SEARCH · Search NASA
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.
Here we continue our exploration of novel genomes discovered in module 1. We will explore different approaches for examining the genome sequence and annotation data, investigating the best approaches for answering our question: Do benthic microbes have functioning circadian clocks?
Quercus variabilis (Fagaceae) is an ecologically and economically important deciduous broadleaved tree species native to and widespread in East Asia. It is a valuable woody species and an indicator of local forest health, and occupies a dominant position in forest ecosystems in East Asia. However, genomic resources from Q. variabilis are still lacking. Here, we present a high-quality Q. variabilis genome generated by PacBio HiFi and Hi-C sequencing. The assembled genome size is 787 Mb, with a contig N50 of 26.04 Mb and scaffold N50 of 64.86 Mb, comprising 12 pseudo-chromosomes. The repetitive sequences constitute 67.6% of the genome, of which the majority are long terminal repeats, accounting for 46.62% of the genome. We used ab initio , RNA sequence-based and homology-based predictions to identify protein-coding genes. A total of 32,466 protein-coding genes were identified, of which 95.11% could be functionally annotated. Evolutionary analysis showed that Q. variabilis was more closely related to Q. suber than to Q. lobata or Q. robur. We found no evidence for species-specific whole genome duplications in Quercus after the species had diverged. This study provides the first genome assembly and the first gene annotation data for Q. variabilis. These resources will inform the design of further breeding strategies, and will be valuable in the study of genome editing and comparative genomics in oak species.
A global network optimization approach for untargeted LC-MS metabolomics data annotation and metabolite discovery
Abstract Motivation Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task. Results We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D. Availability and implementation Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar). Supplementary information Supplementary data are available at Bioinformatics online.
Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.
Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.
Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.
Liquid chromatography–high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak–peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Furthermore, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
GROW Overview We developed the Genome Resolved Open Watersheds database (GROWdb), which aims to increase genomic sampling and understanding of global river microbiomes. An emphasis of GROWdb is to create a publicly available and ever-expanding microbial genome database that is focused on rivers while being interoperable with databases from other ecosystems. GROWdb is based on a network-of-networks approach to move beyond a small collection of well-studied rivers, towards a spatially distributed, global network of systematic observations. GROWdb represents the first microbial, river-focused resource parsed at various scales from genes to MAGs to community level including expression and potential based measurements that will be of interest to microbiologists, ecologists, geochemists, hydrologists, and modelers. Dataset Acknowledgement GROWdb contains data from various research campaigns, please acknowledge the following data generators, as appropriate: WHONDRS derived genomes or samples - include this statement in your acknowledgements: “This study used data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) under the River Corridor Science Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL) that was generated at the U.S. Department of Energy (DOE) Joint Genome Institute User Facility. PNNL is operated by Battelle Memorial Institute for the U.S. DOE under Contract No. DE-AC05-76RL01830. The SFA is supported by the U.S. DOE, Office of Biological and Environmental Research (BER), Environmental System Science (ESS) Program.” Total Samples loaded onto this Narrative: 178 Note: Not all GROW samples may be loaded into KBase Data Availability The data underlying GROWdb are accessible across various platforms to ensure all levels of data structure are widely available. First, all reads and MAGs are publicly hosted on National Center for Biotechnology (NCBI) under Bioproject PRJNA946291. Second, all data related data presented here including MAG annotations, extended data tables, phylogenetic tree files, antibiotic resistance gene database files, and MAG abundance tables are available in Zenodo (link). Beyond the flat database files listed above, our aim for GROWdb was to maximize data use by making the data available in searchable and interactive platforms including the National Microbiome Data Collaborative (NMDC) data portal, the Department of Energy’s Systems Biology Knowledgebase (KBase), and a GROW specific user interface released here, GROWdb Explorer. Each platform provides different ways to interact with GROWdb: NMDC GROWdb formed a pilot project for the NMDC. Specifically, individual GROWdb datasets (metagenomes, metatranscriptomes, etc) are easily accessible and searchable through the NMDC data portal, where they are systematically connected to each other and to a rich suite of sample information and standard analysis results, following Findable, Accessible, Interoperable, and Reusable (FAIR) data practices. KBase GROWdb is publicly available within KBase, including samples (this Narrative), MAGs, and corresponding genome scale metabolic models. Access within KBase allows for immediate access and reuse of data, including comparison to private data using KBase’s 500+ analysis tools. Other linked narratives in KBase: GROW Metagenome Assembled Genomes (MAGs) GROW Metabolic Models GROWdb Explorer GROWdb data is also explorable through a graphical user interface built through the Colorado State University Geospatial Centroid (https://geocentroid.shinyapps.io/GROWdatabase/), allowing users to search and graph microbial and spatial data simultaneously. In summary, this microbial genome resource represents the first publicly available genome collection from rivers and offers data that can be leveraged across microbiome studies. GROWdb is an expanding repository to incorporate and unify global river multi-omic data for the future.
