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397 results for “functional interactions”
Monovalent ion-mediated charge-charge interactions drive aggregation of surface-functionalized gold nanoparticles
<p>Dataset containing files required to run the simulations in "Monovalent ion-mediated charge-charge interactions drive aggregation of surface-functionalized gold nanoparticles"</p>
Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells'
<p><strong>Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells'</strong></p> <p><strong>File descriptions:</strong></p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_1_model_1_fixed.pdb</strong><br> ColabFold output PDB file - Rank 1 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_2_model_2_fixed.pdb</strong><br> ColabFold output PDB file - Rank 2 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_3_model_4_fixed.pdb</strong><br> ColabFold output PDB file - Rank 3 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_4_model_3_fixed.pdb</strong><br> ColabFold output PDB file - Rank 4 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_5_model_5_fixed.pdb</strong><br> ColabFold output PDB file - Rank 5 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_coverage.png</strong><br> ColabFold output chart - MSA sequence coverage</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_PAE.png</strong><br> ColabFold output chart - PAE for each model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_plddt.png</strong><br> ColabFold output chart - predicted IDDT per position</p> <p><strong>Supplementary Excel file 1</strong><br> List of residues predicted to be involved in intermolecular interactions, and the type of interaction (based on PDB files for each models, generated using BIOVIA Discovery Studio 2021)</p>
Harnessing interactions between traits and the environment to improve predictions of ecosystem functioning
<p>The data correspond to the manuscript entitled "Harnessing interactions between traits and the environment to improve predictions of ecosystem functioning". Data needed to reproduce the figure 2 about springtails colonisation of defaunated soil blocks, from two different environments (forest or meadow), with the springtail species classified according to the supposed dispersal ability. S<span>pecies with long legs and antennae, a well-developed jumping apparatus (furcula), and a complete visual apparatus were considered to be able to disperse more rapidly on their own and were thus categorized as “fast” dispersers. Other species with reduced locomotor and vision organs were categorized as “slow” dispersers </span><span>(Ponge <em>et al.</em> 2006a)</span><span>. </span></p> <p><span>The "number of individuals" corresponds to the number of individuals found in each defaunated soil block after one week in the initial data set (Auclarc et al., 2009).</span></p> <p><span>Ponge J-F, Dubs F, Gillet S, et al. 2006a. Decreased biodiversity in soil springtail communities: the importance of dispersal and landuse history in heterogeneous landscapes. </span>Soil Biol Biochem 38: 1158–61.</p> <p><span>Auclerc A, Ponge JF, Barot S, and Dubs F. 2009. Experimental assessment of habitat preference and dispersal ability of soil springtails. </span>Soil Biol Biochem 41: 1596–604.</p>
Data table 5 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<div>An Excel table contains a list of proteins identified by MS from pull-down experiment using cerebellar cortex lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 2_cortex isogenic mouse IgG control dynabeads.xlsx" </div> <div> </div>
Data table 4 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<div>An Excel table contains a list of proteins identified by MS from a pull-down experiment using cerebellum lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 3_cerebellum isogenic mouse IgG control dynabeads.xlsx<span><br></span></div>
Data table 2 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table containing a list of proteins identified by MS from samples after a pull-down experiment using cerebral cortex lysates with Dyna beads coated with control isogenic mouse IgG. The proteins from the list were considered false positive interactors of ataxin-3 in the cortex.</p>
Data table 1 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table contains LFQ intensity and other raw MS data for proteins identified in fractions 3,4,5 (i), 11, 12, 13, (ii) 18, 19, and 20 (iii) from ki150 and ki21 model brains. These fractions showed enrichment in ATXN3 protein. The fractions are visualized in Figure 4 (<a href="http://doi.org/10.3389/fnmol.2023.1122308" target="_blank" rel="noopener">doi.org/10.3389/fnmol.2023.1122308</a>)</p>
Functional interactions among neurons within single columns of macaque V1
<p>Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.</p>
The associations of gene-gene interactions among the 6 variants in 3 genes related to inflammation and endothelial function with carotid stenosis were performed by the GMDR approach
<p><span>The associations of gene-gene interactions among the 6 variants in 3 genes related to inflammation and endothelial function with carotid stenosis were performed by the GMDR approach. The best model for carotid stenosis including <em>ITGA2</em> rs4865756 and <em>HABP2</em> rs7923349 scored 8/10 for cross-validation consistency and 10/10 for sign testing. </span></p>
