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3,264 results for “FAST”
Raw Data: Enhancing Stroke Risk Prediction in Patients with Transient Ischemic Attack: Insights from a Prospective Cohort Study Implementing Fast-Track Care
<p>Background and aims: Fast-track care have been proved to reduce the short-term risk of stroke after transient ischemic attack (TIA). We aimed to investigate stroke risk and to characterize short-and long-term stroke predictors in a large cohort of TIA patients undergoing fast-track management.Methods: Prospective study, enrolling consecutive TIA patients admitted to a Northern Italy emergency department from August 2010 to December 2017. All patients underwent fasttrack care within 24 hours of admission. The primary outcome was defined as the first stroke recurrence at 90 days, 12 and 60 months after TIA. Stroke incidence with 95% confidence interval (CI) at each timepoint was calculated using Poisson regression. Predictors of stroke recurrence were evaluated with Cox regression analysis. The number needed to treat (NNT) of fast-track care in preventing 90-day stroke recurrence in respect to the estimates based on baseline ABCD2 score was also calculated.We enrolled 1035 patients (54.2% males). Stroke incidence was low throughout the follow-up with rates of 2.2% [95% CI 1.4%-3.3%] at 90 days, 2.9% [95% CI 1.9%-4.2%] at 12 months and 7.1% [95% CI 5.4%-9.0%] at 60 months. Multiple TIA, speech disturbances and presence of ischemic lesion at neuroimaging predicted stroke recurrence at each timepoint. Male sex and increasing age predicted 90-day and 60-month stroke risk, respectively. Hypertension was associated with higher 12-month and 60-month stroke risk.No specific TIA etiology predicted higher stroke risk throughout the follow-up. The NNT for fast-track care in preventing 90-day stroke was ] in the overall cohort and 6.8 [95% CI 4.6-13.5] in patients with baseline ABCD2 of 6 to 7.Our findings support the effectiveness of fast-track care in preventing both short-and long-term stroke recurrence after TIA.</p>
Datasets for paper "Global biochemical profiling of fast-growing Antarctic bacteria isolated from meltwater ponds by high-throughput FTIR spectroscopy"
<p><span>Fourier transform infrared (FTIR) spectroscopy is a biophysical techniq</span><span>u<span>e used for non-destructive biochemical profiling of biological samples. It can provide comprehensive information about the total cellular biochemical profile of microbial cells. In this study, FTIR spectroscopy was used to perform biochemical characterization of twenty-nine bacterial strains isolated from the Antarctic meltwater ponds. The bacteria were grown on two forms of brain heart infusion (BHI) medium: agar at six different temperatures </span>(4, 10, 18, 25, 30, and 37 °C) and on broth at 18 °C. Multivariate data analysis approaches such as principal component analysis (PCA) and correlation analysis were used to study the difference in biochemical profiles induced by the cultivation conditions.</span> <span>The observed results indicated a strong correlation between FTIR spectra and the phylogenetic relationships among the studied bacteria. The most accurate taxonomy-aligned clustering was achieved with bacteria cultivated on agar</span><span>. Cultivation on two forms of BHI medium provided biochemically different bacterial biomass. The impact of temperature on the total cellular biochemical profile of the studied bacteria was </span><span>species-specific</span><span>, however, similarly for all bacteria, lipid spectral region was the least affected while polysaccharide region was the most affected by different temperatures. <span>T</span>he biggest temperature-triggered changes of the cell chemistry were detected for bacteria with a wide temperature tolerance such <em><span>Pseudomonas lundensis </span></em><span>strains<em> </em>and<em> Acinetobacter lwoffii </em>BIM B-1558. </span></span></p>
The dispersion measure and rotation measure from fast radio burst host galaxies based on the TNG50 simulation
<p>Supplementary data to the paper "The dispersion measure and rotation measure from fast radio burst host galaxies based on the TNG50 simulation" accepted at A&A.</p> <p>The public TNG50 simulation data of the IllustrisTNG project are obtained through the website: https://www.tng-project.org/data/</p> <p>We placed a total of 16.5 million FRBs in 16 500 TNG50 galaxies at redshifts of 0<z<2, with stellar masses in the range 9<log(M*/M_sun)<12. We calculated the DM and RM host galaxy values, which we publish together with the galaxy IDs in TNG50, positions of the FRBs, galaxy inclinations, and the electron density and gas density at the position of the FRBs.</p>
