Enabling knowledge discovery: case study - taxonomy development for NASA
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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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Explore the source record for details and available documents.
Internal reaction mass (IRM) wave energy converters (WEC) capture wave energy by reacting against a moving mass that is suspended within their hulls. Due to their hermetically sealed Power Take Off (PTO), compact form factor and relatively simple mooring requirements, the National Renewable Energy Laboratory has identified IRM WECs are good candidates for applications such as ocean observation. A literature review of existing IRM mechanisms lead to the classification of IRM WECs based on their principal method of energy capture and principal axes of operation. The IRM categories include sliding mass, pendular and gyroscopic devices. A narrow down study was conducted to ascertain the most promising mechanisms for further investigation; devices were evaluated based on a decision matrix of weighted criteria that included device power output, prototypability and power to mass ratio. The narrow down study identified the issue of achieving resonance with pendulum-based designs, as well as the poor response produced by reaction to long period waves. Chaotic pendula and methods to multiply agitation were identified as a means to increase power output. Alternative methods of power generation, such as triboelectric nanogenerators, were also identified as a technology that would benefit from multiplied agitation and chaotic motion. This work was funded under the U.S. Department of Energy's Water Power Technologies Office's Powering the Blue Economy portfolio.
Automatic differentiation is a popular technique for computing derivatives of computer programs. While automatic differentiation has been successfully used in countless engineering, science, and machine learning applications, it can sometimes nevertheless produce surprising results. In this paper, we categorize problematic usages of automatic differentiation, and illustrate each category with examples such as chaos, time-averages, discretizations, fixed-point loops, lookup tables, linear solvers, and probabilistic programs, in the hope that readers may more easily avoid or detect such pitfalls. We also review debugging techniques and their effectiveness in these situations.
Energy efficiency and thermal comfort can be improved by independent control of temperature and humidity. In recent work, an emerging class of cooling systems known as separate sensible and latent cooling (SSLC) systems have demonstrated 14 to 47% energy savings, while simultaneously providing better comfort control than conventional air conditioning. In this work, SSLC is defined as using two or more cooling processes with unequal sensible heat ratio (SHR) to control temperature and humidity independently and dynamically. Fundamental efficiency limits of SSLC systems are developed for the first time, demonstrating that the Carnot efficiency for SSLC is higher than that of conventional systems. It is revealed that SSLC has particularly high potential for moderate outdoor temperatures, high sensible building loads, and drier indoor conditions. Additionally, this work proposes a classification scheme for SSLC systems, revealing an enormous number of feasible cycle permutations. This provides a framework for the important research task of developing SSLC systems that will save energy, be practical to construct, and be controllable.
Lorchels, also known as false morels (Gyromitra sensu lato), are iconic due to their brain-shaped mushrooms and production of gyromitrin, a deadly mycotoxin. Molecular phylogenetic studies have hitherto failed to resolve deep-branching relationships in the lorchel family, Discinaceae, hampering our ability to settle longstanding taxonomic debates and to reconstruct the evolution of toxin production. We generated 75 draft genomes from cultures and ascomata (some collected as early as 1960), conducted phylogenomic analyses using 1542 single-copy orthologs to infer the early evolutionary history of lorchels, and identified genomic signatures of trophic mode and mating-type loci to better understand lorchel ecology and reproductive biology. Our phylogenomic tree was supported by high gene tree concordance, facilitating taxonomic revisions in Discinaceae. We recognized 10 genera across two tribes: tribe Discineae (Discina, Maublancomyces, Neogyromitra, Piscidiscina, and Pseudodiscina) and tribe Gyromitreae (Gyromitra, Hydnotrya, Paragyromitra, Pseudorhizina, and Pseudoverpa); Piscidiscina was newly erected and 26 new combinations were formalized. Paradiscina melaleuca and Marcelleina donadinii formed their own family-level clade sister to Morchellaceae, which merits further taxonomic study. Genome size and CAZyme content were consistent with a mycorrhizal lifestyle for the truffle species (Hydnotrya spp.), whereas the other Discinaceae genera possessed genomic properties of a saprotrophic habit. Lorchels were found to be predominantly heterothallic-either MAT1-1 or MAT1-2-but a single occurrence of colocalized mating-type idiomorphs indicative of homothallism was observed in Gyromitra esculenta strain CBS101906 and requires additional confirmation and follow-up study. Lastly, we confirmed that gyromitrin has a phylogenetically discontinuous distribution, having been detected exclusively in two distantly related genera (Gyromitra and Piscidiscina) belonging to separate tribes. Our genomic dataset will facilitate further investigations into the gyromitrin biosynthesis genes and their evolutionary history. With additional sampling of Geomoriaceae and Helvellaceae-two closely related families with no publicly available genomes-these data will enable comprehensive studies on the independent evolution of truffles and ecological diversification in an economically important group of pezizalean fungi.
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Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and “transduce” signals to adjust internal processes. We hypothesized that an ecosystem’s unique stimuli leave a sensor “fingerprint,” able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection’s nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient’s disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.
This is a script and the associated test input/output that will accompany a new manuscript (soon to be submitted for publication). It can be used to detect and classify virophage, a specific group of viruses. This script is released as documentation for the manuscript, but there is no specific invention, and most bioinformaticians would be able to recreate a comparable script in a matter of days (if not hours) based on the description in the manuscript.
GTNet is a neural network capable of identifying the species from which a genomic sequence was derived. Its main use will be in processing metagenomic sequences generated in microbiome research.