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979 results for “HR”
A Trial to Evaluate Efficacy and Safety of Ribociclib With Endocrine Therapy as Adjuvant Treatment in Patients With HR+/HER2- Early Breast Cancer
ClinicalTrials.gov study NCT03701334. IPD Sharing: YES. Countries: 20. Publications: 3.
EPIC-HR: Study of Oral PF-07321332/Ritonavir Compared With Placebo in Nonhospitalized High Risk Adults With COVID-19
ClinicalTrials.gov study NCT04960202. IPD Sharing: YES. Countries: 20. Publications: 4.
Rainfall associated with tropical and post-tropical cyclones in the high resolution CNRM-CM6-1-HR model
Open the record for dataset details and reuse information.
HR-pQCT demo images
<p>Demo images used in Jupyter notebooks of the JC MSK community for educational purpose</p>
Crack nucleation using combined crystal plasticity modelling, HR-DIC and HR-EBSD in a superalloy containing non-metallic inclusions under fatigue
<p>The uploaded data are required to reproduce the experimental results in the paper. </p> <p>To replicate figure 4, both GID.mat and thermal_E11.mat should be loaded into matlab. </p> <p>To replicate figure 6, strain_11.mat should be uploaded. Then fDIC_GB.m should be executed. </p> <p>If the reader has further questions, please contact Tiantian Zhang at tiantian.zhang08@imperial.ac.uk or tzhang6@wpi.edu</p>
MAR-MPI-ESM1-2-HR HIST (1961-2014) and SSP245 European Alps (2015-2100)
<p>This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the MPI-ESM1-2-HR GCM (CMIP6 version) for the historical period (HIST, 1961 to 2014) and the SSP245 projection (SSP245, 2015 to 2100). The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr)<br> The realization used is r1i1p1f1.<br> Data are available at the daily frequency, with one variable per file (10 years of data per file).<br> The available variables in this deposit are :</p> <p><strong>CC:</strong> Cloud cover, [0-1]<br> <strong>MB:</strong> Total snow water equivalent, [mm.We]<br> <strong>MBrr:</strong> Daily rainfall, [mm.We] (5)<br> <strong>MBsf:</strong> Daily snowfall, [mm.We] (5)<br> <strong>QQz: </strong> Near-surface specific humidity at constant height, [g/kg] (2)</p> <p><strong>TTmax:</strong> Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C]<br> <strong>TTmin:</strong> Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]</p> <p><strong>TTz: </strong>Near-surface mean air temperature at constant-height, [C]<br> <strong>UUz:</strong> Near-surface zonal component of wind speed at constant height, [m/s]<br> <strong>VVz:</strong> Near surface Meridional component of wind speed at constant height, [m/s]<br> <strong>ZN3:</strong> Total snow height, [m]</p> <p>Other variables are available upon request (see email above).<br> AL : Surface albedo, [0-1]<br> CD: Low level Cloud cover, [0-1]<br> CM: Middle level Cloud cover, [0-1]<br> CU: High level Cloud cover, [0-1]<br> SP: Surface pressure, [hPa]<br> ST: Surface temperature, [C]<br> TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1)<br> TTp: Constant pressure-level mean air temperature, [C] (4)<br> ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m]</p> <p>LWD: Surface downward longwave radiation, [W/m2]<br> LWU: Surface upward longwave radiation, [W/m2]</p> <p>SWD: Surface downward shortwave radiation, [W/m2]<br> SWU: Surface upward shortwave radiation, [W/m2]</p> <p>SHF: Surface sensible heat flux, [W/m2] LHF: Surface latent heat flux, [W/m2]</p> <p>* Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUy.nc file</p> <p>(1) For variable TT, ZZ model constant sigma level of 0.9997479, 0.999496, 0.9989921<br> (2) For variables TTz, QQz constant height level are : 2, 10, 50 and 100m<br> (3) For variables UUz, VVz constant height level are : 10, 50 and 100m<br> (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa<br> (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results.<br> Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City<br> ~ </p>
files associated with HR_1
<p>Files associated with tagged-profilin manuscript.</p>
HR Measures From Four Consumer-Grade Optical Heart Rate Sensors with Gold Standard ECG Reference
<p>Accompanying paper that explains data collection, research methods, cleaning etc. is under review as:</p> <p>"Validity of Four Consumer-Grade Optical Heart Rate Sensors for Assessing Volume and Intensity Distribution of Physical Activity"</p> <p>Currently with: Scandinavian Journal of Medicine & Science in Sports TBD, 2024-12-31</p> <p>Four files contain HR data at 1 Hz for each of the devices (Garmin Venu2S and Polar Vantage M2, and the upper arm-worn Polar Verity Sense and Scosche Rhythm24) [device column] paired with medical-grade ECG [ecg column], Type: EP830, Amedtec ECGpro, Aue, Germany</p>
MOBSTER - At the crossroads of stellar astrophysics in the upper HR Diagram
<p>Contrarily to low-mass stars, in which dynamo-generated surface magnetism is essentially ubiquitous, only about 10% of stars with radiative envelopes (O, B and A) exhibit detectable magnetic fields at their surfaces. Yet, this small magnetic subpopulation includes potential progenitors of exotic astronomical objects, such as high-mass stellar black holes (even at solar metallicity) and magnetars. Therefore, there is a strong motivation to learn more about the as-yet misunderstood formation of these stars, and what is currently lacking is a robust statistical basis to interpret their properties. In this talk, I will briefly review massive star magnetism and some of the critical gaps in knowledge that currently plague their study. I will then discuss the observational limitations that prevent us from learning more, and argue that all-sky broadband photometric missions such as the Transiting Exoplanet Survey Satellite (TESS) offer a valuable indirect method of addressing these limitations. Finally, I will present the MOBSTER (Magnetic OB[A] Stars with TESS: probing their Evolutionary and Rotational properties) collaboration, and highlight some of its early results, as well as offer a glimpse of the road that lies ahead.</p>
