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62 results for “physiological measurement”
A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures
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Evaluating the efficacy of drone-based thermal images for measuring wildlife abundance and physiology
<p>Monitoring the population dynamics and behaviors of wildlife is crucial for effective conservation. Although drones can provide a promising alternative to traditional monitoring methods, validation studies must be done to quantify the accuracy of drone-based abundance and distribution estimates in various biological systems. Here, we investigate the use of drones equipped with high-resolution Red-Green-Blue (RGB) and thermal cameras, along with machine learning techniques, for assessments of abundance and physiology in northern elephant seals (<em>Mirounga</em> <em>angustirostris</em>). Aerial images of N=3,415 northern elephant seals were collected at Año Nuevo Reserve during N=24 drone flights, along with ambient air temperatures, wind speed, and time-of-day data. The two-dimensional footprints and surface temperatures of seals were measured from the images. Machine learning algorithms were applied to detect seals in the imagery, and model performance was evaluated. Our findings indicate that seal detection was more accurate using RGB images compared to Thermal images, but that Thermal images could be used to determine that time of day and ambient temperature (but not wind speed or body size) strongly influenced seal external skin temperature. In other words, RGB and Thermal cameras have different strengths and weaknesses that should be carefully considered when designing research studies. Our study highlights the promising integration of drones, thermal imaging, and machine learning for wildlife research, contributing to faster, safer, cheaper, less disruptive, and more accurate wildlife monitoring and conservation efforts.</p>
Fig. 3 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures
Fig. 3. Image illustrating under water visibility at the beginning of the snorkeling excursion and the presence of tourists (Lima, 2008).
Fig. 1 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures
Fig. 1. Map showing the location of the study area: Sucuri River (C), município of Bonito area (B), Brazil (A). Adapted from Miranda & Coutinho (2004).
Fig. 8 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures
Fig. 8. Variation of behaviour patterns between before (8h00) and after (9h00) the first disturbance of tourists in the river (mean and SEM) for M. bonita; (a) Tourism and (b) No Tourism. Lighter bars = 8h00; darker bars = 9h00 (Mann-Whitney U-test). N = 70; * p <0.05; ** p <0.01.
Fig. 9 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures
Fig. 9. Variation (mean and SEM) of cortisol responses to restraining stress in Moenkhausia bonita individuals at the No Tourism and Tourism sites (Mann-Whitney U-test, N = 6; Z = -2.95; p <0.005).
Intrinsic factors influence a physiological measure across a forest bird community
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Evaluating the efficacy of drone-based thermal images for measuring wildlife abundance and physiology
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Fig. 2 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures
Fig. 2. Image illustrating tourists at the beginning of the snorkeling excursion (Lima, 2008).
Physiological and morphological growth and hydraulic measures in Asclepias syriaca and A. speciosa under drought
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Data from: Assessing fish movement and physiological traits along a salinity gradient by measuring stable oxygen isotope values in fish blood water and muscle water
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fMRI during lower body negative pressure (LBNP) with concurrent physiological measurements
