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51 results for “biophysical model”
Data, scripts, and R Notebook for Carneiro et al 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology DOI:10.1111/1749-4877.12707
<p>Files provided as supporting information for the paper by Carneiro et al. 2023. Flight performance and wing morphology in the bat <em>Carollia perspicillata</em>: biophysical models and energetics. Integrative Zoology. DOI:10.1111/1749-4877.12707</p> <p>File descriptions</p> <p>ArmTA.txt - Temperature and surface areas for arms of <em>C. perspicillata</em> after flight experiment<br> BodyTA.txt - Temperature and surface areas for body of <em>C. perspicillata</em> after flight experiment<br> HeadTA.txt - Temperature and surface areas for head of <em>C. perspicillata</em> after flight experiment<br> WingTA.txt - Temperature and surface areas for wings (patagium) of <em>C. perspicillata</em> after flight experiment<br> WingMorph.txt - Morphological variables measured in the body and wings of <em>C. perspicillata</em><br> HeatLoss.R - Function to estimate heat loss (Qt)<br> PowFlight.R - Function to estimate minimum power required to fly<br> Script-HeatLoss-FlightPerformance.R - R script with set of analyses performed<br> SupportingInformationFile.docx - R notebook with set of analyses performed, word format<br> SupportingInformationFile.nb.html - R notebook with set of analyses performed, html format<br> SupportingInformationFile.Rmd - R notebook with set of analyses performed (R markdown)</p> <p>For the R scripts (Script-HeatLoss-FlightPerformance.R) and notebook (<br> SupportingInformationFile.Rmd) to work and be compiled, all files need to be copied to the same folder.</p>
Biophysical effects of vegetation cover change from satellite and models
<p>Vegetation cover changes associated with land use and land cover change (LULCC) can perturb the local surface energy balance, which in turn can affect the local climate. Land surface models (LSMs) can be used to simulate such land-climate interactions, but their capacity to model these biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. This dataset provides idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) that are harmonized with estimations obtained from satellite observations, enabling the inter-comparison and benchmarking of LSM performances and which can serve to identify model limitations and prioritize efforts in model development. The dataset provides the change in latent heat flux, in combined sensible and ground heat flux and in net radiation caused by 15 specific vegetation cover transitions on a 1° by 1° grid at monthly time scale for a synthetic year based on data from 2008 until 2012. The dataset was generated from a collaborative effort lead by JRC within the FP7 LUC4C project (luc4c.eu).</p>
PLOS Comput. Biol. "Biophysically detailed mathematical models of multiscale cardiac active mechanics": datasets
<p>This repository contains the data accompanying the PLOS Computational Biology paper "<em>Biophysically detailed mathematical models of multiscale cardiac active mechanics</em>", by Francesco Regazzoni, Luca Dedè and Alfio Quarteroni.</p> <p>It contains the following datasets:</p> <ul> <li><strong>steady_state.csv</strong>: steady-state active tension for constant calcium concentration and sarcomere length (Figs. 11, 12, 13 ,14).</li> <li><strong>isometric_twitches.csv</strong>: active tension transients in isometric conditions (Figs. 15, 16, 17).</li> <li><strong>force_velocity_relationship.csv</strong>: force-velocity relationship at different calcium concentrations and sarcomere lenghts (Fig. 18).</li> <li><strong>fast_transient_response.csv</strong>: tension-elongation curve after a fast step in length (Fig. 19).</li> </ul> <p>CSV headers refer to the following variables (and measure units):</p> <ul> <li><strong>Ca</strong> (<em>μM</em>): intracellular calcium concentration.</li> <li><strong>SL</strong> (<em>μm</em>): sarcomere length.</li> <li><strong>active_tension</strong> (<em>kPa</em>): active tension.</li> <li><strong>Delta_L</strong> (<em>nm/hs</em>): step length.</li> <li><strong>velocity</strong> (<em>hs/s</em>): shortening velocity.</li> <li><strong>time</strong> (<em>s</em>): time.</li> </ul>
A biophysical model of two interacting cortical areas
