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NASA NTRS2004

Role of shielding in modulating the effects of solar particle events: Monte Carlo calculation of absorbed dose and DNA complex lesions in different organs

Distributions of absorbed dose and DNA clustered damage yields in various organs and tissues following the October 1989 solar particle event (SPE) were calculated by coupling the FLUKA Monte Carlo transport code with two anthropomorphic phantoms (a mathematical model and a voxel model), with the main aim of quantifying the role of the shielding features in modulating organ doses. The phantoms, which were assumed to be in deep space, were inserted into a shielding box of variable thickness and material and were irradiated with the proton spectra of the October 1989 event. Average numbers of DNA lesions per cell in different organs were calculated by adopting a technique already tested in previous works, consisting of integrating into "condensed-history" Monte Carlo transport codes--such as FLUKA--yields of radiobiological damage, either calculated with "event-by-event" track structure simulations, or taken from experimental works available in the literature. More specifically, the yields of "Complex Lesions" (or "CL", defined and calculated as a clustered DNA damage in a previous work) per unit dose and DNA mass (CL Gy-1 Da-1) due to the various beam components, including those derived from nuclear interactions with the shielding and the human body, were integrated in FLUKA. This provided spatial distributions of CL/cell yields in different organs, as well as distributions of absorbed doses. The contributions of primary protons and secondary hadrons were calculated separately, and the simulations were repeated for values of Al shielding thickness ranging between 1 and 20 g/cm2. Slight differences were found between the two phantom types. Skin and eye lenses were found to receive larger doses with respect to internal organs; however, shielding was more effective for skin and lenses. Secondary particles arising from nuclear interactions were found to have a minor role, although their relative contribution was found to be larger for the Complex Lesions than for the absorbed dose, due to their higher LET and thus higher biological effectiveness. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

Non-NASA Center↗
NASA NTRS2012

Achievable Performance and Effective Interrogator Design for SAW RFID Sensor Tags

For many NASA missions, remote sensing is a critical application that supports activities such as environmental monitoring, planetary science, structural shape and health monitoring, non-destructive evaluation, etc. The utility of the remote sensing devices themselves is greatly increased if they are passive V that is, they do not require any on-board power supply such as batteries V and if they can be identified uniquely during the sensor interrogation process. Additional passive sensor characteristics that enable greater utilization in space applications are small size and weight, long read ranges with low interrogator power, ruggedness, and operability in extreme environments (vacuum, extreme high/low temperature, high radiation, etc.) In this paper, we consider one very promising passive sensor technology, called surface acoustic wave (SAW) radio-frequency identification (RFID), that satisfies all of these criteria. In general, RFID is a method of identifying items using radio waves to interrogate tags encoded with a unique identifier that are affixed to the items of interest. In the case of passive tags, only the interrogator, which transmits power to the tags in the form of radio-frequency electromagnetic radiation, requires access to a power supply. Passive RFID technologies are used today in many applications, including asset tracking and management, security and access control, and remote sensing. To date, most of the development and application in RFID technology has focused on either asset/inventory tracking and control or security and access control because these are the largest commercial application areas. Recently however, there has been growing interest in using passive RFID technology for remote sensing applications, and SAW devices are at the forefront of RFID sensing technology development. Although SAW RFID tags have great potential for use in numerous space-based remote sensing applications, the limited collision resolution capability of current generation tags limits the performance in a cluttered sensing environment. That is, as more SAW-based sensors are added to the environment, numerous tag responses are superimposed at the receiver and decoding all or even a subset of the telemetry becomes increasingly difficult. Background clutter generated by reflectors other than the sensors themselves is also a problem, as is multipath interference and signal distortion, but the limiting factor in many remote sensing applications can be expected to be tag mutual interference. This problem may be greatly mitigated by proper design of the SAW tag waveform, but that remains an open research problem, and in the meantime, several other related questions remain to be answered including: (1) What are the fundamental relationships between tag parameters such as bit-rate, time-bandwidth-product, SNR, and achievable collision resolution? (2) What are the differences in optimal or near-optimal interrogator designs between noise-limited environments and interference-limited environments? (3) What are the performance characteristics of different interrogator designs in term of parameters such as transmitter power level, range, and number of interfering tags? In this paper, we will present the results of a research effort aimed at providing at least partial answers to all of these questions.

