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This white paper provides the background for why it is important to take the next steps with the NASA taxonomy including test and validation, XML schema development, integration with the FirstGov federal search engine, the OneNASA portal and its supporting web content management system.
Life cycle assessments (LCAs) are essential for understanding the environmental impacts of material production. However, gaps in life cycle inventory (LCI) data for material and chemical inputs present a key challenge for LCA practitioners, especially in the early design stages. Strategies for filling in these gaps require additional time and expertise, which can hinder the LCA’s completion. This study combined automatic material classification and probabilistic under-specification to create a time-efficient method to fill material LCI data gaps. To illustrate the proposed method, proxy environmental impact distributions were generated using publicly available material LCI data classified into the ChemOnt chemical taxonomy using the open-source chemical classification software ClassyFire. Input materials with data gaps were then classified into the same taxonomy, where proxy environmental impact values could be selected from the available distributions to quickly fill in any data gaps. Although these methods were applied to classify material production processes available in the Federal LCA Commons and Ecoinvent databases, they can be applied to any LCA database. This study shows that classifying materials by their chemical structure produces taxonomies with increased granularity relative to industrial classification, improving the ability of under-specified proxy data to be used for differentiating the environmental impacts of competing designs.
Industrial processes drive the exchange of materials, energy, and currency throughout the economy. These processes are powered by electricity and direct combustion, with variation in their operation even within the same industry. This heterogeneity makes it difficult for large models, including the National Energy Modeling System (US), to project their energy use while remaining tractable. Decarbonization and ensuing changes to the energy system require changes to industrial processes while offering opportunities for process innovation, but the extent and nature of changes are difficult to model with current classification schemes and corresponding data. The North American Industrial Classification (NAICS) is an economic taxonomy of industries, but its categories are less meaningful from an energy and material flow perspective. For example, a facility that makes steel from iron ore in a blast furnace/basic oxygen furnace is categorized under the same NAICS code as a facility that makes steel from scrap in an electric arc furnace despite the scale, use of recycled scrap versus iron ore, and energy use differences in the two facility types. Exploratory analysis is performed on a large dataset used for plant-level energy assessment in order to detect clusters that can aid in better modeling of industry for energy analysis in an evolving system with breakthrough technologies.
The development of rapid, economical genome sequencing has shed new light on the classification of viruses. As of October 2016, the National Center for Biotechnology Information (NCBI) database contained >2 million viral genome sequences and a reference set of ~4000 viral genome sequences that cover a wide range of known viral families. Whole-genome sequences can be used to improve viral classification and provide insight into the viral tree of life . However, due to the lack of evolutionary conservation amongst diverse viruses, it is not feasible to build a viral tree of life using traditional phylogenetic methods based on conserved proteins. In this study, we used an alignment-free method that uses k-mers as genomic features for a large-scale comparison of complete viral genomes available in RefSeq. To determine the optimal feature length, k (an essential step in constructing a meaningful dendrogram), we designed a comprehensive strategy that combines three approaches: (1) cumulative relative entropy, (2) average number of common features among genomes, and (3) the Shannon diversity index. This strategy was used to determine k for all 3,905 complete viral genomes in RefSeq. Lastly, the resulting dendrogram shows consistency with the viral taxonomy of the ICTV and the Baltimore classification of viruses.
Purpose: Various causal taxonomies have been developed for healthcare incidents and for radiation therapy in particular. The causal analysis of incidents leads to corrective actions which can also be organized into a taxonomy. Such a corrective action classification system would provide information about the situational context, the action type, and the leverage of the action in order to detect patterns in the corrective actions frequently employed in radiation therapy. It would also provide practical guidance to the radiation therapy community for determining the appropriateness and potential effectiveness of proposed corrective actions. Materials: A review of causal analysis reports and corrective action plans was conducted using the following sources: US NRC medical event reports, IAEA reports, ROSIS submissions, US Veterans Health Administration reports, and singleincident report sources. The corrective actions presented in the published sources were then mapped onto four corrective action taxonomy prototypes: role-based, safety-context-based, responsibility-based, and hierarchy of hazard control. The resulting corrective action taxonomy was then validated through use of the published sources. Results: The responsibility-based taxonomy and hierarchy of hazard taxonomy provided more intuitive and sensible categories than the role-based taxonomy or the safety-context taxonomy. The most frequent corrective actions were added safety barriers, training, process standardization, and development of a quality improvement program where one was lacking. Conclusion: Published corrective action statements in radiation therapy emphasize what to do more so than whom the recipient is or which process step is affected. The hierarchy of hazard taxonomy provides a suitable framework for radiation therapy and has the advantage of providing insight into the likelihood that a particular corrective action will mitigate the recurrence of the error it was meant to correct. This information would be useful to medical center administration, safety personnel, and regulators who must assess the projected efficacy of corrective actions. Derek Brown is a director of TreatSafely, LLC.