<p>We present a large-scale, data-driven, biophysically-detailed computational model of two interacting cortical areas, based on data from rodent somatosensory cortex.</p> <p>This model is derived from a previous model (described in two manuscripts: <a href="https://doi.org/10.7554/eLife.99688.1" target="_blank" rel="noopener">anatomy</a>, <a href="https://doi.org/10.1101/2023.05.17.541168" target="_blank" rel="noopener">physiology</a>), but it consists of a reduced setting tailored to the study of inter-areal interactions in cortical sensory processing. Details of the model and initial results can be found <a href="https://doi.org/10.1101/2024.10.13.618022" target="_blank" rel="noopener">here</a>.</p> <h3>Description</h3> <p>The model describes a system of two otherwise isolated cortical areas (X and Y), where area X is a primary sensory and area Y is the first higher-order area in a cortical processing hierarchy. Each area consists of about 200K morphologically-detailed conductance-based neurons, distributed across six cortical layers and of 60 different morphological types and 212 morpho-electrical types.</p> <p>The model incorporates the following connectivity:</p> <ul> <li>Local touch-based connectivity within each area.</li> <li>Thalamocortical innervation from both VPM (core-type) and POm (matrix-type) nuclei to area X.</li> <li>Long-range data-driven projections between both areas with characteristic laminar termination profiles.</li> </ul> <p>Additionally, each area receives nonspecific background noise to exhibit spontaneous activity comparable to experimental recordings of per-layer mean firing rates.</p> <h3>Setup</h3> <p>The model is provided in the <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007696" target="_blank" rel="noopener">SONATA</a> format and can be run using <a href="https://github.com/BlueBrain/neurodamus/" target="_blank" rel="noopener">Neurodamus</a>, a simulator frontend for <a href="https://www.neuron.yale.edu/neuron/" target="_blank" rel="noopener">NEURON</a>. Synaptic and ion channel mechanisms specific for <a href="https://github.com/BlueBrain/neurodamus-models/tree/main/neocortex" target="_blank" rel="noopener">neocortical</a> neurons are also required to run this model (build instructions <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files" target="_blank" rel="noopener">here</a>).</p> <p>To setup the model, all files must be placed in the same directory and all the <a href="https://www.nongnu.org/lzip/" target="_blank" rel="noopener">Lzip</a>-compressed TAR archives must be extracted. The total uncompressed size is 219 GB.</p> <pre><code>$ mkdir model_root # download all files into model_root $ cd model_root $ for file in *.tar.lz; do tar -xf $file; done</code></pre> <h3>Simulation</h3> <p>We provide some example configuration files (under<em> </em><strong>example_simulation_configs</strong>) for simulations of spontaneous and evoked activity, as well as some network manipulations (layer-wise pathway blocks and TTX application).</p> <p>In order to run a simulation, copy <strong>simulation_config.json</strong> into a new directory and set the <em>network</em> key to the path of the directory containing the extracted model (optionally, set the <em>output</em> key as well). Instructions for running a simulation can be found <a href="https://github.com/BlueBrain/neurodamus?tab=readme-ov-file#examples" target="_blank" rel="noopener">here</a> and documentation for the simulation configuration file can be found <a href="https://sonata-extension.readthedocs.io/en/latest/sonata_simulation.html" target="_blank" rel="noopener">here</a>.</p> <h3>Analysis</h3> <p>Analysis of model composition and connectivity, as well as of simulation outputs, can be performed using <a href="https://github.com/BlueBrain/snap" target="_blank" rel="noopener">Blue Brain SNAP</a> or by directly accessing the HDF5 files with <a href="https://github.com/BlueBrain/libsonata" target="_blank" rel="noopener">libsonata</a>. Documentation on the SONATA format for all files making up the model can be found <a href="https://sonata-extension.readthedocs.io/en/latest/sonata_overview.html" target="_blank" rel="noopener">here</a>.</p> <h3>Computational resources</h3> <p>Approximate scaling of computational resources is as follows (based on simulations of 5 s biological time running on a cluster with 40 cores @ 2.5 GHz and 376 GB of RAM per node, one MPI process per core, using <a href="https://doi.org/10.3389/fninf.2019.00063" target="_blank" rel="noopener">CoreNEURON</a>):</p> <ul> <li>Memory per process = 1106 GB / N ** 0.87</li> <li>Simulation time = 4278 h / N ** 0.93</li> </ul> <p>For example, running with N = 1000 processes (25 nodes) results in 107 GB memory usage per node and 7h16m simulation time for 5 s biological time. Longer simulations scale approximately linearly in time, taking 13h13m for 10 s biological time and 21h48m for 15 s biological time.</p> <h3>Changelog</h3> <p>v1.0.1<br>Fixed (unused) key "node_sets_file" in example simulation configuration files.</p>
Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels"
<p>Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels". </p> <p>migrationmatrix14.txt contains the particle tracking matrix, with the total number of particles that migrated from row i to column j (out of a total of 2217864 particles released per population).</p> <p>mussel_microsat_Genepop.txt contains the microsatellite data for each population in Genepop format.</p>
Fig.1 in Preliminary Biophysical Assessment Of Forest Ecosystem Services: Two Model Area Examples
Fig.1. Ecosystem service class: biomass energy products. Indicator: potential energy wood supply within felling limits.
