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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Exercise training hypotension - Implications for plasma volume, renin, and vasopressin

The relation of changes in plasma volume, plasma renin activity and arginine vasopressin to changes in resting blood pressure during exercise training is investigated. Resting supine, sitting, and standing systolic and fifth-phase diastolic blood pressures were measured in ten men before and after an eight-day training period on a cycle ergometer in either a hot (39.8 C) or cool (23.8 C) environment, and compared with plasma volume, renin and vasopressin levels, heart rates, maximal oxygen uptakes, rectal temperatures and sweat rates. Following acclimatization, resting supine and sitting diastolic pressures are observed to decrease by 6 and 9 mm Hg, respectively, while no significant changes are found in the diastolic pressures of the control group or the systolic pressures of either group. Resting plasma volume is found to increase by 12.2% in the controls and by 17.6% after acclimatization following the exercise training. Results suggest that the resting hypotension produced is not attributable to changes in resting plasma volume, renin or vasopressin, although heat acclimatization, which leads to large decreases in plasma volume and increases in vasopressin and renin activity, may be useful in the treatment of hypertension.

Greenleaf, J. E.↗
NASA NTRS1993

Autogenic feedback training experiment: A preventative method for space motion sickness

Space motion sickness is a disorder which produces symptoms similar to those of motion sickness on Earth. This syndrome has affected approximately 50 percent of all astronauts and cosmonauts exposed to microgravity in space, but it differs from what is commonly known as motion sickness in a number of critical ways. There is currently no ground-based method for predicting susceptibility to motion sickness in space. Antimotion sickness drugs have had limited success in preventing or counteracting symptoms in space, and frequently caused debilitating side effects. The objectives were: (1) to evaluate the effectiveness of Autogenic-Feedback Training as a countermeasure for space motion sickness; (2) to compare physiological data and in-flight symptom reports to ground-based motion sickness data; and (3) to predict susceptibility to space motion sickness based on pre-flight data of each treatment group crew member.

Cowings, Patricia S.↗
DOE OSTI2023

A flexible event reconstruction based on machine learning and likelihood principles

Event reconstruction is a central step in many particle physics experiments, turning detector observables into parameter estimates; for example estimating the energy of an interaction given the sensor readout of a detector. A corresponding likelihood function is often intractable, and approximations need to be constructed. Here, in our work, we first show how the full likelihood for a many-sensor detector can be broken apart into smaller terms, and secondly how we can train neural networks to approximate all terms solely based on forward simulation. Our technique results in a fast, flexible, and close-to-optimal surrogate model proportional to the likelihood and can be used in conjunction with standard inference techniques allowing for a consistent treatment of uncertainties. We illustrate our technique for parameter inference in neutrino telescopes based on maximum likelihood and Bayesian posterior sampling. Given its great flexibility, we also showcase our method for geometry optimization enabling to learn optimal detector designs. Lastly, we apply our method to realistic simulation of a ton-scale water-based liquid scintillator detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1992

Use of sorption technology for treatment of humidity condensate for potable water

This research focused on the testing of the original potable water processor aboard Space Station Freedom that was to produce potable water from the humidity condensate and additional water generated by carbon dioxide reduction. Humidity condensate was simulated by an influent water model 'Ersatz'. The humidity condensate was treated with multifiltration (MF) beds that consisted of a train of sorption beds (referred to as 'Unibed') designed to remove specific contaminants. For the complete simulated MF system runs tested for 100 bed volumes (BV) (volume processed/total column volume), 0.6 percent of the TOC was removed by the SAC/IRN 77 (Strong Acid Cation exchange resin), 39.6 percent of the total organic carbon (TOC) was removed by the WBA/IRA 68 (Weak Base Anion exchange resin), 13.2 percent of the TOC was removed by activated carbon adsorption (580-26), and the remaining sorbent media acted as polishing units to remove an additional 1.6 percent of the TOC at steady state. At steady state, 45 percent of the influent TOC passed through the MF bed.