Vollständige Transkripte der Expert*inneninterviews zur Masterarbeit "Digitale Badges als Kompetenznachweis – Erwartungen aus Sicht von HR-Verantwortlichen in Österreich"
<p>Vollständige Transkripte der Expert*inneninterviews zur Masterarbeit „Digitale Badges als Kompetenznachweis – Erwartungen aus Sicht von HR-Verantwortlichen in Österreich“</p>
Supplementary material 2 from: Hubancheva A, Bozicevic V, Morinière J, Goerlitz HR (2023) DNA metabarcoding data from faecal samples of the lesser (Myotis blythii) and the greater (Myotis myotis) mouse-eared bats from Bulgaria. Metabarcoding and Metagenomics 7: e106844. https://doi.org/10.3897/mbmg.7.106844
Taxonomic relationships and relative abundance of prey and parasite species in faecal samples from M. myotis and M. blythii from Bulgaria
Fig. 5 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 5. Analysis of 2D Scores Plot by Partial Least Squares Discriminant Analysis (PLS-DA) of characterized flavonoids in soybean leaves from the C9 and WT genotypes, infected (I) or noninfected (NI) by P. s. pv. tomato 36 h after inoculation. Points represent replicates analyzed, whereas ellipses indicate 95% confidence region.
Fig. 9 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 9. Expression analysis of target genes performed by qRT-PCR involved in plant bacterial interactions from BiP-overexpressing (C9) and wild-type (WT) soybean plants infected (I) or noninfected (NI) with P. syringae pv. tomato. The expression levels were obtained using the 2-ΔCT method. Bars (mean SE; ± n = 4) with the same capital letters indicate no significant difference between control and inoculated treatments and those followed by the same lowercase letters indicate no significant difference among genotypes within the same treatment (Student's test: P <0.05).
Fig. 8 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 8. Relative abundance of the flavonoid derivatives from BiP-overexpressing (C9) and wild-type (WT) soybean plants infected (I) or noninfected (NI) with P. syringae pv. tomato. Bars (mean ± SE; n = 4) with the same capital letters indicate no significant difference between control and inoculated treatments and those followed by the same lowercase letters indicate no significant difference among genotypes within the same treatment (Student's test: P <0.05).
Fig. 4 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 4. Absolute concentrations of phytohormones (A) and of flavonoid aglycones (B) by UHPLC/MS QqQ. The data represent the mean ± standard error. Bars (mean ± SE; n = 4) with the same capital letters indicate no significant difference between control and inoculated treatments and those followed by the same lowercase letters indicate no significant difference among genotypes within the same treatment (Student's test: P <0.05).
Fig. 3 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 3. Clustering analysis by Heat Map method of the characterized metabolites by GC/MS in soybean leaves from the WT and C9 genotypes, infected (I) or noninfected (NI) by P. syringae pv. tomato 36 h after inoculation. Differences in the abundance of the metabolites are indicate in response to treatments.
Fig. 6 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 6. Clustering analysis by Heat Map method of the characterized flavonoids by LC QqQ in soybean leaves from the WT and C9 genotypes, infected (I) or noninfected (NI) by P. syringae pv. tomato 36 h after inoculation. This shows the differences in the abundance of the flavonoids analyzed by LC-MS in response to bacterial infection. Differences in the abundances of the detected flavonoids are indicate in response to treatments. Green color represents a decrease, and red color an increase.
Fig. 7 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 7. Schematic overview of flavonoid biosynthesis pathway reconstructed using the characterized compounds from soybean leaves. Each colored square box is indicative of the abundance levels of the metabolites involved in the flavonoid biosynthesis and identified for each WT and C9 genotypes, infected (I) or noninfected (NI) by P. syringae pv. tomato. The main flavonoids of pathway are sketched by continuous line while compounds not detected, but intermediate of the pathway, are sketched by dashed line. Compounds marked by blue asterisk were more abundant in the inoculated C9 genotype while compounds marked by red asterisk were more abundant in the inoculated WT genotype.
Fig. 2 in BiP-overexpressing soybean plants display accelerated hypersensitivity response (HR) affecting the SA-dependent sphingolipid and flavonoid pathways
Fig. 2. Analysis of the metabolic profiles of the C9 and WT genotypes in soybean leaves from the WT and C9 genotypes infected (I) or noninfected (NI) by P. syringae pv tomato 36 h after inoculation. In (A) 2D Scores Plot generated by Partial Least Squares Discriminant Analysis (PLS-DA) of all the metabolites. Points represent analyzed replicates, whereas ellipses indicate 95% confidence region. In (B) Major metabolites responsible for discrimination between inoculated and mock inoculated soybean groups identified by VIP score. Green color represents a decrease, and red color an increase.
Efficacy of an Intravaginal Treatment With Carboxymethyl-β-glucan and Polycarbophil in HR-HPV Clearance
ClinicalTrials.gov study NCT04781023. IPD Sharing: NO. Countries: 1. Publications: 7.
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.