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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Planetary Protection Technologies for Planetary Science Instruments, Spacecraft, and Missions: Report of the NASA Planetary Protection Technology Definition Team (PPTDT)

Planetary bodies like Mars, Europa, and Enceladus pose the question, "How to study them without contaminating them and destroying future prospects to detect life, if it is there?" The natural trade-off, of course, is that the cleaner your spacecraft, the more you can explore such a body without risk of contaminating it. As chartered by NASA Headquarters, the Planetary Protection Technology Definition Team (PPTDT) was asked to provide a report covering six different areas related to the engineering and technology challenges of implementing planetary protection requirements on solar system exploration missions, including: Assessment of technical and engineering challenges to applying available microbial-reduction methods, including recontamination prevention, to spacecraft hardware and instruments, to meet current NASA requirements on preventing the forward contamination of potentially habitable worlds by future spacecraft missions (orbiters, atmospheric missions, landers, penetrators, and drills); Identification of spacecraft and instrument materials known to be compatible with existing planetary protection protocols; Planetary protection protocols/processes available or which appear promising, and areas ripe for technological development; The technical and engineering challenges in ensuring that spacecraft hardware and instruments can meet organic cleanliness requirements needed to ensure high confidence in differentiating Earth contamination from extraterrestrial signals to avoid false negative as well as false positive results; Approaches for mitigating the identified challenges that would allow instruments to be flown successfully at the required levels of cleanliness and microbial reduction, beginning with identification of commonly used materials and spacecraft hardware that are compatible (or particularly vulnerable) to planetary protection protocols; Engineering, technology, and scientific research and development that could be funded by NASA to provide future capabilities to field scientific instruments and spacecraft on missions that require either subsystem or system-level microbial reduction and recontamination prevention.

John D. Rummel↗
DOE OSTI2015

Assessment of Radiation Background Variation for Moving Detection Systems

The introduction points out that radiation backgrounds fluctuate across very short distances: factors include geology, soil composition, altitude, building structures, topography, and other manmade structures; and asphalt and concrete can vary significantly over short distances. Brief descriptions are given of the detection system, experimental setup, and background variation measurements. It is concluded that positive and negative gradients can greatly reduce the detection sensitivity of an MDS: negative gradients create opportunities for false negatives (nondetection), and positive gradients create a potentially unacceptable FAR (above 1%); the location of use for mobile detection is important to understand; spectroscopic systems provide more information for screening out false alarms and may be preferred for mobile use; and mobile monitor testing at LANL accounts for expected variations in the background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2011

A Prospective Evaluation of Staging and Target Volume Definition of Lymph Nodes by {sup 18}FDG PET/CT in Patients With Squamous Cell Carcinoma of Thoracic Esophagus

