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Results for “Respiration rate”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Rhythmic variation in heart rate and respiration rate during space flight - Apollo 15

As part of the operational biomedical monitoring for Apollo manned missions, ECG and respiration rate are telemetered at selected intervals to mission control. The data were collected as part of this monitoring program. These data were evaluated for circadian and ultradian rhythmicity because of their uniqueness. The ability to detect and quantitate biorhythms in living systems during space flight is an important aspect of evaluating hypotheses concerning the underlying mechanisms of these phenomena. Circadian variation in heart rate during space flight is demonstrated here. In analyzing generated time series data it has been found that period discrimination is much better than the theoretical limit.

Rummel, J. A.↗
NASA NTRS1989

Respiration rate in maize roots is related to concentration of reduced nitrogen and proliferation of lateral roots

The relationship between specific rate of respiration (respiration rate per unit root dry weight) and concentration of reduced nitrogen was examined for maize (Zea mays L.) roots. Plants with 2 primary nodal root axes were grown for 8 days in a split-root hydroponic system in which NO3- was supplied to both axes at 1.0 mol m-3, to one axis at 1.0 mol m-3 and the other axis at 0.0 mol m-3, or to both axes at 0.0 mol m-3. Respiration rates and root characteristics were measured at 2-day intervals. Specific rate of respiration was positively correlated in a nonlinear relationship with concentration of reduced nitrogen. The lowest specific rates of respiration occurred when neither axis received exogenous NO3- and the concentration of reduced nitrogen in the axes was less than 9 mg g-1. The greatest rates occurred in axes that were actively absorbing NO3- and contained more than 35 mg g-1 of reduced nitrogen. At 23 mg g-1 of reduced nitrogen, below which initiation of lateral branches was decreased by 30-50%, specific rate of respiration was 17% greater for roots actively absorbing NO3- than for roots not absorbing NO3-. Increases in specific rate of respiration associated with concentrations of reduced nitrogen greater than 23 mg g-1 were concluded to be attributable primarily to proliferation of lateral branches.

Non-NASA Center↗
DOE OSTI2024

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Newly developed sugarbeet lines with altered postharvest respiration rates differ in transcription factor and glycolytic enzyme expression

Respiration is the principal cause for postharvest sucrose loss in sugarbeet (Beta vulgaris L.) roots. Although reductions in respiration rate could mitigate these losses, developing sugarbeet cultivars and storage procedures that reduce respiration are hindered by a lack of knowledge of the genetic and metabolic factors that control storage respiration rate. Research was conducted to identify genes and gene products that affect storage respiration rate by creating two sugarbeet lines that differ in respiration rate and characterizing gene expression differences between these lines. Sugarbeet lines F1056 and F1057, which differ by up to 42% in respiration rate, were created by divergent selection of a sugarbeet population using root respiration rate after 30 d in storage as the principal selection criterion. RNA sequencing identified 287 differentially expressed genes (DEGs) between these lines on the day of harvest and after 28-d storage. Of these DEGs, nine encoded transcription factors and five encoded enzymes involved in the respiratory pathway. Other DEGs contributed to a variety of biological and molecular functions based on gene ontology classifications. Of respiratory pathway DEGS, genes for NAD+- and NADP+-dependent forms of glyceraldehyde-3-phosphate dehydrogenase were of note due to their high upregulation in the high respiring line and for their established role in glycolysis, a pathway identified as a likely bottleneck in respiratory substrate production. Overall, lines F1056 and F1057 provide new tools for investigating genetic and physiological differences in storage respiration rate, and their DEGs identify candidates for genes affecting sugarbeet root respiration rate.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1969

Direct reading of electrocardiograms and respiration rates

Technique for reading heart and respiration rates is more accurate and direct than the previous method. Index of a plastic calibrated card is aligned with a point on the electrocardiogram. Complexes are counted as indicated on the card and heart or respiration rate is read directly from the appropriate scale.

Wise, J. P.↗
DOE OSTI2023

Maximum respiration rates in hyporheic zone sediments are primarily constrained by organic carbon concentration and secondarily by organic matter chemistry

