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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An integrated multi-omics approach identifies the landscape of interferon-α-mediated responses of human pancreatic beta cells

Proinflammatory cytokines are important mediators of pancreatic beta cell dysfunction and demise in the early stages of type 1 diabetes (T1D). Interferon-a (IFNa), a type I interferon member, is expressed in the islets of T1D individuals and it is expression and signaling is regulated by both genetic (T1D risk variants) and environmental factors (viral infections) associated to T1D. We presently characterized human beta cells responses to IFNa by combining ATAC-seq, RNA-seq and proteomics assays. The initial beta cell response to IFNa was characterized by major chromatin remodeling, followed by marked changes in transcriptional and translational regulation. IFNa-induced changes in alternative splicing (AS) and first exon usage increased the diversity of transcripts expressed by beta cells. This, combined with changes observed on protein modification/degradation, ER stress and MHC class I, may significantly expand the peptide repertoire presented by beta cells to the immune system. On the other hand, beta cells up-regulated checkpoint proteins, such as PDL1 and HLA-E, that may protect them against the autoimmune assault. Data mining of the present multi-omics analysis led to the identification of two compound classes that revert IFNa effects on human beta cells and may be translated to clinical trials.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1977

Theoretic aspects of the identification of the parameters in the optimal control model

The identification of the parameters of the optimal control model from input-output data of the human operator is considered. Accepting the basic structure of the model as a cascade of a full-order observer and a feedback law, and suppressing the inherent optimality of the human controller, the parameters to be identified are the feedback matrix, the observer gain matrix, and the intensity matrices of the observation noise and the motor noise. The identification of the parameters is a statistical problem, because the system and output are corrupted by noise, and therefore the solution must be based on the statistics (probability density function) of the input and output data of the human operator. However, based on the statistics of the input-output data of the human operator, no distinction can be made between the observation and the motor noise, which shows that the model suffers from overparameterization.

Vanwijk, R. A.↗
DOE OSTI2020

Detection of a multi–disease biomarker in saliva with graphene field effect transistors

Human carbonic anhydrase 1 (CA1) has been suggested as a biomarker for identification of several diseases including cancers, pancreatitis, diabetes, and Sjogren’s syndrome. However, the lack of a rapid, cheap, accurate, and easy-to-use quantification technique has prevented widespread utilization of CA1 for practical clinical applications. To this end, we present a label-free electronic biosensor for detection of CA1 utilizing highly sensitive graphene field effect transistors (G-FETs) as a transducer and specific RNA aptamers as a probe. The binding of CA1 with aptamers resulted in a positive shift in Dirac voltage V D of the G-FETs, the magnitude of which depended on target concentration. These aptameric G-FET biosensors showed the binding affinity (K D ) of ~2.3 ng/ml (70 pM), which is four orders lower than that reported using a gel shift assay. This lower value of K D enabled us to achieve a detection range (10 pg/ml - 100 ng/ml) which is well in line with the clinically relevant range. These highly sensitive devices allowed us to further prove their clinical relevance by successfully detecting the presence of CA1 in human saliva samples. In conclusion, the utilization of this label-free biosensor could facilitate the early stage identification of various diseases associated with changes in concentration of CAs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2002

Mutations in the dopamine beta-hydroxylase gene are associated with human norepinephrine deficiency

Norepinephrine (NE), a key neurotransmitter of the central and peripheral nervous systems, is synthesized by dopamine beta-hydroxylase (DBH) that catalyzes oxidation of dopamine (DA) to NE. NE deficiency is a congenital disorder of unknown etiology, in which affected patients suffer profound autonomic failure. Biochemical features of the syndrome include undetectable tissue and circulating levels of NE and epinephrine, elevated levels of DA, and undetectable levels of DBH. Here, we report identification of seven novel variants including four potentially pathogenic mutations in the human DBH gene (OMIM 223360) from analysis of two unrelated patients and their families. Both patients are compound heterozygotes for variants affecting expression of DBH protein. Each carries one copy of a T-->C transversion in the splice donor site of DBH intron 1, creating a premature stop codon. In patient 1, there is a missense mutation in DBH exon 2. Patient 2 carries missense mutations in exons 1 and 6 residing in cis. We propose that NE deficiency is an autosomal recessive disorder resulting from heterogeneous molecular lesions at DBH. Copyright 2002 Wiley-Liss, Inc.

Case Reports↗
NASA NTRS2017

Autonomous System Technologies for Resilient Airspace Operations

Increasing autonomous systems within the aircraft cockpit begins with an effort to understand what autonomy is and developing the technology that encompasses it. Autonomy allows an agent, human or machine, to act independently within a circumscribed set of goals; delegating responsibility to the agent(s) to achieve overall system objective(s). Increasingly Autonomous Systems (IAS) are the highly sophisticated progression of current automated systems toward full autonomy. Working in concert with humans, these types of technologies are expected to improve the safety, reliability, costs, and operational efficiency of aviation. IAS implementation is imminent, which makes the development and the proper performance of such technologies, with respect to cockpit operation efficiency, the management of air traffic and data communication information, vital. A prototype IAS agent that attempts to optimize the identification and distribution of "relevant" air traffic data to be utilized by human crews during complex airspace operations has been developed.