Barton Richard J.↗
NASA NTRS1996

Clusters of DNA damage induced by ionizing radiation: formation of short DNA fragments. II. Experimental detection

The basic 30-nm chromatin fiber in the mammalian cell consists of an unknown (possibly helical) arrangement of nucleosomes, with about 1.2 kb of DNA per 10-nm length of fiber. Track-structure considerations suggest that interactions of single delta rays or high-LET particles with the chromatin fiber might result in the formation of multiple lesions spread over a few kilobases of DNA (see the accompanying paper: W.R. Holley and A. Chatterjee, Radiat. Res. 145, 188-199, 1996). In particular, multiple DNA double-strand breaks and single-strand breaks may form. To test this experimentally, primary human fibroblasts were labeled with [3H]thymidine and exposed at 0 degrees C to X rays or accelerated nitrogen or iron ions in the LET range of 97-440 keV/microns. DNA was isolated inside agarose plugs and subjected to agarose gel electrophoresis under conditions that allowed good separation of 0.1-2 kb size DNA. The bulk of DNA remained in the well or migrated only a small distance into the gel. It was found that DNA fragments in the expected size range were formed linearly with dose with an efficiency that increased with LET. A comparison of the yield of such fragments with the yield of total DNA double-strand breaks suggests that for the high-LET ions a substantial proportion (20-90%) of DNA double-strand breaks are accompanied within 0.1-2 kb by at least one additional DNA double-strand break. It is shown that these results are in good agreement with theoretical calculations based on treating the 30-nm chromatin fiber as the target for ionizing particles. Theoretical considerations also predict that the clusters will contain numerous single-strand breaks and base damages. It is proposed that such clusters be designated "regionally multiply damaged sites." Postirradiation incubation at 37 degrees C resulted in a decline in the number of short DNA fragments, suggesting a repair activity. The biological significance of regionally multiply damaged sites is presently unknown.

Non-NASA Center↗
NASA NTRS2014

Forecasting Sensorimotor Adaptability from Baseline Inter-Trial Correlations

One of the greatest challenges surrounding adaptation to the spaceflight environment is the large variability in symptoms, and corresponding functional impairments, from one crewmember to the next. This renders preflight training and countermeasure development difficult, as a "one-size-fits-all" approach is inappropriate. Therefore, it would be highly advantageous to know ahead of time which crewmembers might have more difficulty adjusting to the novel g-levels inherent to spaceflight. Such knowledge could guide individually customized countermeasures, which would enable more efficient use of crew time, both preflight and inflight, and provide better outcomes. The primary goal of this project is to look for a baseline performance metric that can forecast sensorimotor adaptability without exposure to an adaptive stimulus. We propose a novel hypothesis that considers baseline inter-trial correlations, the trial-to-trial fluctuations in motor performance, as a predictor of individual sensorimotor adaptive capabilities. To-date, a strong relationship has been found between baseline inter-trial correlations and adaptability in two oculomotor systems. For this project, we will explore an analogous predictive mechanism in the locomotion system. METHODS: Baseline Inter-trial Correlations: Inter-trial correlations specify the relationships among repeated trials of a given task that transpire as a consequence of correcting for previous performance errors over multiple timescales. We can quantify the strength of inter-trial correlations by measuring the decay of the autocorrelation function (ACF), which describes how rapidly information from past trials is "forgotten." Processes whose ACFs decay more slowly exhibit longer-term inter-trial correlations (longer memory processes), while processes whose ACFs decay more rapidly exhibit shorterterm inter-trial correlations (shorter memory processes). Longer-term correlations reflect low-frequency activity, which is more easily measured in the frequency domain. Therefore, we use the power spectrum (PS), which is the Fourier transform of the ACF, to describe our inter-trial correlations. The decay of the PS yields a straight line on a log-log frequency plot, which we quantify by Beta = - (slope of PS on log-log axes). Hence, Beta is a measure of the strength of inter- trial correlations in the baseline data. Larger Beta values are indicative of longer inter-trial correlations. Experimental Approach: We will begin by performing a retrospective analysis of treadmill-gait adaptation data previously collected by Dr. Bloomberg and colleagues. Specifically, we will quantify the strength of inter-trial correlations in the baseline step cadence and heart rate data and compare it to the locomotor adaptability performance results already described by these investigators. Incorporating these datasets will also allow us to explore the applicability of (and potential limitations surrounding) the use of Beta in forecasting physiological performance. We will also perform a new experiment, in which Beta will be derived from baseline data collected during over-ground (non-treadmill) walking, which will enable us to consider locomotor performance, through the parameter Beta, under the most functionallyrelevant, natural gait condition. This experiment will incorporate two baseline and five post-training over-ground locomotion tests to explore the consistency and potential adaptability of the Beta values themselves. HYPOTHESES: We hypothesize that the strength of baseline inter-trial correlations of step cadence and heart rate will relate to locomotor adaptability. Specifically, we anticipate that individuals who show weaker longer-term inter-trial correlations in baseline step cadence data will be the better adaptors, as step cadence can be modified in real-time (i.e., online corrections are an inherent property of the locomotor system; analogous to results observed in the VOR). Conversely, because heart rate is not altered mid-beat, we expect that individuals who demonstrate stronger longer-term correlations in heart rate will be the better adaptors (analogous to results observed in the saccadic system). CONCLUSIONS: At the conclusion of this project we hope to uncover a baseline predictor of locomotor adaptability. If our hypotheses hold true, our results will demonstrate that the temporal structure of baseline behavioral data contains important information that may aid in forecasting adaptive capacities. The ability to predict such adaptability in the sensorimotor system has significant implications for spaceflight, where astronauts must adjust their motor programs following a change in g-level to retain movement accuracy.

Beaton, K. H.↗
NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