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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structural basis of tRNA recognition by the widespread OB fold

The widespread oligonucleotide/oligosaccharide-binding (OB)-fold recognizes diverse substrates from sugars to nucleic acids and proteins, and plays key roles in genome maintenance, transcription, translation, and tRNA metabolism. OB-containing bacterial Trbp and yeast Arc1p proteins are thought to recognize the tRNA elbow or anticodon regions. Here we report a 2.6 Å co-crystal structure of Aquifex aeolicus Trbp111 bound to tRNA Ile , which reveals that Trbp recognizes tRNAs solely by capturing their 3' ends. Structural, mutational, and biophysical analyses show that the Trbp/EMAPII-like OB fold precisely recognizes the single-stranded structure, 3' terminal location, and specific sequence of the 3' CA dinucleotide — a universal feature of mature tRNAs. Arc1p supplements its OB – tRNA 3' end interaction with additional contacts that involve an adjacent basic region and the tRNA body. This study uncovers a previously unrecognized mode of tRNA recognition by an ancient protein fold, and provides insights into protein-mediated tRNA aminoacylation, folding, localization, trafficking, and piracy.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

Identification of Geostructures of the Continental Crust Particularly as They Relate to Mineral Resource Evaluation

The author has identified the following significant results. A pattern of very old geostructures was recognized, reflecting structures in the crust. This pattern is not peculiar to Alaska, but can be recognized throughout the northern cordillera. A new metallogenic hypothesis for Alaska was developed, based on the relationship of space image linears to known mineral deposits. Using image linear analysis, regional geologic features were also recognized; these features may be used to guide in the location of undiscovered oil and/or gas accumulations in northern Alaska. The effectiveness of ERTS data in enhancing medium and small scale mapping was demonstrated. ERTS data were also used to recognize and monitor the state of large scale vehicular scars on Arctic tundra.

Lathram, E. H.↗
NASA NTRS1986

Crew Communication as a Factor in Aviation Accidents

The crew communication process is analyzed. Planning and explanation are shown to be well-structured discourse types, described by formal rules. These formal rules are integrated with those describing the other most important discourse type within the cockpit: the command-and-control speech act chain. The latter is described as a sequence of speech acts for making requests (including orders and suggestions), for making reports, for supporting or challenging statements, and for acknowledging previous speech acts. Mitigation level, a linguistic indication of indirectness and tentativeness in speech, was an important variable in several hypotheses, i.e., the speech of subordinates is more mitigated than the speech of superiors, the speech of all crewmembers is less mitigated when they know that they are in either a problem or emergency situation, and mitigation is a factor in failures of crewmembers to initiate discussion of new topics or have suggestions ratified by the captain. Test results also show that planning and explanation are more frequently performed by captains, are done more during crew- recognized problems, and are done less during crew-recognized emergencies. The test results also indicated that planning and explanation are more frequently performed by captains than by other crewmembers, are done more during crew-recognized problems, and are done less during-recognized emergencies.

Goguen, J.↗
NASA NTRS1988

Intelligent interfaces for expert systems

Vital to the success of an expert system is an interface to the user which performs intelligently. A generic intelligent interface is being developed for expert systems. This intelligent interface was developed around the in-house developed Expert System for the Flight Analysis System (ESFAS). The Flight Analysis System (FAS) is comprised of 84 configuration controlled FORTRAN subroutines that are used in the preflight analysis of the space shuttle. In order to use FAS proficiently, a person must be knowledgeable in the areas of flight mechanics, the procedures involved in deploying a certain payload, and an overall understanding of the FAS. ESFAS, still in its developmental stage, is taking into account much of this knowledge. The generic intelligent interface involves the integration of a speech recognizer and synthesizer, a preparser, and a natural language parser to ESFAS. The speech recognizer being used is capable of recognizing 1000 words of connected speech. The natural language parser is a commercial software package which uses caseframe instantiation in processing the streams of words from the speech recognizer or the keyboard. The systems configuration is described along with capabilities and drawbacks.

Villarreal, James A.↗
NASA NTRS1990

Immunoreactive prohormone atrial natriuretic peptides 1-30 and 31-67 - Existence of a single circulating amino-terminal peptide

Sep-Pak C18 extraction of human plasma and radioimmunoassay using antibodies which recognize atrial natriuretic peptide (99-128) and the prohormone sequences 1-30 and 31-67 resulted in mean values from 20 normal subjects of 26.2 (+/- 9.2), 362 (+/- 173) and 368 (+/- 160) pg/ml, respectively. A high correlation coefficient between values obtained using antibodies recognizing prohormone sequences 1-30 and 31-67 was observed (R = 0.84). Extracted plasma immunoreactivity of 1-30 and 31-67 both eluted at 46 percent acetonitrile. In contrast, chromatographic elution of synthetic peptides 1-30 and 31-67 was observed at 48 and 39 percent acetonitrile, respectively. Data suggest that the radioimmunoassay of plasma using antibodies recognizing prohormone sequences 1-30 and 31-67 may represent the measurement of a unique larger amino-terminal peptide fragment containing antigenic sites recognized by both antisera.

