Space-based centrifuge
Engineering and biomedical studies of therapeutic and training potential of space-based centrifuge
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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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Ultrasonic wave propagation in trabecular bone predicted by the stratified model
The objective of this study was to investigate ultrasound propagation in trabecular bone by considering the wave reflection and transmission in a multilayered medium. The use of ultrasound to identify those at risk of osteoporosis is a promising diagnostic method providing a measure of bone mineral density (BMD). A stratified model was proposed to study the effect of transmission and reflection of ultrasound wave within the trabecular architecture on the relationship between ultrasound and BMD. The results demonstrated that ultrasound velocity in trabecular bone was highly correlated with the bone apparent density (r=0.97). Moreover, a consistent pattern of the frequency dependence of ultrasound attenuation coefficient has been observed between simulation using this model and experimental measurement of trabecular bone. The normalized broadband ultrasound attenuation (nBUA) derived from the simulation results revealed that nBUA was nonlinear with respect to trabecular porosity and BMD. The curve of the relationship between nBUA and BMD was parabolic in shape, and the peak magnitude of nBUA was observed at approximately 60% of bone porosity. These results agreed with the published experimental data and demonstrated that according to the stratified model, reflection and transmission were important factors in the ultrasonic propagation through the trabecular bone.
Non-NASA Center↗
Earth benefits from space life sciences
Contributions of space exploration which are widely recognized are those dealing with the impact of space technology on public health and medical services in both urban and remote rural areas. Telecommunications, image enhancement, 3-dimensional image reconstructions, miniaturization, automation, and data analysis, have transformed the delivery of medical care and have brought about a new impetus to the field of biomedicine. Many areas of medical care and biological research have been affected. These include technological breakthroughs in such areas as: (1) diagnosis, treatment, and prevention of cardiovascular diseases, (2) new approaches to the understanding of osteoporosis, (3) early detection of genetic birth defects, (4) emergency medical care, and (5) treatment of chronic metabolic disorders. These are but a few examples where technology originally developed to support space medicine or space research has been applied to solving medical and health care delivery problems on Earth.
NASA Center HQS↗
Use of a genetic algorithm for the analysis of eye movements from the linear vestibulo-ocular reflex
It is common in vestibular and oculomotor testing to use a single-frequency (sine) or combination of frequencies [sum-of-sines (SOS)] stimulus for head or target motion. The resulting eye movements typically contain a smooth tracking component, which follows the stimulus, in which are interspersed rapid eye movements (saccades or fast phases). The parameters of the smooth tracking--the amplitude and phase of each component frequency--are of interest; many methods have been devised that attempt to identify and remove the fast eye movements from the smooth. We describe a new approach to this problem, tailored to both single-frequency and sum-of-sines stimulation of the human linear vestibulo-ocular reflex. An approximate derivative is used to identify fast movements, which are then omitted from further analysis. The remaining points form a series of smooth tracking segments. A genetic algorithm is used to fit these segments together to form a smooth (but disconnected) wave form, by iteratively removing biases due to the missing fast phases. A genetic algorithm is an iterative optimization procedure; it provides a basis for extending this approach to more complex stimulus-response situations. In the SOS case, the genetic algorithm estimates the amplitude and phase values of the component frequencies as well as removing biases.
Non-NASA Center↗
Shear stress reduces protease activated receptor-1 expression in human endothelial cells
Shear stress has been shown to regulate several genes involved in the thrombotic and proliferative functions of endothelial cells. Thrombin receptor (protease-activated receptor-1: PAR-1) increases at sites of vascular injury, which suggests an important role for PAR-1 in vascular diseases. However, the effect of shear stress on PAR-1 expression has not been previously studied. This work investigates effects of shear stress on PAR-1 gene expression in both human umbilical vein endothelial cells (HUVECs) and microvascular endothelial cells (HMECs). Cells were exposed to different shear stresses using a parallel plate flow system. Northern blot and flow cytometry analysis showed that shear stress down-regulated PAR-1 messenger RNA (mRNA) and protein levels in both HUVECs and HMECs but with different thresholds. Furthermore, shear-reduced PAR-1 mRNA was due to a decrease of transcription rate, not increased mRNA degradation. Postshear stress release of endothelin-1 in response to thrombin was reduced in HUVECs and HMECs. Moreover, inhibitors of potential signaling pathways applied during shear stress indicated mediation of the shear-decreased PAR-1 expression by protein kinases. In conclusion, shear stress exposure reduces PAR-1 gene expression in HMECs and HUVECs through a mechanism dependent in part on protein kinases, leading to altered endothelial cell functional responses to thrombin.
