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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Towards Feature-Preserving 2D and 3D Vector Field Compression

The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type change in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress input data together with the error bound field using a modified lossy compressor. Our compression algorithm can be also embarrassingly parallelized for large data handling and in situ processing. We benchmark our method by comparing it with existing lossy compressors in terms of false positive/negative/type rates, compression ratio, and various vector field visualizations with several scientific applications.

critical points↗
DOE OSTI2021

Hidden Markov Model: a shortest unique representative approach to detect the protein toxins, virulence factors and antibiotic resistance genes

Objective: Currently, next generation sequencing (NGS) is widely used to decode potential novel or variant pathogens both in emergent outbreaks and in routine clinical practice. However, the efficient identification of novel or diverged pathogenomic compositions remains a big challenge. It is especially true for short DNA sequence fragments from NGS, since sequence similarity searching is vulnerable to false negatives or false positives, as is mismatching or matching with unrelated proteins. Therefore, this study aimed to establish a bioinformatics approach that can generate unique motif sequences for profiling searching, resulting in high specificity and sensitivity. Results: In this study, we introduced a Shortest Unique Representative Hidden Markov Model (HMM) approach to identify bacterial toxin, virulence factor (VF), and antimicrobial resistance (AR) in short sequence reads. We first construct unique representative domain sequences of toxin genes, VFs, and ARs to avoid potential false positives, and then to use HMM models to accurately identify potential toxin, VF, and AR fragments. The benchmark shows this approach can achieve relatively high specificity and sensitivity if the appropriate cutoff value is applied. Our approach can be used to recognize the protein sequences of known toxins and pathogens, identifies their common characteristics and then searches for similar sequences in other organisms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Human versus automation in responding to failures: an expected-value analysis

A simple analytical criterion is provided for deciding whether a human or automation is best for a failure detection task. The method is based on expected-value decision theory in much the same way as is signal detection. It requires specification of the probabilities of misses (false negatives) and false alarms (false positives) for both human and automation being considered, as well as factors independent of the choice--namely, costs and benefits of incorrect and correct decisions as well as the prior probability of failure. The method can also serve as a basis for comparing different modes of automation. Some limiting cases of application are discussed, as are some decision criteria other than expected value. Actual or potential applications include the design and evaluation of any system in which either humans or automation are being considered.

NASA Discipline Space Human Factors↗
NASA NTRS2012

Multi-Parameter Observation and Detection of Pre-Earthquake Signals in Seismically Active Areas

The recent large earthquakes (M9.0 Tohoku, 03/2011; M7.0 Haiti, 01/2010; M6.7 L Aquila, 04/2008; and M7.9 Wenchuan 05/2008) have renewed interest in pre-anomalous seismic signals associated with them. Recent workshops (DEMETER 2006, 2011 and VESTO 2009 ) have shown that there were precursory atmospheric /ionospheric signals observed in space prior to these events. Our initial results indicate that no single pre-earthquake observation (seismic, magnetic field, electric field, thermal infrared [TIR], or GPS/TEC) can provide a consistent and successful global scale early warning. This is most likely due to complexity and chaotic nature of earthquakes and the limitation in existing ground (temporal/spatial) and global satellite observations. In this study we analyze preseismic temporal and spatial variations (gas/radon counting rate, atmospheric temperature and humidity change, long-wave radiation transitions and ionospheric electron density/plasma variations) which we propose occur before the onset of major earthquakes:. We propose an Integrated Space -- Terrestrial Framework (ISTF), as a different approach for revealing pre-earthquake phenomena in seismically active areas. ISTF is a sensor web of a coordinated observation infrastructure employing multiple sensors that are distributed on one or more platforms; data from satellite sensors (Terra, Aqua, POES, DEMETER and others) and ground observations, e.g., Global Positioning System, Total Electron Content (GPS/TEC). As a theoretical guide we use the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model to explain the generation of multiple earthquake precursors. Using our methodology, we evaluated retrospectively the signals preceding the most devastated earthquakes during 2005-2011. We observed a correlation between both atmospheric and ionospheric anomalies preceding most of these earthquakes. The second phase of our validation include systematic retrospective analysis for more than 100 major earthquakes (M>5.9) in Taiwan and Japan. We have found anomalous behavior before all of these events with no false negatives. Calculated false alarm ratio for the for the same month over the entire period of analysis (2003-2009) is less than 10% and was d as the earthquakes. The commonalities in detecting atmospheric/ionospheric anomalies show that they may exist over both land and sea in regions of maximum stress (i.e., along plate boundaries) Our results indicate that the ISTF model could provide a capability to observe pre-earthquake atmospheric/ionospheric signals by combining this information into a common framework.