Purpose: To determine an optimal standardized uptake value (SUV) threshold for detecting lymph node (LN) metastases in esophageal cancer using {sup 18}F-Fluorodeoxyglucose positron emission tomography/computer tomography ({sup 18}FDG PET/CT) and to define the resulting nodal target volume, using histopathology as a 'gold standard.' Methods: Sixteen patients with esophageal squamous cell carcinoma who underwent radical esophagectomy and three-field LN dissection after {sup 18}FDG PET/CT and CT scans were enrolled into this study. Locations of LN groups were recorded according to a uniform LN map. Diagnostic performance of different SUV thresholds was assessed by receiver operating characteristic analysis. The optimal cutoff SUV was determined by plotting the false-negative rate (FNR) and false-positive rate (FPR), the sum of both error rates (FNR+FPR), and accuracy against a hypothetical SUV threshold. For each patient, nodal gross tumor volumes (GTVNs) were generated with CT alone (GTVNCT), PET/CT (GTVNPET), and pathologic data (GTVNpath). GTVNCT or GTVNPET was compared with GTVNpath by means of a conformity index (CI), which is the intersection of the two GTVNs divided by the sum of them minus the intersection, e.g., CI{sub CT} and {sub path} = GTVN{sub CT} and {sub path}/(GTVN{sub CT}+ GTVN{sub path} - GTVN{sub CT} and {sub path}). Results: LN metastases occurred in 21 LN groups among the 144 specimens taken from the 16 patients. The area under the receiver operating characteristic curve was 0.9017 {+-} 0.0410. The plot of error rates showed a minimum of FNR+FPR for an SUV of 2.36, at which the sensitivity, specificity, and accuracy were 76.19%, 95.93%, and 93.06%, respectively, whereas those of CT were 33.33%, 94.31%, and 85.42% (p values: 0.0117, 0.7539, and 0.0266). Mean GTVN{sub CT}, GTVN{sub PET}, and GTVN{sub path} were 1.52 {+-} 2.38, 2.82 {+-} 4.51, and 2.68 {+-} 4.16cm{sup 3}, respectively. Mean CI{sub CT} and {sub path} and CI{sub PET} and {sub path} were 0.31 and 0.65 (p value = 0.0352). Conclusions: Diagnostic superiority of PET/CT at an SUV threshold of 2.36 over CT has potential value in nodal target volume definition, but whether this can contribute to better treatment outcomes needs prospective analyses of recurrences in a larger cohort of patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Data for Training and Testing Radiation Detection Algorithms in an Urban Environment

The US government routinely performs radiological response deployments to search for the presence of illicit nuclear materials (e.g., highly enriched uranium and weapons-grade plutonium) in a specified area. The deployments can be intelligence driven, in support of law enforcement, and for planned events such as WrestleMania, presidential inaugurations, or political conventions. In a typical deployment, radiation detection systems carried by human operators or mounted on vehicles move in a clearing pattern through the search area. Search teams rely on radiation detection algorithms running on these systems in real time to alert them to the presence of an illicit threat source. The detection and identification of sources is complicated by large variation of natural radiation background throughout a search area and the potential presence of localized non-threat sources such as patients undergoing treatment with medical isotopes. As a result, detection algorithms must be carefully balanced between missing real sources (false negatives) and reporting too many false alarms (false positives).The purpose of this data set is to spur innovations in detecting, identifying, and localizing nuclear materials inurban search missions.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Deconvolute individual genomes from metagenome sequences through short read clustering

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Estimating coccidioidomycosis endemicity while accounting for imperfect detection using spatio - temporal occupancy modeling

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhaling Coccidioides fungal spores. Incidence has risen in recent years, and it is believed the endemic region for Coccidioides is expanding in response to climate change. While Valley fever case data can help us understand trends in disease risk, using case data as a proxy for Coccidioides endemicity is not ideal because case data suffers from imperfect detection, including false positives (e.g., travel-related cases reported outside of endemic area) and false negatives (e.g., misdiagnosis or underreporting). Here we proposed a Bayesian, spatio-temporal occupancy model to relate monthly, county-level presence/absence data on Valley fever cases to latent endemicity of Coccidioides, accounting for imperfect detection. We used our model to estimate endemicity in the western United States. We estimated high probability of endemicity in southern California, Arizona, and New Mexico, but also in regions without mandated reporting, including western Texas, eastern Colorado, and southeastern Washington. We also quantified spatio-temporal variability in detectability of Valley fever, given an area is endemic to Coccidioides. We estimated an inverse relationship between lagged 3- and 9-month precipitation and case detection, and a positive association with agriculture. This work can help inform public health surveillance needs and identify areas that would benefit from mandatory case reporting.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies

