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119 results for “computer simulation”
Unlocking the power of computer modelling and simulation across the life sciences product lifecycle
<p><strong>Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle</strong></p> <p>In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare.</p> <p><strong>The Fusion of Technology and Life Sciences</strong></p> <p><em>In Silico Methods: A New Frontier in Medicine</em></p> <p>The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials.</p> <p><em>Bridging the Gap with Computational Modeling</em></p> <p>Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles.</p> <p><strong>The Impact on Clinical Research and Patient Care</strong></p> <p><em>Enhancing Precision and Efficiency</em></p> <p>One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs.</p> <p><em>Promoting Medical Equitability</em></p> <p>The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy.</p> <p><strong>Conclusion: The Future is Now</strong></p> <p>The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.</p>
A Resilient Workflow to Control a Biomedical HPC Simulation in an Urgent Computing Setting
<p><span><span><span><span>We demonstrate a resilient workflow enabled by the LEXIS Platform, running a time- and safety-critical biomedical simulation of virtual stent placement in intracranial arteries using the HemoFlow application. The workflow, as captured on the video, gracefully handles failures of single computing steps or entire computing systems and thus lends itself to urgent computing applications. <br><br><span><span>The concept of this workflow has potential for realising ab-initio computational biomedical simulations which can provide live, targeted guidance to surgeons.</span></span></span></span></span></span></p>
Additional Artifacts - Supplements to: A Resilient Workflow to Control a Biomedical HPC Simulation in an Urgent Computing Setting
<p>In this dataset, we have collected supplementary artifacts to support an understanding of the workflow presented in the submission cited (see related identifiers).</p> <p>These artifacts are (cf. README.md in the main folder of the tar.gz archive):</p> <p>A1: modified HemoFlow code (cf. https://github.com/gzavo/hemoflow) for our workflow experiments (subfolder "hemoflowcfd");<br>A2: workflow descriptions in python for Apache Airflow (subfolder "workflow");<br>A3: inputs (.xml/.npz) and output (.txt) for the example (subfolder "case").</p> <p> </p>
Simulated X-ray micro-computed tomography based particle tracking velocimetry dataset for validation purposes
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Validation dataset for micro-computed tomography based particle tracking velocimetry: a simulated micro-CT based velocimetry experiment with associated ground-truth particle trajectories</p> <p>- The ground truth trajectories were based on randomly dropping virtual particles in the pore space, and tracking their movement through a CFD-based velocity field (see below). The positions were calculated for the time corresponding to each radiograph of a micro-CT experiment. The folder "GroundTruthData" contains the locations of all particles at the central time of each micro-CT scan, as well as their radii. Check the associated readme file to read the data file.</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 7 time steps (70 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains an image of the pore space without particles, matching with the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 6 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
Source code and simulation results for the computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators
<p><strong>Summary</strong></p> <p>Data and source code relate to the article "Computation of eigenfrequency sensitivities using Riesz projections for<br> efficient optimization of nanophotonic resonators" [<a href="https://doi.org/10.1038/s42005-022-00977-1">1</a>]. It combines direct differentiation of scattering problems with a contour integral method [<a href="https://doi.org/10.1016/j.jcp.2020.109678">2</a>] to compute eigenfrequency sensitivities. An optimization is used to demonstrate the relevance of the method.</p> <p><strong>Structure</strong></p> <p>The most important elements of this publication are the MATLAB scripts 'sensitivities.m' and 'optimization.m', which can be used to reproduce the most important results of the paper. The directories <strong>code</strong>, <strong>scattering</strong> and <strong>results </strong>contain the software RPExpand [<a href="https://doi.org/10.1016/j.softx.2021.100763">3</a>], input files for JCMsuite [<a href="https://doi.org/10.1002/pssb.200743192">4</a>] and results produced with the scripts, respectively. Furthermore, the latter contains the subfolder <strong>tabulated,</strong> which contains text files tabulating data presented in Figures 2 and 4 of the paper. Eventually, the function 'code/observation.m' evaluates the target for the optimization.