Houston, Vincent E.↗
DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

Today’s large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet Autoencoder (AE) based Cyber-Physical Anomaly Detection System (Cy-Phy ADS) for detecting anomalies in EV Controller Area Network (CAN) protocol communication. It consists of four main components: Cyber-Physical Feature Extractor, ResNet AE-based Anomaly Detection Framework, Cyber-Physical Health Metric (CPHM), and Visualization Dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory. The presented Cy-Phy ADS compared against six widely used unsupervised anomaly detection algorithms: One Class Support Vector Machine (OCSVM), Variational Autoencoder (VAE), LSTM Autoencoder (LSTM AE), Isolation Forest (IForest), Principle Component Analysis (PCA) and Local Outlier Factor (LOF). Here the presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and False positive rate. It also showed the lowest training and inference time compared to the neural network-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1984

Pilot interaction with automated airborne decision making systems

Two project areas were pursued: the intelligent cockpit and human problem solving. The first area involves an investigation of the use of advanced software engineering methods to aid aircraft crews in procedure selection and execution. The second area is focused on human problem solving in dynamic environments, particulary in terms of identification of rule-based models land alternative approaches to training and aiding. Progress in each area is discussed.

Rouse, W. B.↗
NASA NTRS2013

Ubiquitous Wireless Smart Sensing and Control

Need new technologies to reliably and safely have humans interact within sensored environments (integrated user interfaces, physical and cognitive augmentation, training, and human-systems integration tools). Areas of focus include: radio frequency identification (RFID), motion tracking, wireless communication, wearable computing, adaptive training and decision support systems, and tele-operations. The challenge is developing effective, low cost/mass/volume/power integrated monitoring systems to assess and control system, environmental, and operator health; and accurately determining and controlling the physical, chemical, and biological environments of the areas and associated environmental control systems.

Wagner, Raymond↗
NASA NTRS2013

Ubiquitous Wireless Smart Sensing and Control. Pumps and Pipes JSC: Uniquely Houston

Need new technologies to reliably and safely have humans interact within sensored environments (integrated user interfaces, physical and cognitive augmentation, training, and human-systems integration tools).Areas of focus include: radio frequency identification (RFID), motion tracking, wireless communication, wearable computing, adaptive training and decision support systems, and tele-operations. The challenge is developing effective, low cost/mass/volume/power integrated monitoring systems to assess and control system, environmental, and operator health; and accurately determining and controlling the physical, chemical, and biological environments of the areas and associated environmental control systems.

Wagner, Raymond↗
NASA NTRS2000

Multi-Agent Diagnosis and Control of an Air Revitalization System for Life Support in Space

An architecture of interoperating agents has been developed to provide control and fault management for advanced life support systems in space. In this adjustable autonomy architecture, software agents coordinate with human agents and provide support in novel fault management situations. This architecture combines the Livingstone model-based mode identification and reconfiguration (MIR) system with the 3T architecture for autonomous flexible command and control. The MIR software agent performs model-based state identification and diagnosis. MIR identifies novel recovery configurations and the set of commands required for the recovery. The AZT procedural executive and the human operator use the diagnoses and recovery recommendations, and provide command sequencing. User interface extensions have been developed to support human monitoring of both AZT and MIR data and activities. This architecture has been demonstrated performing control and fault management for an oxygen production system for air revitalization in space. The software operates in a dynamic simulation testbed.

Malin, Jane T.↗
NASA NTRS1982

Modeling of human operator dynamics in simple manual control utilizing time series analysis

Time series analysis is applied to model human operator dynamics in pursuit and compensatory tracking modes. The normalized residual criterion is used as a one-step analytical tool to encompass the processes of identification, estimation, and diagnostic checking. A parameter constraining technique is introduced to develop more reliable models of human operator dynamics. The human operator is adequately modeled by a second order dynamic system both in pursuit and compensatory tracking modes. In comparing the data sampling rates, 100 msec between samples is adequate and is shown to provide better results than 200 msec sampling. The residual power spectrum and eigenvalue analysis show that the human operator is not a generator of periodic characteristics.

Agarwal, G. C.↗
NASA NTRS2003

Revolutionary Concepts for Human Outer Planet Exploration (HOPE)

This paper summarizes the content of a NASA-led study performed to identify revolutionary concepts and supporting technologies for Human Outer Planet Exploration (HOPE). Callisto, the fourth of Jupiter's Galilean moons, was chosen as the destination for the HOPE study. Assumptions for the Callisto mission include a launch year of 2045 or later, a spacecraft capable of transporting humans to and from Callisto in less than five years, and a requirement to support three humans on the surface for a minimum of 30 days. Analyses performed in support of HOPE include identification of precursor science and technology demonstration missions and development of vehicle concepts for transporting crew and supplies. A complete surface architecture was developed to provide the human crew with a power system, a propellant production plant, a surface habitat, and supporting robotic systems. An operational concept was defined that provides a surface layout for these architecture components, a list of surface tasks, a 30-day timeline, a daily schedule, and a plan for communication from the surface.