Chen, Yu-Ming↗
NASA NTRS1994

Object recognition and pose estimation of planar objects from range data

The Extravehicular Activity Helper/Retriever (EVAHR) is a robotic device currently under development at the NASA Johnson Space Center that is designed to fetch objects or to assist in retrieving an astronaut who may have become inadvertently de-tethered. The EVAHR will be required to exhibit a high degree of intelligent autonomous operation and will base much of its reasoning upon information obtained from one or more three-dimensional sensors that it will carry and control. At the highest level of visual cognition and reasoning, the EVAHR will be required to detect objects, recognize them, and estimate their spatial orientation and location. The recognition phase and estimation of spatial pose will depend on the ability of the vision system to reliably extract geometric features of the objects such as whether the surface topologies observed are planar or curved and the spatial relationships between the component surfaces. In order to achieve these tasks, three-dimensional sensing of the operational environment and objects in the environment will therefore be essential. One of the sensors being considered to provide image data for object recognition and pose estimation is a phase-shift laser scanner. The characteristics of the data provided by this scanner have been studied and algorithms have been developed for segmenting range images into planar surfaces, extracting basic features such as surface area, and recognizing the object based on the characteristics of extracted features. Also, an approach has been developed for estimating the spatial orientation and location of the recognized object based on orientations of extracted planes and their intersection points. This paper presents some of the algorithms that have been developed for the purpose of recognizing and estimating the pose of objects as viewed by the laser scanner, and characterizes the desirability and utility of these algorithms within the context of the scanner itself, considering data quality and noise.

Pendleton, Thomas W.↗
NASA NTRS1999

History of Ozone Research: From Schonbein to the Present

In 1840, C.F. Schonbein recognized that the smell generated in several different electrical and chemical processes was a single substance. He named this substance "ozein" from the Greek for "to smell". This substance we know today as ozone. Several periods can be distinguished in the continued development of our understanding of ozone. Throughout the late 19th century, the identity and properties of ozone were established and described. Ozone was recognized to be a constituent of normal air and tests were established to measure its concentration. Its disinfectant properties were recognized. New methods were developed for making ozone in the laboratory. In 1879, ultraviolet spectroscopic techniques were applied to the measurement of the solar spectrum and it was discovered by Comu that the spectrum was cut off at about 300 nm wavelength. Hartley suggested, based on laboratory measurements, that this cutoff was due to ozone in the atmosphere which he correctly asserted was somewhere in the upper atmosphere. This began the period of development of the amount and distribution of ozone throughout the atmosphere. In 1930, Chapman put forward the first theory of the formation and destruction of ozone. By the mid-1960s it was becoming obvious that the description of the chemical loss term was inadequate. By the early 1970s the chemical destruction of ozone by the oxides of hydrogen, nitrogen, chlorine, and bromine was recognized as an essential element in the chemical balance determining the ozone concentration. Today, ozone is a broad research project which crosses the boundaries of traditional disciplines. Stratospheric ozone loss due to chlorofluorocarbons is a newsworthy item. The Antarctic ozone hole opens up every spring. The provisions of the Montreal Protocol were agreed upon by countries around the world and promise to reduce the future levels of ozone-destroying chlorine in the stratosphere. Ozone concentrations in polluted cities are a subject of local and national regulations to limit its potential for causing health problems and corrosive effects on materials. A lot is known about ozone but many new questions are still being developed.

Stolarski, Richard S.↗
NASA NTRS1997

Covisualization in living onion cells of putative integrin, putative spectrin, actin, putative intermediate filaments, and other proteins at the cell membrane and in an endomembrane sheath

