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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Library Screening, In Vivo Confirmation, and Structural and Bioinformatic Analysis of Pentapeptide Sequences as Substrates for Protein Farnesyltransferase

Protein farnesylation is a post-translational modification where a 15-carbon farnesyl isoprenoid is appended to the C-terminal end of a protein by farnesyltransferase (FTase). This process often causes proteins to associate with the membrane and participate in signal transduction pathways. The most common substrates of FTase are proteins that have C-terminal tetrapeptide CaaX box sequences where the cysteine is the site of modification. However, recent work has shown that five amino acid sequences can also be recognized, including the pentapeptides CMIIM and CSLMQ. In this work, peptide libraries were initially used to systematically vary the residues in those two parental sequences using an assay based on Matrix Assisted Laser Desorption Ionization–Mass Spectrometry (MALDI-MS). In addition, 192 pentapeptide sequences from the human proteome were screened using that assay to discover additional extended CaaaX-box motifs. Selected hits from that screening effort were rescreened using an in vivo yeast reporter protein assay. The X-ray crystal structure of CMIIM bound to FTase was also solved, showing that the C-terminal tripeptide of that sequence interacted with the enzyme in a similar manner as the C-terminal tripeptide of CVVM, suggesting that the tripeptide comprises a common structural element for substrate recognition in both tetrapeptide and pentapeptide sequences. Molecular dynamics simulation of CMIIM bound to FTase further shed light on the molecular interactions involved, showing that a putative catalytically competent Zn(II)-thiolate species was able to form. Bioinformatic predictions of tetrapeptide (CaaX-box) reactivity correlated well with the reactivity of pentapeptides obtained from in vivo analysis, reinforcing the importance of the C-terminal tripeptide motif. This analysis provides a structural framework for understanding the reactivity of extended CaaaX-box motifs and a method that may be useful for predicting the reactivity of additional FTase substrates bearing CaaaX-box sequences.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2010

Feature Extraction and Selection Strategies for Automated Target Recognition

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

computer vision↗
NASA NTRS1978

Evaluation of wavelength groups for discrimination of agricultural cover types

Multispectral scanner data in twelve spectral channels, in the wavelength range 0.46 to 11.7 mm, acquired in July 1971 for three flightlines, were analyzed by applying automatic pattern recognition techniques. These twelve spectral channels were divided into four wavelength groups (W1, W2, W3 and W4), each consisting of three wavelength channels -- with respect to their estimated probability of correct classification (P sub c) in discriminating agricultural cover types. The same analysis was also done for the data acquired in August, to investigate the effect of time on these results. The effect of deletion of each of the wavelength groups on P sub C in the subsets of one to nine channels, is given. Values of P sub C for all possible combinations of wavelength groups, in the subsets of one to eleven channels are also given.

Kumar, R.↗
NASA NTRS1993

A theoretical and experimental investigation of impact control for manipulators

This article describes a simple control strategy for stable hardon-hard contact of a manipulator with the environment. The strategy is motivated by recognition of the equivalence of proportional gain explicit force control and impedance control. It is shown that negative proportional force gains, or impedance mass ratios less than unity, can equivalently provide excellent impact response without bouncing. This result is indicated by an analysis performed with an experimentally determined arm/sensor/environment model. The results are corroborated by experimental data from implementation of the control algorithms on the CMU DD Arm II system. The results confirm that manipulator impact against a stiff environment without bouncing can be readily handled by this novel control strategy.

Volpe, Richard↗
NASA NTRS2003

Quantum dots as strain- and metabolism-specific microbiological labels

Biologically conjugated quantum dots (QDs) have shown great promise as multiwavelength fluorescent labels for on-chip bioassays and eukaryotic cells. However, use of these photoluminescent nanocrystals in bacteria has not previously been reported, and their large size (3 to 10 nm) makes it unclear whether they inhibit bacterial recognition of attached molecules and whether they are able to pass through bacterial cell walls. Here we describe the use of conjugated CdSe QDs for strain- and metabolism-specific microbial labeling in a wide variety of bacteria and fungi, and our analysis was geared toward using receptors for a conjugated biomolecule that are present and active on the organism's surface. While cell surface molecules, such as glycoproteins, make excellent targets for conjugated QDs, internal labeling is inconsistent and leads to large spectral shifts compared with the original fluorescence, suggesting that there is breakup or dissolution of the QDs. Transmission electron microscopy of whole mounts and thin sections confirmed that bacteria are able to extract Cd and Se from QDs in a fashion dependent upon the QD surface conjugate.

NASA Center JPL↗
NASA NTRS1971

Interactive display/graphics systems for remote sensor data analysis.

Using a color-television display system and interactive graphics equipment on-line to an IBM 360/44 computer, investigators at the Manned Spacecraft Center have developed a variety of interactive displays which aid in analyzing remote sensor data. This paper describes how such interactive displays are used to: (1) analyze data from a multispectral scanner, (2) develop automatic pattern recognition systems based on multispectral scanner measurements, and (3) analyze data from nonimaging sensors such as the infrared radiometer and microwave scatterometer.

