Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Recognition”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Structural analysis of the endogenous glycoallergen Hev b 2 (endo-β-1,3-glucanase) from Hevea brasiliensis and its recognition by human basophils

This study describes the three-dimensional structure of the endogenous glycosylated allergen Hev b 2 (endo-β-1,3-glucanase), which exhibits three post-translational modifications that form a patch on the surface of the molecule that is proposed to be an allergenic IgE epitope. Endogenous glycosylated Hev b 2 (endo-β-1,3-glucanase) from Hevea brasiliensis is an important latex allergen that is recognized by IgE antibodies from patients who suffer from latex allergy. The carbohydrate moieties of Hev b 2 constitute a potentially important IgE-binding epitope that could be responsible for its cross-reactivity. Here, the structure of the endogenous isoform II of Hev b 2 that exhibits three post-translational modifications, including an N-terminal pyroglutamate and two glycosylation sites at Asn27 and at Asn314, is reported from two crystal polymorphs. These modifications form a patch on the surface of the molecule that is proposed to be one of the binding sites for IgE. A structure is also proposed for the most important N-glycan present in this protein as determined by digestion with specific enzymes. To analyze the role of the carbohydrate moieties in IgE antibody binding and in human basophil activation, the glycoallergen was enzymatically deglycosylated and evaluated. Time-lapse automated video microscopy of basophils stimulated with glycosylated Hev b 2 revealed basophil activation and degranulation. Immunological studies suggested that carbohydrates on Hev b 2 represent an allergenic IgE epitope. In addition, a dimer was found in each asymmetric unit that may reflect a regulatory mechanism of this plant defence protein.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Structural and molecular dynamics studies of a C1-oxidizing lytic polysaccharide monooxygenase from Heterobasidion irregulare reveal amino acids important for substrate recognition

Lytic polysaccharide monooxygenases (LPMOs) are a group of recently discovered enzymes that play important roles in the decomposition of recalcitrant polysaccharides. Here, we report the biochemical, structural, and computational characterization of an LPMO from the white-rot fungus Heterobasidion irregulare (HiLPMO9B). This enzyme oxidizes cellulose at the C1 carbon of glycosidic linkages. The crystal structure of HiLPMO9B was determined at 2.1 A resolution using X-ray crystallography. Unlike the majority of the currently available C1-specific LPMO structures, the HiLPMO9B structure contains an extended L2 loop, connecting ..beta..-strands ..beta..2 and ..beta..3 of the ..beta..-sandwich structure. Molecular dynamics (MD) simulations suggest roles for both aromatic and acidic residues in the substrate binding of HiLPMO9B, with the main contribution from the residues located on the extended region of the L2 loop (Tyr20) and the LC loop (Asp205, Tyr207, and Glu210). Asp205 and Glu210 were found to be involved in the hydrogen bonding with the hydroxyl group of the C6 carbon of glucose moieties directly or via a water molecule. Two different binding orientations were observed over the course of the MD simulations. In each orientation, the active-site copper of this LPMO preferentially skewed toward the pyranose C1 of the glycosidic linkage over the targeted glycosidic bond. This study provides additional insight into cellulose binding by C1-specific LPMOs, giving a molecular-level picture of active site substrate interactions.

09 BIOMASS FUELS↗
NASA NTRS1974

On the recognition of complex structures: Computer software using artificial intelligence applied to pattern recognition

An approach to simultaneous interpretation of objects in complex structures so as to maximize a combined utility function is presented. Results of the application of a computer software system to assign meaning to regions in a segmented image based on the principles described in this paper and on a special interactive sequential classification learning system, which is referenced, are demonstrated.

Yakimovsky, Y.↗
DOE OSTI2016

Automatic anatomy recognition in whole-body PET/CT images

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

The Pandora multi-algorithm approach to automated pattern recognition in LAr TPC detectors

Pattern recognition is the identification of structures and regularities in data. In high energy physics, it is a vital stage in the reconstruction of events recorded by fine-granularity detectors. The development and operation of Liquid Argon Time Projection Chambers (LAr TPCs) for neutrino physics has created a need for new approaches to pattern recognition, in order to fully exploit the superb imaging capabilities offered by this technology. Whereas the human brain excels at identifying features in the recorded events, it is a significant challenge to develop an automated solution. The Pandora Software Development Kit (SDK) provides functionality to aid the process of designing, implementing and running pattern recognition algorithms. In particular, it promotes the use of a multi-algorithm approach to pattern recognition: individual algorithms each aim to address a specific task in a particular topology; a series of many tens of algorithms then carefully build-up a picture of the event and, together, provide a robust automated pattern recognition solution. Building on successful use of the Pandora SDK for pattern recognition at collider experiments, a sophisticated chain of algorithms has been created to perform pattern recognition for neutrino experiments utilising LAr TPCs like MicroBooNE. The input to the Pandora pattern recognition is a list of 2D Hits. The output from the chain of over 70 algorithms is a hierarchy of reconstructed 3D Particles, each with an identified particle type, vertex and direction. In this document, we present details of the Pandora pattern recognition algorithms used to reconstruct cosmic-ray and neutrino events in LAr TPCs. We also present metrics that assess the current reconstruction performance using simulated data from MicroBooNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