Ouzounov, D.↗
NASA NTRS2012

In-Situ Wire Damage Detection System

An In-Situ Wire Damage Detection System (ISWDDS) has been developed that is capable of detecting damage to a wire insulation, or a wire conductor, or to both. The system will allow for realtime, continuous monitoring of wiring health/integrity and reduce the number of false negatives and false positives while being smaller, lighter in weight, and more robust than current systems. The technology allows for improved safety and significant reduction in maintenance hours for aircraft, space vehicles, satellites, and other critical high-performance wiring systems for industries such as energy production and mining. The integrated ISWDDS is comprised of two main components: (1) a wire with an innermost core conductor, an inner insulation film, a conductive layer or inherently conductive polymer (ICP) covering the inner insulation film, an outermost insulation jacket; and (2) smart connectors and electronics capable of producing and detecting electronic signals, and a central processing unit (CPU) for data collection and analysis. The wire is constructed by applying the inner insulation films to the conductor, followed by the outer insulation jacket. The conductive layer or ICP is on the outer surface of the inner insulation film. One or more wires are connected to the CPU using the smart connectors, and up to 64 wires can be monitored in real-time. The ISWDDS uses time domain reflectometry for damage detection. A fast-risetime pulse is injected into either the core conductor or conductive layer and referenced against the other conductor, producing transmission line behavior. If either conductor is damaged, then the signal is reflected. By knowing the speed of propagation of the pulse, and the time it takes to reflect, one can calculate the distance to and location of the damage.

Williams, Martha↗
NASA NTRS2021

Data-driven landslide nowcasting at the global scale

Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.

XGBoos↗
DOE OSTI2019

Assessment of geophysical monitoring methods for detection of brine and CO 2 leakage in drinking water aquifers

Here, we evaluated and compared the effectiveness of four surface-based geophysical monitoring methods to downhole pressure and chemical methods to detect brine and CO 2 leakage in underground sources of drinking water overlying a CO 2 storage reservoir. This assessment uses synthetic monitoring data generated from 400 simulated aquifer impact data sets. Here, the six monitoring techniques can detect impacted groundwater once 20,000 tonnes of CO 2 have leaked into the drinking water resource. Geophysical methods are most effective at detecting shallow plumes. Although downhole monitoring methods outperform geophysical methods in detecting deep plumes, geophysical methods may help reduce the false negative during the post injection site care because they detect impacted groundwater where downhole sensors may be absent.

58 GEOSCIENCES↗
NASA NTRS2018

Exoplanet Biosignatures: Understanding Oxygen as a Biosignature in the Context of Its Environment