Fitting parameters for "Concurrent Observation of High-frequency EMIC Waves and Low-harmonic Fast MS Waves within a Magnetic Dip in the Inner Magnetosphere"
<p>1. whamp_input_ionfit_*.mat</p> <p>The fitting parameters for the growth rate calculation of HFEMIC waves, which includes: </p> <div> <div>Bi-Maxwellian distribution parameters:</div> <div>PlasmaModel.B - magnetic field in nT</div> <div>PlasmaModel.Species.m - mass/mass_proton (0 means electron)</div> <div>PlasmaModel.Species.n - density in cm^-3</div> <div>PlasmaModel.Species.t - temperature in eV</div> <div>PlasmaModel.Species.a - anisotropy, Tperp/Tpara</div> <div> </div> </div> <p>2. shell_dist_*.mat</p> <p>The fitting parameters for the growth rate calculation of MS waves, which includes:</p> <div>Bi-Maxwellian distribution parameters:</div> <div>PlasmaModel.B - magnetic field in nT</div> <div>PlasmaModel.Species.m - mass/mass_proton (0 means electron)</div> <div>PlasmaModel.Species.n - density in cm^-3</div> <div>PlasmaModel.Species.t - temperature in eV</div> <div>PlasmaModel.Species.a - anisotropy, Tperp/Tpara</div> <div> </div> <div>Partial shell distribution parameters:</div> <div> <div>PlasmaModel.Shell.m - mass/mass_proton (0 means electron)</div> <div>PlasmaModel.Shell.ns - density in cm^-3</div> <div>PlasmaModel.Shell.sigma - the degree of pitch angle anisotropy</div> <div>PlasmaModel.Shell.Vs - the shell speed in m/s <div>PlasmaModel.Shell.theta_s - the thermal spread of the shell in m/s</div> </div> </div>
Data for "The Nature of Right-handed Polarized Ion-scale Waves In the Near-Sun Solar Wind and Extended Solar Corona: the Antisunward Fast-Magnetosonic Whistler Wave or the Sunward Ion Cyclotron Wave?" by Shi et al.
<h2>The database includes all theoretical analysis results based on the linear model.</h2> <h2>Captions:</h2> <p><strong>data_fig1.mat</strong> file is used to plot Figure 1, which is the data of the instabilities driven by the interplay of the temperature anisotropy and relative streaming speed of the proton beam component.</p> <div>x_axis: X axis data, relative streaming speed</div> <div>y_axis: Y axis data, temperature anisotropy</div> <div>gamma_max: normalized growth rate \gamma, used in figure 1a</div> <div>frequency_max: normalized frequency f in the plasma frame, used in figure 1b</div> <div>theta_max: wave propagating angle \theta in the plasma frame , used in figure 1c</div> <div>ellip_max: ellipticity \epsilon in the plasma frame, used in figure 1d</div> <div>theta_max_sc: wave propagating angle \theta in the spacecraft frame , used in figure 1e</div> <div>ellip_max_sc: ellipticity \epsilon in the spacecraft frame, used in figure 1f</div> <div> </div> <div> </div> <p><strong>data_fig2.mat </strong>file is used to plot Figure 2, which is the data of the dependence of the instability in regime I on the relative steaming speed of the proton beam component.</p> <div> sICW_x_axis1: X axis data of figure 2a, normalized k </div> <div> sICW_y_axis1: Y axis data of figure 2a, relative streaming speed</div> <div> sICW_gamma_all: normalized growth rate \gamma of sunward ICW in k and relative steaming speed space, used in figure 2a</div> <div> sICW_x_axis2: X axis data of figure 2b-2e, normalized k</div> <div> sICW_frequency: normalized frequency f of sunward ICW, used in figure 2b</div> <div> sICW_gamma: normalized growth rate \gamma of sunward ICW, used in figure 2c</div> <div> sICW_etr_b: the energy transfer rate of proton beam, used in figure 2d</div> <div> sICW_etr_c: the energy transfer rate of proton core, used in figure 2e</div> <div> </div> <div> asICW_x_axis1: X axis data of figure 2f, normalized k </div> <div> asICW_y_axis1: Y axis data of figure 2f, relative streaming speed</div> <div> asICW_gamma_all: normalized growth rate \gamma of antisunward ICW in k and relative steaming speed space, used in figure 2f</div> <div> asICW_x_axis2: X axis data of figure 2g-2j, normalized k</div> <div> asICW_frequency: normalized frequency f of antisunward ICW, used in figure 2g</div> <div> asICW_gamma: normalized growth rate \gamma of antisunward ICW, used in figure 2h</div> <div> asICW_etr_b: the energy transfer rate of proton beam, used in figure 2i</div> <div> asICW_etr_c: the energy transfer rate of proton core, used in figure 2j</div> <div> </div> <div> </div> <p><strong>data_fig3.mat</strong> file is used to plot Figure 3, which is the data of the dependence of the instability in regime I on the temperature anisotropy of the proton core component.