Background The MG-RAST API provides search capabilities and delivers organism and function data as well as raw or annotated sequence data via the web interface and its RESTful API. For casual users, however, RESTful APIs are hard to learn and work with. Results We created the graphical MG-RAST API explorer to help researchers more easily build and export API queries; understand the data abstractions and indices available in MG-RAST; and use the results presented in-browser for exploration, development, and debugging. Conclusions The API explorer lowers the barrier to entry for occasional or first-time MG-RAST API users.
Preliminary: This final report corresponds to a grant (DE-SC0021216) that was awarded to the University of Montana. Mid-way through the grant period, I relocated from the University of Montana to the University of Arizona. The grant was ended at University of Montana in late 2022, with all efforts concluding on 08/26/22; the remaining funds supporting the project were relinquished by University of Montana, and were later awarded to University of Arizona under a new grant, with start date 04/01/23. This report focuses on results of research efforts at UMontana through 08/26/22. Results: We made progress in each of the three aims of the proposal. We released software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes. We made substantial progress in developing software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we made notable progress in developing AI methods (specifically: a neural embedding model) for identifying similarities between protein sequences based on amino-wise latent vectors. These efforts were supplemented by development of methods for protein modeling in support of predicting protein-drug binding activity, and by my leadership of a team in the NIH/DOE 2021 Petabyte-Scale Sequence Search hack-a-thon.
We fulfilled all original three aims of the proposal. Following the earlier release (during the first phase of the project at Montana) of software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes, we have nearly completed a second gap-filling tool that improves accuracy and explainability. We completed software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we completed a neural embedding model for identifying similarities between protein sequences based on amino-wise latent vectors.
Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.
We present that peptide cofragmentation leads to chimeric MS/MS spectra that negatively impact traditional single-peptide match-per-spectrum (sPSM) search strategies in proteomics. The collection of chimeric spectra is influenced by peptide coelution and the width of precursor isolation windows. Although peptide cofragmentation can be reduced by advanced chromatography, such as UHPLC and 2D-HPLC separation schemes, and narrower isolation windows, chimeric spectra can still be as high as 30–50% of the total MS/MS spectra collected. Alternatively, cofragmented peptides in chimeric spectra and the use of wider isolation windows benefit multiple-peptide matches-per-spectrum (mPSM) algorithms, such as CharmeRT, which facilitate the identification of several cofragmented peptides. Considering recent advancements in LC and mPSM methodologies, we present a comprehensive examination of the levels of chimeric spectra collected in the analysis of a HeLa digest measured using different LC modes of separation and isolation windows and compare the depth of identifications obtained when these data are annotated using a sPSM or a mPSM approach. Our results demonstrate that MS/MS data derived from 1D-HPLC strategies under different gradient schemes and searched with CharmeRT yielded higher average numbers of PSMs (11%–49%), peptide analytes (10%–16%), and peptide sequences (3%–10%) compared to data derived from 1D-UHPLC runs but searched with a sPSM strategy. Interestingly, data from a 2D-HPLC separation strategy benefits more from the application of CharmeRT results when compared to a 50 cm 1D-UHPLC column employing a 500 min gradient. In conclusion, these results provide new insights into how to better configure LC-MS/MS measurements for improved throughput and peptide identification in complex proteomes.
Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.
Modern experiments are typically conducted by large, extended, where researchers rely on other team members to produce much of the data they use. The experiments record very large numbers of measurements which can be difficult for users to find, access and understand. We are developing a system for users to annotate their data products with structured metadata, providing data consumers with a discoverable, browsable data index. Machine understandable metadata captures the underlying semantics of the recorded data, which can then be consumed by both programs, and interactively by users. Collaborators can use these metadata to select and understand recorded measurements.
Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.
Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.