Non-NASA Center↗
Influence of gravity on cardiac performance
Results obtained by the investigators in ground-based experiments and in two parabolic flight series of tests aboard the NASA KC-135 aircraft with a hydraulic simulator of the human systemic circulation have confirmed that a simple lack of hydrostatic pressure within an artificial ventricle causes a decrease in stroke volume of 20%-50%. A corresponding drop in stroke volume (SV) and cardiac output (CO) was observed over a range of atrial pressures (AP), representing a rightward shift of the classic CO versus AP cardiac function curve. These results are in agreement with echocardiographic experiments performed on space shuttle flights, where an average decrease in SV of 15% was measured following a three-day period of adaptation to weightlessness. The similarity of behavior of the hydraulic model to the human system suggests that the simple physical effects of the lack of hydrostatic pressure may be an important mechanism for the observed changes in cardiac performance in astronauts during the weightlessness of space flight.
NASA Discipline Cardiopulmonary↗
Evaluation of dual-tip micromanometers during 21-day implantation in goats
Investigative research efforts using a cardiovascular model required the determination of central circulatory haemodynamic and arterial system parameters for the evaluation of cardiovascular performance. These calculations required continuous beat-to-beat measurement of pressure within the four chambers of the heart and great vessels. Sensitivity and offset drift, longevity, and sources of error for eight 3F dual-tipped micromanometers were determined during 21 days of implantation in goats. Subjects were instrumented with pairs of chronically implanted fluid-filled access catheters in the left and right ventricles, through which dual-tipped (test) micromanometers were chronically inserted and single-tip (standard) micromanometers were acutely inserted. Acutely inserted sensors were calibrated daily and measured pressures were compared in vivo to the chronically inserted sensors. Comparison of the pre- and post-gain calibration of the chronically inserted sensors showed a mean sensitivity drift of 1.0 +/- 0.4% (99% confidence, n = 9 sensors) and mean offset drift of 5.0 +/- 1.5 mmHg (99% confidence, n = 9 sensors). Potential sources of error for these drifts were identified, and included measurement system inaccuracies, temperature drift, hydrostatic column gradients, and dynamic pressure changes. Based upon these findings, we determined that these micromanometers may be chronically inserted in high-pressure chambers for up to 17 days with an acceptable error, but should be limited to acute (hours) insertions in low-pressure applications.
Non-NASA Center↗
Electrical signal transmission in a bone cell network: the influence of a discrete gap junction
A refined electrical cable model is formulated to investigate the role of a discrete gap junction in the intracellular transmission of electrical signals in an electrically coupled system of osteocytes and osteoblasts in an osteon. The model also examines the influence of the ratio q between the membrane's electrical time constant and the characteristic time of pore fluid pressure, the circular, cylindrical geometry of the osteon, and key simplifying assumptions in our earlier continuous cable model (see Zhang, D., S. C. Cowin, and S. Weinbaum. Electrical signal transmission and gap junction regulation in a bone cell network: A cable model for an osteon. Ann. Biomed. Eng. 25:379-396, 1997). Using this refined model, it is shown that (1) the intracellular potential amplitude at the osteoblastic end of the osteonal cable retains the character of a combination of a low-pass and a high-pass filter as the corner frequency varies in the physiological range; (2) the presence of a discrete gap junction near a resting osteoblast can lead to significant modulation of the intracellular potential and current in the osteoblast for measured values of the gap junction coupling strength; and (3) the circular, cylindrical geometry of the osteon is well simulated by the beam analogy used in Zhang et al.
Non-NASA Center↗
Wireless Frequency‐Multiplexed Acoustic Array‐Based Acoustofluidics
Abstract Acoustofluidics has shown great potential in enabling on‐chip technologies for driving liquid flows and manipulating particles and cells for engineering, chemical, and biomedical applications. To introduce on‐demand liquid sample processing and micro/nano‐object manipulation functions to wearable and embeddable electronics, wireless acoustofluidic chips are highly desired. This paper presents wireless acoustofluidic chips to generate acoustic waves carrying sufficient energy and achieve key acoustofluidic functions, including arranging particles and cells, generating fluid streaming, and enriching in‐droplet particles. To enable these functions, the wireless acoustofluidic chips leverage mechanisms, including inductive coupling‐based wireless power transfer (WPT), frequency multiplexing‐based control of multiple acoustic waves, and the resultant acoustic radiation and drag forces. For validation, the wirelessly generated acoustic waves are measured using laser vibrometry when different materials (e.g., bone, tissue, and hand) are inserted between the WPT transmitter and receiver. Moreover, the wireless acoustofluidic chips successfully arrange nanoparticles into different patterns, align cells into parallel pearl chains, generate streaming, and enrich in‐droplet microparticles. This research is anticipated to facilitate the development of embeddable wireless on‐chip flow generators, wearable sensors with liquid sample processing functions, and implantable devices with flow generation and acoustic stimulation abilities for engineering, veterinary, and biomedical applications.
