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146 results for “body measurements”
A Study in Healthy People to Measure the Amount of BI 655130 in the Blood After Injecting Different Doses Into Different Parts of the Body
ClinicalTrials.gov study NCT03617835. IPD Sharing: NO. Countries: 1. Publications: 1.
Empirical measurements of ammonium excretion in kelp forest fishes: Effects of body size, taxonomy and trophic guild
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Predicting body mass in Ruminantia using postcranial measurements
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Data from: Inferring the feeding history of the castor bean tick, Ixodes ricinus from lipid and body measurements
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Body size measurements and stomach contents of Alligator mississippiensis on Jekyll Island, Georgia
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Data from: Deriving birth timing of roe deer fawns from body measurements to limit mowing mortality
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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>
FIGURES 1–5. Body measurements. 1 in Revision of the species-group of Mischocyttarus (Omega) filiformis (de Saussure 1854), with description of three new species (Hymenoptera, Vespidae)
FIGURES 1–5. Body measurements. 1 (head): clypeus height (HCLP) and width (WCLP), head width between eyes at ocular sinus (INTOW), and height (FHH); 2 (mesosoma part.): width of pronotal carina (WCAR), and mesoscutum length (LMS) and width (WMS); 3: mesopleuron height (HMP); 4: first metasomal tergum length (LSI), and apical width (WSI); 5: fore wing discal cell length (LDIS).
Data from: Evaluation of two methods for minimally invasive peripheral body temperature measurement in birds
Body temperature (Tb) is a valuable parameter when assessing the physiological state of animals, but its widespread measurement is often constrained by methods that are invasive or require frequent recapture of animals. Alternatives based on automated remote sensing of peripheral Tb show promise, but little is known about their strengths and limitations. We measured peripheral Tb in great tits Parus major with subcutaneously implanted passive integrated transponders (PIT tags) and externally attached radio transmitters to determine repeatability of measurements, sensitivity of each method to variation in ambient temperature (Ta) and wind speed, the relationship between methods, and their ability to capture circadian variation in Tb. Repeatability of measurements by radio transmitters was high (> 80%) when readings were taken within 20 min, but reduced to 16% when measures were spaced 3.5 h apart. PIT tag data for the 3.5 h interval were more repeatable (33%) and less variable (cv). Data were affected by Ta with a stronger effect on the externally attached transmitters, but the influence of wind speed was small for both methods. There was a significant positive relationship between transmitter- and PIT tag temperature during both days and nights. Both methods were equally suited to detect diel changes in peripheral Tb. However, transmitters offered longer detection distance and better temporal resolution. These qualities should be considered when deciding how to collect Tb data remotely. If properly deployed, both methods allow measurement of peripheral Tb over a wide range of natural systems and conditions in small, free-ranging, birds.
FIGURE 4. Mean reflectance spectra for ten body regions measured from three Melozone leucotis subspecies, M. l in Phenotypic variation and vocal divergence reveals a species complex in White-eared Ground-sparrows (Cabanis) (Aves: Passerellidae)
FIGURE 4. Mean reflectance spectra for ten body regions measured from three Melozone leucotis subspecies, M. l. leucotis (solid lines, N = 13), M. l. nigrior (dotted lines, N = 13), and M. l. occipitalis (dashed lines, N = 8). The gray area around each line represents standard error of the mean calculated at every 1nm.
Figure 5 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 5. Box plots with whiskers showing the variation among locomotion modes of percentage prediction errors (%PE) for the multiple regression analysis (MR; grey) and the single regression analysis (SR; white) equations adjusted in modern birds. Box length shows the interquartile range (25th and 75th percentiles). The horizontal line within boxes indicates the median. Vertical lines show the 5–95% confidence limits. Points indicate values outside these limits (i.e. outliers). Black horizontal lines between the grey and white boxes show the %PE estimates obtained with the equation based on humeral length (HL), the osteological variable less affected by ecological groupings (as shown by its lower F-statistic values; see Tables 6 and 7). %PE values greater than zero indicate an underestimation and %PE values lower than zero indicate an overestimation. A, %PE variation with respect to groups of aerial locomotion. Abbreviations: CF, continuous flapping; FG, flapping and gliding; FB, flapping and bounding; S, soaring. B, %PE variation with respect to groups of non-aerial locomotion. Abbreviations: A, aquatic; TG, terrestrial ground-dwelling; TN, terrestrial non-ground-dwelling; ATG, aquatic and grounddwelling; TGN, perching and ground-dwelling.