The Epsilonproteobacteria is the fifth validly described class of the phylum Proteobacteria, known primarily for clinical relevance and for chemolithotrophy in various terrestrial and marine environments, including deep-sea hydrothermal vents. As 16S rRNA gene repositories have expanded and protein marker analysis become more common, the phylogenetic placement of this class has become less certain. A number of recent analyses of the bacterial tree of life using both 16S rRNA and concatenated marker gene analyses have failed to recover the Epsilonproteobacteria as monophyletic with all other classes of Proteobacteria. In order to address this issue, we investigated the phylogenetic placement of this class in the bacterial domain using 16S and 23S rRNA genes, as well as 120 single-copy marker proteins. Singleand concatenated-marker trees were created using a data set of 4,170 bacterial representatives, including 98 Epsilonproteobacteria. Phylogenies were inferred under a variety of tree building methods, with sequential jackknifing of outgroup phyla to ensure robustness of phylogenetic affiliations under differing combinations of bacterial genomes. Based on the assessment of nearly 300 phylogenetic tree topologies, we conclude that the continued inclusion of Epsilonproteobacteria within the Proteobacteria is not warranted, and that this group should be reassigned to a novel phylum for which we propose the name Epsilonbacteraeota (phyl. nov.). We further recommend the reclassification of the order Desulfurellales (Deltaproteobacteria) to a novel class within this phylum and a number of subordinate changes to ensure consistency with the genome-based phylogeny. Phylogenomic analysis of 658 genomes belonging to the newly proposed Epsilonbacteraeota suggests that the ancestor of this phylum was an autotrophic, motile, thermophilic chemolithotroph that likely assimilated nitrogen from ammonium taken up from the environment or generated from environmental nitrate and nitrite by employing a variety of functional redox modules. The emergence of chemoorganoheterotrophic lifestyles in several Epsilonbacteraeota families is the result of multiple independent losses of various ancestral chemolithoautotrophic pathways. Our proposed reclassification of this group resolves an important anomaly in bacterial systematics and ensures that the taxonomy of Proteobacteria remains robust, specifically as genome-based taxonomies become more common.
Abstract Summary CONSTAX—the CONSensus TAXonomy classifier—was developed for accurate and reproducible taxonomic annotation of fungal rDNA amplicon sequences and is based upon a consensus approach of RDP, SINTAX and UTAX algorithms. CONSTAX2 extends these features to classify prokaryotes as well as eukaryotes and incorporates BLAST-based classifiers to reduce classification errors. Additionally, CONSTAX2 implements a conda-installable command-line tool with improved classification metrics, faster training, multithreading support, capacity to incorporate external taxonomic databases and new isolate matching and high-level taxonomy tools, replete with documentation and example tutorials. Availability and implementation CONSTAX2 is available at https://github.com/liberjul/CONSTAXv2, and is packaged for Linux and MacOS from Bioconda with use under the MIT License. A tutorial and documentation are available at https://constax.readthedocs.io/en/latest/. Data and scripts associated with the manuscript are available at https://github.com/liberjul/CONSTAXv2_ms_code. Supplementary information Supplementary data are available at Bioinformatics online.