The structure and ecological function of the interactions between plants and arbuscular mycorrhizal fungi through multilayer networks
<ol> <li>Arbuscular mycorrhizas are one of the most frequent mutualisms in terrestrial ecosystems. Although studies on plant mutualistic interaction networks suggest that they may leave their imprint on plant community structure and dynamics, this has not been explicitly assessed. Thus, in the context of plant-fungi interactions, studies explicitly linking plant-mycorrhizal fungi interaction networks with key ecological functions of plant communities, such as recruitment, are lacking. </li> <li>In this study, we analyse, in two Mediterranean forest communities of southern Iberian Peninsula, how plant-AMF networks modulate plant-plant recruitment interaction networks. We use a new approach integrating plant-AMF and plant recruitment networks into a single multilayer structure. We also develop a new metric (Interlayer Node Neighbourhood Integration, INNI) to explore the impact of a given node on the structure across layers.</li> <li>Similarity of plant species in their AMF communities is positively related to the observed frequency of recruitment interactions in the field. Results reveal that properties of plant-AMF networks, such as plant degree and centrality, contribute to explaining properties of the plant recruitment network, such as in- and out-degree (i.e. sapling bank and canopy service) and its modular structure. However, these relationships differed between the two forest communities. Finally, we identify particular AMF that contribute to integrating the neighbourhood of recruitment interactions between plants.</li> <li>This multilayer network approach is useful to explore the role of plant-AMF interactions on recruitment, a key ecosystem function enhanced by fungi. Results provide evidence that the complex structure of plant-AMF interactions impacts functional and structurally plant-plant interactions, which in turn may potentially influence plant community dynamics, through their effects on the structure of the recruitment network.</li> </ol>
Functional anaysis of miR-143-3p/KSR2 interaction and oncogenic function in JURKAT and ALL-SIL T-cell acute lymphoblastic leukemia cell lines
<p>1. FCS files from GFP competition assay performed in ALL-SIL and JURKAT cell lines upon transduction with hsa-mir-143 expression vector (pCDH miR-143-3p) or empty vector (pCDH EV) as control.</p><p>2. Uncropped chemiluminescent immunoblot in JURKAT and ALL-SIL cell lines transduced with hsa-mir-143 expression vector (pCDH miR-143-3p) or empty vector (pCDH EV) as control. Upper band is KSR2 protein (~100 kDa) and lower band is loading control GAPDH protein (~37 kDa). Order of samples on the membrane: JURKAT pCDH miR-143-3p replicate 1, pCDH EV replicate 1, pCDH EV replicate 2, pCDH miR-143-3p replicate 2, pCDH EV replicate 3, pCDH miR-143-3p replicate 3, ALL-SIL pCDH miR-143-3p replicate 1, pCDH miR-143-3p replicate 2, pCDH EV replicate 1, pCDH miR-143-3p replicate 3, pCDH EV replicate 2, pCDH EV replicate 3.</p><p>3. RT-qPCR amplification data for relative quantification of <i>KSR2 </i>expression in reference to <i>ACTB </i>and <i>GAPDH </i>in JURKAT and ALL-SIL cell lines expressing deadCas9-KRAB system for transcriptional repression, upon transduction with sgRNA targeting <i>KSR2 </i>transcription start site vector (<i>KSR2 </i>sgRNA1 and <i>KSR2 </i>sgRNA2) or non-targeting sgRNA vector (NT) as control.</p><p>4. FCS files from GFP competition assay performed in ALL-SIL and JURKAT cell lines expressing deadCas9-KRAB system for transcriptional repression, upon transduction with sgRNA targeting <i>KSR2 </i>transcription start site vector (<i>KSR2 </i>sgRNA1 and <i>KSR2 </i>sgRNA2) or non-targeting sgRNA vector (NT) as control.</p>
Interaction Between Cannabidiol, Meal Ingestion, and Liver Function
ClinicalTrials.gov study NCT04971837. IPD Sharing: NO. Countries: 1. Publications: 1.
interACTION: A Portable Joint Function Monitoring and Training System for Remote Rehabilitation Following TKA
ClinicalTrials.gov study NCT02646761. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Predictive Executive Functioning Models Using Interactive Tangible-Graphical Interface Devices
ClinicalTrials.gov study NCT01711372. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Species interactions and environmental context affect intraspecific behavioural trait variation and ecosystem function
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Data from: A dominant plant species and insects interactively shape plant community structure and an ecosystem function
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The structure and ecological function of the interactions between plants and arbuscular mycorrhizal fungi through multilayer networks
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The roles of isolation and interspecific interaction in generating the functional diversity of an insular mammal radiation
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Data from: A global genetic interaction network maps a wiring diagram of cellular function
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Data from: Gα and regulator of G-protein signaling (RGS) protein pairs maintain functional compatibility and conserved interaction interfaces throughout evolution despite frequent loss of RGS proteins in plants
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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.