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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Flight Crew Health Stabilization Program

This document establishes the policy and procedures for the HSP and is authorized through the Director, Johnson Space Center (JSC). This document delineates the medical operations requirements for the HSP. The HSP goals are accomplished through an awareness campaign and procedures such as limiting access to flight crewmembers, medical screening, and controlling flight crewmember activities. NASA's Human Space Flight Program uses strategic risk mitigation to achieve mission success while protecting crew health and safety. Infectious diseases can compromise crew health and mission success, especially in the immediate preflight period. The primary purpose of the Flight Crew Health Stabilization Program (HSP) is to mitigate the risk of occurrence of infectious disease among astronaut flight crews in the immediate preflight period. Infectious diseases are contracted through direct person-to-person contact, and through contact with infectious material in the environment. The HSP establishes several controls to minimize crew exposure to infectious agents. The HSP provides a quarantine environment for the crew that minimizes contact with potentially infectious material. The HSP also limits the number of individuals who come in close contact with the crew. The infection-carrying potential of these primary contacts (PCs) is minimized by educating them in ways to avoid infections and avoiding contact with the crew if they are or may be sick. The transmission of some infectious diseases can be greatly curtailed by vaccinations. PCs are strongly encouraged to maintain updated vaccinations.

Johnston, Smith L.↗
NASA NTRS1991

A two dimensional artificial reality

The current presumption is that it is necessary to don goggles, gloves and a data suit to experience artificial reality. However, there is another technology that offers an alternative or complement to the encumbering techniques associated with NASA. In VIDEOPLACE, your image appears in a 2D graphic world created by a computer. The VIDEOPLACE computer can analyze a person's image in 1/30 second and can detect when an object is touched. Thus, it can generate a graphic or auditory response to an illusory contact. VIDEOPLACE technology exists in two formats: the VIDEODESK and the VIDEOPLACE. In the VIDEODESK environment, the image of your hands can be used to perform the normal mouse functions, such as menuing and drawing. In addition, you have the advantage of multipoint control. For instance, you can use the thumbs and forefingers of each hand as control points for a spline curve. Perhaps most important, the image of your hands can be compressed and transmitted to a colleague over an ISDN voice channel to appear on the remote screen superimposed over identical information. Likewise, the image of your colleague's hands can appear on both screens. The result is that the two of you can use your hands to point to features on your respective screens as you speak, exactly as you would if you were sitting together. In the VIDEOPLACE environment, you can interact with graphic creatures and the images of other people in other locations in a graphic world. Your whole body can be moved, scaled and rotated in real-time without regard to the laws of physics. Thus, VIDEOPLACE can be used to create a fantasy world in which the laws of cause and effect are composed by an artist.

Krueger, Myron W.↗
DOE OSTI2021

A cross-study analysis of drug response prediction in cancer cell lines

Abstract To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1987

The desktop interface in intelligent tutoring systems

The interface between an Intelligent Tutoring System (ITS) and the person being tutored is critical to the success of the learning process. If the interface to the ITS is confusing or non-supportive of the tutored domain, the effectiveness of the instruction will be diminished or lost entirely. Consequently, the interface to an ITS should be highly integrated with the domain to provide a robust and semantically rich learning environment. In building an ITS for ZetaLISP on a LISP Machine, a Desktop Interface was designed to support a programming learning environment. Using the bitmapped display, windows, and mouse, three desktops were designed to support self-study and tutoring of ZetaLISP. Through organization, well-defined boundaries, and domain support facilities, the desktops provide substantial flexibility and power for the student and facilitate learning ZetaLISP programming while screening the student from the complex LISP Machine environment. The student can concentrate on learning ZetaLISP programming and not on how to operate the interface or a LISP Machine.