Covisualizations with wide-field computational optical-sectioning microscopy of living epidermal cells of the onion bulb scale have evidenced two major new cellular features. First, a sheath of cytoskeletal elements clads the endomembrane system. Similar elements clad the inner faces of punctate plasmalemmal sites interpreted as plasmalemmal control centers. One component of the endomembrane sheath and plasmalemmal control center cladding is anti-genicity-recognized by two injected antibodies against animal spectrin. Immunoblots of separated epidermal protein also showed bands recognized by these antibodies. Injected phalloidin identified F-actin with the same cellular distribution pattern, as did antibodies against intermediate-filament protein and other cytoskeletal elements known from animal cells. Injection of general protein stains demonstrated the abundance of endomembrane sheath protein. Second, the endomembrane system, like the plasmalemmal puncta, contains antigen recognized by an anti-beta 1 integrin injected into the cytoplasm. Previously, immunoblots of separated epidermal protein were shown to have a major band recognized both by this antibody prepared against a peptide representing the cytosolic region of beta 1 integrin and an antibody against the matrix region of beta 1 integrin. The latter antiboby also identified puncta at the external face of protoplasts. It is proposed that integrin and associated transmembrane proteins secure the endomembrane sheath and transmit signals between it and the lumen or matrix of the endoplasmic reticulum and organellar matrices. This function is comparable to that proposed for such transmembrane linkers in the plasmalemmal control centers, which also appear to bind cytoskeleton and a host of related molecules and transmit signals between them and the wall matrix. It is at the plasmalemmal control centers that the endoplasmic reticulum, a major component of the endomembrane system, attaches to the plasma membrane.

Non-NASA Center↗
NASA NTRS2004

Automated Recognition of Geologically Significant Shapes in MER PANCAM and MI Images

Autonomous recognition of scientifically important information provides the capability of: 1) Prioritizing data return; 2) Intelligent data compression; 3) Reactive behavior onboard robotic vehicles. Such capabilities are desirable as mission scenarios include longer durations with decreasing interaction from mission control. To address such issues, we have implemented several computer algorithms, intended to autonomously recognize morphological shapes of scientific interest within a software architecture envisioned for future rover missions. Mars Exploration Rovers (MER) instrument payloads include a Panoramic Camera (PANCAM) and Microscopic Imager (MI). These provide a unique opportunity to evaluate our algorithms when applied to data obtained from the surface of Mars. Early in the mission we applied our algorithms to images available at the mission web site (http://marsrovers.jpl.nasa.gov/gallery/images.html), even though these are not at full resolution. Some algorithms would normally use ancillary information, e.g. camera pointing and position of the sun, but these data were not readily available. The initial results of applying our algorithms to the PANCAM and MI images are encouraging. The horizon is recognized in all images containing it; such information could be used to eliminate unwanted areas from the image prior to data transmission to Earth. Additionally, several rocks were identified that represent targets for the mini-thermal emission spectrometer. Our algorithms also recognize the layers, identified by mission scientists. Such information could be used to prioritize data return or in a decision-making process regarding future rover activities. The spherules seen in MI images were also autonomously recognized. Our results indicate that reliable recognition of scientifically relevant morphologies in images is feasible.

Morris, Robert↗
DOE OSTI2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Nucleolar localization signal and histone methylation reader function is required for SPIN1 to promote rRNA gene expression

Highlights: • A NoLS in the N-terminus of SPIN1 mediates its nucleolar localization and function. • The H3R8me2a-recognizing capacity of SPIN1 is indispensable for its promotion of rRNA expression. • SPIN1 is involved the formation of SL1 complex on rDNA loci. Spindlin1 (SPIN1), a histone modification reader protein, was enriched in the cell nucleolus and facilitated rRNA expression. However, how SPIN1 localizes to the nucleolus and its functional role in rRNA gene expression remain unresolved. Here, we identified a nucleolar localization signal in the N-terminal region of SPIN1 that is essential for its enrichment and function in the nucleolus. We also discovered that, in addition to its H3K4me3 recognizing activity, the H3R8me2a-recognizing capacity of SPIN1 is also indispensable for stimulating rRNA expression. Chromatin immunoprecipitation results indicated that SPIN1 is required for the association or assembly of selective factor 1 (SL1) complex, probably facilitating the initiation of rDNA transcription through its H3 K4me3-R8me2a reader function.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹ instrument, verb, target › combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. Here, the challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. Here, a total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Cryo-EM Structures of Human Drosha and DGCR8 in Complex with Primary MicroRNA

Metazoan microRNAs require specificmaturation steps initiated by Microprocessor, comprising Drosha and DGCR8. Lack of structural information for the assembled complex has hindered an understanding of how Microprocessor recognizes primary microRNA transcripts (pri-miRNAs). In this paper we present a cryoelectron microscopy structure of human Microprocessor with a pri-miRNA docked in the active site, poised for cleavage. The basal junction is recognized by a four-way intramolecular junction in Drosha, triggered by the Belt and Wedge regions that clamp over the ssRNA. The belt is important for efficiency and accuracy of pri-miRNA processing. TwodsRBDs form a molecular ruler tomeasure the stem length between the twodsRNA-ssRNAjunctions. The specific organization of thedsRBDs near the apical junction is independent of Drosha core domains, as observed in a second structure in the partially docked state. Collectively, we derive a molecular model to explain how Microprocessor recognizes a pri-miRNA and accurately identifies the cleavage site.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

