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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Structure of FcγRI in complex with Fc reveals the importance of glycan recognition for high-affinity IgG binding

Fc gamma receptor I (FcγRI) contributes to protective immunity against bacterial infections, but exacerbates certain autoimmune diseases. The sole high-affinity IgG receptor, FcγRI plays a significant role in immunotherapy. To elucidate the molecular mechanism of its high-affinity IgG binding, we determined the crystal structure of the extracellular domains of human FcγRI in complex with the Fc domain of human IgG 1 . FcγRI binds to the Fc in a similar mode as the low-affinity FcγRII and FcγRIII receptors. In addition to many conserved contacts, FcγRI forms additional hydrogen bonds and salt bridges with the lower hinge region of Fc. Unique to the high-affinity receptor-Fc complex, however, is the conformation of the receptor D2 domain FG loop, which enables a charged KHR motif to interact with proximal carbohydrate units of the Fc glycans. Both the length and the charge of the FcγRI FG loop are well conserved among mammalian species. Ala and Glu mutations of the FG loop KHR residues showed significant contributions of His-174 and Arg-175 to antibody binding, and the loss of the FG loop–glycan interaction resulted in an ~20- to 30-fold decrease in FcγRI affinity to all three subclasses of IgGs. Furthermore, deglycosylation of IgG 1 resulted in a 40-fold loss in FcγRI binding, demonstrating involvement of the receptor FG loop in glycan recognition. Here, these results highlight a unique glycan recognition in FcγRI function and open potential therapeutic avenues based on antibody glycan engineering or small molecular glycan mimics to target FcγRI for certain autoimmune diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Chiral Recognition: A Spin-Driven Process in Chiral Oligothiophene. A Chiral-Induced Spin Selectivity (CISS) Effect Manifestation

In this paper it is experimentally demonstrated that the electron-spin/molecular-handedness interaction plays a fundamental role in the chiral recognition process. This conclusion is inferred comparing current versus potential (I-V) curves recorded using chiral electrode surfaces, which are obtained via chemisorption of an enantiopure thiophene derivative: 3,3'-bibenzothiophene core functionalized with 2,2'-bithiophene wings (BT 2 T 4 ). The chiral recognition capability of these chiral-electrodes is probed via cyclic voltammetry measurements, where, Ag nanoparticles (AgNPs) capped with enantiopure (BT 2 T 4 ) (BT 2 T 4 )@AgNP) are used as the chiral redox probe. Then, the interface handedness is explored by recording spin-polarized (I-V) curves in spin-dependent electrochemistry (SDE) and magnetic-conductive atomic force microscopy (mc-AFM) experiments. The quality of the interfaces is thoroughly cross-checked using X-ray photoemission spectroscopy, Raman, electrodesorption measurements, which further substantiate the metal(electrode)-sulfur(thiophene) central role in the chemisorption process. Spin-polarization values of about 15% and 30% are obtained in the case of SDE and mc-AFM experiments, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Pattern Recognition Algorithm for Quantum Annealers

Abstract The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to a large increase in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a quadratic unconstrained binary optimization (QUBO), which allows algorithms to be run on classical and quantum annealers. While the overall timing of the proposed approach and its scaling has still to be measured and studied, we demonstrate that, in terms of efficiency and purity, the same physics performance of the LHC tracking algorithms can be achieved. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2010

Structural Basis for Langerin Recognition of Diverse Pathogen and Mammalian Glycans through a Single Binding Site

Langerin mediates the carbohydrate-dependent uptake of pathogens by Langerhans cells in the first step of antigen presentation to the adaptive immune system. Langerin binds to an unusually diverse number of endogenous and pathogenic cell surface carbohydrates, including mannose-containing O-specific polysaccharides derived from bacterial lipopolysaccharides identified here by probing a microarray of bacterial polysaccharides. Crystal structures of the carbohydrate-recognition domain from human langerin bound to a series of oligomannose compounds, the blood group B antigen, and a fragment of β-glucan reveal binding to mannose, fucose, and glucose residues by Ca 2+ coordination of vicinal hydroxyl groups with similar stereochemistry. Oligomannose compounds bind through a single mannose residue, with no other mannose residues contacting the protein directly. There is no evidence for a second Ca 2+ -independent binding site. Likewise, a β-glucan fragment, Glcβ1–3Glcβ1–3Glc, binds to langerin through the interaction of a single glucose residue with the Ca 2+ site. The fucose moiety of the blood group B trisaccharide Galα1–3(Fucα1–2)Gal also binds to the Ca 2+ site, and selective binding to this glycan compared to other fucose-containing oligosaccharides results from additional favorable interactions of the nonreducing terminal galactose, as well as of the fucose residue. Surprisingly, the equatorial 3-OH group and the axial 4-OH group of the galactose residue in 6SO 4 –Galβ1–4GlcNAc also coordinate Ca 2+ , a heretofore unobserved mode of galactose binding in a C-type carbohydrate-recognition domain bearing the Glu-Pro-Asn signature motif characteristic of mannose binding sites. Salt bridges between the sulfate group and two lysine residues appear to compensate for the nonoptimal binding of galactose at this site.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

