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302 results for “pacemaker”
Source data for "Feed-forward metabotropic signaling by Cav1 Ca2+ channels supports pacemaking in pedunculopontine cholinergic neurons"
<p><strong>Fig.1A_ChAT.tif</strong></p><p>Confocal image (green channel, anti-ChAT staining) for Fig.1A</p><p> </p><p><strong>Fig.1A_tdTomato.tif </strong></p><p>Confocal image (red channel, tdTomato) for Fig.1A</p><p> </p><p><strong>Fig.1B_ChAT.tif</strong></p><p>Confocal image (green channel, anti-ChAT staining) for Fig.1B</p><p> </p><p><strong>Fig.1B_tdTomato.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.1B</p><p> </p><p><strong>Fig.1C_DIC.png</strong></p><p>Differential interference contrast micrograph for Fig.1C left</p><p> </p><p><strong>Fig.1C_Fluo.png</strong></p><p>Epifluorescent illumination micrograph for Fig. 1C right</p><p> </p><p><strong>Fig.1DEH.xlsx</strong></p><p>Numerical data for the charts in Fig. 1D, Fig.1E, Fig.1H</p><p> </p><p><strong>Fig.1F.tif</strong></p><p>MAX projection of z-stack of 2PLSM images (red channel, Alexa 594) used to generate Fig.1F </p><p> </p><p><strong>Fig.1F_inset.tif</strong></p><p>2PLSM image (green channel, Fura-2) for the right inset of Fig.1F</p><p> </p><p><strong>Fig.2A_inset.tif</strong></p><p>Confocal image (green channel, GFP) for the higher magnification inset of Fig.2A</p><p> </p><p><strong>Fig.2A.tif</strong></p><p>Confocal image (green channel, GFP) for Fig.2A</p><p> </p><p><strong>Fig.2B_bottom.tif</strong></p><p>Confocal image (green channel, GFP) for Fig.2B (bottom and overlay panels)</p><p> </p><p><strong>Fig.2B_top.tif</strong></p><p>Confocal image (red channel, td Tomato) for Fig.2B (top and overlay panels)</p><p> </p><p><strong>Fig.2CE.xlsx</strong></p><p>Numerical data for the charts in Fig. 2C, Fig. 2E</p><p> </p><p><strong>Fig.3B.tif</strong></p><p>Confocal image (green channel, MitoGCaMP6) for Fig.3B and overlay in Fig.3D</p><p> </p><p><strong>Fig.3C.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.3C and overlay in Fig.3D</p><p> </p><p><strong>Fig.3E.tif</strong></p><p>2PLSM image (green channel, MitoGCaMP6) for Fig.3E</p><p> </p><p><strong>Fig.3GIJ.xlsx</strong></p><p>Numerical data for the charts in Fig. 3G, Fig. 3I, Fig.3J</p><p> </p><p><strong>Fig.4B.tif</strong></p><p>2PLSM image (green channel, MitoGCaMP6) for Fig.4B</p><p> </p><p><strong>Fig.4DFG.xlsx</strong></p><p>Numerical data for the charts in Fig.4D, Fig.4F, Fig.4G</p><p> </p><p><strong>Fig.5A.tif</strong></p><p>Confocal image (green channel, PercevalHR) for Fig.5A and overlay in Fig.5C</p><p> </p><p><strong>Fig.5B.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.5B and overlay in Fig.5C</p><p> </p><p><strong>Fig.5D.tif</strong></p><p>2PLSM image (green channel, PercevalHR) for Fig.5D</p><p> </p><p><strong>Fig.5GHJ.xlsx</strong></p><p>Numerical data for the charts in Fig.5G, Fig.5H, Fig.5J</p><p> </p><p><strong>Fig.6BCD.xlsx</strong></p><p>Numerical data for the charts in Fig.6b, Fig.6C, Fig.6D</p><p> </p><p><strong>Fig.7A.tif</strong></p><p>Confocal image (green channel, mito-roGFP) for Fig.7A and overlay in Fig.7C</p><p> </p><p><strong>Fig.7B.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.7B and overlay in Fig.7C</p><p> </p><p><strong>Fig.7D.tif</strong></p><p>2PLSM image (green channel, mito-roGFP) for Fig.7D</p><p> </p><p><strong>Fig.7F.xlsx</strong></p><p>Numerical data for the charts in Fig.7F</p>
Recovery of the full in vivo firing range in post-lesion surviving DA SN neurons associated with Kv4.3-mediated pacemaker plasticity
Open the record for dataset details and reuse information.