Applying current guidance and practices for common-cause failure (CCF) mitigation to digital instrumentation and control (I&C) systems has proven problematic, and the regulatory environment has been unpredictable. The potential for CCF vulnerability inhibits I&C modernization, thereby challenging the long-term sustainability of existing plants. For new plants and advanced reactor concepts, concern about CCF vulnerability in highly integrated digital I&C systems imposes a design burden that results in higher costs and increased complexity. The regulatory uncertainty in determining which mitigation strategies will be acceptable (e.g., what diversity is needed and how much is sufficient) drives designers to adopt complicated, costly solutions devised for existing plants. To address the conditions that constrain the transition to digital I&C technology by the US nuclear industry, crosscutting research is needed to resolve uncertainty, demonstrate necessary characteristics, and establish an objective basis for qualification of digital technology for nuclear power plant (NPP) I&C applications. To fulfill this research need, Oak Ridge National Laboratory is investigating mitigation of CCF vulnerability for nuclear-qualified applications. The outcome of this research is expected to contribute to a fundamentally sound, comprehensive basis to qualify digital technology for nuclear power applications. This report documents the development of a CCF taxonomy. The basis for the CCF taxonomy was generated by determining consistent terminology and establishing a classification approach. The terminology is based on definitions from standards, guides, and relevant nuclear power industry technical reports. The classification approach is derived from identified classification schemes focused on I&C systems and key characteristics, including failure modes. The CCF taxonomy provides the basis for a systematic organization of key systems aspects relevant to analyzing the potential for CCF vulnerability and the suitability of mitigation techniques. Development of an effective CCF taxonomy will help to provide a framework for establishing the objective analysis and assessment capabilities desired to facilitate rigorous identification of fault types and triggers that are the fundamental elements of CCF.
Taxonomy is often criticized for being too conservative and too slow and having limited relevance because it has not taken into consideration the latest methods and findings. Yet the cumulative work product of its practitioners underpins contemporary microbiology and serves as a principal means of shaping and referencing knowledge. Here, using methods drawn from the field of exploratory data analysis, this minireview examines the current state of the field as it transitions from a taxonomy based on 16S rRNA gene sequences to one based on whole-genome sequences and tests the validity of some commonly held beliefs.
Before interpreting properties of meteorites in terms of putative processes and conditions in the early solar system, it is necessary to understand just what sort of objects meteorites are. Such understanding begins with classification. In this chapter, the current taxonomy of meteorites is summarized, and it is shown how certain stone meteorites, the chondrites, possess chemical and petrographic features that make them potentially attractive as probes of the early solar system. The prevalence of secondary alteration effects, often capable of perturbing the primitive record even in chondrites, is also emphasized.
The link between the E-type asteroids and the enstatite achondrites (aubrites) was first proposed for the original E-asteroid, 44 Nysa. The association was based on the high albedos and the featureless spectra shared by the E-asteroids and the aubrites. Among the plausible geologic and meteoritic materials, only enstatite (the magnesium end-member of the pyroxene solid solution series) is sufficiently abundant to comprise asteroid-sized bodies. However, the presence of a weak 0.89 m absorption feature in the spectrum of 44 Nysa indicates that its pyroxene contains a small amount of Fe(2+) but still substantially more than any aubrite present in the meteorite collection. The original E-class was defined based on its high albedo and flat to slightly reddish spectrum. In the absence of albedo data, the E-type was degenerate with the M- and P-types, and together these were designated as X-types. Recently, a taxonomy has been proposed to identify E-types in the absence of albedo data. In this newer classification system three subdivisions of the X-type have been proposed, including Xc, Xe and Xk. Of nine albedo-defined E-types [d], this newer non-albedo based taxonomy produced the following classifications: X-1 asteroid; Xc-2 asteroids; Xe-5 asteroids; and Xk-1 asteroid. Although the Xe subtype includes the largest number of albedo-defined E-types, most of the remaining 24 Xe-types can be excluded based on their low measured IRAS albedos, ranging from 0.116 to 0.329, which are below the lower albedo limit of the E-class (0.34) and substantially below that of the lowest albedo an actual E-type asteroid (0.41). The present discussion will be limited to unambiguous E-type asteroids determined on albedo criteria.
The ecological importance of viruses is now widely recognized, yet our limited knowledge of viral sequence space and virus–host interactions precludes accurate prediction of their roles and impacts. In this study, we mined publicly available bacterial and archaeal genomic data sets to identify 12,498 high-confidence viral genomes linked to their microbial hosts. These data augment public data sets 10-fold, provide first viral sequences for 13 new bacterial phyla including ecologically abundant phyla, and help taxonomically identify 7–38% of ‘unknown’ sequence space in viromes. Genome- and network-based classification was largely consistent with accepted viral taxonomy and suggested that (i) 264 new viral genera were identified (doubling known genera) and (ii) cross-taxon genomic recombination is limited. Further analyses provided empirical data on extrachromosomal prophages and coinfection prevalences, as well as evaluation of in silico virus–host linkage predictions. Together these findings illustrate the value of mining viral signal from microbial genomes.