New surface characterization tools for alumina based refractory material exposed to cavitation - Image analysis and pattern recognition approach

Highlights: • Surface degradation was monitored via mass loss measurement. • Observation of surface damage destruction was done using image analysis. • Shape parameters of surface defects change during cavitation. • Shape parameters changes are correlated to mass loss measurements. - Abstract: The aim of this study was to examine the influence of cavitation erosion on the morphology of defects formed on the surfaces of alumina-based materials. The alumina-based samples were exposed to cavitation erosion for 42 min. The damages that occurred were monitored at predefined time intervals by measuring the mass loss and observing the sample surfaces. Digital images of surfaces were processed using an image analysis software package to define morphological characteristics of the damages occurred. The calculated morphological parameters were subjected to analysis by pattern recognition techniques to correlate the appearance of particular morphological characteristics with the behavior of the material during cavitation erosion. The newly established method in describing cavitation erosion which uses pattern recognition approach for treatments of morphological parameters calculated for damages by image analysis tools gave the results efficient for material characterization as the traditional one.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Transcranial direct current stimulation of dorsolateral prefrontal cortex during encoding improves recall but not recognition memory

Prior work demonstrates that application of transcranial direct current stimulation (tDCS) improves memory. In this study, we investigated tDCS effects on face-name associative memory using both recall and recognition tests. Participants encoded face-name pairs under either active (1.5 mA) or sham (.1 mA) stimulation applied to the scalp adjacent to the left dorsolateral prefrontal cortex (dlPFC), an area known to support associative memory. Participants’ memory was then tested after study (day one) and then again after a 24-h delay (day two), to assess both immediate and delayed stimulation effects on memory. Results indicated that active relative to sham stimulation led to substantially improved recall (more than 50%) at both day one and day two. Recognition memory performance did not differ between stimulation groups at either time point. These results suggest that stimulation at encoding improves memory performance by enhancing memory for details that enable a rich recollective experience, but that these improvements are evident only under some testing conditions, especially those that rely on recollection. Altogether, stimulation of the dlPFC could have led to recall improvement through enhanced encoding from stimulation or from carryover effects of stimulation that influenced retrieval processes, or both.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

42 ENGINEERING↗
DOE OSTI2014

Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model

The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of the identified systems, the posterior probabilities and sufficient statistics calculations represent a computational bottleneck in both enrollment and testing. We propose a multi-layered hash system, employing a tree-structured GMM–UBM which uses Runnalls’ Gaussian mixture reduction technique, in order to reduce the number of these calculations. Moreover, with this tree-structured hash, we can trade-off reduction in computation with a corresponding degradation of equal error rate (EER). As an example, we also reduce this computation by a factor of 15× while incurring less than 10% relative degradation of EER (or 0.3% absolute EER) when evaluated with NIST 2010 speaker recognition evaluation (SRE) telephone data.

42 ENGINEERING↗
DOE OSTI2021

Quantum pattern recognition algorithms for charged particle tracking

High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Point Adversarial Self-Mining: A Simple Method for Facial Expression Recognition

In this article, we propose a simple yet effective approach, called point adversarial self mining (PASM), to improve the recognition accuracy in facial expression recognition (FER). Unlike previous works focusing on designing specific architectures or loss functions to solve this problem, PASM boosts the network capability by simulating human learning processes: providing updated learning materials and guidance from more capable teachers. Specifically, to generate new learning materials, PASM leverages a point adversarial attack method and a trained teacher network to locate the most informative position related to the target task, generating harder learning samples to refine the network. The searched position is highly adaptive since it considers both the statistical information of each sample and the teacher network capability. Other than being provided new learning materials, the student network also receives guidance from the teacher network. After the student network finishes training, the student network changes its role and acts as a teacher, generating new learning materials and providing stronger guidance to train a better student network. Here, the adaptive learning materials generation and teacher/student update can be conducted more than one time, improving the network capability iteratively. Extensive experimental results validate the efficacy of our method over the existing state of the arts for FER.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An evaluation of GPT models for phenotype concept recognition

Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation. The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations. The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches. Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Pheromone Recognition and Selectivity by ComR Proteins among Streptococcus Species

