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NASA NTRS1999

High-Resolution Mapping of Lunar Crustal Magnetic Fields: Correlations with Albedo Markings of the Reiner Gamma Class

During the last eight months of the Lunar Prospector mission (December 1999-July 1999), the spacecraft was placed in a relatively low-altitude (15-30-km perapsis), near-polar orbit that allowed high-resolution mapping of crustal magnetic fields. We report here initial studies of the correlation of locally strong magnetic anomalies with unusual, swirl-like albedo markings of the Reiner Gamma class. Based on this correlation, which is known from earlier studies of Apollo subsatellite magnetometer data, it has been proposed that the swirls represent regions whose higher albedos have been preserved via deflection of the solar-wind ion bombardment by strong crustal fields. This model in turn depends on the hypothesis that solar-wind implanted H is at least one component of the process that optically matures exposed silicate surfaces in the inner solar system . Specifically, it is hypothesized that implanted H acts as an effective reducing agent to enhance the rate of production of nanophase metallic Fe particles from preexisting silicates during micrometeoroid impacts. According to the model, the curvilinear shapes of these albedo markings are caused, at least in part, by the geometry of ion deflections in a magnetic field. The improved resolution and coverage of the Prospector data allow more detailed mapping of the fields, especially on the lunar farside. This permits a more quantitative test of whether all albedo markings of this class are associated with strong local magnetic fields.Only if the latter condition is met can the solar-wind deflection hypothesis he valid. The basic procedure for mapping crustal magnetic fields using Lunar Prospector magnetometer data follows that developed for analysis of Apollo subsatellite magnetometer data. The specific mapping steps are (1) selection of mission time intervals suitable for mapping crustal fields; these are limited essentially either to times when the Moon is in a lobe of the geomagnetic tail or to times when the Moon is in the solar wind but the spacecraft is in the lunar wake; the data are transformed to a radial, east, and north coordinate system with measurements given as a function of spacecraft latitude, longitude, and altitude; (2) visual editing of individual orbit segments selected for minimal external field disturbances; (3) minimization of remaining low-frequency external fields for individual orbit data segments by quadratic detrending; and (4) two-dimensional filtering of individual orbit segments to produce a vector field map along the slightly curved surface defined by the spacecraft altitude; maps of the three field components (radial, east, and north), the field magnitude, and the spacecraft altitude are constructed. For data obtained at low to middle latitudes, the horizontal resolution of the field maps is limited by the orbit-track separation (about 30 km at the equator). Maps of the field magnitude have been constructed within limited selenographic regions based mainly on data acquired in March, April, and May of 1999. This was a time period when the orbit plane was nearly aligned with the Sun-Moon line so that field mapping was possible at times when the Moon was in the solar wind as well as when the Moon was in the geomagnetic tail. Most of the coverage is across the lunar farside. However, a shows an example of a field map produced from solar-wind wake data for a region including Reiner Gamm on western Oceanus Procellarum (location: 58.5W, 7.5N). The contour interval is 3 nT and the mean spacecraft altitude is 18 km to within the accuracy allowed by the resolution of the map (30 km or about 1 deg.); strong magnetic anomalies correlate closely with swirl locations. Individual orbit profiles (whose resolution along the orbit track is comparable to the spacecraft altitude of 18 km) also demonstrate a good correlation of field magnitude with surface albedo. In order to investigate the correlation of magnetic fields with the location of swirl features, we have reexamined available lunar imagery (Lunar Orbiter, Apollo, and Clementine) to identify and map swirl locations within regions where swirls have previously been mapped. In these images, swirls were distinguished from other high-albedo features such as crater rays by their curvilinear shapes and increased visibility in forward-scattered light. Digital maps of swirls identified by all available imagery were then superposed on maps of the field magnitude at the spacecraft altitude. Based upon analysis of these composite magnetic/geologic maps, we draw the preliminary conclusion that swirl features are associated with magnetic anomalies revealed by Lunar Prospector. Detailed maps of these swirl features are currently being constructed for the magnetically strong regions antipodal to the Imbrium, Serenitatis, and Crisium Basins. Additional information contained in the original,