Baudendistel, Stephen↗
NASA NTRS1997

Individual Differences in Response to Sleep Deprivation: Assessment of Fatigue Following Sleep Loss

Previous work has indicated that a small but significant number of participants in sleep deprivation studies or in simulated shift work experiments manifests an exaggerated performance decrement when they reach a critical point in the experiment, usually near the trough of the circadian cycle or the middle of the night. Those who show this exaggerated response do not appear to differ from other non-nal volunteers in any substantial way according to usual screening criteria or baseline values. The present study aims to examine factors that may provide the basis for this extreme response. We propose that a preexisting sleep deficit-as manifested by low values on the Multiple Sleep Latency Test (MSLT)-may account for extreme responders. It has been shown that among normal volunteers screened for a variety of studies, approximately 20 to 25 percent show low (< 6 minutes) MSLT scores on a consistent basis, whereas a like proportion shows consistently high MSLT scores (> 13 minutes). Additionally, studies by this group have indicated that subjects with low MSLT scores may suffer from chronic insufficient sleep, as further substantiated by the finding that they have consistently higher nocturnal sleep efficiency and that their MSLT scores rise to normal values when sleep is extended. We hypothesize that the short MSLT subjects have a significant long-term sleep deficit that leads to a marked intolerance for sleep deprivation or shift work. We further suggest that this sleep debt may signify an increased sleep need in these individuals that is not met either due to personal preference or to societal pressures (or both). If this speculation is accurate, then we predict that the tolerance for sleep deprivation in such individuals can be increased by "pretreatment" with sleep extension. Thus, the present study is designed to test the following two hypotheses: (1) subjects with nominal sleep patterns who have low MSLT scores (e.g., Sleepy subjects) will show an exaggerated response (performance decrement) to sleep loss compared to subjects who have high MSLT scores (Alert subjects) on a nominal sleep schedule; (2) when permitted to extend sleep--thus discharging their sleep debt-the Sleepy subjects will show a sleep-loss response resembling that of the Alert subjects.

Carskadon, Mary A.↗
NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗
NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗
NASA NTRS1997

Individual Differences in Response to Sleep Deprivation: Assessment of Fatigue Following Sleep Loss

Previous work has indicated that a small but significant number of participants in sleep deprivation studies or in simulated shift work experiments manifests an exaggerated performance decrement when they reach a critical point in the experiment, usually near the trough of the circadian cycle or the middle of the night. Those who show this exaggerated response do not appear to differ from other non-nal volunteers in any substantial way according to usual screening criteria or baseline values. The present study aims to examine factors that may provide the basis for this extreme response. We propose that a preexisting sleep deficit-as manifested by low values on the Multiple Sleep Latency Test (MSLT)-may account for extreme responders. Roth and colleagues (1993) have shown that among normal volunteers screened for a variety of studies, approximately 20 to 25 percent show low (< 6 minutes) MSLT scores on a consistent basis, whereas a like proportion shows consistently high MSLT scores (> 13 minutes). Additionally, studies by this group have indicated that subjects with low MSLT scores may suffer from chronic insufficient sleep (Roth et al., 1993), as further substantiated by the finding that they have consistently higher nocturnal sleep efficiency and that their MSLT scores rise to normal values when sleep is extended (Roehrs et al., 1996). We hypothesize that the short MSLT subjects have a significant long-term sleep deficit that leads to a marked intolerance for sleep deprivation or shift work. We further suggest that this sleep debt may signify an increased sleep need in these individuals that is not met either due to personal preference or to societal pressures (or both). If this speculation is accurate, then we predict that the tolerance for sleep deprivation in such individuals can be increased by "pretreatment" with sleep extension. Thus, the present study is designed to test the following two hypotheses: subjects with nominal sleep patterns who have low MSLT scores (e.g., Sleepy subjects) will show an exaggerated response (performance decrement) to sleep loss compared to subjects who have high MSLT scores (Alert subjects) on a nominal sleep schedule. when permitted to extend sleep-thus discharging their sleep debt-the Sleepy subjects will show a sleep-loss response resembling that of the Alert subjects.