Ajjarapu, Sundara R. M.↗
DOE OSTI2022

Development of a prediction model for radiotherapy response among patients with head and neck squamous cell carcinoma based on the tumor immune microenvironment and hypoxia signature

Abstract Introduction The immune system and hypoxia are major factors influencing radiosensitivity in patients with different cancer types. This study aimed at developing a model to predict radiotherapy response in patients with head and neck squamous cell carcinoma (HNSCC) based on the tumor immune microenvironment and hypoxia signature. Materials and Methods We first evaluated the hypoxia status and tumor immune microenvironment in the Cancer Genome Atlas (TCGA) cohort by using transcriptomic data. Differentially expressed genes (DEGs) were identified between the “high immunity and low hypoxia” and “low immunity and high hypoxia” groups and those DEGs significantly associated with disease‐specific survival in the univariate Cox regression analysis were selected as the prognostic DEGs. We selected the immune hypoxia–related genes (IHRGs) by intersecting prognostic DEGs with immune and hypoxia gene sets. We used the IHRGs to train a multivariate Cox regression model in the TCGA cohort, based on which we calculated the IHRG prognostic index (IHRGPI) for each patient and validated its efficacy in predicting radiotherapy response in the Gene Expression Omnibus cohorts. Furthermore, we explored potential mechanisms and effective combinational treatment strategies for different IHRGPI groups. Results Five IHRGs were used to construct the IHRGPI, which was used to dichotomize the cohorts. The patients with lower IHRGPI showed a better radiotherapy response across different cohorts and endpoints, including overall survival, progression‐free survival, and recurrence‐free survival ( p < 0.05). Patients with higher IHRGPI showed greater hypoxia and lesser immune cell infiltration. A lower IHRGPI indicated a better immunotherapy response, while a higher IHRGPI indicated a better chemotherapy response. Conclusions IHRGPI is promising for predicting radiotherapy response and guiding combinational treatment strategies in patients with HNSCC.

Zhu, Guang‐Li↗
NASA NTRS2006

NASA Tech Briefs, March 2006

Topics covered include: Medical Signal-Conditioning and Data-Interface System; Instruments for Reading Direct-Marked Data-Matrix Symbols; Processing EOS MLS Level-2 Data; Ground Processing of Data From the Mars Exploration Rovers; Estimating Total Electron Content Using 1,000+ GPS Receivers; NASA Solar Array Demonstrates Commercial Potential; Improved Control of Charging Voltage for Li-Ion Battery; Programmable Pulse-Position-Modulation Encoder; Wavelength-Agile External-Cavity Diode Laser for DWDM; Pattern-Recognition Processor Using Holographic Photopolymer; Submicrosecond Power-Switching Test Circuit; Three-Function Logic Gate Controlled by Analog Voltage; Integrated System for Autonomous Science; Montage Version 3.0; Utilizing AI in Temporal, Spatial, and Resource Scheduling; Satellite Image Mosaic Engine; Architecture for Control of the K9 Rover; HFGMC Enhancement of MAC/GMC; Automated Activation and Deactivation of a System Under Test; Cleaning Carbon Nanotubes by Use of Mild Oxygen Plasmas; Generating Aromatics From CO2 on Mars or Natural Gas on Earth; Attaching Thermocouples by Peening or Crimping; Heat Treatment of Friction-Stir-Welded 7050 Aluminum Plates; Generating Breathable Air Through Dissociation of N2O; High-Performance Scanning Acousto-Ultrasonic System; Correction for Thermal EMFs in Thermocouple Feedthroughs; Using Quasiparticle Poisoning To Detect Photons; Estimating Resolution Lengths of Hybrid Turbulence Models; Education and Training Module in Alertness Management; Cargo-Positioning System for Next-Generation Spacecraft; Micro-Imagers for Spaceborne Cell-Growth Experiments; Holographic Solar Photon Thrusters; Plasma-Based Detector of Outer-Space Dust Particles; and Generation of Data-Rate Profiles of Ka-Band Deep-Space Links.

Source record↗
NASA NTRS2011

Atrial Arrhythmias in Astronauts. Summary of a NASA Summit

This slide presentation reviews the findings of a panel of heart experts brought together to study if atrial arrhythmias more prevalent in astronauts, and potential risk factors that may predispose astronauts to atrial arrhythmias. The objective of the panel was to solicit expert opinion on screening, diagnosis, and treatment options, identify gaps in knowledge, and propose relevant research initiatives. While Atrial Arrhythmias occur in approximately the same percents in astronauts as in the general population, they seem to occur at younger ages in astronauts. Several reasons for this predisposition were given: gender, hypertension, endurance training, and triggering events. Potential Space Flight-Related Risk factors that may play a role in precipitating lone atrial fibrillation were reviewed. There appears to be no evidence that any variable of the space flight environment increases the likelihood of developing atrial arrhythmias during space flight.