Hood, L. L.↗

NASA NTRS1999

Modeling the Activity of Single Genes

The central dogma of molecular biology states that information is stored in DNA, transcribed to messenger RNA (mRNA) and then translated into proteins. This picture is significantly augmentated when we consider the action of certain proteins in regulating transcription. These transcription factors provide a feedback pathway by which genes can regulate one another's expression as mRNA and then as protein. To review: DNA, RNA and proteins have different functions. DNA is the molecular storehouse of genetic information. When cells divide, the DNA is replicated, so that each daughter cell maintains the same genetic information as the mother cell. RNA acts as a go-between from DNA to proteins. Only a single copy of DNA is present, but multiple copies of the same piece of RNA may be present, allowing cells to make huge amounts of protein. In eukaryotes (organisms with a nucleus), DNA is found in the nucleus only. RNA is copied in the nucleus then translocates(moves) outside the nucleus, where it is transcribed into proteins. Along the way, the RNA may be spliced, i.e., may have pieces cut out. RNA then attaches to ribosomes and is translated to proteins. Proteins are the machinery of the cell other than DNA and RNA, all the complex molecules of the cell are proteins. Proteins are specialized machines, each of which fulfills its own task, which may be transporting oxygen, catalyzing reactions, or responding to extracellular signals, just to name a few. One of the more interesting functions a protein may have is binding directly or indirectly to DNA to perform transcriptional regulation, thus forming a closed feedback loop of gene regulation. The structure of DNA and the central dogma were understood in the 50s; in the early 80s it became possible to make arbitrary modifications to DNA and use cellular machinery to transcribe and translate the resulting genes; more recently, genomes (i.e., the complete DNA sequence) of many organisms have been sequenced. This large-scale sequencing began with simple organisms, viruses and bacteria, progressed to eukaryotes such as yeast, and more recently (1998) progressed to a multi-cellular animal, the nematode Caenorhabditis elegans. Sequencers have now moved on to the fruit fly Drosophila melanogaster, whose sequence is slated for completion by the end of 1999. The human genome project is expected to determine the complete sequence of all 3 billion bases of human DNA within the next five years. In the wake of genome-scale sequencing, further instrumentation is being developed to assay gene expression and function on a comparably large scale. Much of the work in computational biology focuses on computational tools used in sequencing, finding genes that are related to a particular gene, finding which parts of the DNA code for proteins and which do not, understanding what proteins will be formed from a given length of DNA, predicting how the proteins will fold from a one-dimensional structure into a three dimensional structure, and so on. Much less computational work has been done regarding the function of proteins. One reason for this is that different proteins function very differently, and so work on protein function is very specific to certain classes of proteins. There are, for example, proteins such enzymes that catalyze various intracellular reactions, receptors that respond to extracellular signals and ion channels that regulate the flow of charged particles into and out of the cell. In this chapter, we will consider a particular class of proteins called transcription factors(TFs), which are responsible for regulating when a certain gene is expressed in a certain cell, which cells it is express in, and how much is expressed. Understanding these processes will involve developing a deeper understanding of transcription, translation, and the cellular processes that control those processes. All of these elements fall under the aegis of gene regulation or more narrowly transcriptional regulation. Some of the key questions in gene regulation are: What genes are expressed in a certain cell at a certain time? How does gene expression differ from cell to cell in a multicellular organism? Which proteins act as transcription factors, i.e., are important in regulating gene expression? From questions like these, we hope to understand which genes are important for various macroscopic processes. Nearly all of the cells of a multicellular organism contain the same DNA. Yet this same genetic information yields a large number of different cell types. The fundamental difference between a neuron and a liver cell, for example, is which genes are expressed. Thus understanding gene regulation is an important step in understanding development. Furthermore, understanding the usual genes that are expressed in cells may give important clues about various diseases. Some diseases, such as sickle cell anemia and cystic fibrosis, are caused by defects in single, non-regulatory genes; others, such as certain cancers, are caused when the cellular control circuitry malfunctions - an understanding of these diseases will involve pathways of multiple interacting gene products. There are numerous challenges in the area of understanding and modeling gene regulation. First and foremost, biologists would like to develop a deeper understanding of the processes involved, including which genes and families of genes are important, how they interact, etc. From a computation point of view, there has been embarrassingly little work done. In this chapter there are many areas in which we can phrase meaningful, non-trivial computational questions, but questions that have not been addressed. Some of these are purely computational (what is a good algorithm for dealing with a model of type X) and others are more mathematical (given a system with certain characteristics, what sort of model can one use? How does one find biochemical parameters from system-level behavior using as few experiments as possible?). In addition to biological and algorithmic problems, there is also the ever-present issue of theoretical biology - what general principles can be derived from these systems, what can one do with models other than just simulate time-courses, what can be deduced about a class of systems without knowing all the details? The fundamental challenge to computationalists and theorists is to add value to the biology - to use models, modeling techniques and algorithms to understand the biology in new ways.