A rare entity - percutaneous lead extraction in a very late onset pacemaker endocarditis - case report and review of literature
<p><strong>Video 1.</strong> Transthoracic two-dimensional echocardiography apical 4-chamber view: large hypoechogenic hyper-pedunculated mobile mass at the level of tricuspid valve; <strong>Video 2.</strong> Transesophageal two-dimensional echocardiography: low echogenicity pedunculated mass attached to the pacemaker lead, with multiple sites of binding, with no supplementary involvement of the tricuspid valve and myxomatous appearance of the posterior leaflet with hypermobility and rupture of chordae; <strong>Video 3. </strong>Fluoroscopy during the lead extraction procedure, after freeing the lead from adhesions: traction of the lead from the right ventricular apex, through the tricuspid valve, right atrium, superior vena cava and left subclavian vein, to complete lead removal; <strong>Video 4.</strong> Postprocedural transesophageal two-dimensional echocardiography: posterior leaflet chordae rupture, no residual vegetation, no pericardial effusion; <strong>Video 5.</strong> Transthoracic two-dimensional echocardiography parasternal short axis, no additional cardiac masses, posterior leaflet chordae rupture of the tricuspid valve; <strong>Video 6.</strong> Transthoracic two-dimensional echocardiography parasternal short axis, color Doppler: mild tricuspid regurgitation, with two small thin jets.</p>
Lanthanum modulated reaction pacemakers on a single catalytic nanoparticle - Database
<p><strong>Supplementary Data to the associated "Nature Communications" article (doi: 10.1038/s41467-023-43026-3) containing the FEM measurements and timeseries simulated by the microkinetic modelling.</strong></p><p>FEM measurements of the oscillating hydrogen oxidation reaction on Rh at T = 453 K at constant pressures of pH2 = 5.0 x 10-6 and pO2 = 4.4 x 10-6 mbar on a clean Rh tip (Data 1) and Lanthanum modulated surface (Data 2).</p>
Model output derived from SWE pacemaker experiments using MIROC6-AGCM
<p><strong>This data set is model output derived from numerical simulations using MIROC6-AGCM (Onuma et al., 2024, Journal of Climate).</strong></p> <p><strong>The contents are as below.</strong></p> <p>- <strong>python</strong>: Python scripts for the visualization<br><br>- <strong>figure</strong>: PNG files created by the Python scripts<br><br>- <strong>data</strong>: model output data for the Python scripts. The data directory includes the below contents.<br><br>-- <strong>LMIP</strong>: Output data simulated using MATSIRO6 with GSWP3 reanalysis data (LMIP experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from LMIP experiment (netCDF and csv files, respectively).</p> <p>-- <strong>AMIP</strong>: Output data simulated using MIROC6 with prescribed sea surface temperature and sea ice concentration (AMIP experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from AMIP ensemble experiments (netCDF and csv files, respectively).</p> <p>-- <strong>AMIP+SWE</strong>: Output data simulated using MIROC6 with prescribed sea surface temperature, sea ice concentration and snow water equivalent (AMIP+SWE experiment) are included. Monthly time-series data and regional averages for 1901-2010 derived from AMIP+SWE ensemble experiments (netCDF and csv files, respectively). Difference in a surface energy budget between AMIP+SWE and AMIP is also included (feedback_*.csv).</p> <p>-- <strong>AS-A</strong>: Statistical results analyzed from output data in AMIP+SWE and AMIP experiments (csv files). R_a (R_b) means the correlation coefficients of the outputs in AMIP+SWE (AMIP) for inter-annual change from 1901 to 2010 with those in LMIP. P means p-value. RMSD_a (RMSD_b) means the root mean square difference of the outputs in AMIP+SWE (AMIP) for inter-annual change from 1901 to 2010 with those in LMIP. Tr_a (Tr_b, Tr_ref) means the inter-annual trends from 1901 to 2010 in AMIP+SWE (AMIP, LMIP).<br><br>-- <strong>else</strong>: CMIP6 amip multi-models output, GlobSnow3 observation data and mask files for the regional averages. CMIP6 and GlobSnow3 data have been processed for analysis.</p> <p>*Regarding AMIP and AMIP+SWE data, the uploaded data are limited to ensemble mean data and run01 data due to data size that can be uploaded. If you need model outputs from run02 to run10, please contact Yukihiko Onuma (yonuma613@gmail.com). </p> <p><strong>The abbreviations of the output files are below.