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9,152 results for “Networking”
Cell Anomaly Localisation using Structured Uncertainty Prediction Networks
<p>Fluorescent and brightfield datasets for "Cell Anomaly Localisation using Structured Uncertainty Prediction Networks", part of MIDL 2022.</p>
Data for paper: Comparing Measures of Centrality in Bipartite Social Networks: A Study of Drug Seeking for Opioid Analgesics
<p>Data and code for paper: Comparing Measures of Centrality in Bipartite Social Networks: A Study of Drug Seeking for Opioid Analgesics</p>
Supporting Information: Assessing the Social Dimension in Strategic Network Design for a Sustainable Development: The Case of Bioethanol Production in the EU
<p>Supporting Information S2 provides all parameters of the optimization model, both those associated with the social objective and hotspot functions of the study at hand, and those from the underlying model by Wietschel et al. (2021). To put the method of the study into perspective, it also gives an overview of referenced frameworks (GSLCAPO, SHDB, SDGs) and their categories, subcategories, and indicators. Furthermore, it includes detailed results for all social, environmental, and economic objective functions in all scenarios, and of the Pareto optimization. Lastly, the underlying data for all figures of the manuscript and Supporting Information S1 is provided.</p>
From Networked to Isolated: Observing Water Hydrogen Bonds in Concentrated Electrolytes with Two Dimensional Infrared Spectroscopy
<p>The files contain the data that are shown in the figures of the Main Text and of the Supplementary Materials of the research article:</p> <p>From Networked to Isolated: Observing Water Hydrogen Bonds in Concentrated Electrolytes with Two Dimensional Infrared Spectroscopy</p> <p>Nicholas H. C. Lewis, Bogdan Dereka, Yong Zhang, Edward J. Maginn, and Andrei Tokmakoff</p> <p>J. Phys. Chem. B, (2022)</p>
The appendix for dynamic model of respiratory infectious disease transmission by population mobility based on city network
<p>First, a scale-free city network was established, and the shortest path between any two nodes was determined. Second, the movement path of tourists was designed based on the shortest path. Subsequently, every infected person's information, such as the city, infection time, onset, and hospitalisation, was confirmed based on their movement path. Third, the features of the transmission path and time distribution of the epidemic were characterised after summarising the information. Finally, the reliability of the model was verified.</p>
Dataset (MATLAB format) from Chen Kang et al (2021) Modularity and robustness of frontal cortex networks. Cell, 184(14):3717-3730.
<p><strong>Summary</strong></p> <p>These experiments simultaneously measure neuronal responses from the two hemispheres of premotor cortex (anterior lateral motor cortex, ALM) of adult mice performing pole location discrimination with a short-term memory. We inactivate activity of one hemisphere (left or right) in some trials and both hemispheres in others. Data from 135 recording sessions are included in this release.</p> <p>This dataset contains data from 39 mice (age > P60). 9 VGAT-ChR2-EYFP mice (Jackson laboratory, JAX Stock#014548) and 30 PV-IRES-Cre (JAX Stock#008069) crossed to Rosa26-LSL-ReaChR, red-shifted channelrhodopsin reporter mice (JAX 24846), were used for simultaneous electrophysiology and photoinhibition. The experiments (including experiment methods) are described in the following publication:</p> <p><em>Chen, G., Kang, B., Lindsey, J., Druckmann, S., Li, N. (2021). Modularity and robustness of frontal cortical networks. Cell 184, 1-14. doi: 10.1016/j.cell.2021.05.026</em></p> <p> </p> <p><strong>How to cite the data</strong></p> <p>If you publish any work using the data, please cite the Chen et. al., (2021) publication above and also cite the dataset in the following recommended format:</p> <p>Chen G, Li N (2021); Data and simulations related to: Modularity and robustness of frontal cortical networks. Chen et al (2021) Cell, 184(14):3717-3730.