Barr, Yael↗
NASA NTRS2017

Medical Optimization Network for Space Telemedicine Resources

INTRODUCTION: Long-duration missions beyond low Earth orbit introduce new constraints to the space medical system. Beyond the traditional limitations in mass, power, and volume, consideration must be given to other factors such as the inability to evacuate to Earth, communication delays, and limitations in clinical skillsets. As NASA develops the medical system for an exploration mission, it must have an ability to evaluate the trade space of what resources will be most important. The Medical Optimization Network for Space Telemedicine Resources (MONSTR) was developed over the past year for this reason, and is now a system for managing data pertaining to medical resources and their relative importance when addressing medical conditions. METHODS: The MONSTR web application with a Microsoft SQL database backend was developed and made accessible to Tableau v9.3 for analysis and visualization. The database was initially populated with a list of medical conditions of concern for an exploration mission taken from the Integrated Medical Model (IMM), a probabilistic model designed to quantify in-flight medical risk. A team of physicians working within the Exploration Medical Capability Element of NASA's Human Research Program compiled a list diagnostic and treatment medical resources required to address best- and worst-case scenarios of each medical condition using a terrestrial standard of care and entered this data into the system. This list included both tangible resources (e.g. medical equipment, medications) and intangible resources (e.g. clinical skills required to perform a procedure). The physician team then assigned criticality values to each instance of a resource, representing the importance of that resource to diagnosing or treating its associated condition(s). Medical condition probabilities of occurrence during a Mars mission were pulled from the IMM and imported into the MONSTR database for use within a resource criticality-weighting algorithm. DISCUSSION: The MONSTR tool is a novel approach to assess the relative value of individual resources needed for the diagnosis and treatment of medical conditions. Future work will add resources for prevention and long term care of these conditions. Once data collection is complete, MONSTR will provide the operational and research communities at NASA with information to support informed decisions regarding areas of research investment, future crew training, and medical supplies manifested as part of any exploration medical system.

Rubin, D.↗
DOE OSTI2021

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

The photosynthetic capacity or the CO 2 -saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C 4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that V max can be well predicted from spectra (r=0.81). Here, the integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of V max (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to V max prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Data from Unique Contributions of Chlorophyll and Nitrogen to Predict Crop Photosynthetic Capacity from Leaf Spectroscopy

The photosynthetic capacity or the CO2-saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that Vmax can be well predicted from spectra (r=0.81). The integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of Vmax (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to Vmax prediction.

Biomass Analytics↗
DOE OSTI2023

Revealing the Galaxy–Halo Connection through Machine Learning

Abstract Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of star formation, feedback from supernovae, and the radiative transfer of ionizing photons can capture the processes relevant for establishing these connections. The complexity of these physics can prove difficult to disentangle and obfuscate how mass-dependent trends in the galaxy population originate. Here, we train a machine-learning method called Explainable Boosting Machines (EBMs) to infer how the stellar mass and star formation rate of nearly 6 million galaxies simulated by the Cosmic Reionization on Computers project depend on the physical properties of halo mass, the peak circular velocity of the galaxy during its formation history v peak , cosmic environment, and redshift. The resulting EBM models reveal the relative importance of these properties in setting galaxy stellar mass and star formation rate, with v peak providing the most dominant contribution. Environmental properties provide substantial improvements for modeling the stellar mass and star formation rate in only ≲10% of the simulated galaxies. We also provide alternative formulations of EBM models that enable low-resolution simulations, which cannot track the interior structure of dark matter halos, to predict the stellar mass and star formation rate of galaxies computed by high-resolution simulations with detailed baryonic physics.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Influence of simulated microgravity on the VO2 max of nontrained and trained rats

Head-down suspension (HDS) of rats has evolved as a useful model for the simulation of a microgravity environment. Previous HDS experiments with rats have shown an impaired capacity to perform aerobic exercise as demonstrated by reductions in maximum oxygen consumption (VO2 max), treadmill run time (RT), and mechanical efficiency (ME) of treadmill running at submaximal conditions. To determine whether endurance training (TR) before HDS would modify exercise performance, male Sprague-Dawley rats were assigned to nontrained (NT) or TR groups for 6 wk and exposed to HDS or cage control (CC) conditions for 29 days. The rats were tested for VO2 max, RT, and ME before treatment and on days 7, 14, 21, and 28. In addition, water and electrolyte excretion was measured on days 1 and 21 of the experimental period. Before HDS, the TR rats had significantly higher measures of VO2 max (15%) and RT (22%) than the NT rats. On day 28, HDS was associated with significant reductions in absolute VO2 max (ml/min) in TR (-30%) and NT (-14%) rats. Relative VO2 max (ml.min-1.kg-1) was significantly reduced in TR (-15%) but not NT rats. Similar reductions in RT occurred in TR (-37%) and NT (-35%) rats by day 28. ME was reduced 22% in both TR and NT rats after 28 days of suspension. HDS elicited diuresis, natriuresis, and kaliuresis in TR rats after 21 days but not after 24 h. In contrast, HDS-NT rats exhibited no diuretic, natriuretic, or kaliuretic responses.(ABSTRACT TRUNCATED AT 250 WORDS).