</p> <div> sICW_x_axis1: X axis data of figure 3a, normalized k </div> <div> sICW_y_axis1: Y axis data of figure 3a, temperature anisotropy of the proton core</div> <div> sICW_gamma_all: normalized growth rate \gamma of sunward ICW in k and temperature anisotropy of the proton core space, used in figure 3a</div> <div> sICW_x_axis2: X axis data of figure 3b-3e, normalized k</div> <div> sICW_frequency: normalized frequency f of sunward ICW, used in figure 3b</div> <div> sICW_gamma: normalized growth rate \gamma of sunward ICW, used in figure 3c</div> <div> sICW_etr_b: the energy transfer rate of proton beam, used in figure 3d</div> <div> sICW_etr_c: the energy transfer rate of proton core, used in figure 3e</div> <div> </div> <div> asICW_x_axis1: X axis data of figure 3f, normalized k </div> <div> asICW_y_axis1: Y axis data of figure 3f, temperature anisotropy of the proton core</div> <div> asICW_gamma_all: normalized growth rate \gamma of antisunward ICW in k and temperature anisotropy of the proton core space, used in figure 3f</div> <div> asICW_x_axis2: X axis data of figure 3g-3j, normalized k</div> <div> asICW_frequency: normalized frequency f of antisunward ICW, used in figure 3g</div> <div> asICW_gamma: normalized growth rate \gamma of antisunward ICW, used in figure 3h</div> <div> asICW_etr_b: the energy transfer rate of proton beam, used in figure 3i</div> <div> asICW_etr_c: the energy transfer rate of proton core, used in figure 3j</div> <div> </div> <div> </div> <p><strong>data_fig4</strong>.mat file is used to plot Figure 4, which is the data of the dependence of the wave frequency on the bulk flow speed.</p> <div> x_axis: X axis of figure 4, bulk flow speed.</div> <div> y_axis1: Y axis of figure 4a, normalized relative streaming speed of regime III and V</div> <div> y_axis2: Y axis of figure 4b, normalized relative streaming speed of regime I</div> <div> fVsw_fmw: the normalized frequency distributions in spacecraft frame of antisunward fast-magnetosonic whistler waves in regimes III and V, used in figure 4a</div> <div> fVsw_sic: the normalized frequency distributions in spacecraft frame of sunward ion cyclotron waves in regimes I, used in figure 4b</div> <div> fVsw_fmw_n1: spacecraft frequency of antisunward fast-magnetosonic whistler waves on the 1.5VA bulk flow speed, used in figure 4c</div> <div> fVsw_fmw_n2: spacecraft frequency of antisunward fast-magnetosonic whistler waves on the 2.5VA bulk flow speed, used in figure 4c</div> <div> fVsw_sic_n1: spacecraft frequency of sunward ion cyclotron waves on the 0.25VA bulk flow speed, used in figure 4c</div> <div> fVsw_sic_n2: spacecraft frequency of sunward ion cyclotron waves on the 0.5VA bulk flow speed, used in figure 4c</div> <div> fVsw_sic_n3: spacecraft frequency of sunward ion cyclotron waves on the 0.75VA bulk flow speed, used in figure 4c</div>
Data for "Resolving Structures of Paramagnetic Systems in Chemistry and Materials Science by Solid-State NMR: the Revolving Power of Ultra-Fast MAS"
<p>Raw NMR data</p>
Data from: Different food sources elicit fast changes to bacterial virulence
Environmentally transmitted, opportunistic bacterial pathogens have a life cycle that alternates between hosts and environmental reservoirs. Resources are often scarce and fluctuating in the outside-host environment, whereas overcoming the host immune system could allow pathogens to establish a new, resource abundant and stable niche within the host. We tested if shortterm exposure to different outside-host resource types and concentrations affect Serratia marcescens—(bacterium)'s virulence in Galleria mellonella (moth). As expected, virulence was mostly dictated by the bacterial dose, but we also found a clear increase in virulence when the bacterium had inhabited a low (versus high) resource concentration, or animal-based (versus plant-based) resources for 48 h prior to injection. The results suggest that temporal changes in pathogen's resource environment can induce very rapid changes in virulence and affect infection severity. Such changes could also play an important role in shifts from environmental lifestyle to pathogenicity or switches in host range and have implications for the management of opportunistic pathogens and disease outbreaks.
