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68 results for “Flow networks”
Supplementary data for the article "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."
<p>Supplementary figures, tables and raw data for the article " "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."</p> <p><strong>Food sharing can occur in both social and non-social species but is crucial in eusocial species in which only some group members collect food. This food collection but also intranidal food distribution through trophallactic (i.e. mouth-to-mouth) exchanges are fundamental issues in eusocial insects. However, the behavioural rules underlying the regulation and the dynamics of food intake and the resulting networks of exchanges are poorly understood. In this study, we provide new insights </strong><strong>into</strong><strong> the behavioural rules underlying the structure of trophallactic networks and food dissemination dynamics within the colony. We build a simple data-driven model that implements interindividual variability and division of labour to investigate the processes of food accumulation/dissemination inside the nest, both at the individual and collective </strong><strong>levels</strong><strong>. We also test the alternative hypotheses (no variability and no division of labour). Division of labour with inter-individual variability predicts contrary to other models the food dynamics and exchange networks. We establish the links between the interindividual heterogeneity of the trophallactic behaviours, the food flow dynamics and network of trophallactic events</strong>.<strong> Despite the relative simplicity of the model rules, efficient trophallactic networks may emerge as the ones observed in ants leading to better understanding of evolution of such societies.</strong></p> <p> </p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for multi-capillary dilation scenarios mimicking pericyte ablation
<p>Documentation to reproduce in silico analyses related to the manuscript<br> <strong>Pericyte remodelling is deficient in the aged brain and contributes to impaired capillary flow and structure</strong></p> <p>by</p> <p>Andrée-Anne Berthiaume, Franca Schmid, Stefan Stamenkovic, Vanessa Coelho-Santos, Cara D. Nielson, Bruno Weber, Mark W. Majesky and Andy Y. Shih</p> <p>Published in<br> Nature Communications (doi: 10.1038/s41467-022-33464-w)</p> <p>All simulations are performed based on the in silico blood flow model with discrete red blood cell (RBC) tracking as described in Schmid et al., 2017, PLoS Comp Biol (doi: <a href="https://doi.org/10.1371/journal.pcbi.1005392">10.1371/journal.pcbi.1005392</a>). The bi-phasic blood flow simulations have been performed in two realistic microvascular networks from the somatosensory cortex of the mouse first published in Blinder et al., 2013, Nature Neuroscience (doi: 10.1038/nn.3426). </p> <p>For further information and instructions please contact Franca Schmid (franca.schmid@unibe.ch, orcid.org/0000-0002-0689-9366).</p> <p><br> <strong>Simulation results:</strong></p> <p>All time-averaged simulation results are saved as vascular graphs building on the python library igraph and stored as python pickle files (Python 2.7). For each simulation two files are available: <em>verticesDict.pkl</em> and <em>edgesDict.pkl</em>containing all vertex and edge specific data, respectively. A summary of the vertex and edge attributes is provided below. The folder <em>Baseline</em> contains the simulation results for microvascular network 1 (MVN1) and MVN2 for the reference simulation, i.e. without any dilation. Folder <em>Dilated</em> contains the simulation results mimicking the four pericyte ablation scenarios. Subfolders <em>dc_x.x</em> contain the simulation results for the different diameter changes. Note that, folder <em>dc_0.0</em> contains no new simulation results but is a dummy folder containing the information about the vessels to be dilated for the different dilation scenarios (namely edge attribute: <em>toDilate</em> and <em>base_capillary</em>). </p> <p> </p> <p><strong>Reproducing figure 8:</strong></p> <p>Panels a-c: created by illustrating the simulation results with the open source software Paraview (v5.7.0).<br> Panels d-f & h: can be generated by executing make_all_figures.py in Python 2.7 within the provided folder structure.<br> Panel g: can be generated by executing make_figure_8g.py after installation of the the vgm-framework (further information see below). </p> <p><br> Output: All created Figures are saved in the folder <em>Figures</em>. The associated source data is available in Excel format in the folder <em>SourceData</em>.