Abstract. River corridors are fundamental components of the Earth system, and their biogeochemistry can be heavily influenced by processes in subsurface zones immediately below the riverbed, referred to as the hyporheic zone. Within the hyporheic zone, organic matter (OM) fuels microbial respiration, and OM chemistry heavily influences aerobic and anaerobic biogeochemical processes. The link between OM chemistry and respiration has been hypothesized to be mediated by OM molecular diversity, whereby respiration is predicted to decrease with increasing diversity. Here we test the specific prediction that aerobic respiration rates will decrease with increases in the number of unique organic molecules (i.e., OM molecular richness, as a measure of diversity). We use publicly available data across the United States from crowdsourced samples taken by the Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS) consortium. Our continental-scale analyses rejected the hypothesis of a direct limitation of respiration by OM molecular richness. In turn, we found that organic carbon (OC) concentration imposes a primary constraint over hyporheic zone respiration, with additional potential influences of OM richness. We specifically observed respiration rates to decrease nonlinearly with the ratio of OM richness to OC concentration. This relationship took the form of a constraint space with respiration rates in most systems falling below the constraint boundary. A similar, but slightly weaker, constraint boundary was observed when relating respiration rate to the inverse of OC concentration. These results indicate that maximum respiration rates may be governed primarily by OC concentration, with secondary influences from OM richness. Our results also show that other variables often suppress respiration rates below the maximum associated with the richness-to-concentration ratio. An important focus of future research will identify physical (e.g., sediment grain size), chemical (e.g., nutrient concentrations), and/or biological (e.g., microbial biomass) factors that suppress hyporheic zone respiration below the constraint boundaries observed here.

58 GEOSCIENCES↗
DOE OSTI2024

Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

13C↗
DOE OSTI2023

Spatial Study 2022: Water Column, Sediment, and Total Ecosystem Respiration Rates across the Yakima River Basin, Washington, USA (v2)

This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin and is associated with the manuscript “Sediment-associated processes account for most of the spatial variation in ecosystem respiration in the Yakima River basin” submitted to Nature Communications Earth & Environment (Garayburu-Caruso et al., in review). The dataset provides ecosystem metabolism estimates generated from streamMetabolizer (Appling et al.; 2018) using data collected during the same five-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. Additionally, it includes the scripts used for the analysis and producing the figures in the manuscript. The contents include streamMetabolizer inputs and outputs and additional relevant data needed to generate the main manuscript results. The data included are: total ecosystem respiration, water respiration, calculated sediment-associated respiration, gross primary production outputs from the river corridor model for the Yakima River Basin, median grain size (d50), depth, dissolved oxygen, water temperature, pressure, and annual oxygen consumption. The associated GitHub repository can be found at https://github.com/river-corridors-sfa/SSS_metabolism. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” Raw time series sensor data, total suspended solids, and depth data from SSS were published at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1969566. A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Field metadata for the samples in this da This dataset is comprised of one main data folder with four subfolders. The main data folder contains of (1) file-level metadata; (2) data dictionary; (3) total/water column/sediment respiration; (4) gross primary production (GPP); (5) median grain size (d50); and (6) annual oxygen consumption. The “Figures” subfolder contains the figures used in the paper and all intermediate files (including geospatial files). The “Published_Data” contains a readme directing the user to download the public data to reproduce analyses and figures. The “Scripts” folder contains all scripts used in the analyses that were not part of running StreamMetabolizer. Lastly, the “Stream_Metabolizer” folder contains all files associated with running StreamMetabolizer including (1) model input files, (2) model output files, (3) processing scripts, (4) histogram plots of the outputs, and (5) an R project. All files are .csv, .pdf, .R, .Rmd, .Rproj, .html, .png, .txt, .qgz, .cpg, .dbf, .prj, .shp, .shp.ea.iso.xml, .shp.iso.xml, .shx, .sbn. ta package can be found at either link. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Schneider Springs Fire Study 2023 for Ecosystem Respiration Rates: Surface Water Chemistry and Hydrologic Sensor Data across the Yakima River Basin, Washington, USA (v2)

This dataset supports a broader study examining the drivers of spatial variability in wildfire impacts across the Yakima River Basin. Data provided within this dataset were generated from sample collection across 17 total sites (8 sites affected by a recent wildfire, 9 sites unaffected by a recent wildfire) within multiple rivers throughout the Yakima River Basin in Washington, USA from May-July 2023. Fire affected sites are defined as those affected by the 2021 Schneider Springs Fire, based on the drainage area of the streams being within the 2021 Schneider Springs Fire burn perimeter or not (Figure 1, below). The contents include surface water geochemistry data (dissolved organic carbon; total dissolved nitrogen; total suspended solids); short-term sonde data (specific conductivity; turbidity; pH; chlorophyll A; temperature); stream depth data; stream velocity; manual chamber open channel respiration data; sensor time-series data (oxygen; water pressure; barometric pressure); field metadata (including qualitative information on in stream and river corridor characteristics); and environmental context photos taken in the field. The dataset also includes a summary file of the sensor data and plots of the sensor data. Sensors were only recovered at 15 out of the 17 sites, and not all sensors were recovered at all 15 sites (see Methods section for more details), therefore all data does not exist at all sites. Data from a 2022 study at the same sites, as well as additional sites, can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566. The data package was originally published in November 2023. It was updated in June 2025 (v2; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one folder with field photos and one main data folder with two subfolders. The main data folder consists of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) field protocol; (5) readme; (6) international generic sample number (IGSN) mapping file; and (7) stream depth and averages. The sensor data subfolder consists of (1) sensor installation methods summary; (2) stream velocity; and (3) six subfolders. The BarotrollAtm (barometric pressure; temperature), DepthHOBO (water pressure; temperature), MantaRiver (specific conductivity; turbidity; pH; chlorophyll A; temperature), EXO (specific conductivity; pH; temperature), miniDOT (dissolved oxygen; temperature), and miniDOTManualChamber (dissolved oxygen; temperature) contain time-series data, plots, and summary files. The sample data subfolder consists of (1) total suspended solids (TSS) data; (2) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (3) total dissolved nitrogen (TN) data and averages; and (4) methods codes. All files are .csv, .pdf, .jpg, .jpeg, or .mov.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1964