</strong><br>alb: land surface albedo<br>clt: total cloud cover fraction<br>ebflx: soil evaporation<br>gdt: surface air temperature (GSWP3)<br>hfls: latent heat flux<br>hfss: sensible heat flux<br>mrsos: surface soil moisture<br>pr: total precipitation<br>rlds: downward longwave radiation at the surface<br>rldscs: clear-sky downward longwave radiation at the surface<br>rls: net longwave radiation at the surface<br>rlus: upward longwave radiation at the surface<br>rsds: downward shortwave radiation at the surface<br>rsdscs: clear-sky downward shortwave radiation at the surface<br>rss: net shortwave radiation at the surface<br>rsus: upward shortwave radiation at the surface<br>snc: snow cover fraction<br>snow: snow water equivalent<br>T2: surface air temperature<br>tsl_z0: surface soil temperature (top 0.05 m)<br>tsl_z1: sub-surface soil temperature (top 0.05-0.25 m)</p>
Reorganisation of circadian activity and the pacemaker circuit under novel light regimes
<p>Many environmental features are cyclic, with predictable changes across the day, seasons and latitudes. Additionally, anthropogenic, artificial light-induced changes in photoperiod or shiftwork-driven novel light/dark cycles also occur. Endogenous timekeepers or circadian clocks help organisms cope with such changes. The remarkable plasticity of clocks is evident in the waveforms of behavioural and molecular rhythms they govern. Despite detailed mechanistic insights into the functioning of the circadian clock, practical means to manipulate activity waveform are lacking. Previous studies using a nocturnal rodent model showed that novel light regimes caused locomotor activity to bifurcate such that mice showed two bouts of activity restricted to the dimly lit phases. Here, we explore the generalizability of these findings and leverage the genetic toolkit of Drosophila melanogaster to obtain mechanistic insights into this unique phenomenon. We find that dim scotopic illumination of specific durations induces circadian photoreceptor CRYPTOCHROME-dependent activity bifurcation in male flies. We show circadian re-organisation of the pacemaker circuit wherein the 'evening' neurons regulate the timing of both bouts of activity under novel light regimes. Our findings indicate that environmental regimes can be exploited to design light regimes to ease the circadian waveform into synchronising with challenging conditions. </p>
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 1 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p> <p> </p> <p> </p>
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 2 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p>
Efficacy Study of Pacemakers to Treat Slow Heart Rate in Patients With Heart Failure
ClinicalTrials.gov study NCT02145351. IPD Sharing: NO. Countries: 1. Publications: 1.
Physical Activity and Pacemaker Study
ClinicalTrials.gov study NCT03052829. IPD Sharing: YES. Countries: 1. Publications: 1.
The Influence of Heart Rate Limitation on Exercise Tolerance in Pacemaker Patients.
ClinicalTrials.gov study NCT02247245. IPD Sharing: Not stated. Countries: 1. Publications: 1.
AV Node Ablation and Pacemaker Therapy Compared to Drug Therapy for Atrial Fibrillation - Pilot Study
ClinicalTrials.gov study NCT00589303. IPD Sharing: Not stated. Countries: 2. Publications: 6.
The LEADLESS II IDE Study for the Nanostim Leadless Pacemaker
ClinicalTrials.gov study NCT02030418. IPD Sharing: Not stated. Countries: 3. Publications: 2.
Patient Preferences for Leadless Pacemakers
ClinicalTrials.gov study NCT05327101. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Effectiveness of Pacemaker With Closed Loop Stimulation Compared to Pacemakers With and Without Standard Rate Response
ClinicalTrials.gov study NCT00355797. IPD Sharing: Not stated. Countries: 1. Publications: 1.
AV Delay Optimization vs. Intrinsic Conduction in Pacemaker Patients With Long PR Intervals
ClinicalTrials.gov study NCT02154750. IPD Sharing: NO. Countries: 1. Publications: 4.
OMNI Study--Assessing Therapies in Medtronic Pacemaker, Defibrillator, and Cardiac Resynchronization Therapy Devices.
ClinicalTrials.gov study NCT00277524. IPD Sharing: Not stated. Countries: 1. Publications: 4.
ProMRI Study of the Entovis Pacemaker System
ClinicalTrials.gov study NCT01761162. IPD Sharing: Not stated. Countries: 1. Publications: 1.
RF Surgical Sponge-Detecting System on the Function of Pacemakers and Implantable Cardioverter Defibrillators
ClinicalTrials.gov study NCT02111980. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Cardioneuroablation Versus Pacemaker Implantation for the Treatment of Symptomatic Sinus Node Dysfunction
ClinicalTrials.gov study NCT05186220. IPD Sharing: YES. Countries: 1. Publications: 3.
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