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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

The total microbiome functions in bacteria and fungi

Unveiling the relationship between phylogeny and function of the microbiome is crucial to determine its contribution to ecosystem functioning. However, while there is a considerable amount of information on microbial phylogenetic diversity, our understanding of its relationship to functional diversity is still scarce. Here we predicted the total microbiome functions of bacteria and fungi on Earth using the total known functions from level 3 of KEGG Orthology by modelling the increase of functions with increasing diversity of bacteria or fungi. For bacteria and fungi, the unsaturated model described the data significantly better (for both P?<2.2e-16), suggesting the presence of two types of functions. Widespread functions ubiquitous in every living organism that make up two thirds of our current knowledge of microbiome functions are separated from rare functions from specialised enzymes present in only a few species. Given previous estimates on species richness, we predicted a global total of 35.5 million functions in bacteria and 3.2 million in fungi; of which only 0.02% and 0.14% are known today. Our approach highlights the necessity of novel and more sophisticated methods to unveil the entirety of rare functions to fully understand the involvement of the microbiome in ecosystem functioning. SIGNIFICANCE: The functionality of and within a microbial community is generally inferred based on the taxonomic annotation of the organism. However, our understanding of functional diversity and how it relates to taxonomy is still limited. Here we predict the total microbiome functionality in bacteria and fungi on Earth using known and annotated protein-coding sequences in species accumulation curves. Our estimates reveal that the majority of functionality (>99%) could be assigned to yet unknown and rare functions, highlighting that our current knowledge is incomplete and functional inference is thus lackluster.

Bacteria↗
DOE OSTI2026

Generalist multimodal AI: A review of architectures, challenges and opportunities

Multimodal models are expected to be a critical component to future advances in artificial intelligence. Here, this field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.

Artificial intelligence (AI)↗
DOE OSTI2023

Modeling, testing, and mitigation of electromagnetic pulse on PV systems

Electromagnetic Pulse (EMP) poses a significant threat to the normal operation of power systems, especially with the increasing penetration of renewable energy. Without adequate protection, EMP can severely damage equipment or result in circuit breakdowns or short circuits. Solar photovoltaic (PV) facilities are particularly susceptible to EMP since PV systems are outdoors and exposed to EMP radiation. To assess and mitigate this threat, this paper summarizes various models and tests used to study the effects of EMP on PV systems, assesses the nature of the threat, and identifies measures to mitigate it. The paper provides a comprehensive review by categorizing analytical methods based on their pros and cons, introducing and describing EMP-related events, and providing a taxonomy of analytical methods for studying EMP. This paper compares the processes of modeling, testing, and mitigating EMP at both the component and system levels of PV systems. Furthermore, it also presents a case study that reveals the vulnerability of solar inverters to EMP using the pulse current injection method. Finally, the paper discusses some of the remaining challenges that should be considered in future solar PV system design and deployment.

14 SOLAR ENERGY↗
DOE OSTI2022

Expanding the genomic encyclopedia of Actinobacteria with 824 isolate reference genomes

The phylum Actinobacteria includes important human pathogens like Mycobacterium tuberculosis and Corynebacterium diphtheriae and renowned producers of secondary metabolites of commercial interest, yet only a small part of its diversity is represented by sequenced genomes. Here, we present 824 actinobacterial isolate genomes in the context of a phylum-wide analysis of 6,700 genomes including public isolates and metagenome-assembled genomes (MAGs). We estimate that only 30%–50% of projected actinobacterial phylogenetic diversity possesses genomic representation via isolates and MAGs. A comparison of gene functions reveals novel determinants of host-microbe interaction as well as environment-specific adaptations such as potential antimicrobial peptides. We identify plasmids and prophages across isolates and uncover extensive prophage diversity structured mainly by host taxonomy. Analysis of >80,000 biosynthetic gene clusters reveals that horizontal gene transfer and gene loss shape secondary metabolite repertoire across taxa. Our observations illustrate the essential role of and need for high-quality isolate genome sequences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

MARLOWE: An Untargeted Proteomics, Statistical Approach to Taxonomic Classification for Forensics

