Alternatives for Navigating Small Unmanned Air Vehicles without GPS

Considering the increased reliance on GPS navigation for the Army’s Unmanned Aircraft Systems, adversaries have invested in capabilities to deny our systems access to genuine GPS signals. Although significant effort has been put forth in the areas of anti-jamming and anti-spoofing in GPS receivers, a need for alternative navigation methods in a GPS denied environment has grown in importance. This report outlines the recommendation and analysis completed for Mr. Lars Ericsson of the Army Project Manager Unmanned Aircraft Systems (PM-UAS).  The report includes background research in the domain space, comprehensive stakeholder analysis, derived system requirements and functional requirements, ending with alternative generation, value scoring, costing, and provided findings for a recommended alternative for future consideration. 

Selenoprotein gene nomenclature

The human genome contains 25 genes coding for selenocysteine-containing proteins (selenoproteins). These proteins are involved in a variety of functions, most notably redox homeostasis. Selenoprotein enzymes with known functions are designated according to these functions: TXNRD1, TXNRD2, and TXNRD3 (thioredoxin reductases), GPX1, GPX2, GPX3, GPX4 and GPX6 (glutathione peroxidases), DIO1, DIO2, and DIO3 (iodothyronine deiodinases), MSRB1 (methionine-R-sulfoxide reductase 1) and SEPHS2 (selenophosphate synthetase 2). Selenoproteins without known functions have traditionally been denoted by SEL or SEP symbols. However, these symbols are sometimes ambiguous and conflict with the approved nomenclature for several other genes. Therefore, there is a need to implement a rational and coherent nomenclature system for selenoprotein-encoding genes. Our solution is to use the root symbol SELENO followed by a letter. This nomenclature applies to SELENOF (selenoprotein F, the 15 kDa selenoprotein, SEP15), SELENOH (selenoprotein H, SELH, C11orf31), SELENOI (selenoprotein I, SELI, EPT1), SELENOK (selenoprotein K, SELK), SELENOM (selenoprotein M, SELM), SELENON (selenoprotein N, SEPN1, SELN), SELENOO (selenoprotein O, SELO), SELENOP (selenoprotein P, SeP, SEPP1, SELP), SELENOS (selenoprotein S, SELS, SEPS1, VIMP), SELENOT (selenoprotein T, SELT), SELENOV (selenoprotein V, SELV) and SELENOW (selenoprotein W, SELW, SEPW1). This system, approved by the HUGO Gene Nomenclature Committee, also resolves conflicting, missing and ambiguous designations for selenoprotein genes and is applicable to selenoproteins across vertebrates

Genetic Variation in Selenoprotein Genes, Lifestyle, and Risk of Colon and Rectal Cancer

BACKGROUND: Associations between selenium and cancer have directed attention to role of selenoproteins in the carcinogenic process. METHODS: We used data from two population-based case-control studies of colon (n = 1555 cases, 1956 controls) and rectal (n = 754 cases, 959 controls) cancer. We evaluated the association between genetic variation in TXNRD1, TXNRD2, TXNRD3, C11orf31 (SelH), SelW, SelN1, SelS, SepX, and SeP15 with colorectal cancer risk. RESULTS: After adjustment for multiple comparisons, several associations were observed. Two SNPs in TXNRD3 were associated with rectal cancer (rs11718498 dominant OR 1.42 95% CI 1.16,1.74 pACT 0.0036 and rs9637365 recessive 0.70 95% CI 0.55,0.90 pACT 0.0208). Four SNPs in SepN1 were associated with rectal cancer (rs11247735 recessive OR 1.30 95% CI 1.04,1.63 pACT 0.0410; rs2072749 GGvsAA OR 0.53 95% CI 0.36,0.80 pACT 0.0159; rs4659382 recessive OR 0.58 95% CI 0.39,0.86 pACT 0.0247; rs718391 dominant OR 0.76 95% CI 0.62,0.94 pACT 0.0300). Interaction between these genes and exposures that could influence these genes showed numerous significant associations after adjustment for multiple comparisons. Two SNPs in TXNRD1 and four SNPs in TXNRD2 interacted with aspirin/NSAID to influence colon cancer; one SNP in TXNRD1, two SNPs in TXNRD2, and one SNP in TXNRD3 interacted with aspirin/NSAIDs to influence rectal cancer. Five SNPs in TXNRD2 and one in SelS, SeP15, and SelW1 interacted with estrogen to modify colon cancer risk; one SNP in SelW1 interacted with estrogen to alter rectal cancer risk. Several SNPs in this candidate pathway influenced survival after diagnosis with colon cancer (SeP15 and SepX1 increased HRR) and rectal cancer (SepX1 increased HRR). CONCLUSIONS: Findings support an association between selenoprotein genes and colon and rectal cancer development and survival after diagnosis. Given the interactions observed, it is likely that the impact of cancer susceptibility from genotype is modified by lifestyle