Natural transformation, or competence, is an ability inherent to bacteria for the uptake of extracellular DNA. This process is central to bacterial evolution and allows for the rapid acquirement of new traits, such as antibiotic resistance in pathogenic microorganisms. For the Gram-positive bacteria genus Streptococcus, genes required for competence are under the regulation of quorum sensing (QS) mediated by peptide pheromones. One such system, ComRS, consists of a peptide (ComS) that is processed (XIP), secreted, and later imported into the cytoplasm, where it binds and activates the transcription factor ComR. ComR then engages in a positive feedback loop for the expression of ComS and the alternative sigma-factor SigX. Although ComRS are present in the majority of Streptococcus species, the sequence of both ComS/XIP and ComR diverge significantly, suggesting a mechanism for species-specific communication. To study possible cross-talk between streptococcal species in the regulation of competence, and to explore in detail the molecular interaction between ComR and XIP we undertook an interdisciplinary approach. We developed a ‘test-bed’ assay to measure the activity of different ComR proteins in response to cognate and heterologous XIP peptides in vivo, revealing distinct ComR classes of strict, intermediate, and promiscuous specificity among species. We then solved an X-ray crystal structure of ComR from S. suis to further understand the interaction with XIP and to search for structural features in ComR proteins that may explain XIP recognition. Using the structure as a guide, we probed the apo conformation of the XIP-binding pocket by site-directed mutagenesis, both in test-bed cultures and biochemically in vitro. In alignments with ComR proteins from other species, we find that the pocket is lined by a variable and a conserved face, where residues of the conserved face contribute to ligand binding and the variable face discriminate among XIP peptides. Together, our results not only provide a model for XIP recognition and specificity, but also allow for the prediction of novel XIP peptides that induce ComR activity.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2002

High speed optical object recognition processor with massive holographic memory

Real-time object recognition using a compact grayscale optical correlator will be introduced. A holographic memory module for storing a large bank of optimum correlation filters, to accommodate the large data throughput rate needed for many real-world applications, has also been developed. System architecture of the optical processor and the holographic memory will be presented. Application examples of this object recognition technology will also be demonstrated.

512 X 512 grayscale optical correlator holographic↗
NASA NTRS1996

A Compact Prototype of an Optical Pattern Recognition System

In the Technology 2006 Case Studies/Success Stories presentation, we will describe and demonstrate a prototype of a compact optical pattern recognition system as an example of a successful technology transfer and continuuing development of state-of-the-art know-how by the close collaboration among government, academia, and small business via the NASA SBIR program. The prototype consists of a complete set of optical pattern recognition hardware with multi-channel storage and retrieval capability that is compactly configured inside a portable 1'X 2'X 3' aluminum case.

MSF↗
NASA NTRS2011

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

pattern recognition↗
DOE OSTI2024

Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions

In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolution of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.

LiDAR, machine learning, neural network, object re↗
DOE OSTI2023

Structural insights into the unique recognition module between α-synuclein peptide and nanobody

Nanobodies are single-domain fragments of antibodies with comparable specificity and affinity to antibodies. They are emerging as versatile tools in biology due to their relatively small size. Here, we report the crystal structure of a specific nanobody Nbα-syn01, bound to a 14 amino acid long peptide of α-synuclein (αSyn), a 140-residue protein whose aggregation is associated with Parkinson's disease. The complex structure exhibits a unique binding pattern where the αSyn peptide replaces the N-terminal region of nanobody. Recognition is mediated principally by extended main chain interaction of the αSyn peptide and specificity of the interaction lies in the central 48–52 region of αSyn peptide. Structure-guided truncation of Nbα-syn01 shows tighter binding to αSyn peptide and improved inhibition of α-synuclein aggregation. The structure of the truncated complex was subsequently determined and was indistinguishable to full length complex as the full-length form had no visible electron density for the N-terminal end. These findings reveal the molecular basis for a previously unobserved binding mode for nanobody recognition of α-synuclein, providing an explanation for the enhanced binding, and potential for an alternate framework for structure-based protein engineering of nanobodies to develop better diagnostic and therapeutic tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A Chaos Theoretic Approach to Animal Activity Recognition

Animal activity is a descriptor that can be potentially used to track the health and well-being, which is obviously very important to improve the management process and productivity of farms. This paper deals with an animal behavior recognition problem using a chaos theory approach where we adopt such a technique for automatic classification of calves behavioral states. Two main mutually exclusive behaviors are of interest, namely, lying and standing/walking with six possible activities: feeding, drinking water, drinking milk, playing, rumination, and neutral. The time series generated by ear-tags with a 3D-accelerometer after a wavelet denoising transformation and frequency stabilization are treated as representations of the nonlinear dynamical system. The dynamical system of a certain animal state exhibits specific strange attractor in a phase space. A characterization of such an attractor is performed through metric, dynamic, and topological invariants including Lyapunov exponent, correlation dimension, length of a phase trajectory, sum of edges forming a convex hull and others. These measures are used as a feature vector for the subsequent classification. In a cross validation scheme, six classifiers are built on each training set, and the hyper-parameters are optimized using an inner validation set. The classifier that reaches the highest accuracy on the inner validation set is used to classify the outer validation set. It is shown that this approach can be useful at predicting activity states as an alternative methodology for the animal behavior state recognition problem with acceptable classification accuracy. Furthermore it is possible to include this procedure as part of an ensemble method in machine learning where a combination of different models is used.

97 MATHEMATICS AND COMPUTING↗