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615 results for “tuning”
Data from: Endothelial and systemic upregulation of miR-34a-5p fine-tunes senescence in progeria
<p>Endothelial defects significantly contribute to cardiovascular pathology in the premature aging disease Hutchinson-Gilford progeria syndrome (HGPS). Using an endothelium-specific progeria mouse model, we identify a novel, endothelium-specific microRNA (miR) signature linked to the p53-senescence pathway and a senescence-associated secretory phenotype (SASP). Progerin-expressing endothelial cells exert profound cell-non-autonomous effects initiating senescence in non-endothelial cell populations and causing immune cell infiltrates around blood vessels. Comparative miR expression analyses revealed unique upregulation of senescence-associated miR34a-5p in endothelial cells with strong accumulation at atheroprone aortic arch regions but also, in whole cardiac- and lung tissues as well as in the circulation of progeria mice. Mechanistically, miR34a-5p knockdown reduced not only p53 levels but also late-stage senescence regulator p16 with no effect on p21 levels, while p53 knockdown reduced miR34a-5p and partially rescued p21-mediated cell cycle inhibition with a moderate effect on SASP. These data demonstrate that miR34a-5p reinforces two separate senescence regulating branches in progerin-expressing endothelial cells, the p53- and p16-associated pathways, which synergistically maintain a senescence phenotype that contributes to cardiovascular pathology. Thus, the key function of circulatory miR34a-5p in endothelial dysfunction-linked cardiovascular pathology offers novel routes for diagnosis, prognosis and treatment for cardiovascular aging in HGPS and potentially geriatric patients.</p>
Backscatter tuned laser absorption spectroscopy in additive manufacturing
<p>Raw data accompanying our paper </p> <p><strong>Backscatter absorption spectroscopy for process monitoring in powder bed fusion</strong></p> <p> </p>
Raw Experimental Data for work presented in 'Optimally diverse communication channels in disordered environments with tuned randomness'
<p>This is the raw experimental data for the work presented in 'Optimally diverse communication channels in disordered environments with tuned randomness', published in Nature Electronics.</p> <p> </p> <p><a href="https://doi.org/10.1038/s41928-018-0190-1">https://doi.org/10.1038/s41928-018-0190-1</a> </p> <p> </p> <p>See the README file for an explanation of the data.</p>
CodonTransformer - Genomic and CodonTransformer-Generated Sequences for Fine-tuned Organisms
<p>This dataset is used in creating Fig. 2a and Supplementary Figs. 2-16 of the paper, mainly including the predictions of base (pretrained) and finetuend CodonTransformer model along with various metrics. </p>
Neural pathways and computations that achieve stable contrast processing tuned to natural scenes
<h1>Gur et al. 2024 database</h1> <p>Source data of the paper Gür et al. 2024, “Neural pathways and computations that achieve stable contrast processing tuned to natural scenes”, Nature Communications. This work contains an analysis of post-receptor luminance gain in the Drosophila visual system, focusing on the circuitry and algorithms for implementation of rapid luminance gain control.</p> <p>All data can be analyzed using the code provided in the Github repository: <a href="https://github.com/silieslab/Gur-etal-2024">https://github.com/silieslab/Gur-etal-2024</a>. Please go to the “Readme” file in the repository for how to use the code.</p> <h2>Raw data</h2> <p>All raw data is located in the folder “raw_data”. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <h2>Processed data</h2> <p>All processed data is located in the folder “processed_data”. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <p>- 2p_imaging_python_pickle: Processed data stored as .pickle files. <br>- Dm12_Figure7_Mat_files: Processed data for Dm12 imaging and optogenetics experiments presented in Figure 7 stored as .mat files.<br>- EM_data: Processed data for EM analysis done in Figure 7.<br>- Figure 6 Tm9 flpSTOP: tdTomato expression data for Figure 6 Tm9 flpSTOP experiments.<br>- Figure S5 Tm1 flpSTOP: tdTomato expression data for FigureS5 Tm1 flpSTOP experiments.</p>
Tuned Bis-Layered Supported Ionic Liquid Catalyst (SILCA) for Competitive Activity in the Heck Reaction of Iodobenzene and Butyl Acrylate