Eppler, W. G.↗
NASA NTRS1993

Preliminary system design of a Three Arm Capture Mechanism (TACM) flight demonstration article

The overall objective of the Three Arm Capture Mechanism (TACM) is to serve as a demonstration of capability for capture of objects in space. These objects could be satellites, expended boosters, pieces of debris, etc.; anything of significant size. With this capability we can significantly diminish the danger of major collisions of debris with valuable space assets and with each other, which would otherwise produce many smaller, high velocity pieces of debris which also become concerns. The captured objects would be jettisoned into the atmosphere, relocated in 'parking' orbits, or recovered for disposition or refurbishment. The dollar value of satellites launched into space continues to grow along with the cost of insurance; having a capture capability takes a positive step towards diminishing this added cost. The effort covered is a planning step towards a flight demonstration of the satellite capture capability. Based on the requirement to capture a communication class satellite, its associated booster, or both, a preliminary system definition of a retrieval kit is defined. The objective of the flight demonstration is to demonstrate the techniques proposed to perform the mission and to obtain data on technical issues requiring an in situ space environment. The former especially includes issues such as automated image recognition techniques and control strategies that enable an unmanned vehicle to rendezvous and capture a satellite, contact dynamics between the two bodies, and the flight segment level of automation required to support the mission. A development plan for the operational retrieval capability includes analysis work, computer and ground test simulations, and finally a flight demonstration. A concept to perform a selected mission capturing a precessing communications satellite is described. Further development efforts using analytical tools and laboratory facilities are required prior to reaching the point at which a full commitment to the flight demonstration design can be made.

Schaefer, Otto↗
NASA NTRS2018

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed.

tropical cyclone intensity↗
NASA NTRS1977

Recognition of compact astrophysical objects

NASA's Laboratory for High Energy Astrophysics and the Dept. of Physics and Astrophysics at the Univ. of Md. collaberated on a graduate level course with this title. This publication is an edited version of notes used as the course text. Topics include stellar evolution, pulsars, binary stars, X-ray signatures, gamma ray sources, and temporal analysis of X-ray data.

Ogelman, H.↗
NASA NTRS1990

Pattern recognition of clouds and ice in polar regions

The study is based on AVHRR imagery and results from Landsat high-spatial-resolution scenes. Among the textual features investigated are the gray level difference vector (GLDV), and sum and difference histogram (SADH) approaches as well as gray level run length, spatial-coherence, and spectral-histogram measures. The traditional stepwise discriminant analysis and neural-network analysis are used for the identification of 20 Arctic surface and cloud classes. A principal-component analysis and hybrid architecture employing a modularized competitive learning layer are utilized. It is pointed out that the cloud-classification accuracy comparable to that of back-propagation could be achieved with a training time two orders of magnitude faster.

Welch, R. M.↗
NASA NTRS1993

Digital mammography, cancer screening: Factors important for image compression

The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers.

Clarke, Laurence P.↗
NASA NTRS2010

Optimization of a Multi-Stage ATR System for Small Target Identification

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

false alarm rate↗
NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing and Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

Biology↗
NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing And Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

life science↗
NASA NTRS1971

Mixing models and the recognition of end-member groups in Apollo 11 and 12 soils.

Lunar soils returned from the Apollo 11 and 12 sites appear to consist of more than one source material. Investigators have suggested that the soil can be described by as few as two end members and possibly as many as five or six. In the present study Q-mode factor analysis is used to establish the end members more rigorously and suggests that major-element chemistry of Apollo 11 and 12 lunar soils can be adequately explained in terms of a three-component mixing system. The end members isolated by Q-mode factor analysis can be regarded as basaltic, anorthositic, and noritic (or KREEP) components. Three-component mixing models fitted by least squares to the eight major oxides indicate that six of the Apollo 12 soils investigated are dominated by the basaltic component, whereas the other five are dominated by the noritic or KREEP component. Anorthosite is present in all soils except sample 12033, which is a simple two-component mixture of basalt and KREEP. The Apollo 11 soil is also a three-component mixture but is more basaltic than the Apollo 12 soils. Anorthositic and KREEP materials are important components of the lunar lithosphere and may be distributed on the lunar surface areally in an independent manner.

Lindsay, J. F.↗
DOE OSTI2025

Streptococcus pneumoniae HtrA is a dynamic and monomeric virulence factor capable of forming larger oligomeric complexes

Abstract High‐temperature requirement A (HtrA) proteases are a conserved family of serine proteases central to protein quality control and bacterial virulence. While Gram‐negative and human HtrAs are structurally well studied, Gram‐positive homologs remain essentially uncharacterized. Here, we present the first integrated structural and mechanistic analysis of a Gram‐positive HtrA, from Streptococcus pneumoniae , a virulence factor essential for adhesion and infection in vivo. Proteomic profiling of an htrA knockout and cleavage assays demonstrate that S. pneumoniae HtrA is required for protein quality control, with the PDZ domain mediating substrate recognition. Biochemically, S. pneumoniae HtrA exists exclusively as a monomer in solution, a striking divergence from canonical trimeric HtrAs that we show is shared with other Gram‐positive homologs. NMR analyses reveal that the monomer dynamically samples open and closed conformations, while cryo‐EM of a catalytic mutant identifies a hexamer stabilized by a unique LoopA–PDZ interaction. Together, these findings define S. pneumoniae HtrA as a dynamic monomer with interdomain coupling between its protease and PDZ domains, establishing Gram‐positive HtrAs as a mechanistically divergent subgroup within the HtrA family.

Lee, Eunjeong [Department of Biochemistry and Mole↗