Non-NASA Center↗
NASA NTRS1995

Exercise and Human Immunodeficiency Virus (HIV-1) Infection

The human immune system is highly efficient and remarkably protective when functioning properly. Similar to other physiological systems, it functions best when the body is maintained with a balanced diet, sufficient rest and a moderately stress-free lifestyle. It can be disrupted by inappropriate drug use and extreme emotion or exertion. The functioning of normal or compromised immune systems can be enhanced by properly prescribed moderate exercise conditioning regimens in healthy people, and in some human immunodeficiency virus (HIV-1)-infected patients but not in others who unable to complete an interval training program. Regular exercise conditioning in healthy people reduces cardiovascular risk factors, increases stamina, facilitates bodyweight control, and reduces stress by engendering positive feelings of well-being. Certain types of cancer may also be suppressed by appropriate exercise conditioning. Various exercise regimens are being evaluated as adjunct treatments for medicated patients with the HIV-1 syndrome. Limited anecdotal evidence from patients suggests that moderate exercise conditioning is per se responsible for their survival well beyond expectancy. HIV-1-infected patients respond positively, both physiologically and psychologically, to moderate exercise conditioning. However, the effectiveness of any exercise treatment programme depends on its mode, frequency, intensity and duration when prescribed o complement the pathological condition of the patient. The effectiveness of exercise conditioning regimens in patients with HIV-1 infection is reviewed in this article. In addition, we discuss mechanisms and pathways, involving the interplay of psychological and physiological factors, through which the suppressed immune system can be enhanced. The immune modulators discussed are endogenous opioids, cytokines, neurotransmitters and other hormones. Exercise conditioning treatment appears to be more effective when combined with other stress management procedures.

Lawless, DeSales↗
DOE OSTI2021

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ~80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. Further, we show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1980

Prophylaxis and treatment of seasickness

Depending upon the dominant type of symptoms, seasickness is divided into three forms: nervous, gastro-intestinal, and cardiovascular. Various medications are recommended appropriate to these forms. The first goal is normalization of impaired system functions as well as metabolism and the electrolyte and acid-base condition of the organism. Dietary recommendations are made and specific suggestions on the use of physical exercise, including prophylatic vestibular training exercises.

Yefremenko, M.↗
NASA NTRS1999

Space Medicine: A Surgeon's Perspective

For the first four decades of human space flight NASA's priorities in life sciences and medical programs have been preventative medicine (astronaut selection and training); assessment of the physiologic effects of microgravity and other unique aspects of space flight, implementation of countermeasures to protect against adverse effects, and amelioration of these adverse effects. Because most of the U.S. space flight experience has been on short duration missions, the need for medical and diagnostic treatment capabilities have been limited.The first long-term crews will arrive on the International Space Station (ISS) in early 2000. This will usher in a new era, an era of sustained human presence in Low Earth Orbit. One of the principal purposes of the ISS program is to increase the knowledge of the effects of long duration space flight on humans, a pre-requisite to future exploration class missions beyond Low Earth Orbit (e.g., a return to the Moon or an exploration of Mars). Areas of particular interest include protection from radiation, muscle atrophy, bone loss, cardiovascular alterations, immune dysfunction, adverse psychological response to hazards and confinement, and neurovestibular alterations. In addition, long duration space flight requires the development of autonomous medical care capabilities, as the distances involved eliminate the possibility of real-time telemedicine or robotic intervention, and prevent a mission abort and a rapid return to Earth. The objectives of this presentation include: 1. A description of the International Space Station project, including its research facilities and on-orbit medical capabilities; 2. An overview of the physiological and medical problems associated with microgravity in space flight; 3. A review of NASA's biomedical research priorities and ongoing work to develop clinical care capabilities for space flight crews (including surgical interventions) and; 4. An overview of current and proposed research priorities for NASA Research Announcements, NASA Space Biomedical Research Institute, Small Business Innovation Research Grant, and other funding sources.

Dawson, David L.↗