<p>This dataset contains the measurement data for figures (graphs) published in journal article:</p> <p>Tuned Bis-Layered Supported Ionic Liquid Catalyst (SILCA) for Competitive Activity in the Heck Reaction of Iodobenzene and Butyl Acrylate</p> <p>by Nemanja Vucetic, Pasi Virtanen, Ayat Nuri, Andrey Shchukarev, Jyri-Pekka Mikkola, and Tapio Salmi</p> <p>Published in: Catalysts 2020, 10(9), 963 </p> <p>https://doi.org/10.3390/catal10090963 </p>
Dataset for ''Tuning the proximity induced spin - orbit coupling in bilayer graphene/WSe2 heterostructures with pressure''
<p>This is the measurement dataset for the article ''Tuning the proximity induced spin - orbit coupling in bilayer graphene/WSe2 heterostructures with pressure''. The .py file is also included which is used for the modelling.</p>
A Proportional Control Strategy for Stiffness Tuning of Parallel Manipulators
<p>MBDyn models for the paper "A Proportional Control Strategy for Stiffness Tuning of Parallel Manipulators"</p>
Fine Tune, Sample of Training Set
Open the record for dataset details and reuse information.
High-resolution mapping of the period landscape reveals polymorphism in cell cycle frequency tuning
<p>Biological oscillators adapt to environmental changes with widely tunable frequencies, a property theoretical studies attributed to positive feedbacks. However, no experiments have tested this theory. Here, we created synthetic cells to independently tune the frequency and feedback strength of a cell-cycle oscillator, enabling continuous mapping of period landscape in response to network perturbations. We found that although inhibiting positive feedback of cyclin-dependent kinase (Cdk1) reduces the tunability, the reduction is not as significant as theoretically predicted, and the Cdk1-counteracting phosphatase, PP2A, provides additional machinery to ensure frequency regulation. Additionally, cells exhibit polymorphic responses to PP2A inhibition, showing a monomodal distribution of oscillatory cells at low or high PP2A inhibition or a bimodal distribution at both low and high inhibitions. We explained the polymorphism by a model of two interlinked bistable switches of Cdk1 and PP2A where cell-cycle oscillations exhibit two modes in the presence or absence of PP2A bistability.</p>
Data from: Fine-tuning biodiversity assessments: A framework to pair eDNA metabarcoding and morphological approaches
<p><span>Accurate quantification of biodiversity can be demanding and expensive. Although environmental DNA (eDNA) metabarcoding can facilitate biodiversity assessments through non-invasive, cost-efficient, and rapid surveys, the approach struggles to outperform traditional morphological approaches in providing reliable quantitative estimates for surveyed species (e.g., abundance and biomass).</span></p> <p><span>We present an integrated methodology for improving biodiversity surveys that pairs eDNA metabarcoding with morphological data, following a series of taxonomic and geographic filters. We demonstrate its power by applying it to a new spatiotemporal dataset generated on an Iberian-wide distributed aquatic mesocosm infrastructure that spans a wide biogeographic gradient.</span></p> <p><span>By building upon the strengths that these two approaches offer, our framework improved taxonomic resolution for 30% of the taxa and enabled species' traits (e.g., body-size) and abundance to be assigned to 85% of the taxa in hybrid datasets.</span></p> <p><span>These results indicate that eDNA-based assessments can complement, but not always replace, conventional approaches. Integrating conventional and modern eDNA metabarcoding approaches, already available in the ecologist's toolbox, will greatly enhance biodiversity assessments.</span></p>
Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level - MD simulations Dataset
<p>Models of SARS-CoV-2 virus spike protein bound to 1-3 of ACE2 receptors embedded in lipid nanodisks. Systems include all files in GROMACS format needed to reproduce simulations performed in the "Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level" article.</p> <p> </p> <table> <caption>Details</caption> <thead> <tr> <th scope="col">system</th> <th scope="col">box size (x-y-z) [nm]</th> <th scope="col">Number of atoms</th> </tr> </thead> <tbody> <tr> <td>Spike +<br> 1x ACE2, full length</td> <td>33.44834 28.96711 57.95573</td> <td>5,665,217</td> </tr> <tr> <td>Spike +<br> 1x ACE2, truncated</td> <td>28.05757 24.29856 46.74417</td> <td>3,203,907</td> </tr> <tr> <td>Spike +<br> 2x ACE2, truncated</td> <td>28.30864 21.23141 48.40873</td> <td>2,936,398</td> </tr> <tr> <td>Spike +<br> 3x ACE2, truncated</td> <td>28.32733 21.24544 48.30436</td> <td>2,936,588</td> </tr> </tbody> </table>