</p> <p> </p> <p><strong>Edge attributes:</strong></p> <p>diameter: vessel diameter [µm]<br> mainAV: 1 if ascending venule main branch, 0 otherwise<br> connectivity: vertex tuple to define location of edge<br> flow: flow rate [µm<sup>3</sup>/ms]<br> mainDA: 1 if descending arteriole main branch, 0 otherwise<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> htt: tube hematocrit [-]<br> toDilate: 1 if vessel is dilated for the current dilation scenario, 0 otherwise<br> base_capillary: 1 if vessel is the base capillary of the current dilation scenario, 0 otherwise</p> <p> </p> <p><strong>Vertex attributes:</strong></p> <p>index: vertex index<br> pressure: pressure [mmHg]<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> coords: vertex coordinates x,y,z [µm]<br> pBC: pressure boundary conditions at inflow vertices [mmHg], None at internal nodes</p> <p> </p> <p><strong>Obtaining simulation results:</strong><br> General:</p> <ul> <li>Running bi-phasic blood flow simulations requires setting-up the vgm-framework available at: <a href="https://github.com/Franculino/vgm.git">https://github.com/Franculino/vgm.git</a> (v.1.0).</li> <li>vgm is written in Python 2.7 and builds on standard python libraries.</li> <li>vgm has been used on macOS, Ubuntu and Windows Systems.</li> <li>Installation time < 5min. Further details available within the vgm README.</li> <li>Runtime depends on the network size, the chosen blood flow model and the initial conditions (e.g. ~8hrs for a Restart simulation of MVN1 with the bi-phasic blood flow model, see Restarty.py).</li> <li>scripts/Test.py provides an example how a simulation can be initiated. A Demo case is provided (details see below).</li> <li>Output: sampledict_BackUp_xx.pkl</li> <li>The bi-phasic blood flow model can be applied on all kind of microvascular graphs.</li> </ul> <p>Specific for current application:</p> <ul> <li>Simulations are a restart on the statistical steady state of the baseline cases.</li> <li>All relevant pre-processing functions for the current study are available in scripts/find_stroke_locations.py. Further details are available from the definition of the different functions.</li> <li>The simulations are initiated with scripts/Restart.py.</li> <li>To obtain the time-averaged simulation results scripts/01_put_together_sampledicts.py and scripts/02_convergenceDiscrete.py need to be executed. This results in the file G_averaged.pkl that is used for further analyses.</li> </ul> <p>Demo:</p> <ul> <li>Contains a small hexagonal microvascular network to test the code.</li> <li>1) Run Test.py to start the simulation</li> <li>2) Run 01_put_together_sampledicts.py</li> <li>3) Run 02_convergenceDiscrete.py to obtain time-averaged results (<em>G_averaged.pkl</em>)</li> </ul>
The dataset of "Characteristics of Urban Interaction Network in Shandong Province from the Perspective of Traffic Flow and Information Flow"
<p>There are the datasets of the research named "Characteristics of Urban Interaction Network in Shandong Province from the Perspective of Traffic Flow and Information Flow".</p>
Dataset for Bike Flow Prediction with Multi-Graph Convolutional Networks, Chai et al. (https://github.com/Di-Chai/GraphCNN-Bike/)
<p>This is contains the preprocessed, and raw data used to run the replication package from: <br>https://github.com/Di-Chai/GraphCNN-Bike/</p>
Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
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Modeling fluid flow in ship systems for controller tuning using an artificial neural network
<p>Dataset used to develop ANN NARX models</p>
Fig. 2 in Using haplotype networks, estimation of gene flow and phenotypic characters to understand species delimitation in fungi of a predominantly Antarctic Usnea group (Ascomycota, Parmeliaceae)
Fig. 2 Enlargement of the Usnea aurantiaco-atra group of the Bayesian inference (Fig. 1) depicting 101 taxa. Posterior probabilities ≥ 0.95 are visualized by bold branches. Colors match the sampling site colours in Fig. 4a. A! fertile specimen with apothecia, S! vegetative reproduction via soralia. Most important clades of the nested clade analysis (Fig. 4a,b) are plotted on the phylogenetic tree
Fig. 2. A–G, Aphaena discolor. A–B in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 2. A–G, Aphaena discolor. A–B, specimen on his host-tree, Kirirom, 9.V.2015 (J. Constant). C, habitat in Kirirom, 9.V.2015 (J. Constant). D, Pursat, Cardamom, 2.I.2009 (J. Holden). E, Koh Kong, Tatai, 5.III.2012 (G. Chartier). F–G, Dichoptera sp. Chambok, 5.V.2015 (J. Constant). H, Kalidasa nigromaculata, Siem Reap, Angkor, 10.VIII.2014 (S. De Greef). I, Penthicodes atomaria tended by a cockroach, Cardamom Mts, 4.VIII.2013 (A. Anker). J, P. pulchella, Siem Reap, 18.IX.2013 (S. De Greef). K, P. variegata, Mondulkiri, O Reang District, 19.V.2015 (B. Barca). L–M, Polydictya tricolor, Siem Reap, Angkor, 1.VIII.2013 (S. De Greef). N–O, Polydictya sp., 8 km NNW Angkor, 6.XI.2013 (E. Smith).
Fig. 1 in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 1. Call to collaboration to the study of Fulgoridae of Cambodia posted on Facebook on May 18th, 2015.