Mjolsness, Eric↗

NASA NTRS1992

Elemental Analyses of Hypervelocity Microparticle Impact Sites on Interplanetary Dust Experiment Sensor Surfaces

The Interplanetary Dust Experiment (IDE) had over 450 electrically active ultra-high purity metal-oxide-silicon impact detectors located on the six primary sides of the Long Duration Exposure Facility (LDEF). Hypervelocity microparticles (approximately 0.2 to approximately 100 micron diameter) that struck the active sensors with enough energy to breakdown the 0.4 or 1.0 micron thick SiO2 insulator layer separating the silicon base (the negative electrode), and the 1000 A thick surface layer of aluminum (the positive electrode) caused electrical discharges that were recorded for the first year of orbit. The high purity Al-SiO2-Si substrates allowed detection of trace (ppm) amounts of hypervelocity impactor residues. After sputtering through a layer of surface contamination, secondary ion mass spectrometry (SIMS) was used to create two-dimensional elemental ion intensity maps of microparticle sites on the IDE sensors. The element intensities in the central craters of the impacts were corrected for relative ion yields and instrumental conditions and then normalized to silicon. The results classification resulted from the particles' origins as 'manmade', 'natural', or 'indeterminate'. The last classification resulted from the presence of too little impactor residue, analytical interference from high background contamination, the lack of information on silicon and aluminum residues, or a combination of these circumstances. Several analytical 'blank' discharges were induced on flight sensors by pressing down on the sensor surface with a pure silicon shard. Analyses of these blank discharges showed that the discharge energy blasts away the layer of surface contamination. Only Si and Al were detected inside the discharge zones, including the central craters, of these features. Thus far, a total of 79 randomly selected microparticle impact sites from the six primary sides of the LDEF were analyzed: 36 from tray C-9 (Leading (ram), or east, side), 18 from tray C-3 (Trailing (wake), or west, side), 12 from tray B-12 (north side), 4 from tray D-6 (south side), 3 from tray H-11 (space end), and 6 from tray G-10 (earth end). Residue from manmade debris was identified in craters on all trays (aluminum oxide particle residues were not detectable on the Al/Si substrates). These results were consistent with the IDE impact record which showed highly variable long term microparticle impact flux rates on the west, space, and Earth sides of the LDEF which could not be ascribed to astronomical variability of micrometeorite density. The IDE record also showed episodic bursts of microparticle impacts on the east, north, and south sides of the satellite, denoting passage through orbital debris clouds or rings.