Here we review how environmental context can be used to interpret whether O 2 is a biosignature in extrasolar planetary observations. This paper builds on the overview of current biosignature research discussed in Schwieterman et al. (2017), and provides an in-depth, interdisciplinary example of biosignature identification and observation that serves as a basis for the development of the general framework for biosignature assessment described in Catling et al., (2017). O 2 is a potentially strong biosignature that was originally thought to be an unambiguous indicator for life at high-abundance. In exploring O 2 as a biosignature, we describe the coevolution of life with the early Earth's environment, and how the interplay of sources and sinks in the planetary environment may have resulted in suppression of O 2 release into the atmosphere for several billion years, a false negative for biologically generated O 2 . False positives may also be possible, with recent research showing potential mechanisms in exoplanet environments that may generate relatively high abundances of atmospheric O 2 without a biosphere being present. These studies suggest that planetary characteristics that may enhance false negatives should be considered when selecting targets for biosignature searches. Similarly our ability to interpret O 2 observed in an exoplanetary atmosphere is also crucially dependent on environmental context to rule out false positive mechanisms. We describe future photometric, spectroscopic and time-dependent observations of O 2 and the planetary environment that could increase our confidence that any observed O 2 is a biosignature, and help discriminate it from potential false positives. The rich, interdisciplinary study of O 2 illustrates how a synthesis of our understanding of life's evolution and the early Earth, scientific computer modeling of star-planet interactions and predictive observations can enhance our understanding of biosignatures and guide and inform the development of next-generation planet detection and characterization missions. By observing and understanding O 2 in its planetary context we can increase our confidence in the remote detection of life, and provide a model for biosignature development for other proposed biosignatures.

Victoria S Meadows↗
DOE OSTI2020

Misclassification of primary liver cancer in the Life Span Study of atomic bomb survivors

Primary liver cancer is difficult to diagnose accurately at death, due to metastases from nearby organs and to concomitant diseases, such as chronic hepatitis and cirrhosis. Trends in diagnostic accuracy could affect radiation risk estimates for incident liver cancer by altering background rates or by impacting risk modification by sex and age. We quantified the potential impact of death‐certificate inaccuracies on radiation risk estimates for liver cancer in the Life Span Study of atomic bomb survivors. True‐positive and false‐negative rates were obtained from a previous study that compared death‐certificate causes of death with those based on pathological review, from 1958 to 1987. We assumed various scenarios for misclassification rates after 1987. We obtained estimated true positives and estimated false negatives by stratified sampling from binomial distributions with probabilities given by the true‐positive and false‐negative rates, respectively. Poisson regression methods were applied to highly stratified person‐year tables of corrected case counts and accrued person years. During the study period (1958–2009), there were 1,885 cases of liver cancer, which included 383 death‐certificate‐only (DCO) cases; 1,283 cases with chronic liver disease as the underlying cause of death; and 150 DCO cases of pancreatic cancer among 105,444 study participants. Across the range of scenarios considered, radiation risk estimates based on corrected case counts were attenuated, on average, by 13–30%. Our results indicated that radiation risk estimates for liver cancer were potentially sensitive to death‐certificate inaccuracies. Additional data are needed to inform misclassification rates in recent years.

French, Benjamin↗
DOE OSTI2019

Diagnostic performance of magnetic resonance cholangiopancreatography (MRCP) versus endoscopic retrograde cholangiopancreatography (ERCP) in the pediatric population: a clinical effectiveness study

Purpose: To determine the diagnostic performance of magnetic resonance cholangiopancreatography (MRCP) for detecting biliary and pancreatic abnormalities in a pediatric population, using endoscopic retrograde cholangiopancreatography (ERCP) as the reference standard. Materials and methods: Institutional review board approval with a waiver of informed consent was obtained for this retrospective investigation. Records from the Cincinnati Children’s Hospital Medical Center, Division of Gastroenterology and Department of Radiology were used to identify patients aged ≤ 18 years who had undergone both ERCP and MRCP within a 4-week interval between January 2013 and May 2017. Biliary and pancreatic duct findings were documented for each modality to determine the diagnostic performance of MRCP (with 95% confidence intervals), using ERCP as the reference standard. Results: 54 patients met inclusion criteria. Mean patient age at time of ERCP was 10.4 ± 4.9 years, and 25 (46%) were male. Mean interval between ERCP and MRCP was 11.2 ± 9.7 days. For detection of any abnormality (n = 99 ERCP findings), MRCP had a sensitivity of 76.8% (67.5–84.0%) and a positive predictive value (PPV) of 81.7% (72.7–88.3%). MRCP was 75.7% (59.9–86.6%) sensitive, with a PPV of 84.9% (69.1–93.4%) for biliary findings (n = 37) and 73.5% (59.7–83.8%) sensitive, with a PPV of 78.3% (64.4–87.7%) for pancreatic findings (n = 49). For pancreatobiliary abnormalities (n = 13), MRCP had a sensitivity of 92.3% (66.7–99.6%) and a PPV of 85.7% (60.1–97.5%). Conclusion: In clinical practice, MRCP is moderately sensitive for biliary and pancreatic abnormalities, with false-negative and false-positive examinations being relatively common.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

