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1,904 results for “Breathing”
Data from: Who is taking our roe deer's breath away? – Prevalence and promotive factors of lung parasites in roe deer (Capreolus capreolus) in southeast Germany
<p>The existence of bronchopulmonary nematodes in German roe deer (<em>Capreolus capreolus</em>) is well documented, with two types of lung parasites that have been described previously (<em>Dictyocaulus capreolus </em>and<em> Varestrongylus capreoli)</em>. However, little is known about the impact of these parasites on their host animal or which parameters influence outbreak and intensity of infection. The aim of this study was to obtain new information on the relevance of factors such as season, environmental conditions or age, sex, and body mass of the infected roe deer. To obtain our results, the respiratory tracts of 762 roe deer from south-eastern Germany were examined.</p> <p>In the sample, 42.5 % of roe deer were infested with <em>V. capreoli</em> and 14.0 % with <em>D. capreolus</em>, 51.3 % of animals were completely free of lung parasites. Testing for influencing variables, our regression models found both sex and age of the roe deer to statistically influence infestation, with male sex and younger age correlating with both stronger infestation and higher infestation rates. Accordingly, in male animals, the infestation rates with <em>V. capreoli</em> and <em>D. capreolus</em> (45.1 % and 20.1 %) were higher than in females (39.4 % and 8.0 %).</p> <p>The overall infestation rate of juvenile animals was remarkably higher (73 %) than those of sub-adults (38.3 %) or adults (28.4 %).</p> <p>Across all age groups, infested animals showed lower body weights compared to non-infested animals. According to our multiple linear regression model Roe deer infected with <em>D. capreolus</em> on average weighed 0.65 kg less than their healthy counterparts, in case of <em>V. capreoli</em> 0.72 kg less on average. While the burden on the well-being of infested animals can only be assumed, these concrete figures (reduced body weight in infested compared to healthy animals) demonstrate the economic damage lung parasites cause to meat harvesting from bagged roe deer.</p>
FIGURE 2 in Breathing of the Nevado del Ruiz volcano reservoir, Colombia, inferred from repeated seismic tomography
FIGURE 2: Ancestral biogeographic reconstruction of tropical, temperate and cosmopolitan occurrence of oribatid mites as reconstructed with ancestral character state mapping in Mesquite 3.10 using parsimony algorithms. See text for details. The tree is based on the BI phylogeny of the 18S rRNA and partial 28S rDNA (see Fig. 1).
Linear vs. non-linear metrics of Autonomic Nervous System: study on healthy volunteers during controlled breathing
<h1>Please cite this article as reference article:</h1> <p>Uryga A, Najda M, Berent I, Mataczyński C, Urbański P, Kasprowicz M, Buchner T. The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability. Physiol Meas. 2024 Sep 16;45(9). doi: 10.1088/1361-6579/ad7778. </p> <h1>Funding</h1> <p>SONATA 18 UMO-2022/47/D/ST7/00229 National Science Centre, Poland (dataset 2)</p> <p>SONATA-BIS UMO-2013/10/E/ST7/00117 National Science Centre, Poland (dataset 1)</p> <h1>General information</h1> <p>Two datasets were used in this study.</p> <p>The dataset 1 includes 49 healthy volunteers (28 females, 21 males, median age: 23 years, range: 18-31 years) who were measured at the Neuroengineering Laboratory at Wroclaw University of Science and Technology (WUST) between October 2014 and June 2015 (Biomedical Committee Agreement number: KB-170/2014).</p> <p>The dataset 2 includes 21 healthy volunteers (14 females, 7 males, median age: 22 years, range: 18-31 years) who were prospectively measured at WUST between October 2023 and January 2024 (Biomedical Committee Agreement number: KB-179/2023/N).</p> <h1>Signal recordings description</h1> <ul> <li>ABP was measured non-invasively by a servo-controlled plethysmograph (Finometer MIDI, FMS Medical Systems, Amsterdam, The Netherlands in all subjects in dataset 1; CNAP, CNSystems Medizintechnik GmbH, Graz, Austria and Finapres Nova, FMS Medical Systems in dataset 2). The cuff was placed on the middle finger of the left hand and held at the level of the heart.</li> <li>Expired end-tidal CO2 (EtCO2), carbon dioxide (CO2) concentration and respiratory rate (RR) were measured via a nasal cannula using a portable capnography monitor (RespSense™, NONIN, Plymouth, USA)</li> <li>Protocol: after a resting epoch lasted at least 5 minutes, a controlled breathing session was initiated with 5-minute recordings at each of the respiratory rate: 6, 10 or 15 breaths/min (0.1 Hz, 0.17 Hz, and 0.25 Hz, respectively), guided by a digital metronome.