</p> <p><strong>Additional Information</strong></p> <p>The applicaton is based on an example from the literature [<a href="https://doi.org/10.1126/science.aaz3985">5</a>]. Using apriori knowledge about the eigenmode of interest, we chose the scalar observable, as defined in Section B of the paper, to be the component of the electric field normal to the plane defining the solid of revolution.</p> <p>The convergence studies are based on the discrete, circular contour <span>\(\tilde{C} = \big\{ c_n~|~ c_n=r_0 e^{2\pi i n/8}, n \in \{0,1,...,7\}\big\}\)</span> with center <span>\(\omega_0 = 2 \pi c/(1600~\mathrm{nm})\)</span> and radius <span>\(r_0 = \omega_0\times10^{-2}\)</span>. For finite element degrees <span>\(d\)</span> higher than 5, the error saturates. For this reason, the differences between results for <span>\(d=5\)</span> and <span>\(d = 6\)</span> may depend on the hardware architecture.</p> <p>A larger radius <span>\(r = 4\times10^{13}\)</span> has been chosen for the optimization to include information from poles located further away from the frequency of interest. The target function <span>\(t(p_1,\dots,p_5) = -q_n \left(1 - \frac{(\omega_n-\omega_0)^2}{r^2} \right)\)</span>is minimized. The first factor is the negative <em>Q-</em>Factor and the second factor ensures that the target is zero at the boundary. If no eigenfrequency <span>\(\omega_n\)</span> is located inside the contour, the target is set to zero. For the purpose of this data publication some numerical parameters have been improved. This resulted in a faster convergence of the optimization.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.0 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p> <p>[2] Felix Binkowski, Lin Zschiedrich, Sven Burger, A Riesz-projection-based method for nonlinear eigenvalue problems, Journal of Computational Physics <strong>419</strong>, 109678 (2020), https://doi.org/10.1016/j.jcp.2020.109678</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[4] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p> <p>[5] Kirill Koshelev, Sergey Kruk, Elizaveta Melik-Gaykazyan, Jae-Hyuck Choi, Andrey Bogdanov, Hong-Gyu Park, Yuri Kivshar, Subwavelength dielectric resonators for nonlinear nanophotonics, Science <strong>367</strong>, 288 (2020), http://dx.doi.org/%2010.1126/science.aaz3985</p>
Data for conductance-based simulations of "Cortical oscillations support sampling-based computations in spiking neural networks"
<p>This repository contains the full data generated by the conductance-based simulations described in: <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009753">Cortical oscillations support sampling-based computations in spiking neural networks</a>. The code is accessible via <a href="https://doi.org/10.5281/zenodo.5512526.">this repository</a>.</p>
Mutually Beneficial Combination of Molecular Dynamics Computer Simulations and Scattering Experiments - DATA
<p>Specular reflectivities of the SoyPC bilayer stack measured at the vertical reflectometer MARIA at Heinz Maier-Leibnitz Zentrum (MLZ) in Garching, Germany.</p> <p>Offspecular reflectivity map (log scale) of the multilayer sample as a function of theangle of incidence (θi) and of the reflection angle (θi).</p> <p>Specular reflectivities of the Si/SiO<sub>2</sub>/DMPC/H2O at 4 different contrasts (H<sub>2</sub>O, D<sub>2</sub>O, SMW and 4MW)</p> <p>Small-angle neutron scattering of the unilamellar SoyPC</p>
Source code and simulation results for computing resonance expansions of quadratic quantities with regularized quasinormal modes
<p>This data publication supplements the article "Resonance expansion of quadratic quantities with regularized quasinormal modes" [1]. Tabulated data related to the figures in the manuscript is provided along with the Matlab scripts used to generate the results. The Riesz projection software package RPExpand [2] has been extended to support quasi normal modes (QNMs) and, in particular, the proposed method for quadratic quantities. A current version is contained in the directory <code>Code</code>. Furthermore, the input files required for scattering and resonance simulations with the finite element method (FEM) solver JCMsuite [3] are contained.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.1 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Felix Binkowski, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger: Resonance expansion of quadratic quantities with regularized quasinormal modes, Physica Status Solidi A <strong>220</strong>, 2370013 (2023)</p> <p>[2] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[3] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p>