Physical asteroid observations are summarized and the classification scheme to describe asteroid surfaces in relation to mineralogical composition is detailed. The principle classes, distinguished on the basis of a number of parameters involving albedo and color, are called C, S, and M.
A common system to classify occupations and skills is essential to support accelerator workforce planning between the Department of Energy (DOE) National Laboratories. In 2025, ten DOE laboratories conducted a census of their accelerator science and engineering workforce and projected their accelerator workforce needs for the next ten years. To support this, revision 3 of the “Common Occupational Classification System” (COCS) was used as a common taxonomy. Modifications were made to include accelerator-specific skills and specialisms. COCS was originally developed for DOE Office of Environmental Restoration and Waste Management in 1996. The framework provides a high-level functional structure that can be expanded with further occupations and specialisms and remains well aligned to DOE laboratory roles nearly 30 years later. Accelerator occupations and specialisms were added to the framework for the 2025 effort to quantify the accelerator workforce. This document is a companion document to COCS and provides a definition for these new occupations and specialisms that do not appear in COCS. Proceeding with a stable taxonomy is seen as essential such that its use becomes easier each year; the taxonomy as used for the 2025 effort is recommended to be continued.
For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.
The Database: Kraken2 [1] database built from a classification tree containing over 700k metagenomic viruses from JGI IMG/VR [2]. (1) Wood, D. E., Lu, J., and Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biol., 20(1), 1–13. doi: 10.1186/s13059-019-1891-0 (2) Paez-Espino D, Chen I-MA, Palaniappan K, Ratner A, Chu K, Szeto E, et al. IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses. Nucleic Acids Res. 2017;45:D457-65. For Paper: Title: A k-mer based approach for virus classification in metatranscriptomic and metagenomic samples identifies viral associations in the Populus phytobiome and autism brains Abstract Background Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an important role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which further limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus' genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples. Methods To identify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses. We then integrated the viral classification tree with the NCBI taxonomy for use with ParaKraken, a metagenomic/transcriptomic classifier. Results To illustrate the breadth of our utility for classifying viruses with ParaKraken, we analyzed data from a plant metagenome study identifying the differences between two Populus genotypes in three different compartments and on a human metatranscriptome study identifying the differences between Autism Spectrum Disorder patients and controls in post mortem brain biopsies. In the Populus study, we identified genotype and compartment specific viral signatures, while in the Autism study we identified a significant increased abundance of eight viral sequences in Autism brain biopsies. Conclusion Viruses represent an important aspect of the microbiome. The ability to classify viruses represents the first step in being able to better understand their role in the microbiome. The viral classification method presented here allows for more complete identification of viral sequences for use in identifying associations between viruses and the host and viruses and other microbiome members. Acknowledgements and Funding This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research was also supported by the Plant-Microbe Interfaces Scientific Focus Area in the Genomic Science Program, the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science, and by the Department of Energy, Laboratory Directed Research and Development funding (ProjectID 8321), at the Oak Ridge National Laboratory. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. This research used resources of the Compute and Data Environment for Science (CADES).
Structural and compositional data are reported as a guide to the classification of 35 iron meteorites. The Xinjiang iron meteorite, previously classified as III AB, is reclassified as III E on the basis of its lower Ga/Ni and Ge/Ni ratios, its wider, swollen kamacite bands, and the ubiquitous presence of haxonite, (Fe,Ni)22C. The Dongling (III CD) appears not to be a new meteorite, but to be paired with the Nantan. Four Antarctic iron meteorites, IAB Allan Hills A77250, A77263, A77289, and A77290, are classified as a paired meteorite because of their similarities in structure and in concentrations of various elements. It is shown that Cu shares certain properties with Ga and Ge, which makes them excellent taxonomic parameters.
NASA tracks over 16,000 technology projects across the Agency, from propulsion systems to software. These projects are classified according to NASA’s Technology Taxonomy to facilitate data search, extraction, and application. In 2020, the Taxonomy was revised to better align strategic goals with project technical disciplines. Manual re-classification of cur-rent and historical projects was estimated to take thousands of technologist labor hours. Instead of manual classification, our team developed T-Rex, a recommender engine, trained on just a small set of manually classified projects. T-Rex was used to classify the projects and then integrate the data into TechPort to recommend classes to users when updating projects. The system and methodology are used in other NASA projects, and T-Rex has achieved 95% accepted accuracy overall.