Tension, Free Space, and Cell Damage in a Microfluidic Wound Healing Assay

We use a novel, microfluidics-based technique to deconstruct the classical wound healing scratch assay, decoupling the contribution of free space and cell damage on the migratory dynamics of an epithelial sheet. This method utilizes multiple laminar flows to selectively cleave cells enzymatically, and allows us to present a 'damage free' denudation. We therefore isolate the influence of free space on the onset of sheet migration. First, we observe denudation directly to measure the retraction in the cell sheet that occurs after cell-cell contact is broken, providing direct and quantitative evidence of strong tension within the sheet. We further probe the mechanical integrity of the sheet without denudation, instead using laminar flows to selectively inactivate actomyosin contractility. In both cases, retraction is observed over many cell diameters. We then extend this method and complement the enzymatic denudation with analogies to wounding, including gradients in signals associated with cell damage, such as reactive oxygen species, suspected to play a role in the induction of movement after wounding. These chemical factors are evaluated in combination with the enzymatic cleavage of cells, and are assessed for their influence on the collective migration of a non-abrasively denuded epithelial sheet. We conclude that free space alone is sufficient to induce movement, but this movement is predominantly limited to the leading edge, leaving cells further from the edge less able to move towards the wound. Surprisingly, when coupled with a gradient in ROS to simulate the chemical effects of abrasion however, motility was not restored, but further inhibited.Massachusetts Institute of Technology. Presidential FellowshipNational Institutes of Health (U.S.). Biotechnology Training FellowshipSingapore-MIT Alliance for Research and TechnologyMassachusetts Institute of Biotechnology Training GrantMassachusetts Institute of Technology (Open-source Funding

Selenium toxicity but not deficient or super-nutritional selenium status vastly alters the transcriptome in rodents

<p>Abstract</p> <p>Background</p> <p>Protein and mRNA levels for several selenoproteins, such as glutathione peroxidase-1 (Gpx1), are down-regulated dramatically by selenium (Se) deficiency. These levels in rats increase sigmoidally with increasing dietary Se and reach defined plateaus at the Se requirement, making them sensitive biomarkers for Se deficiency. These levels, however, do not further increase with super-nutritional or toxic Se status, making them ineffective for detection of high Se status. Biomarkers for high Se status are needed as super-nutritional Se intakes are associated with beneficial as well as adverse health outcomes. To characterize Se regulation of the transcriptome, we conducted 3 microarray experiments in weanling mice and rats fed Se-deficient diets supplemented with up to 5 μg Se/g diet.</p> <p>Results</p> <p>There was no effect of Se status on growth of mice fed 0 to 0.2 μg Se/g diet or rats fed 0 to 2 μg Se/g diet, but rats fed 5 μg Se/g diet showed a 23% decrease in growth and elevated plasma alanine aminotransferase activity, indicating Se toxicity. Rats fed 5 μg Se/g diet had significantly altered expression of 1193 liver transcripts, whereas mice or rats fed ≤ 2 μg Se/g diet had < 10 transcripts significantly altered relative to Se-adequate animals within an experiment. Functional analysis of genes altered by Se toxicity showed enrichment in cell movement/morphogenesis, extracellular matrix, and development/angiogenesis processes. Genes up-regulated by Se deficiency were targets of the stress response transcription factor, Nrf2. Multiple regression analysis of transcripts significantly altered by 2 μg Se/g and Se-deficient diets identified an 11-transcript biomarker panel that accounted for 99% of the variation in liver Se concentration over the full range from 0 to 5 μg Se/g diet.</p> <p>Conclusion</p> <p>This study shows that Se toxicity (5 μg Se/g diet) in rats vastly alters the liver transcriptome whereas Se-deficiency or high but non-toxic Se intake elicits relatively few changes. This is the first evidence that a vastly expanded number of transcriptional changes itself can be a biomarker of Se toxicity, and that identified transcripts can be used to develop molecular biomarker panels that accurately predict super-nutritional and toxic Se status.</p

