Enantioselective Recognition of Ammonium Carbamates in a Chiral Metal–Organic Framework
Abstract not provided
Martell, Jeffrey D.↗
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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.
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Allosteric regulation of lysosomal enzyme recognition by the cation-independent mannose 6-phosphate receptor
Abstract not provided
Olson, Linda J.↗
Structural basis for antibody recognition of the NANP repeats in Plasmodium falciparum circumsporozoite protein
Abstract not provided
Oyen, David↗
Structural basis for a bacterial Pip system plant effector recognition protein
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99 GENERAL AND MISCELLANEOUS↗
A variable DNA recognition site organization establishes the LiaR-mediated cell envelope stress response of enterococci to daptomycin
Abstract not provided
Davlieva, Milya↗
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↗
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.↗
A model for stereospecific recognition of purines as an element of a DNA polypeptide recognition code
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Rein, R.↗
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↗
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 ↗
Enhancing Air Traffic Control Planning with Automatic Speech Recognition
The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.
ATM↗
Higher-Order Neural Networks Applied to 2D and 3D Object Recognition
A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.
Spirkovska, Lilly↗
Incorporating Speech Recognition into a Natural User Interface
The Augmented/ Virtual Reality (AVR) Lab has been working to study the applicability of recent virtual and augmented reality hardware and software to KSC operations. This includes the Oculus Rift, HTC Vive, Microsoft HoloLens, and Unity game engine. My project in this lab is to integrate voice recognition and voice commands into an easy to modify system that can be added to an existing portion of a Natural User Interface (NUI). A NUI is an intuitive and simple to use interface incorporating visual, touch, and speech recognition. The inclusion of speech recognition capability will allow users to perform actions or make inquiries using only their voice. The simplicity of needing only to speak to control an on-screen object or enact some digital action means that any user can quickly become accustomed to using this system. Multiple programs were tested for use in a speech command and recognition system. Sphinx4 translates speech to text using a Hidden Markov Model (HMM) based Language Model, an Acoustic Model, and a word Dictionary running on Java. PocketSphinx had similar functionality to Sphinx4 but instead ran on C. However, neither of these programs were ideal as building a Java or C wrapper slowed performance. The most ideal speech recognition system tested was the Unity Engine Grammar Recognizer. A Context Free Grammar (CFG) structure is written in an XML file to specify the structure of phrases and words that will be recognized by Unity Grammar Recognizer. Using Speech Recognition Grammar Specification (SRGS) 1.0 makes modifying the recognized combinations of words and phrases very simple and quick to do. With SRGS 1.0, semantic information can also be added to the XML file, which allows for even more control over how spoken words and phrases are interpreted by Unity. Additionally, using a CFG with SRGS 1.0 produces a Finite State Machine (FSM) functionality limiting the potential for incorrectly heard words or phrases. The purpose of my project was to investigate options for a Speech Recognition System. To that end I attempted to integrate Sphinx4 into a user interface. Sphinx4 had great accuracy and is the only free program able to perform offline speech dictation. However it had a limited dictionary of words that could be recognized, single syllable words were almost impossible for it to hear, and since it ran on Java it could not be integrated into the Unity based NUI. PocketSphinx ran much faster than Sphinx4 which would've made it ideal as a plugin to the Unity NUI, unfortunately creating a C# wrapper for the C code made the program unusable with Unity due to the wrapper slowing code execution and class files becoming unreachable. Unity Grammar Recognizer is the ideal speech recognition interface, it is flexible in recognizing multiple variations of the same command. It is also the most accurate program in recognizing speech due to using an XML grammar to specify speech structure instead of relying solely on a Dictionary and Language model. The Unity Grammar Recognizer will be used with the NUI for these reasons as well as being written in C# which further simplifies the incorporation.
Chapa, Nicholas↗
Recognition Method of Vehicle Cluster Situation Based on Set Pair Logic considering Driver’s Cognition
The recognition of vehicle cluster situations is one of the critical technologies of advanced driving, such as intelligent driving and automated driving. The accurate recognition of vehicle cluster situations is helpful for behavior decision‐making safe and efficient. In order to accurately and objectively identify the vehicle cluster situation, a vehicle cluster situation model is proposed based on the interval number of set pair logic. The proposed model can express the traffic environment’s knowledge considering each vehicle’s characteristics, grouping relationships, and traffic flow characteristics in the target vehicle’s interest region. A recognition method of vehicle cluster situation is designed to infer the traffic environment and driving conditions based on the connection number of set pair logic. In the proposed model, the uncertainty of the driver’s cognition is fully considered. In the recognition method, the relative uncertainty and relative certainty of driver’s cognition, traffic information, and vehicle cluster situation are fully considered. The verification results show that the proposed recognition method of vehicle cluster situations can realize accurate and objective recognition. The proposed anthropomorphic recognition method could provide a basis for vehicle autonomous behavior decision‐making.
Liu, Shijie↗
Object Recognition using Feature- and Color-Based Methods
An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.
Duong, Tuan↗
Molecular recognition requires dimerization of a VHH antibody
Camelid heavy-chain-only antibodies are a unique class of antibody that possesses only a single variable domain (termed VHH) for antigen recognition. Despite their apparent canonical mechanism of target recognition, where a single VHH domain binds a single target, an anti-caffeine VHH has been observed to possess 2:1 stoichiometry. Here, the structure of the anti-caffeine VHH/caffeine complex enabled the generation and biophysical analysis of variants that were used to better understand the role of VHH homodimerization in caffeine recognition. VHH interface mutants and caffeine analogs, which were examined to probe the mechanism of caffeine binding, suggested caffeine recognition is only possible with the VHH dimer species. Correspondingly, in the absence of caffeine, the anti-caffeine VHH was found to form a dimer with a dimerization constant comparable to that observed with VH:VL domains in conventional antibody systems, which was most stable near physiological temperature. While the VHH:VHH dimer structure (at 1.13 Å resolution) is reminiscent of conventional VH:VL heterodimers, the homodimeric VHH possesses a smaller angle of domain interaction, as well as a larger amount of apolar surface area burial. To test the general hypothesis that the short complementarity-determining region-3 (CDR3) may help drive VHH:VHH homodimerization, an anti-picloram VHH domain containing a short CDR3 was generated and characterized, which revealed it also existed as dimer species in solution. These results suggest homodimer-driven recognition may represent a more common method of VHH ligand recognition, opening opportunities for novel VHH homodimer affinity reagents and helping to guide their use in chemically induced dimerization applications.
60 APPLIED LIFE SCIENCES↗