</li> </ul> <h1>Data description</h1> <ul> <li>Type of database (database 1/database 2)</li> <li>Type of device used to ABP measurement</li> <li>Metadata including: sex (male M, female F), and age</li> <li>Autonomic Nervous System parameters including:</li> </ul> <p>- <strong>Phase-Rectified Signal Averaging</strong> - a non-linear approach used to quantify the acceleration (AC) and deceleration (DC) capacity of the heart; more details could be found here: <em>Campana L M, Owens R L, Clifford G D, Pittman S D and Malhotra A 2010 Phase-rectified signal averaging as a sensitive index of autonomic changes with aging J Appl Physiol 108 1668–73</em></p> <p>- <strong>Entropy</strong>: multiscale entropy (MSEn), approximate entropy (ApEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn) functions calculated for R-R intervals, which were implemented in NeuroKit2</p> <ul> <li> <strong>Heart rate variability (HRV) metrics</strong>: In the frequency domain, the Lomb–Scargle periodogram was used to determine the power spectral density of the interval time series in the low-frequency range (LF, 0.04–0.15 Hz) and the high-frequency range (HF, 0.15–0.40 Hz). Additionally, the total power of the HRV signal (TP, 0.04–0.40 Hz) and the ratio between low and high-frequency components (LF/HF) were calculated. In the time domain, the following metrics were determined: the standard deviation of the R-R intervals (SDNN) and the square root of the mean of the squared successive differences between adjacent R-R intervals (RMSSD), mean of the R-R intervals (meanNN), and the proportion of R-R intervals greater than 20 ms or 50 ms, out of the total number of R-R intervals (pNN20 and pNN50, respectively); appropriate functions were implemented in NeuroKit2</li> </ul> <p> </p> <p>Update ------version 2</p> <p>After the revision process, SDNNref was added, defined according to formula presented in paper of Monfredi et al. (Monfredi O, Lyashkov AE, Johnsen AB, et al. Biophysical characterization of the underappreciated and important relationship between heart rate variability and heart rate. Hypertension. 2014 Dec;64(6):1334-43)</p>
NIH BREATHE mHealth pediatric activity classification dataset
<p>The Biomedical REAl-Time Health Evaluation (BREATHE) platform is part of the Los Angeles PRISMS (Pediatric Research with Integrated Monitoring Systems) NIH-funded Center, which focuses on informatics platforms for mHealth (NIH/NIBIB U54 EB022002; PI: Alex Bui). This dataset was developed to better establish models for child activity classification based on smartwatch sensors. Specifically, a Motorola 360 smartwatch was worn by 21 subjects (age ranged 11-18), generating annotated data across different sensors (3-axis accelerometry, gyroscope, heart rate) for 6 different types of physical activity:three static postures (standing, sitting, lying) and three dynamic activities (walking, walking downstairs and walking upstairs). The study was motivated by the fact that current activity classifiers are typically geared towards <em>adult </em>bodies and patterns of motion; children exhibit different motion patterns, particularly as they grow. This dataset is not intended to be comprehensive, but rather a starting point for exploring differences in motion patterns across children (and relative to adults).</p>
Simulated tracer-gas distribution in a multiscale model of the human lung during multiple-breath nitrogene washout
<p><em><strong>Content</strong></em></p> <p><strong>baseline:</strong></p> <ul> <li>inletFlow (ASCII format data for flow rate at the mouth in m^3/s, sampling frequency 1kHz)</li> <li>primary_results (ASCII format data table with four colums: time in seconds, N2 concentration (normalized), <em>empty </em>-1, pleural pressure in Pascal)</li> </ul> <p><strong>compliance modification (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> <li>duct: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) witin the airway network. Sampling frequency 50Hz (separat vtk-file for each timestep). <em>Inspect for instance with the VisIt (Lawrence Livermore National Laboratory) free visualization software.</em></li> <li>lobule: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) within the trumpet lobules.</li> </ul> <p><strong>compliance modification (regional):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> <li>duct: (same format as described above)</li> <li>lobule: (same format as described above)</li> </ul> <p><strong>size modification (regional):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul> <p><strong>resistance modification (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul> <p><strong>healthy controls (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul>
In-situ Electron Paramagnetic Resonance Investigation of Isotope-selective Breathing in MIL-53 during Dihydrogen Adsorption