CT and MR imaging of chemotherapy-induced hepatopathy

Chemotherapy-induced hepatopathy includes a wide variety of parenchymal and vascular hepatic changes on imaging, including diffuse or focal hepatopathies (i.e. hepatitis, steatosis, fibrosis, pseudocirrhosis, or sinusoidal obstruction). These changes can profoundly alter the hepatic parenchyma on imaging and result in both false negative and false-positive diagnoses of hepatic metastases and lead to errors in patient management strategies. It is therefore important for radiologists to have a comprehensive knowledge of the imaging patterns that may develop following chemotherapy. The purpose of this review is to explore the broad spectrum of hepatic parenchymal and vascular chemotherapy-induced changes on CT and MR imaging.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding

Background Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. New method Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Results Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. Comparison with existing methods We compared our results to four open-access IF-based cell-counting tools available in the literature. Lastly, our method showed improved accuracy for all data samples. Conclusion The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Evaluation of In Silico Multifeature Libraries for Providing Evidence for the Presence of Small Molecules in Synthetic Blinded Samples

The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples. The process is time-consuming and costly, and the majority of molecules are not yet represented by standards. Thus, there is a need to assemble evidence for the presence of small molecules in complex samples through the use of libraries containing calculated chemical properties. To address this need, we developed a Multi-Attribute Matching Engine (MAME) and a library derived in-part from our in silico Chemical Library Engine (ISiCLE). Here, we describe an initial evaluation of these methods in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency’s (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT, Phase 1). For molecules in all mixtures, the initial blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high evidence scores, the FDR was 35%. After unblinding of the sample compositions, we optimized the scoring parameters to better exploit the available evidence, and increased the accuracy for molecules suspected as present. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high evidence scores, the FDR was 10%. This study demonstrates that multi-attribute matching methods in conjunction with in silico libraries may one day enable reduced reliance on experimentally-derived libraries for building evidence for the presence of molecules in complex samples.

Nunez, Jamie↗
DOE OSTI2011

Inspecting the minefield and residual explosives by fast neutron activation method

As an upgrade of a robotic mobile system for antipersonnel land-mine clearance, a fast neutron probe has been considered for the detection of mines and explosive residues. Laboratory tests were made by using the 14 MeV 6 x 10{sup 7} neutrons/sec beam with the associated alpha particle detection and with a LaBr{sub 3} gamma ray detector. Simulant of the anti-personal mine was used as a target. Several measurements were made with the target buried into the soil at different depths. For each depth minimal time measurement was estimated for false negative 0.4 % and false positive equal to 10 %. Tests showed that is possible to detect buried land-mine as well as residual explosives; however, in order to reach the optimal speed of 10 cm/s for de-mining vehicle it is necessarily to use several sealed tube neutron generators and few tens of LaBr{sub 3} gamma ray detectors. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

SU-D-BRF-07: Ultrasound and Fluoroscopy Based Intraoperative Image-Guidance System for Dynamic Dosimetry in Prostate Brachytherapy

