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23 results for “Structure-function”
Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
<p>Dataset linked to the article with the same title</p> <p>The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to <em>O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. </em>Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit.</p> <p><strong>Analyzing the model</strong></p> <p>The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook)</p> <p><strong>Simulating the model</strong></p> <p>To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch <em>nbS1-2023</em>, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a <em>"special"</em> executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where <em>mod-files-from-released-circuit </em>is replaced by the location of <em>O1/mods</em> on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (<em>simulation_config.zip</em>).</p> <p><strong>Analyzing simulation results</strong></p> <p>Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations.</p> <p><strong>Version 2</strong></p> <p>Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1.</p> <p>--</p> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs
<p>The multi-subunit bacterial RNA polymerase (RNAP) and its associated regulators carry out transcription and integrate myriad regulatory signals. Numerous studies have interrogated the inner workings of RNAP, and mutations in genes encoding RNAP drive adaptation of <i>Escherichia coli</i> to many health- and industry-relevant environments, yet a paucity of systematic analyses has hampered our understanding of the fitness benefits and trade-offs from altering RNAP function. Here, we conduct a chemical-genetic analysis of a library of RNAP mutants. We discover phenotypes for non-essential insertions, show that clustering mutant phenotypes increases their predictive power for drawing functional inferences, and demonstrate that some RNA polymerase mutants both decrease average cell length and confer insensitivity to killing by cell-wall targeting antibiotics. Our findings demonstrate that RNAP chemical-genetic interactions provide a general platform for interrogating structure-function relationships <i>in vivo</i> and for identifying physiological trade-offs of mutations, including those relevant for disease and biotechnology. This strategy should have broad utility for illuminating the role of other important protein complexes.</p>
Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs
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Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery
<p>iEEGnetworksFC: Functional Connectivity Networks</p> <p>iEEGnetworkSCPre: Structural Connectivity Networks before surgery</p> <p>iEEGnetworkSCPost: Structural Connectivity Networks expected after surgery</p> <p>iEEGnetworkSCPre5mm: Structural Connectivity Networks before surgery with alternate ROI size of 5mm at each contact</p> <p> </p>
Supplementary material and dataset for article "Structure-Function Relationship of Iron Oxide Nanoflowers: Optimal Sizes for Magnetic Hyperthermia Depending on Alternating Magnetic Field Conditions"
<p>The supporting information file contains additional measurements compared to the main manuscript of the related article: TEM size histograms, SAED and XRD patterns, absorption and fluorescence spectra, DC magnetization curves, AC hysteresis loops, HR-TEM, their FTT patterns and inverse FFT images after applying a mask in the reciprocal space, and various other plots of this multi-parametric study on the structure-properties relations of magnetic iron oxide nanoflowers. Whenever needed for comparison to other experimental results or theoretical fitting, the raw data of all DC magnetization curves, AC hysteresis loops, ZFC-FC magnetization curves vs. temperature (and their derivatives on temperature) are made freely available in this dataset.</p>
Data from: Describing complex structure-function relationships in biomolecules at equilibrium
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Dataset related to "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations"
<p>This is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). It contains the resulting data of the manipulated SSCx network model, such as benchmarks, fitted stochastic models, manipulated connectomes, structural validations, as well as simulation data and analysis results.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <p><strong>Update 25/09/2024 (v2):</strong> Added benchmark results for assessing strong and weak scaling behavior of connectome rewiring.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Supplementary Data for "Exploring structure-function relationships in engineered receptor performance using computational structure prediction"
<p>These data are supplementary data for the manuscript "<strong>Exploring structure-function relationships in engineered receptor performance using computational structure prediction</strong>", which has been submitted for consideration for publication. These data include protein structure predictions used in this study.</p>
Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: a graph signal processing study
<p><strong>Dataset for the publication: 'Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: a graph signal processing study'<br> Rigoni et al. 2023, Clinical Neurophysiology, doi: </strong><a href="https://doi.org/10.1016/j.clinph.2023.05.012">https://doi.org/10.1016/j.clinph.2023.05.012</a></p> <p><strong>Dataset description</strong></p> <p><em>func_data.mat</em> : source-reconstructed EEG traces stored in a Fieldtrip format (data for each subj)</p> <p><em>struct_data</em>: consensus structural connectome</p> <p><em>ROIpatch </em>: mesh used to plot Fig4</p> <p><em>SC_surrogates (W0)</em>: 1000 degree-preserving surrogates of the structural connectome computed with the null_model_und_sign function of the Brani Connectivity Toolbox; W0 are used for broadcasting analyses, section 2.6.2</p> <p><em>SC_surrogates_harmonics (U0)</em> : network harmonics of the surrogate structural connectomes (W0); U0 are used for broadcasting analyses, section 2.6.2<br> <em>BD_surr </em>: Broadcasting-direction (BD) of the degree-preserving surrogates of the structural connectome W0; used to produce FigS3 (data for each subj)</p> <p><em>PHI </em>: matrix of +1/-1 to generate the functional surrogates used to define significance of SDI, as described in section 2.6.3</p> <p><em>data_GSP2_surr </em>: SDI of the functional surrogates used to define significance of SDI, as described in section 2.6.3 (data for each subj)</p> <p> </p> <p>Abbreviations:</p> <p>EEG= electroencephalography;</p> <p>SDI= structure-decoupling index</p>
data for "Structure-function analysis suggests that the photoreceptor LITE-1 is a light-activated ion channel"
<p>Data S1 for Manuscript <strong>Structure-function analysis suggests that the photoreceptor LITE-1 is a light-activated ion channel.</strong></p>
Lung Structure-Function In Survivors of Mild and Severe COVID-19 Infection
ClinicalTrials.gov study NCT04584671. IPD Sharing: NO. Countries: 1. Publications: 13.
Lung Structure-Function In SurVivors of Mild and SEvere COVID-19 Infection: 129Xe MRI
ClinicalTrials.gov study NCT05014516. IPD Sharing: NO. Countries: 1. Publications: 3.
ICF_2024: Multielectron transfer and field-induced slow magnetic relaxation in opto-electroactive spin crossover cobalt(II) complexes: structure-function correlations
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Data from: Structure-function covariation with nonfeeding ecological variables influences evolution of feeding specialization in Carnivora
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Structure-function analysis of fission yeast cleavage and polyadenylation factor (CPF) subunit Ppn1 and its interactions with Dis2 and Swd22
GEO Series GSE161583. Schizosaccharomyces pombe. 12 samples. Type: Expression profiling by high throughput sequencing.
Structure-function Analysis of Rice Immune Receptor BPH14 Reveals Planthopper-resistance Mediated through Protein-protein Interaction with WRKY46/72
GEO Series GSE93607. Oryza sativa. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Mutant APC reshapes plasma membrane cholesterol-dependent Wnt proteolipid condensate structure-function and feedforward oncogenic β-catenin signaling
GEO Series GSE234522. Mus musculus. 31 samples. Type: Expression profiling by high throughput sequencing.
In vivo structure-function analysis of human Dicer reveals directional processing of precursor miRNAs
GEO Series GSE36978. Mus musculus. 16 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Deep mutational scan of a drug efflux pump reveals its structure-function landscape
GEO Series GSE189399. Enterococcus faecalis. 21 samples. Type: Other.
Structure-function analysis of microRNA 3′-end trimming by Nibbler
GEO Series GSE158054. Drosophila melanogaster. 4 samples. Type: Non-coding RNA profiling by high throughput sequencing.
ScienceDex guides
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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.