Prophages are phages in lysogeny that are integrated into, and replicated as part of, the host bacterial genome. These mobile elements can have tremendous impact on their bacterial hosts’ genomes and phenotypes, which may lead to strain emergence and diversification, increased virulence or antibiotic resistance. However, finding prophages in microbial genomes remains a problem with no definitive solution. The majority of existing tools rely on detecting genomic regions enriched in protein-coding genes with known phage homologs, which hinders the de novo discovery of phage regions. In this study, a weighted phage detection algorithm, PhiSpy was developed based on seven distinctive characteristics of prophages, i.e. protein length, transcription strand directionality, customized AT and GC skew, the abundance of unique phage words, phage insertion points and the similarity of phage proteins. The first five characteristics are capable of identifying prophages without any sequence similarity with known phage genes. PhiSpy locates prophages by ranking genomic regions enriched in distinctive phage traits, which leads to the successful prediction of 94% of prophages in 50 complete bacterial genomes with a 6% false-negative rate and a 0.66% false-positive rate.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Fault Management Algorithm Risk Assessment for the NASA Space Launch System

This presentation describes the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. The analysis scope, general assumptions and guide rules, and key modeling concepts were discussed to establish the basis of the risk assessments conducted. Initial analyses indicated a dominance in the total risk by software and firmware failures. This paper presents efforts applied to refine the software risks and the overall impact of implementing those modifications. Current analyses conducted on the detection functions implemented for the SLS Artemis II mission indicate primary risk drivers for the individual FM detection functions are flight software failures, firmware design failures, and hardware Common Cause Failures (CCFs). There still remains issues of how to account for time and redundancy in the software risk estimations.

probability risk analysis↗
NASA NTRSDate not supplied

Fault Management Algorithm Risk Assessment for the NASA Space Launch System

This paper presents the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. The analysis scope, general assumptions and guide rules, and key modeling concepts were discussed to establish the basis of the risk assessments conducted. Initial analyses indicated a dominance in the total risk by software and firmware failures. This paper presents efforts applied to refine the software risks and the overall impact of implementing those modifications. Current analyses conducted on the detection functions implemented for the SLS Artemis II mission indicate primary risk drivers for the individual FM detection functions are flight software failures, firmware design failures, and hardware Common Cause Failures (CCFs). There still remains issues of how to account for time and redundancy in the software risk estimations.

probability risk analysis↗
DOE OSTI2011

Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa. Conclusions: There is a lack of correlation between conventional IMRT QA performance metrics (Gamma passing rates) and dose errors in anatomic regions-of-interest. The most common acceptance criteria and published actions levels therefore have insufficient, or at least unproven, predictive power for per-patient IMRT QA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Feedback-Based, System-Level Properties of Vertebrate-Microbial Interactions

Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered. To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/ EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions. In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D–, or microbial-negative) groups: D+ and D– data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D– data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections. More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Characterization of High Speed Optical and Magnetic Interactions in Superconducting Nanowire Single Photon Detectors

Single-photon detectors are essential tools for quantum photonics. The ideal single photon detector would exhibit a quantum efficiency (QE) of 100% (i.e. no false negatives), zero dark photon counts per second (i.e. no false positives), zero dead time (i.e. the detector is capable of detecting one photon immediately after another), and zero jitter (i.e. the electrical signal produced by the detector perfectly reproduces the timing of the input photon signal). Commercially available avalanche photodiodes (APDs) have generally performed reasonably well for visible wavelengths but they perform poorly at longer wavelengths. For wavelengths spanning the near- ultraviolet (UV) to the mid- infrared (IR), superconducting nanowire single photon detectors (SNSPDs) can exhibit quantum efficiencies exceeding 90% with dark count rates and timing jitter roughly an order of magnitude less than is typically seen in APDs. While SNSPDSs are now commercially available from several small businesses, fundamental questions about the nature of photon interactions with superconducting nanostructures remain.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Multi-site evaluation of the LN34 pan-lyssavirus real-time RT-PCR assay for post-mortem rabies diagnostics