Carskadon, Mary A.↗
NASA NTRS1997

Individual Differences in Response to Sleep Deprivation: Assessment of Fatigue Following Sleep Loss

Previous work has indicated that a small but significant number of participants in sleep deprivation studies or in simulated shift work experiments manifests an exaggerated performance decrement when they reach a critical point in the experiment, usually near the trough of the circadian cycle or the middle of the night. Those who show this exaggerated response do not appear to differ from other normal volunteers in any substantial way according to usual screening criteria or baseline values. The present study aims to examine factors that may provide the basis for this extreme response. We propose that a preexisting sleep deficit-as manifested by low values on the Multiple Sleep Latency Test (MSLT)-may account for extreme responders. Roth and colleagues (1993) have shown that among normal volunteers screened for a variety of studies, approximately 20 to 25 percent show low (< or = 6 minutes) MSLT scores on a consistent basis, whereas a like proportion shows consistently high MSLT scores (> or = 13 minutes). Additionally, studies by this group have indicated that subjects with low MSLT scores may suffer from chronic insufficient sleep (Roth et al., 1993), as further substantiated by the finding that they have consistently higher nocturnal sleep efficiency and that their MSLT scores rise to normal values when sleep is extended (Roehrs et al., 1996). We hypothesize that the short MSLT subjects have a significant long-term sleep deficit that leads to a marked intolerance for sleep deprivation or shift work. We further suggest that this sleep debt may signify an increased sleep need in these individuals that is not met either due to personal preference or to societal pressures (or both). If this speculation is accurate, then we predict that the tolerance for sleep deprivation in such individuals can be increased by "pretreatment" with sleep extension. Thus, the present study is designed to test the following two hypotheses: subjects with nominal sleep patterns who have low MSLT scores (e.g., Sleepy subjects) will show an exaggerated response (performance decrement) to sleep loss compared to subjects who have high MSLT scores (Alert subjects) on a nominal sleep schedule. When permitted to extend sleep-thus discharging their sleep debt-the Sleepy subjects will show a sleep-loss response resembling that of the Alert subjects.

Carskadon, Mary A.↗
NASA NTRS2020

Motion Sickness and Concerns for Urban Air Mobility Vehicles: A Literature Review

Motion sickness is a general term for a constellation of signs and symptoms, generally due to exposure to abrupt, periodic, or unnatural accelerations, especially when traveling in a vehicle. Motion sickness results from a mismatch of the visual and nonvisual (vestibular and kinesthetic) information, the observed scene and the motion felt or lack of it. Motion sickness onset is associated with a pattern of physiological changes in heart rate, peripheral blood flow, respiration, and skin conductance and the pattern is repeatable for a particular subject but variable between subjects. Demographic factors such as gender and age that affect motion sickness are well known with children, women, and older adults more likely to be susceptible. Often motion sickness is assessed and quantified using variations of the motion sickness susceptibility questionnaires including the Pensacola Diagnostic Rating Scale and the Simulator Sickness Questionnaire. Even though symptoms are easily identified by such questionnaires, they commonly are subjective. Tools such as these questionnaires for screening individuals susceptible to motion sickness are useful, however, they are only mildly predictive. Moreover, models for predicting motion sickness, which have largely been developed for sea sickness, do not consider task characteristics. Predictions of motion sickness rates and prevalence for Urban Air Mobility (UAM) vehicles are not possible at present because data from actual flight or full-fidelity simulation are simply not yet available. Extrapolation from other modes of transportation (i.e., automobiles, buses, trains, boats, other types of aircraft) is difficult because of differences in the motion stimulus experienced, trip duration, and other factors. How UAM vehicles will change the social dynamics of passenger interaction and vehicle interior design changes (e.g., seat orientations) is unknown. Should motion sickness prove to be an issue, vehicle design modifications such as having passengers face forward, providing additional seat recline, giving each person their own climate control for airflow, perhaps ensuring the horizon is visible to all passengers (reducing visual occlusion by the headrest) and visually stabilizing displays on carry-on devices (smart phones, tablets, etc.) may benefit passengers. Several commercial companies provide wearable devices for physiological monitoring that have been validated and are suitable for use with passengers in UAM vehicles or high-fidelity simulators. Potential countermeasures for motion sickness include user-worn devices, anti-motion sickness medications, and non-pharmacological approaches such as biofeedback and Autogenic Feedback Training Exercise. Both simulator and in-vehicle UAM research is needed to evaluate the effectiveness of any potential countermeasure.

