scale material under periodic heating. In this study, we made a simulation using a C#-program for time-dependent temperature distribution, based on 2-dimensional heat-diffusion equation including the influence of heat emission from material edges. The simulation was applied to AlN with millimeter-scale dimensions for confirming the validity and accuracy. The simulated thermal diffusivity for 10×75-mm2-area AlN was 1.3×10-4 m2/s, which was larger than the value set in the heat-diffusion equation. This overestimation was also observed in the experiment. Therefore, our simulation can reproduce the unsteady heat conduction and be used for analyzing the ac calorimetry experiment.
This note presents a Laplace transform approach in the determination of the Lagrange multiplier when the variational iteration method is applied to time fractional heat diffusion equation. The ...presented approach is more straightforward and allows some simplification in application of the variational iteration method to fractional differential equations, thus improving the convergence of the successive iterations.
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The present paper deals with the specificities of the thermal response of rubber under cyclic mechanical loading at constant ambient temperature. This question is important, since the stabilized ...thermal response is used in fatigue life criteria, especially for the fast evaluation of fatigue life. For this purpose, entropic coupling in a thermo-hyperelastic framework is first used to predict the variation in the heat source produced or absorbed by the material during cyclic loading. The heat diffusion equation is then used to deduce temperature variations under adiabatic and non-adiabatic conditions. The influence of several parameters on the stabilized thermal response is studied: signal shape, frequency, minimum and maximum stretch levels, multiaxiality of the mechanical state. The results show that, in the steady-state regime, the mean value between the maximum and minimum temperature variations over a mechanical cycle is different from zero. This is due to the specific variation in the heat source, which depends on both the stretch rate and the stretch level. This result has numerous consequences, in particular for fatigue. Indeed, the stabilized mean value between the maximum and minimum temperature variations during fatigue tests does not reflect only fatigue damage, since the entropic coupling also leads to a value different from zero. This is a major difference with respect to materials exhibiting only isentropic coupling, such as metallic materials.
The microstructural changes occurring in steels under cyclic loading are responsible for heat dissipation. Therefore, self-heating measurements can be considered as an efficient way to relate fatigue ...properties to microstructure. However, the dissipative mechanisms are not clearly identified and some questions remain opened. This paper aims to use the experimental estimation of the intrinsic dissipation, in stress amplitude ranges lower than the macroscopic yield strength, to lead to a better understanding of the dissipative mechanisms. First, self-heating measurements enable to evaluate how the free surface affects the spatial distribution of the dissipation sources in the normal direction. Results indicate that, for thin specimens with a high roughness factor, the dissipation sources are mostly activated close to the free surface. Besides, it is experimentally shown that the self-heating curves are not dependent on the mean stress for low stress magnitudes while they are sensitive to the mean stress for higher stress magnitudes. It is deduced that in the loading conditions used to determine the self-heating curves, the dissipative mechanisms imply recoverable strains (anelasticity) for low stress magnitudes and unrecoverable strains (inelasticity) for higher stress magnitudes.
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