Rabies is a fatal zoonotic disease that requires fast, accurate diagnosis to prevent disease in an exposed individual. The current gold standard for post-mortem diagnosis of human and animal rabies is the direct fluorescent antibody (DFA) test. While the DFA test has proven sensitive and reliable, it requires high quality antibody conjugates, a skilled technician, a fluorescence microscope and diagnostic specimen of sufficient quality. The LN34 pan-lyssavirus real-time RT-PCR assay represents a strong candidate for rabies post-mortem diagnostics due to its ability to detect RNA across the diverse Lyssavirus genus, its high sensitivity, its potential for use with deteriorated tissues, and its simple, easy to implement design. Here, we present data from a multi-site evaluation of the LN34 assay in 14 laboratories. A total of 2,978 samples (1,049 DFA positive) from Africa, the Americas, Asia, Europe, and the Middle East were tested. The LN34 assay exhibited low variability in repeatability and reproducibility studies and was capable of detecting viral RNA in fresh, frozen, archived, deteriorated and formalin-fixed brain tissue. The LN34 assay displayed high diagnostic specificity (99.68%) and sensitivity (99.90%) when compared to the DFA test, and no DFA positive samples were negative by the LN34 assay. The LN34 assay produced definitive findings for 80 samples that were inconclusive or untestable by DFA; 29 were positive. Five samples were inconclusive by the LN34 assay, and only one sample was inconclusive by both tests. Furthermore, use of the LN34 assay led to the identification of one false negative and 11 false positive DFA results. Together, these results demonstrate the reliability and robustness of the LN34 assay and support a role for the LN34 assay in improving rabies diagnostics and surveillance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Rapid Response Radiation Sensors for Homeland Security Applications

The National Security Technologies, LLC, Remote Sensing Laboratory is developing a rapid response radiation detection system for homeland security field applications. The intelligence-driven system is deployed only when non-radiological information about the target is verifiable. The survey area is often limited, so the detection range is small; in most cases covering a distance of 10 meters or less suffices. Definitive response is required in no more than 3 seconds and should minimize false negative alarms, but can err on the side of positive false alarms. The detection system is rapidly reconfigurable in terms of size, shape, and outer appearance; it is a plug-and-play system. Multiple radiation detection components (viz., two or more sodium iodide scintillators) are used to independently “over-determine” the existence of the threat object. Rapid response electronic dose rate meters are also included in the equipment suite. Carefully studied threat signatures are the basis of the decision making. The use of Rad-Detect predictive modeling provides information on the nature of the threat object. Rad-Detect provides accurate dose rate from heavily shielded large sources; for example those lost in Mexico were Category 1 radiation sources (~3,000 Ci of 60Co), the most dangerous of five categories defined by the International Atomic Energy Agency. Taken out of their shielding containers, Category 1 sources can kill anyone who is exposed to them at close range for a few minutes to an hour. Whenever possible sub-second data acquisition will be attempted, and, when deployed, the system will be characterized for false alarm rates. Although the radiation detection materials selected are fast (viz., faster scintillators), their speed is secondary to sensitivity, which is of primary importance. Results from these efforts will be discussed and demonstrated.

gamma detector, false alarm rate, sub-second data ↗
DOE OSTI2017

PREDICTION OF SOLAR FLARES USING UNIQUE SIGNATURES OF MAGNETIC FIELD IMAGES

Prediction of solar flares is an important task in solar physics. The occurrence of solar flares is highly dependent on the structure and topology of solar magnetic fields. A new method for predicting large (M- and X-class) flares is presented, which uses machine learning methods applied to the Zernike moments (ZM) of magnetograms observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for a period of six years from 2010 June 2 to 2016 August 1. Magnetic field images consisting of the radial component of the magnetic field are converted to finite sets of ZMs and fed to the support vector machine classifier. ZMs have the capability to elicit unique features from any 2D image, which may allow more accurate classification. The results indicate whether an arbitrary active region has the potential to produce at least one large flare. We show that the majority of large flares can be predicted within 48 hr before their occurrence, with only 10 false negatives out of 385 flaring active region magnetograms and 21 false positives out of 179 non-flaring active region magnetograms. Our method may provide a useful tool for the prediction of solar flares, which can be employed alongside other forecasting methods.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Statistical interpretation of the Amerithrax “morph” assay results