Coarse-grained Near-global Aqua-planet Simulation with Computed Dynamical Tendencies
<p>This dataset includes the coarse-grained 3D state of the near-global CRM simulations (NG-Aqua). The simulation is run at a 4km resolution using the System for Atmospheric Modeling (SAM)</p> <p>A dataset derived from the same simulation is included at the <a href="https://dx.doi.org/10.5281/zenodo.1226370">10.5281/zenodo.1226370</a>. This current posting supplements this dataset with the dynamical tendencies for total water and liquid-ice potential temperature, respectively given by FQT and FSLI. These are computed by initializing SAM run at a 160km resolution with the coarse-grained fields from NG-Aqua; evolving the state forward for 10 30 second time steps; saving the output; and finally computing the difference with the initial condition.</p> <p>This netCDF dataset is split into several part files for more robust uploading/downloading. To download this data, download each "part" file, and combine them with the "cat" linux command:</p> <pre><code>cat noBlur.nc.part?? > noBlur.nc</code></pre> <p>If using this with the uwnet code repository, you should then move this file to "data/processed/training/noBlur.nc", creating that folder if necessary.</p>
Demonstrative simulations of L-PEACH: a computer-based model to understand how peach trees grow
<p>L-PEACH is a computer-based model that simulates source-sink interactions, architecture and physiology of peach trees (Allen et al., 2005, 2006, 2007). The model integrates important concepts related to water transport and carbon assimilation, distribution, and use within the tree (DeJong et al., 2011). L-PEACH is able to simulate crop yield responses to commercial practices such as fruit thinning (Lopez et al., 2008) and pruning (Smith et al., 2008) and could be useful for making fruit growers understand how to optimize these operations. In this work we present several demonstrative simulations of L-PEACH to complement the existing references about L-PEACH and demonstrate its value to study, understand and teach how trees grow (DeJong et al., 2008).</p> <p>The FIRST SIMULATION corresponds with the version of L-PEACH that runs on a daily time-step (L-PEACH-d) (Lopez et al., 2008, 2010). The simulation shows the growth of a peach tree over three years. The color of the stem indicates the direction of the movement of carbon within the tree (white indicates no flux of carbon, increasing apical flux of carbon from light yellow to red, and increasing basal flux of carbon from light blue to deep purple) (see details of colors in Allen et al., 2005). During this simulation the tree was stopped during the dormant season between years and the trees were pruned by the model operator in a manner that is similar to how trees would be pruned when growing in an orchard. Also during the first year of tree growth, grafting is simulated by cutting the tree back in early spring and allowing the tree to grow again as it would in a tree nursery. After this first year the tree is cut back to a single trunk in the same manner as is commonly done when a tree is transplanted from a tree nursery to a commercial fruit orchard.</p> <p>In the SECOND SIMULATION a detailed section of the tree was selected to better appreciate the realism of leaf and fruit growth and in the THIRD SIMULATION we show how to prune a peach tree to a V-system. Responses to pruning were modelled based on the concept of apical dominance as described in Smith et al. (2008) and Lopez et al. (2008).</p> <p>Subsequent simulations correspond to the last version of the L-PEACH model that includes a xylem circuit so that the diurnal water potential of each organ could be simulated along with its physiological functioning and growth. Sub-models for leaf transpiration, soil water potential and the soil-plant interface were also incorporated to provide the driving force and pathway for water flow. In the FOURTH SIMULATION we presented the effect of different irrigation treatments (control irrigation and drought irrigation) on tree development, growth and fruit yield (Da Silva et al., 2011; 2014). L-PEACH-h was also use to illustrate the effect of severity of pruning in tree growth (FIFTH SIMULATION). We tested three levels of pruning: soft, control, and hard. The simulation indicates how trees that received hard pruning are able to recover a similar tree size than control and soft pruned trees due to the generation of vigorous shoots in response to hard pruning.