Simon, C. G.↗

NASA NTRS1993

Elemental analyses of hypervelocity microparticle impact sites on Interplanetary Dust Experiment sensor surfaces

The Interplanetary Dust Experiment (IDE) had over 450 electrically active ultra-high purity metal-oxide-silicon impact detectors located on the six primary sides of the Long Duration Exposure Facility (LDEF). Hypervelocity microparticles (approximately 0.2 to approximately 100 micron diameter) that struck the active sensors with enough energy to break down the 0.4 or 1.0 micron thick SIO2 insulator layer separating the silicon base (the negative electrode), and the 1000 A thick surface layer of aluminum (the positive electrode) caused electrical discharges that were recorded for the first year of orbit. The high purity Al-SiO2-Si substrates allowed detection of trace (ppm) amounts of hypervelocity impactor residues. After sputtering through a layer of surface contamination, secondary ion mass spectrometry (SIMS) was used to create two-dimensional elemental ion intensity maps of microparticle impact sites on the IDE sensors. The element intensities in the central craters of the impacts were corrected for relative ion yields and instrumental conditions and then normalized to silicon. The results were used to classify the particles' origins as 'manmade,' 'natural,' or 'indeterminate.' The last classification resulted from the presence of too little impactor residue, analytical interference from high background contamination, the lack of information on silicon and aluminum residues, or a combination of these circumstances. Several analytical 'blank' discharges were induced on flight sensors by pressing down on the sensor surface with a pure silicon shard. Analyses of these blank discharges showed that the discharge energy blasts away the layer of surface contamination. Only Si and Al were detected inside the discharge zones, including the central craters of these features. Thus far a total of 79 randomly selected microparticle impact sites from the six primary sides of the LDEF have been analyzed: 36 from tray C-9 (Leading (ram), or East, side), 18 from tray C-3 (Trailing (wake), or West, side), 12 from tray B-12 (North side), 4 from tray D-6 (South side), 3 from tray H-11 (Space end), and 6 from tray G-10 (Earth end). Residue from manmade debris was identified in craters on all trays. (Aluminum oxide particle residues were not detectable on the Al/Si substrates.) These results were consistent with the IDE impact record which showed highly variable long term microparticle impact flux rates on the West, Space and Earth sides of the LDEF which could not be ascribed to astronomical variability of micrometeorite density. The IDE record also showed episodic bursts of microparticle impacts on the East, North, and South sides of the satellite, denoting passage through orbital debris clouds or rings.

Simon, Charles G.↗

NASA NTRS2004

Simulations of Solar Wind Plasma Flow Around a Simple Solar Sail

In recent years, a number of solar sail missions of various designs and sizes have been proposed (e.g., Geostorm). Of importance to these missions is the interaction between the ambient solar wind plasma environment and the sail. Assuming a typical 1 AU solar wind environment of 400 km/s velocity, 3.5 cu cm density, ion temperature of approx.10 eV, electron temperature of 40 eV, and an ambient magnetic field strength of 10(exp -4) G, a first order estimate of the plasma interaction with square solar sails on the order of the sizes being considered for a Geostorm mission (50 m x 50 m and 75 m x 75 m corresponding to approx.2 and approx.3 times the Debye length in the plasma) is carried out. First, a crude current balance for the sail surface immersed in the plasma environment and in sunlight was used to estimate the surface potential of the model sails. This gave surface potentials of approx.10 V positive relative to the solar wind plasma. A 3-D, Electrostatic Particle-in-Cell (PIC) code was then used to simulate the solar wind flowing around the solar sail. It is assumed in the code that the solar wind protons can be treated as particles while the electrons follow a Boltzmann distribution. Next, the electric field and particle trajectories are solved self-consistently to give the proton flow field, the electrostatic field around the sail, and the plasma density in 3-D. The model sail was found to be surrounded by a plasma sheath within which the potential is positive compared to the ambient plasma and followed by a separate plasma wake which is negative relative to the plasma. This structure departs dramatically from a negatively charged plate such as might be found in the Earth s ionosphere on the night side where both the plate and its negative wake are contiguous. The implications of these findings are discussed as they apply to the proposed Geostorm solar sail mission.

Garrett, Henry B.↗