Fig. 2 in Preliminary Biophysical Assessment Of Forest Ecosystem Services: Two Model Area Examples
Fig. 2. Ecosystem service class: global climate regulation by reduction of GHG concentration. Indicator: Estimated carbon stock in live above-ground tree biomass.
Data from: Parameters used in the endotherm biophysical model for each species
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Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
<p>The dynamics and organization of the actin cytoskeleton are crucial to many cellular events such as motility, polarization, cell shaping, and cell division. The intracellular and extracellular signaling associated with this cytoskeletal network is communicated through cell membranes. Hence the organization of membrane macromolecules and actin filament assembly are highly interdependent. Although the actin-membrane linkage is known to happen through many routes, the major class of interactions is through the direct interaction of actin-binding proteins with the lipid class containing poly-phosphatidylinositols (PPIs). Among the PPIs, phosphatidylinositol bisphosphate (PI(4,5)P<sub>2</sub>) acts as a significant factor controlling actin polymerization in the proximity of the membrane by binding to actin-associated proteins. The molecular interactions between these actin-binding proteins and the membrane lipids remain elusive. Here, using molecular modeling, analytical theory, and experimental methods, we investigate the binding of three different actin-binding proteins, mDia2, NWASP, and gelsolin, to membranes containing PI(4,5)P<sub>2</sub> lipids. We perform molecular dynamics simulations on the protein-bilayer system and analyze the membrane binding in the form of hydrogen bonds and salt bridges at various PI(4,5)P<sub>2</sub> and cholesterol concentrations. Our experimental study with PI(4,5)P<sub>2</sub>-containing large unilamellar vesicles mimics the computational experiments. Using the multivalencies of the proteins obtained in molecular simulations and the cooperative binding mechanisms of the proteins, we also propose a multivalent binding model that predicts the actin filament distributions at various PI(4,5)P<sub>2 </sub>and protein concentrations.</p>
Data for "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics"
<p>Experimental and precomputed data for the paper "Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics" by Oesterle et al. 2020 (DOI: <a href="https://doi.org/10.7554/eLife.54997">10.7554/eLife.54997</a>).</p> <p>The cone bipolar cell data has been described and published in the paper "Inhibition decorrelates visual feature representations in the inner retina" by Franke et al. 2017 (DOI: <a href="https://doi.org/10.1038/nature21394">10.1038/nature21394</a>). </p> <p>This data is both a supplement to the Oesterle et al. paper and the code for this paper.</p> <p>The code is available in this <a href="http://github.com/berenslab/CBC_inference">GitHub repository</a>.</p> <p>We recommend downloading the GitHub repository and to follow the instructions there.</p>
Supplemental Movie for "The basis of sharp spike onset in standard biophysical models"
<p>Simulation of extracellular field during action potential.</p>
Fig. 4 in Preliminary Biophysical Assessment Of Forest Ecosystem Services: Two Model Area Examples
Fig. 4. Ecosystem class: mediation of noise impacts, indicator: Estimated noise reduction.