</p> <p>The SIXTH SIMULATION was generated to demonstrate that L-PEACH can be also used to simulate the effect of size-controlling rootstock in tree growth (Da Silva et al., 2015). In this simulation we compared tree growth with a standard rootstock (Control) and a size-controlling rootstock (Rootstock) by reducing the hydraulic conductance of the ‘rootstock” piece (base of the trunk) by 50% in the size-controlling rootstock to simulate a reduction in vessel diameters and consequently reduced hydraulic conductance in that part of the tree. After four years of simulated growth, the virtual tree on the dwarfing rootstock was substantially smaller than the virtual tree on the control rootstock.</p> <p>What you can’t see in the movies is that the L-PEACH model calculates the distribution of light in the tree canopy as the tree grows and the rate of photosynthesis in each leaf during a simulated day or hour (depending on whether the daily or hourly models are used for the simulation). Then the distribution and use of photo-assimilates are calculated by the methods described in the papers cited below. The simulations are based on real environmental input data (light, temperature, day length, etc. collected from a real weather station located near a peach orchard) and development of tree architecture is based on developmental principles governing tree growth and detailed measurements of shoots of peach trees (see references).</p> <p><em><strong>Description of files</strong></em></p> <p>Simulation 1: L-PEACH-d over three years of growth.</p> <p>Simulation 2: Detailed growth of leaves and fruit using L-PEACH.</p> <p>Simulation 3: Pruning L-PEACH-d to a v-system.</p> <p>Simulation 4: Control irrigation vs. Drought irrigation using L-PEACH-h.</p> <p>Simulation 5: Reactions to soft, control and hard pruning using L-PEACH-h.</p> <p>Simulation 6: Simulating the effect of size-controlling rootstock using L-PEACH-h.</p>
Simulations from "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors"
<h1>IL6R/IL8R Antibody Binding Model Code</h1> <p>Christina M.P. Ray, Huilin Yang, Jamie B. Spangler, Feilim Mac Gabhann</p> <p>This dataset contains all simulation output files generated for the article "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors". The model is comprised of a coupled set of ordinary differential equations (ODEs) where each individual ODE describes one molecule (antibody or receptor) or molecular complex (antibody + receptor). The terms in the ODEs represent each binding interaction (binding and unbinding processes) in the system.</p> <p>The code for the binding model and for the analysis and visualization results is available on GitHub at <a href="https://github.com/christyray/bispecific-binding-model">christyray/bispecific-binding-model</a>.</p> <h2>Specific Simulations</h2> <p>The <code>.csv</code> and <code>.rds</code> files in the correspond to the results from the simulations performed for the article "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors". These files can be read into R using the <code>import_data()</code> function included in the <a href="https://github.com/christyray/bispecific-binding-model">GitHub repository</a>.</p> <p>The <code>id</code> files contain simulation IDs to link the molecule concentrations (<code>yin</code>) and parameter values (<code>params</code>) with the simulation results (<code>out</code>). When applicable, the <code>norm</code> files contain normalized simulation output, and the <code>occupied</code> files contain receptor fractional occupancy values calculated from the simulation output.</p> <ul> <li><code>optimization</code>: Optimization of binding rate constants (association and dissociation) to experimental <em>in vitro</em> flow cytometry data; results displayed in Figure 2</li> <li><code>binding-curve</code>: Model simulations using the best-fit parameter set for comparison to the experimental data used to fit the model parameters; results displayed in Figure 3</li> <li><code>compare-opt</code>: Model simulations using each of the optimized parameter sets; results displayed in the Supplemental Information</li> <li><code>time</code>: Simulations of antibody binding dynamics over time; results displayed in Figure 4</li> <li><code>concentration</code>: Simulations with varying antibody concentrations and receptor expression levels; results displayed in Figure 5</li> <li><code>monovalent</code>: Simulations restricted to monovalent antibody binding only; results displayed in Figure 6</li> <li><code>compare-ab</code> and <code>compare-recep</code>: Simulations of both the bispecific antibody BS1 and the combination of monospecific antibodies tocilizumab and 10H2 for comparsion; results displayed in Figure 7</li> <li><code>local</code> and <code>global</code>: Local and global univariate sensitivity analyses; results displayed in Figure 8</li> </ul> <h2>References</h2> <blockquote> <p>H. Yang, M. N. Karl, W. Wang, B. Starich, H. Tan, A. Kiemen, A. B. Pucsek, Y.-H. Kuo, G. C. Russo, T. Pan, E. M. Jaffee, E. J. Fertig, D. Wirtz, and J. B. Spangler. Engineered bispecific antibodies targeting the interleukin-6 and -8 receptors potently inhibit cancer cell migration and tumor metastasis. Molecular Therapy, 30(11):3430–3449, Nov. 2022. doi:<a href="https://doi.org/10.1016/j.ymthe.2022.07.008">10.1016/j.ymthe.2022.07.008</a></p> </blockquote>