motion sickness↗
NASA NTRS2014

Spinoff 2013

Topics covered include: Innovative Software Tools Measure Behavioral Alertness; Miniaturized, Portable Sensors Monitor Metabolic Health; Patient Simulators Train Emergency Caregivers; Solar Refrigerators Store Life-Saving Vaccines; Monitors Enable Medication Management in Patients' Homes; Handheld Diagnostic Device Delivers Quick Medical Readings; Experiments Result in Safer, Spin-Resistant Aircraft; Interfaces Visualize Data for Airline Safety, Efficiency; Data Mining Tools Make Flights Safer, More Efficient; NASA Standards Inform Comfortable Car Seats; Heat Shield Paves the Way for Commercial Space; Air Systems Provide Life Support to Miners; Coatings Preserve Metal, Stone, Tile, and Concrete; Robots Spur Software That Lends a Hand; Cloud-Based Data Sharing Connects Emergency Managers; Catalytic Converters Maintain Air Quality in Mines; NASA-Enhanced Water Bottles Filter Water on the Go; Brainwave Monitoring Software Improves Distracted Minds; Thermal Materials Protect Priceless, Personal Keepsakes; Home Air Purifiers Eradicate Harmful Pathogens; Thermal Materials Drive Professional Apparel Line; Radiant Barriers Save Energy in Buildings; Open Source Initiative Powers Real-Time Data Streams; Shuttle Engine Designs Revolutionize Solar Power; Procedure-Authoring Tool Improves Safety on Oil Rigs; Satellite Data Aid Monitoring of Nation's Forests; Mars Technologies Spawn Durable Wind Turbines; Programs Visualize Earth and Space for Interactive Education; Processor Units Reduce Satellite Construction Costs; Software Accelerates Computing Time for Complex Math; Simulation Tools Prevent Signal Interference on Spacecraft; Software Simplifies the Sharing of Numerical Models; Virtual Machine Language Controls Remote Devices; Micro-Accelerometers Monitor Equipment Health; Reactors Save Energy, Costs for Hydrogen Production; Cameras Monitor Spacecraft Integrity to Prevent Failures; Testing Devices Garner Data on Insulation Performance; Smart Sensors Gather Information for Machine Diagnostics; Oxygen Sensors Monitor Bioreactors and Ensure Health and Safety; Vision Algorithms Catch Defects in Screen Displays; and Deformable Mirrors Capture Exoplanet Data, Reflect Lasers.

Source record↗
NASA NTRS2011

Wallops Ship Surveillance System

Approved as a Wallops control center backup system, the Wallops Ship Surveillance Software is a day-of-launch risk analysis tool for spaceport activities. The system calculates impact probabilities and displays ship locations relative to boundary lines. It enables rapid analysis of possible flight paths to preclude the need to cancel launches and allow execution of launches in a timely manner. Its design is based on low-cost, large-customer- base elements including personal computers, the Windows operating system, C/C++ object-oriented software, and network interfaces. In conformance with the NASA software safety standard, the system is designed to ensure that it does not falsely report a safe-for-launch condition. To improve the current ship surveillance method, the system is designed to prevent delay of launch under a safe-for-launch condition. A single workstation is designated the controller of the official ship information and the official risk analysis. Copies of this information are shared with other networked workstations. The program design is divided into five subsystems areas: 1. Communication Link -- threads that control the networking of workstations; 2. Contact List -- a thread that controls a list of protected item (ocean vessel) information; 3. Hazard List -- threads that control a list of hazardous item (debris) information and associated risk calculation information; 4. Display -- threads that control operator inputs and screen display outputs; and 5. Archive -- a thread that controls archive file read and write access. Currently, most of the hazard list thread and parts of other threads are being reused as part of a new ship surveillance system, under the SureTrak project.