Biophysical models of persistent connectivity and barriers on the northern Mid-Atlantic Ridge
<p>This contains four data files that are all matlab binary files (.mat)</p> <p><strong>all_vent_sites.mat</strong></p> <p>This is a Matlab data file containing the <strong>longitude (column 1)</strong>, <strong>latitude (column 2)</strong>, of all vent sites used in the simulations. Column 3 specifies whether a vent-site is a <strong>known vent site (=1)</strong> or a <strong>ghost vent-site (=0)</strong></p> <p> </p> <p><strong>probeData_20W60W_04S45N.mat</strong></p> <p>This is a Matlab data file containing data on Argo probe cycles used to estimate average ocean currents that drive the particle tracking simulations. The variables in the file are:</p> <ul> <li><strong>fl</strong> Argo float ID </li> <li><strong>depth </strong>parking depth of the Argo float (m)</li> <li><strong>longlatStart </strong>longitude and latitude for the start of one dive cycle</li> <li><strong>longlatEnd </strong>longitude and latitude for the end of one dive cycle</li> <li><strong>month</strong> month of the dive cycle</li> <li><strong>year</strong> year of the dive cycle</li> <li><strong>timeStep</strong> number of days between the start and end of a dive cycle</li> <li><strong>distStep</strong> distance between the start and end positions of a cycle (km)</li> <li><strong>velocity</strong> average velocity of the Argo float over one dive cycle (km/day)</li> <li><strong>pos</strong> the mid-point position of the Argos float for each cycle</li> </ul> <p> </p> <p> </p> <p><strong>vent_connectivity_data.mat</strong></p> <p>This is a Matlab data file containing the connectivity data from the particle tracking simulations. The variables in this file are:</p> <ul> <li><strong>bbox_all</strong> The coordinates for the 64 target boxes</li> <li><strong>particleCount</strong> The number of larval particles starting in each of the 64 target boxes. This should be 100000 for all target boxes</li> <li><strong>connectTime</strong> A 64x64x500 array giving number of particles making a connection between two target boxes. connectTime(i,j,t) = number of particles from box i that have passed though box j in a time <= t. The 500 times correspond to the vector tVec.</li> <li><strong>leaveTime </strong>A 64x500 array giving the time taken for particles to leave their initial target box. leaveTime(i,t) = number of particles starting in box i that leave the box in a time <=t. The 500 times correspond to the vector tVec.</li> <li><strong>C_critical </strong> Critical connection probability</li> <li><strong>tVec</strong> A vector of simulation times. This should be 500 time points starting at day 1 up to day 500</li> <li><strong>tMax</strong> The maximum simulation time (days)</li> </ul> <p> </p> <p> </p> <p><strong>sim_larval_dispersal.mat</strong></p> <p>This is a Matlab data file that contains the dispersal distances of all the simulated larval particles for six planktonic larval durations. The variables in this file are:</p> <ul> <li><strong>bbox_all </strong>The coordinates for the 64 target boxes</li> <li><strong>tMax</strong> The maximum simulation time (days). This is the planktonic larval duration.</li> <li><strong>distAll </strong>The dispersal distance (km) within a given planktonic larval duration (tMax)</li> <li><strong>startAll</strong> The target box where a simulated larval particle started. The position of this box is given by bbox_all</li> </ul> <p> </p>
Biophysical larval dispersal models of observed bonefish (Albula vulpes) spawning events in Abaco, The Bahamas: An assessment of population connectivity and ocean dynamics
<p>Biophysical models are a powerful tool for assessing population connectivity of marine organisms that broadcast spawn. <em>Albula</em> <em>vulpes</em> is a species of bonefish that is an economically and culturally important sportfish found throughout the Caribbean and that exhibits genetic connectivity among geographically distant populations. We created ontogenetically relevant biophysical models for bonefish larval dispersal based upon multiple observed spawning events in Abaco, The Bahamas in 2013, 2018, and 2019. Biological parameterizations were informed through active acoustic telemetry, CTD casts, captive larval rearing, and field collections of related albulids and anguillids. Ocean conditions were derived from the Regional Navy Coastal Ocean Model American Seas dataset. Each spawning event was simulated 100 times using the program Ichthyop. Ten thousand particles were released at observed and putative spawning locations and were allowed to disperse for the full 71-day pelagic larval duration for <em>A</em>. <em>vulpes</em>. Settlement densities in defined settlement zones were assessed along with interactions with oceanographic features. The prevailing Northern dispersal paradigm exhibited strong connectivity with Grand Bahama, the Berry Islands, Andros, and self-recruitment to lower and upper Abaco. Ephemeral gyres and flow direction within Northwest and Northeast Providence Channels were shown to have important roles in larval retention to the Bahamian Archipelago. Larval development environments for larvae settling upon different islands showed few differences and dispersal was closely associated with the thermocline. Settlement patterns informed the suggestion for expansion of conservation parks in Grand Bahama, Abaco, and Andros, and the creation of a park in Eleuthera and the Berry Islands to protect fisheries. Further observation of spawning events and the creation of biophysical models will help to maximize protection for bonefish spawning locations and nursery habitat, and may help to predict year-class strength for bonefish stocks throughout the Greater Caribbean.</p>