Multiattribute Decision Modeling in Defense Applications

The role of decision analysis (DA) within military operations research (OR) is discussed. A technically sound and easy-to-apply approach for multiattribute problems is developed, based on an additive value model. This includes developing a value hierarchy based on stakeholder values, the identification of measures of value and the associated scales, the development of single-attribute value functions, and elicitation of attribute swing weights. Cost issues are addressed, as are issues of multiple stakeholders. An example from a military application is presented and developed from beginning to end. The ramifications of decisions under uncertainty are then considered and limitations and cautions identified for application of a linear model. Decision trees are described as the most straightforward way to model a chancy decision. Utility functions are introduced and contrasted with value functions. Some widely useful methods of sensitivity analysis are given. The emphasis is on practical methods that are technically sound but understandable by clients without special training in OR or DA, and on clear methods of presentation of results. The treatment is designed as a sufficient outline for a practitioner, but texts are recommended for those who want to go into more depth in the material

On the Average Probability of Hitting a Satellite during a Laser Counterartillery Engagement

This paper investigates the probability that a high-energy laser fired at an incoming projectile will inadvertently hit (not necessarily damage) a background satellite. This is called the C-RAM mission, for counter rocket, artillery, and mortar. We show how such an engagement can be defined by parameters describing projectile trajectory, laser characteristics, laser firing, and spacecraft orbit parameters, so that a probability of hit can be accurately calculated from the geometry of the situation. The model takes into account laser location, laser pointing and angular sweep, laser beam angular divergence, and orbit height, inclination, and ascending node. It does not take into account atmospheric refraction or absorption, it assumes that the laser beam propagates beyond the intended target into space, and it does not address the probability of damage given a hit. Based on the single-engagement probability calculation, Monte Carlo sampling is then used to find a general probability of hit. For each replication, the threat launcher is placed at a random distance and azimuth, the impact point randomly placed within 1 km of the laser, and the engagement placed randomly in the trajectory. The spacecraft parameters are selected randomly from a comprehensive set of 1417 orbital elements for actual operational or formerly operational spacecraft. A simulation constructed to represent defense against mortar in a near-term counterinsurgency conflict gives a probability of hit of about 15.5 ´ 10-9 per engagement per satellite, or 15.5 nanohits, for satellites in sun-synchronous orbits. Another simulation constructed to represent a hypothetical major combat operation with long-range rocket and artillery threats in addition to mortars. This simulation yields 27.5 nanohits for the same set of satellites. An extensive sensitivity analysis explores how these results vary as the parameters describing spacecraft, projectile, laser, and engagement are changed, giving results from 0 to 549 nanohits. Hits increased as the laser site moved north, as the distance to the threat launch point increased, as the engagement moved away from the midpoint of the projectile trajectory, as laser beam quality deteriorated, as engagement duration increased, and as satellite altitude increased. Over all cases, the statistical 95% confidence interval was ±3-10%. Accumulating the scenario results over a notional three-year counterinsurgency conflict and 20 sun-synchronous spacecraft of interest, we get a total of 0.0034 expected spacecraft hits during the conflict. Accumulating over a notional two-week major combat operation and the same spacecraft, we get 0.015 expected hits

Closed-Form Approximation of Revisit Rate by Low-Altitude Satellites

For many applications of low-altitude spacecraft, it is important to know how often the spacecraft revisits a given target. Average revisits per day can be calculated for a given scenario using astrodynamic software, but that approach can be tedious if a large number of cases are to be compared. This paper develops a closed-form, continuous, and piecewise differentiable function that gives average revisits per day as a function of orbit altitude, orbit inclination, target latitude, and minimum required elevation. The function is compared to the results of astrodynamic simulations and shown to be accurate to within 0.08 passes per day and 1% in almost all cases. It allows instant calculation of an excellent approximation of this important performance parameter, and could be used as part of an optimization routine. The convenience and flexibility provided by this function are demonstrated in a series of charts showing passes per day as a function of one, two, and three variables. The function assumes a circular orbit; the sensitivity of the result to eccentricity is explored using astrodynamic simulations, and is found to decrease by 5.3% as eccentricity increases to 0.07 for a typical case