Power electronics↗
DOE OSTI2021

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Chemical Characterization and Identification of Organosilicon Contaminants in ISS Potable Water

2015 marked the 15th anniversary of continuous human presence on board the International Space Station. During the past year crew members from Expeditions 42-46, including two participating in a one-year mission, continued to rely on reclaimed water as their primary source of potable water. This paper presents and discusses results from chemical analyses performed on ISS water samples returned in 2015. Since the U.S. water processor assembly (WPA) became operational in 2008, there have been 5 instances of organic contaminants breaking through the treatment process. On each occasion, the breakthrough was signaled by an increase in the total organic carbon (TOC) concentration in the product water measured by the onboard TOC analyzer (TOCA). Although the most recent TOC rise in 2015 was not unexpected, it was the first time where dimethylsilanediol (DMSD) was not the primary compound responsible for the increase. Results from ground analysis of a product water sample collected in June of 2015 and returned on Soyuz 41 showed that DMSD only accounted for 10% of the measured TOC. After considerable laboratory investigation, the compound responsible for the majority of the TOC was identified as monomethysilanetriol (MMST). MMST is a low-toxicity compound that is structurally similar to DMSD.

Straub, John E., II↗
NASA NTRSDate not supplied

Human Specimen Repository: Sample Collection for the Future [#133-000445]

In early 2014, an executive memo was released directing the improvement of the management of and access to scientific collections funded by government resources. In the spirit of this memo, NASA’s Human Research Program (HRP) funded a project to review existing residual samples in long-term storage for viability, identification, and scientific significance. These samples were to be collected in a centralized location and a detailed inventory was to be conducted to create the Human Sample Repository (HSR). HSR would work to review data from over 27,000 samples, from flight research projects dating back to 1998. The samples themselves would be reviewed, re-labeled, and organized into easy to retrieve, long-term storage. In addition to the older residual samples, HSR would expand to include pristine samples from all consenting crew who have flown on International Space Station (ISS) missions. All samples inventoried and managed by HSR are intended for the same purpose; to support future research projects which will utilize advanced technologies or techniques, by providing one-of-a-kind sample collections that can not be replicated. With this mission in mind, HSR will continue to collect residual samples from HRP funded projects and pristine samples from consenting crew, to further build this unique collection and increase the diversity of flight samples available to the scientific community.

Human Specimen Repository↗
NASA NTRS2014

Loss of Signal, Aeromedical Lessons Learned from the STS-107 Columbia Space Shuttle Mishap

Loss of Signal, a NASA publication to be available in May 2014 presents the aeromedical lessons learned from the Columbia accident that will enhance crew safety and survival on human space flight missions. These lessons were presented to limited audiences at three separate Aerospace Medical Association (AsMA) conferences: in 2004 in Anchorage, Alaska, on the causes of the accident; in 2005 in Kansas City, Missouri, on the response, recovery, and identification aspects of the investigation; and in 2011, again in Anchorage, Alaska, on future implications for human space flight. As we embark on the development of new spacefaring vehicles through both government and commercial efforts, the NASA Johnson Space Center Human Health and Performance Directorate is continuing to make this information available to a wider audience engaged in the design and development of future space vehicles. Loss of Signal summarizes and consolidates the aeromedical impacts of the Columbia mishap process-the response, recovery, identification, investigative studies, medical and legal forensic analysis, and future preparation that are needed to respond to spacecraft mishaps. The goal of this book is to provide an account of the aeromedical aspects of the Columbia accident and the investigation that followed, and to encourage aerospace medical specialists to continue to capture information, learn from it, and improve procedures and spacecraft designs for the safety of future crews. This poster presents an outline of Loss of Signal contents and highlights from each of five sections - the mission and mishap, the response, the investigation, the analysis and the future.

Stepaniak, Phillip C.↗
NASA NTRS2014

Loss of Signal, Aeromedical Lessons Learned for the STS-I07 Columbia Space Shuttle Mishap

Loss of Signal, a NASA publication to be available in May 2014, presents the aeromedical lessons learned from the Columbia accident that will enhance crew safety and survival on human space flight missions. These lessons were presented to limited audiences at three separate Aerospace Medical Association (AsMA) conferences: in 2004 in Anchorage, Alaska, on the causes of the accident; in 2005 in Kansas City, Missouri, on the response, recovery, and identification aspects of the investigation; and in 2011, again in Anchorage, Alaska, on future implications for human space flight. As we embark on the development of new spacefaring vehicles through both government and commercial efforts, the NASA Johnson Space Center Human Health and Performance Directorate is continuing to make this information available to a wider audience engaged in the design and development of future space vehicles. Loss of Signal summarizes and consolidates the aeromedical impacts of the Columbia mishap process-the response, recovery, identification, investigative studies, medical and legal forensic analysis, and future preparation that are needed to respond to spacecraft mishaps. The goals of this book are to provide an account of the aeromedical aspects of the Columbia accident and the investigation that followed, and to encourage aerospace medical specialists to continue to capture information, learn from it, and improve procedures and spacecraft designs for the safety of future crews.

Patlach, Robert↗