Biophysical larval dispersal models of observed bonefish (Albula vulpes) spawning events in Abaco, The Bahamas: An assessment of population connectivity and ocean dynamics
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Data from: Basolateral amygdala oscillations enable fear learning in a biophysical model
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Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
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Reliable reconstruction of cricket song from biophysical models and preserved specimens
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Data from: Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak-Kattegat region of the eastern North Sea
Maintaining and enabling evolutionary processes within meta-populations is critical to resistance, resilience and adaptive potential. Knowledge about which populations act as sources or sinks, and the direction of gene flow, can help to focus conservation efforts more effectively and forecast how populations might respond to future anthropogenic and environmental pressures. As a foundation species and habitat provider, Zostera marina (eelgrass) is of critical importance to ecosystem functions including fisheries. Here we estimate connectivity of Z. marina in the Skagerrak-Kattegat region of the North Sea based on genetic and biophysical modelling. Genetic diversity, population structure and migration were analysed at 23 locations using 20 microsatellite loci and a suite of analytical approaches. Oceanographic connectivity was analysed using Lagrangian dispersal simulations based on contemporary and historical distribution data dating back to the late 19th century. Population clusters, barriers and networks of connectivity were found to be very similar based on either genetic or oceanographic analyses. A single-generation model of dispersal was not realistic, whereas multi-generation models that integrate stepping-stone dispersal and extant and historic distribution data were able to capture and model genetic connectivity patterns well. Passive rafting of flowering shoots along oceanographic currents is the main driver of gene flow at this spatial-temporal scale and extant genetic connectivity strongly reflects the "ghost of dispersal past" sensu Benzie 1999. The identification of distinct clusters, connectivity hotspots and areas where connectivity has become limited over the last century is critical information for spatial management, conservation and restoration of eelgrass.
Data from: Coalescent and biophysical models of stepping-stone gene flow in Neritid snails
Marine species in the Indo-Pacific have ranges that can span thousands of kilometers, yet studies increasingly suggest that mean larval dispersal distances are less than historically assumed. Gene flow across these ranges must therefore rely to some extent on larval dispersal among intermediate "stepping-stone" populations in combination with long-distance dispersal far beyond the mean of the dispersal kernel. We evaluate the strength of stepping-stone dynamics by employing a spatially explicit biophysical model of larval dispersal in the Tropical Pacific to construct hypotheses for dispersal pathways. We test these hypotheses with coalescent models of gene flow among high-island archipelagos in four Neritid gastropod species. Two of the species live in the marine intertidal, while the other two are amphidromous, living in freshwater but retaining pelagic dispersal. Dispersal pathways predicted by the biophysical model were strongly favored in 16 of 18 tests against alternate hypotheses. In regions where connectivity among high-island archipelagos was predicted as direct, there was no difference in gene flow between marine and amphidromous species. In regions where connectivity was predicted through stepping-stone atolls only accessible to marine species, gene flow estimates between high-island archipelagos were significantly higher in marine species. Moreover, one of the marine species showed a significant pattern of isolation-by-distance consistent with stepping-stone dynamics. While our results support stepping-stone dynamics in Indo-Pacific species, we also see evidence for non-equilibrium processes such as range expansions or rare long-distance dispersal events. This study provides an empirical assessment of a biophysical model that helps to shed light on larval dispersal pathways.
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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.