<p><strong>Description of the dataset:</strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements including CW EPR, Pulsed EPR raw data, N2 isotherm, CO2 isotherm and crystal structure data</li> <li>Files are with filename extensions: <strong>spc, par, dta, dsc, aif, csv, cif, txt and opj</strong></li> <li>Information on <strong>origin of the data</strong>:</li> <li>EPR spectroscopic measurements with filename extensions <strong>spc</strong>, <strong>par dta, dsc</strong>,<strong> txt </strong>and<strong> opj.</strong></li> <li>Data visualisation was conducted using OriginLab version 8 and Microsoft Power Point.</li> <li>X-band CW-EPR spectroscopic measurements data were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Pulsed X-band EPR measurements data were generated by Bruker ELEXYS E580 spectrometer.</li> <li><strong>Additional Information </strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>MIL</strong> – Matériaux de l’Institut Lavoisier</li> <li>definitions of variables: <strong>Magnetic field, Pressures, Microwave power.</strong></li> <li>units of measurement: <strong>Gauss (G), milliTesla (mT), millibar(mbar), microwave power (dB)</strong>.</li> <li>abbreviations on the CW-EPR data filename: Dates, Sample name, H2/D2 ads/des (ads= adsorption, des=desorption), microwave power, D2 or H2 pressures, number scans if indicated.</li> <li>abbreviations on the pulsed EPR data filename: Dates, Sample name, H2/D2 ads/des (ads= adsorption, des=desorption), D2 or H2 pressures, pulse sequences, pulse delay, temperature.</li> </ul> </li> </ul>
Questionnaire data from a study on the perception of breathing in the human brain
<p>This dataset contains anonymized data (questionnaire scores) from a set of questionnaires acquried in a previous study. Version 1 of this dataset (<span><a href="https://doi.org/10.5281/zenodo.10992529" target="_blank" rel="noopener"><span>https://doi.org/10.5281/zenodo.10992529</span></a>)</span> contained summary data of a subset of all participants for a subset of the questionnaires. For details see the Readme.pdf. Version 2 contains all subscores of all questionnaires assessed in this study. See Readme_v2.pdf for details. </p>
Supplementary Dataset for: Enhancement of superexchange due to synergetic breathing and hopping in corner-sharing cuprates
<p>The following data are included in the dataset:</p> <ul> <li>Cluster geometries</li> <li>Point charge embedding files</li> <li>Inputs and output files</li> <li>Orbital files for calculations with large active spaces</li> <li>Data for density difference plots</li> </ul> <p>It is fast to see the contents of the archive with help of <a href="https://github.com/vasi/pixz">pixz</a>:</p> <pre><code class="language-bash">pixz -l data.tar.xz</code></pre> <p>Then the files of interest can be easiliy extracted:</p> <pre><code class="language-bash">pixz -x dir/file < data.tar.xz | tar x </code></pre>
Analysis of Breath Sounds During Surgery
ClinicalTrials.gov study NCT07280546. IPD Sharing: YES. Countries: 1. Publications: 3.
Breathe With Ease: A Unique Approach to Managing Stress (BEAMS)
ClinicalTrials.gov study NCT02374138. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Sleep-Disordered Breathing in Heart Failure - The SchlaHF-Registry
ClinicalTrials.gov study NCT01500759. IPD Sharing: Not stated. Countries: 2. Publications: 3.
Manipulation of Breath Alcohol Tests: Can Specific Techniques Alter Blood Alcohol Concentration Readings?
ClinicalTrials.gov study NCT02580318. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Breathing Exercises Versus Incentive Spirometry in Third-Trimester Pregnancy
ClinicalTrials.gov study NCT07365163. IPD Sharing: NO. Countries: 1. Publications: 12.
13-C Urea Breath Test Using BreathID System and PPIs (Proton Pump Inhibitors)
ClinicalTrials.gov study NCT00825630. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Musically-Guided Paced Breathing Improves Mental Health in War-Affected Adolescents
ClinicalTrials.gov study NCT06988800. IPD Sharing: NO. Countries: 1. Publications: 1.
Slow Yogic-Derived Breathing and Respiration and Cardiovascular Variability in Spinal Cord Injury Patients
ClinicalTrials.gov study NCT05480618. IPD Sharing: NO. Countries: 1. Publications: 1.
Molecular Breath Print of COPD Patients With Exacerbations Despite Triple Inhalational Therapy
ClinicalTrials.gov study NCT04638920. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Neuroimaging During Pure Oxygen Breathing
ClinicalTrials.gov study NCT03268590. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Breath Analysis Technique to Diagnose Pulmonary Embolism
ClinicalTrials.gov study NCT00368836. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Reducing Dynamic Hyperinflation Through Breathing Retraining
ClinicalTrials.gov study NCT01009099. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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International Brain Laboratory public data
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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.