Figure 1. Calibrated phylogeny for the main avian taxa. Tree topology was obtained from O in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 1. Calibrated phylogeny for the main avian taxa. Tree topology was obtained from O'Connor, Chiappe & Bell (2011) and divergence times are based on a 'literal' interpretation of the fossil record from Brockelhurst et al. (2012). Taxa abbreviations: Nth, Neornithes; Orph, Ornithuromorpha; Orn, Ornithothoraces; Orth, Ornithurae; Pyg, Pygostylia.
Figure 4 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 4. Biplots used in the selection of four variables for each fossil group of Mesozoic birds: humeral length (HL), femoral length (FL), diaphyseal craniocaudal width of ulna (dUW), and diaphyseal craniocaudal width of femur (dFWcc). Each predictor variable was plotted against a combination of the 14 remaining variables used for estimating body mass (BM; Table S3). The biplots show the regression line fitted for extant birds (grey circles) with the 95% confidence intervals for BM predictions (dotted lines). Following the procedure of selection of variables (see text), HL and FL were used for generating functions that can be applied to all fossil avian taxa. In contrast, dUW and dFWcc could not be incorporated into the functions adjusted for estimating BM in Archaeopterygidae and Enantiornithes, respectively.
Figure 3 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 3. Effects of weighting for the best-fitting multiple regression equation obtained from the modern data set (see Table 2). A, distribution of familiar residuals for unweighted data, and for data weighted by families. Each box plot with whiskers represents one family. Box length shows the interquartile range (25th and 75th percentiles). Horizontal lines indicate the 5–95% confidence limits. Asterisks show outliers. Abbreviations for families: Ac, Accipitridae; Ad, Alcedinidae; Ae, Aegothelidae; Al, Alcidae; An, Anatidae; Ap, Apodidae; Ar, Ardeidae; At, Artamidae; Au, Alaudidae; Ca, Caprimulgidae; Cc, Cacatuidae; Ch, Charadriidae; Ci, Ciconiidae; Co, Columbidae; Cn, Cinclidae; Cr, Coraciidae; Ct, Catharthidae; Cu, Cuculidae; Cv, Corvidae; Di, Diomedeidae; Fa, Falconidae; Fg, Fringillidae; Fr, Fregatidae; Ga, Gaviidae; Gr, Gruidae; He, Hemiprocnidae; Hi, Hirundinidae; La, Laridae; Ln, Lanidae; Me, Meleagridae; Mg, Megapodidae; Mo, Motacillidae; Mp, Meropidae; Ms, Musophagidae; Mu, Muscicapidae; Ot, Otididae; Pa, Paridae; Pc, Pelecanoididae; Pd, Podicipedidae; Pe, Pelecanidae; Ph, Phasianidae; Pic, Picidae; Pit, Pittidae; Pl, Phalacrocoracidae; Pn, Pandionidae; Po, Podargidae; Pr, Procellaridae; Ps, Psittacidae; Pt, Pteroclidae; Pu, Prunellidae; Ra, Rallidae; Re, Recurvirostridae; Ry, Rynchopidae; Sc, Stercoriidae; Sg, Strigidae; So, Scolopacidae; Sr, Sturnidae; St, Sternidae; Su, Sulidae; Sy, Sylviidae, Te, Tetraonidae; Th, Threskiornithidae; Tt, Tytonidae; Tu, Turdidae; Ty, Tyrannidae; Up, Upupidae. B, plot showing the variations of %MPE with the increase of individuals per family. The dashed line represents the unweighted multiple regression analysis (MR) and the grey line represents the weighted MR.