Purpose: Prostate brachytherapy is a common treatment method for low-risk prostate cancer patients. Intraoperative treatment planning is known to improve the treatment procedure and the outcome. The current limitation of intraoperative treatment planning is the inability to localize the seeds in relation to the prostate. We developed an image-guidance system to fulfill this need to achieve intraoperative dynamic dosimetry in prostate brachytherapy. Methods: Our system is based on standard imaging equipments available in the operating room, including the transrectal ultrasound (TRUS) and the mobile C-arm. A simple fiducial is added to compute the C-arm pose. Three fluoroscopic images and an ultrasound volume of the seeds and the prostate are acquired and processed by four image processing algorithms: seed segmentation, fiducial detection with pose estimation, seed reconstruction, and seeds-to-TRUS registration. The updated seed positions allow the physician to assess the quality of implantation and dynamically adjust the treatment plan during the course of surgery to achieve improved exit dosimetry. Results: The system was tested on 10 phantoms and 37 patients. Seed segmentation resulted in a 1% false negative and 2% false positive rates. Fiducial detection with pose estimation resulted in a detection rate of 98%. Seed reconstruction had a mean reconstruction error of 0.4 mm. Seeds-to-TRUS registration had a mean registration error of 1.3 mm. The total processing time from image acquisition to registration was approximately 1 minute. Conclusion: We present an image-guidance system for intraoperative dynamic dosimetry in prostate brachytherapy. Using standard imaging equipments and a simple fiducial, our system can be easily adopted in any clinics. Robust image processing algorithms enable accurate and fast computation of the delivered dose. Especially, the system enables detection of possible hot/cold spots during the surgery, allowing the physician to address these in the operating room rather than requiring additional therapy afterwards. This research was supported in part by the National Institutes of Health/National Cancer Institute (NIH/NCI) under grant 2R44CA099374 and grant 1R01CA151395, and in part by the Department of Defense (DoD) under grant W81XWH-05-1-0407.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

WE-D-204-07: Development of An ImageJ Plugin for Renal Function Quantification: RenalQuant

Purpose: Commercial workstations usually have their own software to calculate dynamic renal functions. However, usually they have low flexibility and subjectivity on delimiting kidney and background areas. The aim of this paper is to present a public domain software, called RenalQuant, capable to semi-automatically draw regions of interest on dynamic renal scintigraphies, extracting data and generating renal function quantification parameters. Methods: The software was developed in Java and written as an ImageJ-based plugin. The preprocessing and segmentation steps include the user’s selection of one time frame with higher activity in kidney’s region, compared with background, and low activity in the liver. Next, the chosen time frame is smoothed using a Gaussian low pass spatial filter (σ = 3) for noise reduction and better delimitation of kidneys. The maximum entropy thresholding method is used for segmentation. A background area is automatically placed below each kidney, and the user confirms if these regions are correctly segmented and positioned. Quantitative data are extracted and each renogram and relative renal function (RRF) value is calculated and displayed. Results: RenalQuant plugin was validated using retrospective 20 patients’ 99mTc-DTPA exams, and compared with results produced by commercial workstation software, referred as reference. The renograms intraclass correlation coefficients (ICC) were calculated and false-negative and false-positive RRF values were analyzed. The results showed that ICC values between RenalQuant plugin and reference software for both kidneys’ renograms were higher than 0.75, showing excellent reliability. Conclusion: Our results indicated RenalQuant plugin can be trustingly used to generate renograms, using DICOM dynamic renal scintigraphy exams as input. It is user friendly and user’s interaction occurs at a minimum level. Further studies have to investigate how to increase RRF accuracy and explore how to solve limitations in the segmentation step, mainly when background region has higher activity compared to kidneys. Financial support by CAPES.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

glmnetGLR

The primary goal was to build a classifier that accurately predicts a binary response. We utilized a Lasso logistic regression classifier (LLRC) which simultaneously restricts the size of model coefficients and the number of quality metrics included the model. Importantly the model does not treat false-negatives and false-positives as equally deleterious events.

Crowell, Kevin↗