Data for: Caspase-Based Fusion Protein Technology: Substrate Cleavability Described by Computational Modeling and Simulation
<p>This dataset contains all files necessary to set up the simulations conducted in this work. It further contains the scripts that were used to do the stitching and combining of the CASPON-tag and the N-termini of the POIs. The manuscript was just submitted and accepted: <a href="https://doi.org/10.1021/acs.jcim.4c00316">10.1021/acs.jcim.4c00316</a></p>
Computer simulations of photorelaxation dynamics of fluorazene
<p><strong>Description</strong></p> <p>This dataset contains the results of nonadiabatic molecular dynamics (NAMD) simulations of the photorelaxation dynamics of fluorazene in acetonitrile solution. The dilute solution phase is modeled as a single fluorazene molecule at the center of a spherical 500-molecule acetonitrile nanodroplet. The nuclear wavepacket is represented by an ensemble of 60 trajectories, which are numbered trajectory_0001.xyz to trajectory_0060.xyz Each trajectory is formatted as a standard XYZ file, and it can be viewed with molecular editing software such as Jmol, GDIS, or VMD. The atomic coordinates are given in units of Ångström. The frames are written at intervals of 10 fs. Each trajectory lasts 1.5 ps.</p> <p>Each trajectory is accompanied by a CSV file (trajectory_0001.csv etc.) which contains information on the state energies during the given trajectory. The first line is a header: "t(fs),E0(Eh),E1(Eh),E2(Eh),E3(Eh),E4(Eh),Eocc(Eh),Etot(Eh)". The subsequent lines give the time t (in units of fs), the energies of states S0 to S4 in units of Eh (hartree), the energy of the occupied state at time t, and the total energy at time t. (Total energy is not perfectly conserved.)</p> <p><strong>Acknowledgement</strong></p> <p>This research was supported by the Alexander von Humboldt Foundation, and by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 847413.</p>
Figure 6: Simulation computation, at successive steps: iterations 0, 152, 250, 370, 601, 1601. 29
<p>On the figure 6, we present a complete simulation with several centers<br> and several queens [13]. On each center, a queen is emitting several colored<br> pheromons and are able to attract some multi-colored material according to<br> their initial location. Simulation outputs at different iteration times are presented,<br> from RePast implementation and OpenMap GIS visualization.</p>
Optimizing the design of a bioabsorbable metal stent using computer simulation methods: Supporting Data
<p>Data including UMATs and Abaqus input files related to the paper 'Optimizing the design of a bioabsorbable metal stent using computer simulation methods' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.biomaterials.2013.07.010" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.biomaterials.2013.07.010</span></a></p> <p> </p>
Fig. 1 in Hydrodynamic performance of psammosteids: new insights from computational fluid dynamics simulations
Fig. 1. Box-shaped flow domain, mesh and coordinate system used for computational fluid dynamics (A ); A , enlargement view on the mesh.
Fig. 2 in Hydrodynamic performance of psammosteids: new insights from computational fluid dynamics simulations
Fig. 2. Distribution of pressure on the fish bodies at flow velocity 1.5 ms-1, in anterior (A) and lateral (A) views.
Fig. 4. Vorticity patterns using Q-criterion for 0 in Hydrodynamic performance of psammosteids: new insights from computational fluid dynamics simulations
Fig. 4. Vorticity patterns using Q-criterion for 0 angles of attack and flow velocity 1.5 ms-1 in Errivaspis (A), Guerichosteus (B), and Tartuosteus (C). Models displayed in left lateral (A 1 –C 1) and top (A 2 –C 2) views. Iso-vorticity surface is colored by the magnitude of velocity.
Computational simulations show proof-of-concept for optogenetic suppression of ectopic activity in cardiac stem cell therapy
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Population-based computational simulations elucidate mechanisms of focal arrhythmia following stem cell injection
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ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.