<p>This dataset was acquired at Hannover Medical School, Hanover, Germany. The study complied with the Declaration of Helsinki, and was approved by the local ethics committee (# 3404-2016). All subjects gave written informed consent, and consent to publish their data anonimously.</p> <p>We simulated orthostatic stress by means of lower body negative pressure (LBNP). Negative pressure of 30 mmHg was built up inside a custom-made polycarbonate chamber using a vacuum cleaner and a pressure gauge. The stimulation paradigm consisted of four alternating five-minute blocks, two with and two without negative pressure, starting with the negative pressure. Functional MR images and physiological measures were continuously acquired during this cardiovascular challenge. Transitions between pressure states were excluded to avoid excessive movement.</p> <p> </p> <p><strong>Data acquisition</strong><br> <em>MRI data</em><br> All MR images were acquired on a Siemens 3T MAGNETOM Skyra using a 64-channel head/neck coil. The scanning protocol consisted of the following sequences (see Sequences.ods):</p> <ul> <li>func_i: Functional whole brain gradient-echo echo-planar images (EPI) (TR=1230 ms; TE=32 ms; 2mm isotropic resolution; simultaneous multi-slice factor=6; partial Fourier=7/8; 4x233 volumes)</li> <li>func_ref: Reference scan for motion correction and template formation; equivalent to func but without multi-band acceleration (TR=7530 ms)</li> <li>SE_AP_PA: Reference scans for unwarping: Two spin-echo images matched to func in distortion without multi-band acceleration; one with the same, the other one with inverted phase encoding direction.</li> <li>t1: T1-weighted magnetisation-prepared rapid acquisition gradient-echo image (MPRAGE) (TR=2300 ms; TE=2.95 ms; TI=900 ms; resolution: 1.1x1.1x1.2 mm3, in-plane acceleration factor=2)</li> </ul> <p> </p> <p><em>fMRI data preprocessing</em><br> Our preprocessing pipleine (JPreprocessing) is optimised for the brainstem and hypothalamus by avoiding superfluous resampling steps and unnecessary smoothing. On that account, motion correction (MCFLIRT (Jenkinson et al., 2002)) and unwarping (topup (Andersson et al.,2003)) are applied in a single transformation. Afterwards, brain extraction (BET (Smith, 2002)), grand mean scaling and high pass filtering (0.005 Hz) are applied. The data are not smoothed.</p> <p>Two study templates were generated using Advanced Normalization Tools (ANTs (Avants et al., 2008) using antsMultivariateTemplateConstruction2.sh). The first one using the unwarped EPI reference images (func_ref); the second one using the T1-images.</p> <p><br> <em>Physiological data</em><br> The following physiological measures were acquired with an MR-compatible BIOPAC MP150 system</p> <ul> <li>Blood pressure (systolic and dyastolic): Non-invasive continuous blood pressure of the digital artery (pulse decomposition analysis using CareTaker)</li> <li>Electrodermal activity</li> <li>Photoplethysmography</li> <li>Respiration (belt)</li> <li>Electrocardiography</li> </ul> <p><br> <em>Subject information</em><br> 01 m 21 a 182 cm 73 kg 129/62 mmHg<br> 02 f 18 a 167 cm 55 kg 104/58 mmHg<br> 03 m 27 a 200 cm 92 kg 127/62 mmHg<br> 04 f 22 a 160 cm 59 kg 120/73 mmHg<br> 05 f 21 a 170 cm 62 kg 125/67 mmHg<br> 06 f 22 a 171 cm 56 kg 130/70 mmHg<br> 07 f 22 a 174 cm 55 kg 119/61 mmHg<br> 08 f 37 a 171 cm 82 kg 130/71 mmHg<br> 09 m 25 a 170 cm 72 kg 129/62 mmHg<br> 10 f 21 a 173 cm 58 kg 116/71 mmHg<br> 11 m 30 a 185 cm 113 kg 125/65 mmHg<br> 12 f 27 a 157 cm 48 kg 127/76 mmHg<br> 13 m 21 a 184 cm 70 kg 127/80 mmHg<br> 14 f 26 a 173 cm 63 kg 120/68 mmHg<br> 15 m 24 a 200 cm 92 kg 129/72 mmHg<br> 16 m 38 a 196 cm 86 kg 133/67 mmHg<br> 17 m 24 a 186 cm 76 kg 125/65 mmHg<br> 18 f 19 a 178 cm 69 kg 102/53 mmHg<br> 19 f 18 a 168 cm 60 kg 125/66 mmHg<br> 20 f 26 a 171 cm 63 kg 122/70 mmHg<br> 21 f 20 a 175 cm 62 kg 122/76 mmHg<br> 22 f 24 a 169 cm 58 kg 114/64 mmHg</p> <p><br> <em>Missing data</em></p> <ul> <li> sub01: physio.acq and physio_cuts.txt</li> <li>sub14: func_3 has 209 time points</li> <li>sub15: func_2 has 167 time points</li> </ul> <p> </p> <p><strong>Data structure</strong><br> <em>Raw data</em></p> <ul> <li>acqparams.txt: Acquisition parameters needed for topup</li> <li>func_i: raw functional data (dummy volumes already deleted)</li> <li>func_ref: Reference image for motion correction and template generation</li> <li>physio.acq: Physiological measurements <ul> <li>Trigger</li> <li>Blood pressure (systolic and dyastolic)</li> <li>Electrodermal