</p> <p><a href="http://dx.doi.org/10.5281/zenodo.6713616">http://dx.doi.org/10.5281/zenodo.6713616</a></p> <p> </p> <p><strong>How to get started</strong></p> <p>Once downloaded</p> <p>1) unzip “<strong>analysis_scripts</strong>”</p> <p>2) unzip and combine all "<strong>data_structure_*.mat</strong>" and "<strong>meta_data_*.mat</strong>" files into a single folder “<strong>datafiles</strong>”.</p> <p> • <strong>data_structure_*.mat </strong>- contains raw spike data for one session.</p> <p> • <strong>meta_data_*.mat</strong> - contains the meta data information for one session.</p> <p> There is one "<strong>meta_data_*.mat</strong>" file for each "<strong>data_structure_*.mat</strong>"</p> <p>3) Run scripts within "<strong>analysis_scripts</strong>". Follow the instruction in the "<strong>code instruction</strong>".</p> <p> </p> <p><strong>Data analysis</strong></p> <p>The extracellular recording traces were band-pass filtered (300-6 kHz). Events that exceeded an amplitude threshold (4 standard deviations of the background) were subjected to spike sorting to extract single units. Spike sorting was either manual (Guo et al., 2014b) or using Kilosort2 (https://www.github.com/MouseLand/Kilosort2) (Pachitariu et al., 2016) followed by manually curated with the Phy 2.0 beta 1 GUI (https://github.com/cortex-lab/phy) (Rossant et al., 2016) and manual inspection (Guo et al., 2014a). Spike widths were computed as the trough-to-peak interval in the mean spike waveform. Units with spike width < 0.35 ms were defined as fast-spiking (FS) neurons and units with spike widths > 0.45 ms as putative pyramidal neurons. Units with intermediate values (0.35 - 0.45 ms) were excluded. We concentrated our analyses on the putative pyramidal neurons.</p>
Selective area growth of PbTe nanowire networks on InP
<p>data of "Selective area growth of PbTe nanowire networks on InP"</p>
Data of "Universal platform for scalable semiconductor-superconductor nanowire networks '
<p>Data of "Universal platform for scalable semiconductor-superconductor nanowire networks '</p>
Neural network training sets
<p>This dataset contains the synthetically generated particle image datasets used to train a denoising autoencoder (DAE_synthetic_dataset.zip) and a fully convolutional network for semantic segmentation (semanticSegmentation_synthetic_dataset.zip). The necessary models and maps used to generate the dataset are included in synthetic_volumes_and_pdbs.zip. Lastly, the trained neural networks are included in trained_neural_networks.zip.</p>
scMEGA: Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference
<p>The increasing availability of single-cell multi-omics data allow to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) to infer gene regulatory network by combining single cell gene expression and chromatin accessibility profiles. This allows to study complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease development. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction.</p>
Accelerating DNA-PAINT imaging with a deep neural network
<p>(<a href="https://zenodo.org/api/files/7030c526-533f-44ce-8b8c-01a979fd6b6f/RawFrames_P5-IS_20pM.tif?versionId=55a12821-8472-42f5-91b5-eabda1516e2b">RawFrames_P5-IS_20pM.tif</a>) Raw DNA-PAINT SMLM frames with isolated emitters taken on TOM20 labelled MNTB neuronal rat tissue with P5 imaging strand at 20 pM concentration. </p> <p>(<a href="https://zenodo.org/api/files/7030c526-533f-44ce-8b8c-01a979fd6b6f/Binned_HighDensity_30kpatches.tif?versionId=ac01dd00-3c44-4059-a1b1-3b1045fa636d">Binned_HighDensity_30kpatches.tif</a> and <a href="https://zenodo.org/api/files/7030c526-533f-44ce-8b8c-01a979fd6b6f/Binned_HighDensity_30kpatches.csv?versionId=31e792fc-fab5-43cd-af81-a271595586d0">Binned_HighDensity_30kpatches.csv</a>) Artificially summed high-density emitter patches with the corresponding emitter coordinates used for training the DeepSTORM neural network. </p> <p>(DeepSTORM_model_metadata.mat and DeepSTORM_model_weights_best.hdf5) The trained model metadata and weights used for all predicted images in the study.</p> <p>(Figure 4_Large_super-resolution_image.png) The large super-resolution image in Figure 4 and Figure S4.</p> <p>(High-density-frames_Images 1 - 5 .tif) Five high-emitter density raw frames for alpha-tubulin and TOM20 of 400 frames each.