Electronic device simulates respiration rate and depth

An oscillator circuit and a thermistor, in close proximity to a light bulb, periodically alter the heat output of the bulb by varying the voltage across its filament. Use of this simulator permits checkout tests on pneumographs.

Thomas, J. A.↗
NASA NTRS1964

Pneumotachometer counts respiration rate of human subject

To monitor breaths per minute, two rate-to-analog converters are alternately used to read and count the respiratory rate from an impedance pneumograph sequentially displayed numerically on electroluminescent matrices.

Graham, O.↗
NASA NTRS1960

A Flight Evaluation of an Airborne Physiological Instrumentation System, Including Preliminary Results Under Conditions of Varying Accelerations

A physiological instrumentation system capable of recording the electrocardiogram, pulse rate, respiration rate, and systolic and diastolic blood pressures during flight has been developed. This instrumentation system was designed for use during control studies at varied levels of acceleration in order to monitor the well-being of the pilot and at the same time to obtain data for study of the relationships between his various physiological functions and his performance capability. Flights, made in a T-33 aircraft, demonstrated the ability of the system to obtain the desired physiological data in flight. The data obtained in these flights, although limited in nature, indicate a slowing of the pulse rate under the subgravity conditions of brief duration. There appeared to be a proportional nearly in-phase relationship between pulse rate and acceleration. A decrease in diastolic blood pressure together with an increase in pulse pressure was noted during subgravity conditions and an elevation of the diastolic pressure together with a decrease in pulse pressure du-ring increased accelerations. No change worthy of note was seen in the records of the systolic blood pressure, the respiration rate, or the electrocardiogram over the range of acceleration studied (0 to 3 g).

Smedal, Harald A.↗
NASA NTRS1970

Biomedical recording system

System collects medical data directly from patients and permanently records and displays several parameters - electrocardiograph, electroencephalograph, heart rate, respiration rate, auscultatory blood pressure, leg circumference changes, body temperature, and time. Components and operation of the system are described.

Vick, H. A.↗
NASA NTRS1983

The physiological effects of simultaneous exposures to heat and vibration

Determination of the effects of exposure to vibration on the body's ability to handle heat stress, and, if so, identification of the specific vibration parameters (frequency and intensity) for both whole-body (wbv) and segmental-body vibration (sbv) that would have the most detrimental effect on the body's ability to maintain thermal homeostasis were studied. Rectal and skin temperatures, heart rates, localized sweat rates, arm-segment blood perfusion rates, respiration rates, oxygen uptakes, and respiratory exchange ratios were measured in six men (22 to 33 yr) during simultaneous exposures to heat and vibration - either wbv or sbv, and during a heated 50 min recovery period. The heat conditions were T (sub db) = 43.5 + or - 0.5 C (mean + or S.E.M.), and RH = 20 + or - 4%. All vibration exposures were divided into two exposure conditions - identical frequencies but at a high intensity (HI) and a low intensity (LI) level. The HI wbv exposure was for 25 min/day at 5 Hz, 0.37 g-rms; 10 Hz, 0.46 g-rms; 16 Hz, 0.72 g-rms; 30 Hz, 1.40 g-rms; 80 Hz, 3.70 g-rms. The LI wbv exposure was for 2.5 hr/day at the same frequencies but at the following accelerations: 0.14 g-rms; 0.18 g-rms; 0.28 g-rms; 0.55 g-rms; 1.44 g-rms. During the sbv the subject stood and grasped a vibrating, in the Z-axis, hand grip with both hands.

Spaul, W. A.↗
NASA NTRS1985

General Automatic Components of Motion Sickness

A body of investigations performed in support of experiments aboard the space shuttle, and designed to counteract the symptoms of Space Adaptation Syndrome, which resemble those of motion sickness on Earth is reviewed. For these supporting studies, the automatic manifestations of earth-based motion sickness was examined. Heart rate, respiration rate, finger pulse volume and basal skin resistance were measured on 127 men and women before, during and after exposure to nauseogenic rotating chair tests. Significant changes in all autonomic responses were observed across the tests. Significant differences in autonomic responses among groups divided according to motion sickness susceptibility were also observed. Results suggest that the examination of autonomic responses as an objective indicator of motion sickness malaise is warranted and may contribute to the overall understanding of the syndrome on Earth and in Space.

Suter, S.↗