Data from: Fast turnover of genome transcription across evolutionary time exposes entire non-coding DNA to de novo gene emergence
Deep sequencing analyses have shown that a large fraction of genomes is transcribed, but the significance of this transcription is much debated. Here, we characterize the phylogenetic turnover of poly-adenylated transcripts in a comprehensive sampling of taxa of the mouse (genus Mus), spanning a phylogenetic distance of 10 Myr. Using deep RNA sequencing we find that at a given sequencing depth transcriptome coverage becomes saturated within a taxon, but keeps extending when compared between taxa, even at this very shallow phylogenetic level. Our data show a high turnover of transcriptional states between taxa and that no major transcript-free islands exist across evolutionary time. This suggests that the entire genome can be transcribed into poly-adenylated RNA when viewed at an evolutionary time scale. We conclude that any part of the non-coding genome can potentially become subject to evolutionary functionalization via de novo gene evolution within relatively short evolutionary time spans.
Experiment data for the IPC 2018 entry "Fast Downward Merge-and-Shrink"
<p>This bundle contains the data generated for the post-IPC analysis reported in the planner abstract.</p> <p>sievers-ipc2018-raw-data.tar.gz contains the raw data. It contains a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s).</p> <p>sievers-ipc2018-parsed-data.tar.gz contains a "properties" file, which contains a JSON directory with combined data of all runs of the raw data. In essence, the properties file is the union over all properties files generated for each individual planner run.</p>
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
<p>A highly granular calorimeter, similar to CALICE is simulated in Geant4. The calorimeters showers are represented as either images or point clouds. Two state-of-the-art score based diffusion models, on image based and the other point cloud based, are trained on the same set of calorimeter simulations and directly compared to each other.<br> <br> These files include the original Geant4 simulation, an intermediate form of the data required for training, and the samples generated by the image and point cloud models.</p>
Supplementary Video 1 of: Ultra-fast deep-learned CNS tumor classification during surgery
<p><strong>Supplementary Video 1:</strong> An intraoperative sequencing experiment (INTRA_4) was captured on film. Audio has been redacted for privacy reasons. The timeline of this specific experiment is shown as a representative example in Fig. 4.</p>
Preprocedural Fasting and Nitrous Oxide -Oxygen Inhalation Analgesia for Dental Treatment of Children
ClinicalTrials.gov study NCT00263289. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Health Education During Ramadan Fasting in Type 1 Diabetes
ClinicalTrials.gov study NCT03501511. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effect of Fasting on Hypoglycemic Counterregulation in Type 1 Diabetes
ClinicalTrials.gov study NCT05973799. IPD Sharing: NO. Countries: 1. Publications: 0.
Serum Fibroblast Growth Factor-21 Levels During Prolonged Fasting
ClinicalTrials.gov study NCT05792982. IPD Sharing: NO. Countries: 1. Publications: 0.
Perioperative Lung Ultrasonography for Fast-track Cardiac Surgery
ClinicalTrials.gov study NCT03527862. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Fasting Bioequivalence Study of 2 Metformin 1000 mg Prolonged Release Tablets in 28 Healthy Male and Female Volunteers
ClinicalTrials.gov study NCT05123781. IPD Sharing: NO. Countries: 1. Publications: 0.
Relative Bioavailability Study of One Amlodipine 10mg Tablet and One Rosuvastatin 20mg Tablet to Two Fixed Dose Combinations of Amlodipine (10mg) and Rosuvastatin (20mg) in Healthy Subjects Under Fast
ClinicalTrials.gov study NCT02075619. IPD Sharing: YES. Countries: 1. Publications: 0.
Bioavailability Study of 300 mg Trazodone Hydrochloride (New Polymer) vs. 300 mg Trazodone Hydrochloride (Contramid® Prolonged-release Tablets) Under Fasting Conditions
ClinicalTrials.gov study NCT05136521. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Carbohydrate and Insulin Requirements During Prolonged Fasted Exercise
ClinicalTrials.gov study NCT04383015. IPD Sharing: NO. Countries: 1. Publications: 0.
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.