Smith, Donna C.↗
NASA NTRS2018

Pilot Study on the Investigation of Tear Fluid Biomarkers as an Indicator of Ocular, Neurological, and Immunological Health in Astronauts

The purpose of this pilot study is to investigate the collection, preparation, and analysis of tear biomarkers as a means of assessing ocular, neurological, and immunological health. At present, no published data exists on the cytokine profiles of tears from astronauts exposed to long periods of microgravity and space irradiations. In addition, no published data exist on cytokine (biomarker) profiles of tears that have been collected from irradiated non-human biological systems (primates and other animal models). A goal for the proposed pilot study is to discover novel tear biomarkers which can help inform researchers, clinicians, epidemiologist and healthcare providers about the health status of a living biological system, as well as informing them when a disease state is triggered. This would be done via analysis of the onset of expression of pro-inflammatory cytokines, leading up to the full progression of a disease (i.e. cancer, loss of vision, radiation-induced oxidative stress, cardiovascular disorders, fibrosis in major organs, bone loss). Another goal of this pilot study is to investigate the state of disease against proposed medical countermeasures, in order to determine whether the countermeasures are efficacious in preventing or mitigating these injuries. An example of an up and coming tear biomarker technology, Ascendant Dx, a clinical stage diagnostic company, is developing a screening test to detect breast cancer using proteins from tears. The team utilized Liquid Chromatography -Mass Spectrometry with Mass analysis (LC MS/MS) as a discovery platform followed by validation with ELISA to come up with a panel of protein biomarkers that can differentiate breast cancer samples from control ("cancer free") samples with results far surpassing the results of imaging techniques in use today. Continued research into additional proteins is underway to increase the sensitivity and specificity of the test and development efforts are on the way to transfer the test onto a fast, accurate and inexpensive point of care platform. In conclusion, the expected results from this proposed pilot study are to: a) establish an SOP for retrieving/storing/transporting tear fluid samples from multicentre sites b) establish a normal range for relevant biomarkers in tears; and c) establish a database (biobank) of tears of space naïve versus veteran astronauts, to establish a personal baseline for long-term ocular health monitoring

Morton, Stephen↗
NASA NTRS2014

Combinatorial Multidomain Mesoporous Chips and a Method for Fractionation, Stabilization, and Storage of Biomolecules

A new fractionation device shows desirable features for exploratory screening and biomarker discovery. The constituent MSCs may be tailored for desired pore sizes and surface properties and for the sequestration and enrichment of extremely low abundant protein and peptides in desired ranges of the mass/charge spectrum. The MSCs are effective in yielding reproducible extracts from complex biological samples as small as 10 microliter in a time as short as 30 minutes. They are inexpensive to manufacture, and allow for scaled up production to attain the simultaneous processing of a large number of samples. The MSCs are multiplexed, label-free diagnostic tools with the potential of biological recognition moiety modification for enhanced specificity. The MSCs may store, protect and stabilize biological fluids, enabling the simplified and cost-effective collection and transportation of clinical samples. The MSC-based device may serve as a diagnostic tool to complement histopathology, imaging, and other conventional clinical techniques. The MSCs mediated identification of disease-specific protein signatures may help in the selection of personalized therapeutic combinations, in the real-time assessment of therapeutic efficacy and toxicity, and in the rational modulation of therapy based on the changes in the protein networks associated with the prognosis and the drug resistance of the disease.

Ferrari, Mauro↗
NASA NTRS1990

Multiple Access Trade Study

The Personal Access Satellite System (PASS) strawman design uses a hybrid Time Division Multiple Access (TDMA)/Frequency Division Multiple Access (FDMA) implementation. TDMA is used for the forward direction (from Suppliers to Users), and FDMA for the return direction (from Users to Suppliers). An alternative architecture is proposed that will require minimal real time coordination and yet provide a fast access method by using random access Code Division Multiple Access (CDMA). The CDMA system issues are addressed such as connecting suppliers and users, both of whom may be located anywhere in the CONUS, when the user terminals are constrained in size and weight; and providing efficient traffic routing under highly variable traffic requirements. It is assumed that bandwidth efficiency is not of paramount importance. CDMA or Spread Spectrum Multiple Access (SSMA) communication is a method in which a group of carriers operate at the same nominal center frequency but are separable from each other by the low cross correlation of the spreading codes used. Interference and multipath rejection capability, ease of selective addressing and message screening, low density power spectra for signal hiding and security, and high resolution ranging are among the benefits of spread spectrum communications.