Li, Jiali↗
Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.
artificial intelligence↗
Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.
space biology↗
Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.
space biology↗
Advanced Mixed-Mode Pixel Array Detector for X-rays Above 20 keV (Final Report)
The objective of the work reported upon was to perform research and development and test advanced prototype imaging x-ray detectors needed to meet pressing scientific research needs at U.S. x-ray synchrotron user facilities. The high x-ray brightness of the Nation’s x-ray synchrotron sources in the DOE National Laboratories holds promise to advance science and technology in a broad range of fundamental and applied disciplines, including physics, chemistry, biomedical areas and engineering. At present, progress in many areas is being limited not by the x-ray source but by the lack of imaging x-ray detectors capable of meeting experimental needs. Specifically, there is pressing need for imaging detectors with high dynamic range, low noise, high frame rates and the ability to capture x-ray images with x-rays of energy > 20 keV. This Final Technical Report summarizes progress made at Cornell University on novel integrating x-ray detectors that address these characteristics.
36 MATERIALS SCIENCE↗
FAIR Data and Interpretable AI Framework for Architectured Metamaterials
Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.
36 MATERIALS SCIENCE↗
Man/Machine Interaction Dynamics And Performance (MMIDAP) capability
The creation of an ability to study interaction dynamics between a machine and its human operator can be approached from a myriad of directions. The Man/Machine Interaction Dynamics and Performance (MMIDAP) project seeks to create an ability to study the consequences of machine design alternatives relative to the performance of both machine and operator. The class of machines to which this study is directed includes those that require the intelligent physical exertions of a human operator. While Goddard's Flight Telerobotic's program was expected to be a major user, basic engineering design and biomedical applications reach far beyond telerobotics. Ongoing efforts are outlined of the GSFC and its University and small business collaborators to integrate both human performance and musculoskeletal data bases with analysis capabilities necessary to enable the study of dynamic actions, reactions, and performance of coupled machine/operator systems.
Frisch, Harold P.↗
Development of Countermeasures and Exercise Protocols to Reduce the Effects of Microgravity
I have helped scientists at NASA-JSC in analyzing data from many projects. Some of the major ones are: (1) cardiovascular responses to lower body negative pressure (LBNP) following bed rest, (2) the effects of dietary sodium, (3) in-flight cycle exercise mitigates reduced oxygen consumption at submaximal heart rates following space flight, (4) exercise thermoregulation after 13 days of head down bed rest, and (5) bed rest induced orthostatic intolerance. Many of the projects have now been completed and some of them are in the process of being published and others have been presented at national meetings. These projects have helped me be a true statistician and given me a real-life perspective of how interesting and complicated data can be. As a by-product of of these involvements I have been able to write and publish some methodological research that have applications in NASA and elsewhere. For instance, while I was at JSC, I happened to meet Dr. Al Feiveson and got into a discussion of the Space Shuttle Reliability. This led us to rethink about the way the data on the accelerated life testing of space shuttle pressure vessels had been analyzed. This has resulted in a major statistical paper and the paper has appeared in one of the top journals in the field of Statistics. A review of the paper by the editor of the journal was published in AmStatNews, a copy is attached with this report. I have presented these findings at the national/international statistics conference and at other places. I have also written another paper on reliability and a paper on calibration techniques that have applications in the engineering and the biomedical branches of NASA. Further, I am currently in the process of writing at least two more papers that have direct applications in NASA related studies.
Kulkarni, Pandurang M.↗
Investigating Biological Responses to Deep Space Radiation for Missions Beyond Low Earth Orbit (LEO) using Yeast
To enable long-term spaceflight missions and establish habitation on the Moon and Mars, we require a comprehensive understanding of the effects of chronic deep space radiation exposure on humans. BioSentinel is NASA’s first biological CubeSat to venture beyond Low Earth Orbit (LEO). It utilizes Saccharomyces cerevisiae (budding yeast) as a model organism to study biological responses to deep space radiation. Yeast share significant genetic homology with humans, including basic cellular metabolism and DNA repair mechanisms. In addition, unlike human cell cultures, yeast can survive the duration and constraints of a deep space mission. BioSentinel measures biological responses using an optical system and alamarBlue oxidation-reduction (redox) dye. Two strains of yeast are studied - a wild-type and a rad51 mutant strain that is deficient in DNA repair. Changes in metabolism and growth are monitored throughout the nominal 6-month mission. Preliminary tests indicate a significant change in the alamarBlue response to low-dose ionizing radiation (IR). Additionally, rad51 cells have shown an IR dose-dependent decrease in glucose uptake and accumulation of oxidized NADH (NAD+). These biomolecules are involved in reactions responsible for basic cell processes, including growth and development, signaling, and respiration. The current study expanded upon previous data by exposing yeast to deep space-relevant radiation. Glucose and NADH/NAD+ assays were conducted on yeast subjected to varying dosages of high-energy Fe-56 and simulated galactic cosmic rays (GCRs). The resulting data was analyzed using Excel and GraphPad Prism. A particular focus was to identify biomolecules resulting from aerobic respiration, which requires the presence of oxygen, or anaerobic processes. As long-term spaceflight missions draw near, it is increasingly important to characterize biological processes affected by the conditions of deep space. Studying biomolecular damage caused by deep space radiation may enable the development of engineering controls or biomedical therapeutics that mitigate health complications for future astronauts.
Kyra Keenan↗