</p> <p>(<a href="https://zenodo.org/api/files/b98093d2-f689-4fef-8222-220c07ca4fe9/Ground-truth-rendered_Image4_Tubulin.tif">Ground-truth-rendered_Image 1 - 5</a>.tif) Five ground truth images rendered in Picasso (drift-corrected, linked localisations, pixel size 13.37 nm/pixel) for alpha-tubulin and TOM20.</p> <p>(<a href="https://zenodo.org/api/files/e726ff9b-45ba-4d3a-b3ba-3e6eaca03d35/Low%20density%20frames%20for%20GT%20images.zip">Low density frames for GT images.zip</a>) Five low-emitter density (0.5 nM) raw frames for alpha-tubulin and TOM20 of 10000 frames each which were used to render ground truth images.</p> <p>(<a href="https://zenodo.org/api/files/e726ff9b-45ba-4d3a-b3ba-3e6eaca03d35/Bassoon_Homer_datasets.zip">Bassoon_Homer_datasets.zip</a>) Three Bassoon and Homer datasets each with a ground truth image and the corresponding high-density raw frames (5 nM, 800 frames).</p>
Habitat loss increases seasonal interaction rewiring in plant-pollinator networks
<p>Understanding the flexibility of interactions and network rewiring (i.e. reassembly of interactions due to partner-switching) is necessary to comprehend how future anthropogenic changes will affect interspecific interactions and the functioning of communities. A higher rewiring could be expected in more disturbed landscapes because these landscapes contain fewer and more generalist species with more homogeneous traits. We sampled pollination interactions in 20 wild Olea europaea communities along a disturbance gradient to evaluate the hypothesis that the loss of natural habitats increases seasonal (within-year) interaction rewiring in plant-pollinator communities, influencing their functional structure. For this, we particularly tested whether rewiring frequency was negatively related to the extent of natural habitats surrounding the communities, whether interaction rewiring influenced the static structure of networks (nestedness, network specialization –H2'–), and whether a high generalization (low specialization –d'–) and abundance of species in communities made them more prone to rewiring. We show that habitat loss increased seasonal interaction rewiring in networks. Changes in rewiring were related to changes in the cumulative static structure of pollination networks. Nestedness decreased and network specialization (H2') also tended to decrease as interaction rewiring increased, suggesting an indirect effect of habitat loss on the robustness of networks through their dynamics. As expected, generalist insect and plant species were more prone to rewiring. However, flower abundance had different effects on the rewiring probability of plant species depending on the extent of habitat loss, with abundant species rewiring more in disturbed communities and rewiring less in more natural communities. Likely, this is related to the context-dependent foraging behaviour of pollinators, which may switch to more abundant species if the cost of searching for trait-matching resources is high in disturbed habitats. Our work shows the role of partner-switching in generalist species to adapt to new conditions. It also highlights the importance of going beyond general network metrics to understand the underlying processes of community-level interaction assembly, and predict and anticipate the effects of anthropogenic disturbances on pollination services.</p>
Supplementary material 1 from: Behei N, Tryhubchak O, Pryymak B (2022) Development of amlodipine and enalapril combined tablets based on quality by design and artificial neural network for confirming of qualitative composition. Pharmacia 69(3): 779-789. https://doi.org/10.3897/pharmacia.69.e86876
The results of the study of pharmaco-technological parameters of intermediates and amlodipine tablets with enalapril, data of the functions of desirability
Incorporating the image formation process into deep learning improves network performance
<p>These are representative source data for the main figures (Figs, 1d, 2a, 2d, 3c, 4b, 5a) in paper "Incorporating the image formation process into deep learning improves network performance".</p>
Code and Data for "Bridging Gaps in the Climate Observation Network"
<p>The code and data used in the paper "Bridging Gaps in the Climate Observation Network: A Physics-based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data-Driven Stochastic Model"</p>
Datasets for reproducing "Robust Modelling of Internet Delay and Smart Monitoring Schemes for the Automation of Overlay Networks"