Motamedi, Masoud↗
DOE OSTI2022

Development and Validation of a Non-Invasive, Chairside Oral Cavity Cancer Risk Assessment Prototype Using Machine Learning Approach

Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall–precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Flight test of takeoff performance monitoring system

The Takeoff Performance Monitoring System (TOPMS) is a computer software and hardware graphics system that visually displays current runway position, acceleration performance, engine status, and other situation advisory information to aid pilots in their decision to continue or to abort a takeoff. The system was developed at the Langley Research Center using the fixed-base Transport Systems Research Vehicle (TSRV) simulator. (The TSRV is a highly modified Boeing 737-100 research airplane.) Several versions of the TOPMS displays were evaluated on the TSRV B-737 simulator by more than 40 research, United States Air Force, airline and industry and pilots who rated the system satisfactory and recommended further development and testing. In this study, the TOPMS was flight tested on the TSRV. A total of 55 takeoff and 30 abort situations were investigated at 5 airfields. TOPMS displays were observed on the navigation display screen in the TSRV research flight deck during various nominal and off-nominal situations, including normal takeoffs; reduced-throttle takeoffs; induced-acceleration deficiencies; simulated-engine failures; and several gross-weight, runway-geometry, runway-surface, and ambient conditions. All tests were performed on dry runways. The TOPMS software executed accurately during the flight tests and the displays correctly depicted the various test conditions. Evaluation pilots found the displays easy to monitor and understand. The algorithm provides pretakeoff predictions of the nominal distances that are needed to accelerate the airplane to takeoff speed and to brake it to a stop; these predictions agreed reasonably well with corresponding values measured during several fully executed and aborted takeoffs. The TOPMS is operational and has been retained on the TSRV for general use and demonstration.

Middleton, David B.↗
NASA NTRS2014

Using Tests Designed to Measure Individual Sensorimotor Subsystem Perfomance to Predict Locomotor Adaptability

Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the readapation phase following a return to a gravitational environment. These alterations may lead to disruption in the ability to perform mission critical functions during and after these gravitational transitions. Astronauts show significant inter-subject variation in adaptive capability following gravitational transitions. The way each individual's brain synthesizes the available visual, vestibular and somatosensory information is likely the basis for much of the variation. Identifying the presence of biases in each person's use of information available from these sensorimotor subsystems and relating it to their ability to adapt to a novel locomotor task will allow us to customize a training program designed to enhance sensorimotor adaptability. Eight tests are being used to measure sensorimotor subsystem performance. Three of these use measures of body sway to characterize balance during varying sensorimotor challenges. The effect of vision is assessed by repeating conditions with eyes open and eyes closed. Standing on foam, or on a support surface that pitches to maintain a constant ankle angle provide somatosensory challenges. Information from the vestibular system is isolated when vision is removed and the support surface is compromised, and it is challenged when the tasks are done while the head is in motion. The integration and dominance of visual information is assessed in three additional tests. The Rod & Frame Test measures the degree to which a subject's perception of the visual vertical is affected by the orientation of a tilted frame in the periphery. Locomotor visual dependence is determined by assessing how much an oscillating virtual visual world affects a treadmill-walking subject. In the third of the visual manipulation tests, subjects walk an obstacle course while wearing up-down reversing prisms. The two remaining tests include direct measures of knee and ankle proprioception and a functional movement assessment that screens for movement restrictions and asymmetries. To assess each subject's locomotor adaptability subjects walk for twenty minutes on a treadmill that oscillates laterally at 0.3 Hz. Throughout the test metabolic cost provides a measure of exertion and step frequency provides a measure of stability. Additionally, at four points during the perturbation period, reaction time tests are used to probe changes in the amount of mental effort being used to perform the task. As with the adaptive capability observed in astronauts during gravitational transitions, our data shows significant variability between subjects. To aid in the analysis of the results, custom software tools have been developed to enhance in the visualization of the large number of output variables. Preliminary analyses of the data collected to date do not show a strong relationship between adaptability and any single predictor variable. Analysis continues to identify a multifactorial predictor outcome "signature" that do inform us of locomotor adaptability.

Peters, B. T.↗