In the Amerithrax investigation PCR–based “morph assays” were used to link the anthrax letters with the RMR–1029 flask at USAMRIID. Quantitative data reported for several of these assays are not consistent with Poisson sampling statistics, but instead exhibit “Taylor's Law” behavior where the variance greatly exceeds the mean. A plausible statistical model for this behavior can explain the large number of observed negative and “inconclusive” findings, and implies a high likelihood that a repository sample could contain a “morph” mutant at concentrations well above the nominal detection limit but nonetheless give a negative or inconclusive test result. A Bayesian framework relates the assay results to the probability that a sample actually contains all four morph mutants, even though it tested negative for at least one. The analysis implies that the observed false negative rate actually does not significantly weaken the conclusion that the morph assays correctly excluded all but the stocks derived from RMR–1029 as possible sources of the letter powders, at least when the test results were unambiguous. In conclusion, these findings expand upon and resolve some of the issues cited in recent reviews, and indicate the importance of developing a rigorous statistical framework for interpreting “morph” assay data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Pelvic Lymph Node Status Assessed by 18F-Fluorodeoxyglucose Positron Emission Tomography Predicts Low-Risk Group for Distant Recurrence in Locally Advanced Cervical Cancer: A Prospective Study

Purpose: To develop a prediction model to identify a low-risk group for distant recurrence in patients with locally advanced cervical cancer treated by concurrent chemoradiation. Methods and Materials: Prospectively, 62 patients with locally advanced cervical cancer were recruited as a training cohort. Clinical variables and parameters obtained from positron emission tomography (PET) and magnetic resonance imaging were analyzed by logistic regression. For the test set, 54 patients were recruited independently. To identify the low-risk group, negative likelihood ratio (LR) less than 0.2 was set to be a cutoff. Results: Among the training cohort, multivariate logistic analysis revealed that advanced International Federation of Gynecology and Obstetrics (FIGO) stage and a high serum squamous cancer cell (SCC) antigen level were significant risk factors (p = 0.015 and 0.025, respectively). Using the two parameters, criteria to determine a low-risk subset for distant recurrence were postulated: (1) FIGO Stage IIB or less and (2) pretreatment SCC < 2.4 (Model A). Positive pelvic node on PET completely predicted all cases with distant recurrence and thus was considered as another prediction model (Model B). In the test cohort, although Model A did not showed diagnostic performance, Model B completely predicted all cases with distant recurrence and showed a sensitivity of 100% with negative LR of 0. Across the training and test cohort (n = 116), the false negative rate was 0 (95% confidence interval 0%-7.6%). Conclusions: Positive pelvic node on PET is a useful marker in prediction of distant recurrence in patients with locally advanced cervical cancer who are treated with concurrent chemoradiation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2002

Reducing False Positives in Runtime Analysis of Deadlocks

This paper presents an improvement of a standard algorithm for detecting dead-lock potentials in multi-threaded programs, in that it reduces the number of false positives. The standard algorithm works as follows. The multi-threaded program under observation is executed, while lock and unlock events are observed. A graph of locks is built, with edges between locks symbolizing locking orders. Any cycle in the graph signifies a potential for a deadlock. The typical standard example is the group of dining philosophers sharing forks. The algorithm is interesting because it can catch deadlock potentials even though no deadlocks occur in the examined trace, and at the same time it scales very well in contrast t o more formal approaches to deadlock detection. The algorithm, however, can yield false positives (as well as false negatives). The extension of the algorithm described in this paper reduces the amount of false positives for three particular cases: when a gate lock protects a cycle, when a single thread introduces a cycle, and when the code segments in different threads that cause the cycle can actually not execute in parallel. The paper formalizes a theory for dynamic deadlock detection and compares it to model checking and static analysis techniques. It furthermore describes an implementation for analyzing Java programs and its application to two case studies: a planetary rover and a space craft altitude control system.

Bensalem, Saddek↗