activity</li> <li>Photoplethysmogram</li> <li>Respiration</li> <li>Electrocardiogram</li> </ul> </li> <li>physio_cuts.txt: time points in the physio data that correspond to fMRI blocks (start-time end-time TR #volumes)</li> <li>SE_AP_PA: auxiliary image for unwarping</li> <li>slicetiming_i.txt: Slice timing information in seconds</li> <li>t1: Defaced T1-weighted image. (Undefaced images were used for template generation)</li> </ul> <p><br> <em>Templates and masks</em></p> <ul> <li>EPI-template: Generated from unwarped func_ref images <ul> <li>Transformations for all subjects (can be applied using antsApplyTransforms)</li> <li>wb_mask</li> </ul> </li> <li>EPI-2-T1: Transformation from EPI to T1-template</li> <li>MNI-template: T1-template warped into MNI_152 <ul> <li>hyp_mask</li> <li>lower_bs_mask</li> </ul> </li> <li>T1-template: Generated from t1 images <ul> <li>hyp_mask</li> <li>lower_bs_mask</li> </ul> </li> <li>T1-2-MNI: Transformation from T1 to MNI_152-template</li> </ul> <p><br> <em>JPreprocessing</em><br> Preprocessing pipeline</p>
Lonati (2024) - Remote sensing to measure the physiology and foraging ecology of North Atlantic right whales in the Gulf of St. Lawrence, Canada
<h1>Supplementary Material A1.S4 Videos</h1> <h2>Selection of pixels and frames for evaluating intranasal heat</h2> <p>Video A1.S4.1. Time-aligned visible-spectrum (RGB) and infrared thermography (IRT) videos with plot of maximum corrected sensor intensity over time for a normal respiratory cycle from North Atlantic right whale (NARW) Catalog ID #4129 (aligns with Figure A1.S4.1).</p> <p><br>Video A1.S4.2. Time-aligned RGB and IRT videos with plot of maximum corrected sensor intensity over time for a normal respiratory cycle from NARW Catalog ID #3845 (aligns with Figure A1.S4.2).</p> <p><br>Video A1.S4.3. Time-aligned RGB and IRT videos with plot of maximum corrected sensor intensity over time for an anomalous respiratory cycle from NARW Catalog ID #4129, where the exhaled respiratory vapor lingers over the blowholes, obscuring and reducing intranasal heat received by the IRT sensor.</p> <p><br>Video A1.S4.4. Time-aligned RGB and IRT videos with plot of maximum corrected sensor intensity over time for an anomalous respiratory cycle from NARW Catalog ID #3845, where exhaled respiratory vapor and a small wave obscure and reduce intranasal heat received by the IRT sensor.</p> <p><br>Video A1.S4.5. Time-aligned RGB and IRT videos with plot of maximum corrected sensor intensity over time for an anomalous respiratory cycle from the 2021 calf of NARW Catalog ID #4040, where the exhaled respiratory vapor lingers over the blowholes and a non-uniformity correction occurs mid-way through the respiration.</p> <h3><em>G. Lonati - PhD Thesis - University of New Brunswick Saint John</em></h3>
The Physiological Impact of CTO PCI on Coronary Pressure Measurements and Correlation in Donor Vessel
ClinicalTrials.gov study NCT02643940. IPD Sharing: NO. Countries: 1. Publications: 2.
Assessment of Physiological Parameters Measurements (Heart Rate, Respiratory Rate, and Oxygen Saturation) by Standard Acquisition System Compared Remote Photoplethysmography Imaging System l
ClinicalTrials.gov study NCT04660318. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prognostic Perspective of Invasive Hyperemic and Non-Hyperemic Physiologic Indices Measured After Percutaneous Coronary Intervention
ClinicalTrials.gov study NCT04265443. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Impact of Blood Storage Duration on Physiologic Measures
ClinicalTrials.gov study NCT01274390. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Physiologic Volume and Atrophy of Brain and Spinal Cord Measured by Magnetic Resonance Imaging
ClinicalTrials.gov study NCT03706118. IPD Sharing: NO. Countries: 1. Publications: 1.
Behavioural and Physiological Measures of Young Students With Dyslexia
ClinicalTrials.gov study NCT05376696. IPD Sharing: NO. Countries: 1. Publications: 4.
The Effect of Massage, Wipe Bathing and Tub Bathing on Physiological Measurements of Late Premature Newborns
ClinicalTrials.gov study NCT04602130. IPD Sharing: Not stated. Countries: 1. Publications: 16.
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.