Fig. 3. A–B, Pyrops candelaria. A in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 3. A–B, Pyrops candelaria. A, Chambok, 1.IX.2014 (S. Phauk). B, Kampong Tralach, 21.IX.2013 (O. Rodriguez). C–D, P. coelestinus, Chambok, 1.IX.2014 (S. Phauk). E, P. condorinus, Koh Kong, Tatai, 24.V.2015 (G. Chartier). F, P. ducalis, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). G–I, P. peguensis. G, Tumpor, Cardamom, 12.VIII.2009 (J. Holden). H, Chambok, 5.V.2015 (J. Constant). I, idem, biotope. J, P. spinolae, Ratanakiri, Veun Sai Siem Pang, 23.II.2015 (Marduk). K, P. viridirostris, Chambok, 7.V.2015 (J. Constant). L–N, Saiva gemmata. L, nymph, Chambok, 7 May 2015 (J. Constant). M, adult tended by a cockroach, Chambok, 7.V.2015 (J. Constant). N, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). O–Q, Zanna sp. O–P, Koh Kong, Tatai, 1.XI.2012 (G. Chartier). Q, Kampot, 21.XII.2013 (K.W. Meier-Doernberg).
Data for "A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks"
<p>This data is related to the manuscript "A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks". It contains meshes, boundary conditions, and medium properties for discrete fracture networks used to test fluid flow solvers.</p>
Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data
<p>Numerical codes and results for the article: Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data</p>
Exploring functional flow heterogeneity in regulated flow regime: fish species turnover along hydraulic gradients in an artificial waterway network
<p><span>Humans have altered river flows and lateral aquatic habitats. The expansion of agriculture in floodplains has resulted in landscapes dominated by irrigated farmland. A key challenge in water management is to conserve existing ecological communities and habitat heterogeneity, while simultaneously maintaining engineered infrastructure for agriculture. In this study, we focused on an artificial channel network for irrigation with a regulated flow regime and its function as habitat for various fish species. Differences of hydraulic conditions among channels and compositional changes in fish species were examined to clarify functional flow heterogeneity. Species turnover was analyzed using pairwise Simpson dissimilarity among sampling reaches. Species turnover was positively associated with Froude number (flow intensity) differences at intermediate discharges, and with differences in cross-sectional areas (flow magnitude) at low discharges. Drastic changes in inflows should be considered for the effective conservation of flow heterogeneity, even under a regulated flow regime. Improved engineering design to manage the hydraulic environment is one option for maintaining the ecological value of lateral waterbodies in human-dominated landscapes. Our findings provide insights into the importance of functional flow heterogeneity to conserve fish species diversity.</span></p>
Exploring functional flow heterogeneity in regulated flow regime: fish species turnover along hydraulic gradients in an artificial waterway network
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Recognition of dominant driving factors behind sap flow of Liquidambar formosana based on back-propagation neutral network method
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Data from: Ditch network sustains functional connectivity and influences patterns of gene flow in an intensive agricultural landscape
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Data from: The response of migratory populations to phenological change: a Migratory Flow Network modelling approach
1. Declines in migratory species have been linked to anthropogenic climate change through phenological mismatch, which arises due to asynchronies between the timing of life-history events (such as migration) and the phenology of available resources. Long-distance migratory species may be particularly vulnerable to phenological change in their breeding ranges, since the timing of migration departure is based on environmental cues at distant non-breeding sites. 2. Migrants may, however, be able to adjust migration speed en route to the breeding grounds and thus ability of migrants to update their timing of migration may depend critically on stopover frequency during migration; however, understanding how migratory strategy influences population dynamics is hindered by a lack of predictive models explicitly linking habitat quality to demography and movement patterns throughout the migratory cycle. 3. Here, we present a novel modelling framework, the Migratory Flow Network (MFN), in which the seasonally varying attractiveness of breeding, winter, and stopover regions drives the direction and timing of migration based on a simple general flux law. 4. We use the MFN to investigate how populations respond to shifts in breeding site phenology based on their frequency of stopover and ability to detect and adapt to these changes. 5. With perfect knowledge of advancing phenology, 'jump' migrants (low frequency stopover) require more adaptation for populations to recover than 'hop' and 'skip' (high or medium frequency stopover) migrants. If adaptation depends on proximity, hop and skip migrants' populations can recover but jump migrants cannot adjust and decline severely. 6. These results highlight the importance of understanding migratory strategies and maintaining high-quality stopover habitat to buffer migratory populations from climate-induced mismatch. 7. We discuss how MFNs could be applied to diverse migratory taxa, and highlight the potential of MFNs as a tool for exploring how migrants respond to other environmental changes such as habitat loss.
Dataset used in "Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network" by Kamrava et al. in Geophysical Research Letters.
<p>Morphology of Heterogeneous Porous Media</p>
PI Cargo Network Cargo Flows
<p> PI Cargo Network Cargo Flows</p>
Data for: Sociality and signaling activity modulate information flow in river otter communication networks
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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.
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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.