<p>This upload contains the datasets necessary to reproduce the figures and the results of my PhD thesis titled "<a href="https://tel.archives-ouvertes.fr/tel-03666771/document">Robust Modelling of Internet Delay and Smart Monitoring Schemes for the Automation of Overlay Networks</a>" (2020).</p> <p>These datasets are derived from public sources: <a href="https://www.caida.org/projects/manic/">CAIDA MANIC</a> and <a href="https://atlas.ripe.net/">RIPE Atlas</a>.</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>
European power grid network with nodal prices
<p>European power grid network with nodal prices as metadata</p>
Inference and Test Generation Using Program Invariants in Chemical Reaction Networks Artifacts
<p>The artifacts for Inference and Test Generation Using Program Invariants in Chemical Reaction Networks, published at ICSE 2022.</p> <p>The pdf of the paper can be accessed at <a href="https://ieeexplore.ieee.org/document/9794130">IEEEXplore</a>.</p> <p><strong>To cite this work, please use the citation below:</strong></p> <pre>@INPROCEEDINGS{GertenICSE22, author={Gerten, Michael C. and Marsh, Alexis L. and Lathrop, James I. and Cohen, Myra B. and Miner, Andrew S. and Klinge, Titus H.}, booktitle={2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)}, title={Inference and Test Generation Using Program Invariants in Chemical Reaction Networks}, month={May}, year={2022}, pages={1193-1205}, doi={10.1145/3510003.3510176}}</pre> <p>The artifacts are also available on <a href="https://github.com/LavaOps/ICSE-2022-Artifacts">GitHub</a>.</p> <p><strong>This is an updated version of the ChemFlow tool. The update addressed an overflow error when computing gaussian elimination that could result in incorrect invariants with certain model inputs. After verification, all models in this work were not affected by this bug and have the same set of invariants generated by both versions. We have updated the docker file to use the new code as well.</strong></p>
Evolutionary changes in the chromatin landscape contribute to reorganization of a developmental gene network during rapid life history evolution in sea urchins
<p>Chromatin configuration is highly dynamic during embryonic development in animals, exerting an important point of control in transcriptional regulation. Yet there exists remarkably little information about the role of evolutionary changes in chromatin configuration to the evolution of gene expression and organismal traits. Genome-wide assays of chromatin configuration, coupled with whole-genome alignments, can help address this gap in knowledge in several ways. In this study, we present a comparative analysis of regulatory element sequences and accessibility throughout embryogenesis in three sea urchin species with divergent life histories: a lecithotroph <em>Heliocidaris erythrogramma</em>, a closely related planktotroph <em>H. tuberculata</em>, and a distantly related planktotroph <em>Lytechinus variegatus</em>. We identified distinct epigenetic and mutational signatures of evolutionary modifications to the function of putative <em>cis</em>-regulatory elements in <em>H. erythrogramma</em> that have accumulated non-uniformly throughout the genome, suggesting selection, rather than drift, underlies many modifications associated with the derived life history. Specifically, regulatory elements composing the sea urchin developmental gene regulatory network are enriched for signatures of positive selection and accessibility changes which may function to alter binding affinity and access of developmental transcription factors to these sites. Furthermore, regulatory element changes often correlate with divergent expression patterns of genes involved in cell type specification, morphogenesis, and development of other derived traits, suggesting these evolutionary modifications have been consequential for phenotypic evolution in <em>H. erythrogramma</em>. Collectively, our results demonstrate that selective pressures imposed by changes in developmental life history rapidly reshape the <em>cis</em>-regulatory landscape of core developmental genes to generate novel traits and embryonic programs.</p>
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.