The cell cycle-regulated DNA adenine methyltransferase CcrM opens a bubble at its DNA recognition site

The Caulobacter crescentus cell cycle-regulated DNA methyltransferase (CcrM) methylates the adenine of hemimethylated GANTC after replication. Here we present the structure of CcrM in complex with double-stranded DNA containing the recognition sequence. CcrM contains an N-terminal methyltransferase domain and a C-terminal nonspecific DNA-binding domain. CcrM is a dimer, with each monomer contacting primarily one DNA strand: the methyltransferase domain of one molecule binds the target strand, recognizes the target sequence, and catalyzes methyl transfer, while the C-terminal domain of the second molecule binds the non-target strand. The DNA contacts at the 5-base pair recognition site results in dramatic DNA distortions including bending, unwinding and base flipping. The two DNA strands are pulled apart, creating a bubble comprising four recognized base pairs. The five bases of the target strand are recognized meticulously by stacking contacts, van der Waals interactions and specific Watson–Crick polar hydrogen bonds to ensure high enzymatic specificity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural basis of transcription recognition of a hydrophobic unnatural base pair by T7 RNA polymerase

Bacteriophage T7 RNA polymerase (T7 RNAP) is widely used for synthesizing RNA molecules with synthetic modifications and unnatural base pairs (UBPs) for a variety of biotechnical and therapeutic applications. However, the molecular basis of transcription recognition of UBPs by T7 RNAP remains poorly understood. Here we focused on a representative UBP, 7-(2-thienyl)-imidazo[4,5-b]pyridine (Ds) and pyrrole 2-carbaldehyde (Pa), and investigated how the hydrophobic Ds–Pa pair is recognized by T7 RNAP. Our kinetic assays revealed that T7 RNAP selectively recognizes the Ds or Pa base in the templates and preferentially incorporates their cognate unnatural base nucleotide substrate (PaTP or DsTP) over natural NTPs. Our structural studies reveal that T7 RNAP recognizes the unnatural substrates at the pre-insertion state in a distinct manner compared to natural substrates. These results provide mechanistic insights into transcription recognition of UBP by T7 RNAP and provide valuable information for designing the next generation of UBPs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks

Abstract This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Low responsiveness of machine learning models to critical or deteriorating health conditions

Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model’s ability to recognize critical patient conditions. We develop multiple medical ML testing approaches, including a gradient ascent method and neural activation map. We systematically assess these machine learning models’ ability to respond to serious medical conditions using additional test cases, some of which are time series. Guided by medical doctors, our evaluation involves multiple machine learning models, resampling techniques, and four datasets for two clinical prediction tasks. We identify serious deficiencies in the models’ responsiveness, with the models being unable to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our study identifies similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models. Using generated test cases, we find that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Most of the ML models tested fail to respond adequately to critically ill patients. How to incorporate medical knowledge into clinical machine learning models is an important future research direction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A distinct RNA recognition mechanism governs Np 4 decapping by RppH

Dinucleoside tetraphosphates, often described as alarmones because their cellular concentration increases in response to stress, have recently been shown to function in bacteria as precursors to nucleoside tetraphosphate (Np 4 ) RNA caps. Removal of this cap is critical for initiating 5' end-dependent degradation of those RNAs, potentially affecting bacterial adaptability to stress; however, the predominant Np 4 decapping enzyme in proteobacteria, ApaH, is inactivated by the very conditions of disulfide stress that enable Np 4 -capped RNAs to accumulate to high levels. Here, in this study, we show that, in Escherichia coli cells experiencing such stress, the RNA pyrophosphohydrolase RppH assumes a leading role in decapping those transcripts, preferring them as substrates over their triphosphorylated and diphosphorylated counterparts. Unexpectedly, this enzyme recognizes Np 4 -capped 5' ends by a mechanism distinct from the one it uses to recognize other 5' termini, resulting in a one-nucleotide shift in substrate specificity. The unique manner in which capped substrates of this kind bind to the active site of RppH positions the δ-phosphate, rather than the β-phosphate, for hydrolytic attack, generating triphosphorylated RNA as the primary product of decapping. Consequently, a second RppH-catalyzed deprotection step is required to produce the monophosphorylated 5' terminus needed to stimulate rapid RNA decay. The unconventional manner in which RppH recognizes Np 4 -capped 5' ends and its differential impact on the rates at which such termini are deprotected as a prelude to RNA degradation could have major consequences for reprogramming gene expression during disulfide stress.

59 BASIC BIOLOGICAL SCIENCES↗