The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
<p>Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.</p>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 2
<p>This dataset includes model outputs averaged from day 2 to day 15 using the multiscale modeling framework (MMF, also referred to as ``superparameterization'') for</p> <ul> <li>Low-resolution MMF (LR): SP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_32_x_120z1200m.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 32, crm_ny = 1, crm_dx = 1200 m, crm_dt = 5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>High-resolution MMF (HR): UP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 64, crm_ny = 1, crm_dx = 200 m, crm_dt = 0.5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>Same as HR, but considers hyperviscosity with tau = 30s (HRh30): UPhyperlag30_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HR, but considers hyperviscosity with tau = 150s (HRh15): UPhyperlag15_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs15) with tau = 30s and sigmag = 1.5: HPhyper_sedi15_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs12) with tau = 30s and sigmag = 1.2: HPhyper_sedi12_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> </ul>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 1
<p>This dataset includes 6-month simulations using the ne30pg2 grid. Monthly averaged output files from six experiments are included.</p> <ul> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1 <ul> <li>The config options for this control simulation is <pre>CAM_CONFIG_OPTS = -mach summit -phys default -use_MMF -crm samxx -nlev 60 -crm_nz 50 -crm_dt 10 -crm_dx 2000 -crm_nx 64 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -rad rrtmgp -rrtmgpxx -MMF_microphysics_scheme sam1mom -chem none -nlev 125 -crm_nz 115 -crm_dt 2 -crm_dx 200 -crm_nx 256 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -use_MMF_VT -cppdefs ' -DMMF_ESMT -DMMF_USE_ESMT -DMMF_HYPERVISCOSITY -DMMF_SEDIMENTATION ' </pre> </li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED <ul> <li>The HV.SED control case is the same as the control (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1), but considered both hyperciscosity and sedimentation processes.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_1E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_5E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_5E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_5E-05.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_8E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_8E-05.</li> </ul> </li> </ul>
Supplementary Tables and Datasets for publication: Spatial and finely tuned temporal metagenomics of river compartments reveals viral community dynamics in an urban stream
<p>This is a data dump of the tables, genomes, and .faa files that were too large to submit as part of the publication titled: Spatial and finely tuned temporal metagenomics of river compartments reveals viral community dynamics in an urban stream</p> <p> </p> <p>Files here include:</p> <p>-Fasta file containing 1230 vMAGs.</p> <p>-Zip file containing individual fasta files for 125 MAGs</p> <p>-Annotations output for DRAM and DRAM-v for all MAGs and vMAGs</p> <p>-.faa proteins file for the full Freshwater / Wastewater / TARA Oceans dataset that was used for vContact2 biogeography analyses</p>
Supporting Data for "Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins"
<p>Supporting Data for "Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins"</p>
Dataset for Pressure-tuning of minibands in MoS2/WSe2 heterostructures revealed by moire phonons
<p>This is the repository for the spectroscopic data used for the paper Pressure-tuning of minibands in MoS<sub>2</sub>/WSe<sub>2</sub> heterostructures revealed by moiré phonons.</p>
Tuning magnetoelectricity in a mixed-anisotropy antiferromagnet
<p>Dataset including magnetic susceptibility, neutron diffraction, pyrocurrent and Monte Carlo simulations on the mixed-anisotropy antiferromagnet, LiNi<sub>1-x</sub>Fe<sub>x</sub>PO<sub>4</sub>.</p>
Client Centred 'Tune-ups': do They Enhance Community Reintegration After Stroke?
ClinicalTrials.gov study NCT00400712. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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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.