Figure 2 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 2. Illustration of osteological limb measurements used in this study and defined in Table 1: A, cranial aspect of the humerus; B, dorsal aspect of the ulna and the radius; C, dorsal aspect of the carpometacarpus; D, caudal aspect of the femur; E, caudal aspect of the tibiotarsus; F, cranial aspect of the tarsometarsus.
Measurements (including swimming performance data, body sizes/weights)
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Using different body size measures can lead to different conclusions about the effects of climate change
<div> <div> <div> <div> <p><strong>Aim:</strong> Declining animal body size has been proposed as a general response to increasing global temperatures that should be observed across a broad biogeographical scale. However, published studies have shown large variation in both the magnitude and direction of body size trends. We aim to investigate how the way body size is measured (body mass, structural size, body condition) may contribute to differences in body size trends between studies.</p> <p><strong>Location:</strong> Semi-arid Australia.</p> <p><strong>Taxon: </strong>White-plumed honeyeater (Ptilotula penicillatus).</p> <p><strong>Methods:</strong> We studied two separate populations of P. penicillatus over 30 years to investigate the associations between weather and body size. We first investigated how body mass has been affected by weather conditions at each site and then determined how these same local weather conditions affected the constituent components of body mass (structural size and body condition).</p> <p><strong>Results: </strong>The magnitude and direction of weather effects differed with the measure of body size used. Average structural size (wing length) increased with increasing temperatures while average body condition decreased. As body mass is a composite of structural size and body condition, the magnitude and direction of body mass trends was affected by trends in these two other traits. For example, differences in temperature effects on structural size between our two sites led to clear differences in body mass trends.</p> <p><strong>Main Conclusions:</strong> Trends in body size will be strongly affected by the choice of body size measure used in analyses. Change in body mass can be particularly difficult to interpret as it will be a composite of changes in both structural size and body condition. Our results indicate that it is difficult to compare studies using different measures of body size. Using consistent measures of body size will be important to better understand the general effects of climate change on body size.</p> </div> </div> </div> </div>
Movies, temperature and pressure measurements associated with the study "Small amounts of dissolved salt increases the mobility of mud flows on Mars and other extra-terrestrial bodies"
<p>Movies, temperature and pressure measurements logs associated with the study "<em>How salts affect mud mobility at low pressures: implications for Mars and other extraterrestrial bodies</em>" that are showing behavior of muds with various content of different types of salts.</p>
Raw data set of pigeon body mass measurements
<p>1. Animal-borne logging devices are now commonly used to record and monitor the movements, physiology and behaviours of free-living animals. It is imperative that the impacts these devices have on the animals themselves is minimised.</p> <p>2. One important consideration is the interaction between the body mass of the animal, and the mass of the device.</p> <p>3. Using captive homing pigeons, we demonstrate that birds lose the equivalent amount of body mass compared to that of the logging device attached. With our experiments, we calculated that the compensatory mass loss because of the logging device equates to a total loss of 1,140 kJ of energy to the bird, over the 25-day period. This equates to 32% per day of their total daily energy budget.</p> <p>4. We suggest that practitioners of biologging give due consideration to the possibility of a device-induced decrease in body mass when making decisions regarding device size, and when considering the period of the time of the year at which devices are attached.</p> <p>5. It appears, based on the results of the present study, that device attachment is likely to be most disruptive during periods of regulated mass change, especially when periods of mass gain precede periods in which stored energy reserves are extensively utilised.</p> <p>6. These findings have significant consequences for anyone using biologging technology on both wild and captive volant animals. Further studies utilising captive birds are now needed to fully understand how context- and species-dependent physiological responses to externally attached devices are.</p>
FIG. 2 in Maternal Body Size and Condition Predict Measures of Reproductive Success and Future Reproductive Allocation in Territorial Eastern Red-Backed Salamanders
FIG. 2. The regression of number of surviving offspring (at 185 d posthatching) on (A) maternal body size (SVL in mm) and (B) maternal condition (residuals of the regression of maternal mass, mm, on maternal body size, SVL in mm).
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.