General proteomics research for fundamental science typically addresses laboratory- or patient-derived samples of known origin and composition. However, in a few research areas, such as environmental proteomics, clinical identification of infectious organisms, archeology, art/cultural history, and forensics, attributing the origin of a protein-containing sample to the organisms that produced it is a central focus. A small number of groups have approached this problem and developed software tools for taxonomic characterization and/or identification using bottom-up proteomics. Most such tools identify peptides via database search, and many rely on organism-specific peptides as markers. Our group recently introduced MARLOWE, a software tool for taxonomic characterization of unknown samples based on de novo peptide identification and signal-erosion-resistant strong peptides, which are shared peptides distributed in a taxonomy-dependent manner. In the current work, we further characterize the utility of MARLOWE using publicly available proteomics data from forensically-relevant samples. MARLOWE characterizes samples based on their protein profile, and returns ranked organism lists of potential contributors and taxonomic scores based on shared strong peptides between organisms. Overall, the correct characterization rate ranges between 44 and 100%, depending on the sample type and data acquisition parameters (with lower numbers associated with lower-quality data sets). MARLOWE demonstrates successful characterization of true contributors and close relatives, and provides sufficient specificity to distinguish certain microbial species. MARLOWE demonstrates its ability to provide insight into potential taxonomic sources for a wide range of sample types without prior assumptions about sample contents. As a result, this approach can find utility in forensic science and also broadly in bioanalytical applications that utilize proteomics approaches for taxonomic characterization.

Bacteria↗
DOE OSTI2020

Targeted assemblies of cas1 suggest CRISPR-Cas’s response to soil warming

Abstract There is an increasing interest in the clustered regularly interspaced short palindromic repeats CRISPR-associated protein (CRISPR-Cas) system to reveal potential virus–host dynamics. The universal and most conserved Cas protein, cas1 is an ideal marker to elucidate CRISPR-Cas ecology. We constructed eight Hidden Markov Models (HMMs) and assembled cas1 directly from metagenomes by a targeted-gene assembler, Xander, to improve detection capacity and resolve the diverse CRISPR-Cas systems. The eight HMMs were first validated by recovering all 17 cas1 subtypes from the simulated metagenome generated from 91 prokaryotic genomes across 11 phyla. We challenged the targeted method with 48 metagenomes from a tallgrass prairie in Central Oklahoma recovering 3394 cas1. Among those, 88 were near full length, 5 times more than in de-novo assemblies from the Oklahoma metagenomes. To validate the host assignment by cas1, the targeted-assembled cas1 was mapped to the de-novo assembled contigs. All the phylum assignments of those mapped contigs were assigned independent of CRISPR-Cas genes on the same contigs and consistent with the host taxonomies predicted by the mapped cas1. We then investigated whether 8 years of soil warming altered cas1 prevalence within the communities. A shift in microbial abundances was observed during the year with the biggest temperature differential (mean 4.16 °C above ambient). cas1 prevalence increased and even in the phyla with decreased microbial abundances over the next 3 years, suggesting increasing virus–host interactions in response to soil warming. This targeted method provides an alternative means to effectively mine cas1 from metagenomes and uncover the host communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Orbital stability analysis and photometric characterization of the second Earth Trojan asteroid 2020 XL5

Trojan asteroids are small bodies orbiting around the L 4 or L 5 Lagrangian points of a Sun-planet system. Due to their peculiar orbits, they provide key constraints to the Solar System evolution models. Despite numerous dedicated observational efforts in the last decade, asteroid 2010 TK 7 has been the only known Earth Trojan thus far. Here we confirm that the recently discovered 2020 XL 5 is the second transient Earth Trojan known. To study its orbit, we used archival data from 2012 to 2019 and observed the object in 2021 from three ground-based observatories. Our study of its orbital stability shows that 2020 XL 5 will remain in L 4 for at least 4 000 years. With a photometric analysis we estimate its absolute magnitude to be ${H}_{r}=18.5{8}_{-0.15}^{+0.16}$, and color indices suggestive of a C-complex taxonomy. Assuming an albedo of 0.06 ± 0.03, we obtain a diameter of 1.18 ± 0.08 km, larger than the first known Earth Trojan asteroid.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Missing microbial eukaryotes and misleading meta-omic conclusions

Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and selecting and evaluating these databases is a critical part of correctly annotating protistan taxonomy in environmental datasets. We argue that ongoing curation of genetic resources is crucial in accurately annotating protists in in situ meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Soil microbiome predictability increases with spatial and taxonomic scale

Soil microorganisms shape ecosystem function, yet it remains an open question whether we can predict the composition of the soil microbiome in places before observing it. Furthermore, it is unclear whether the predictability of microbial life exhibits taxonomic- and spatial-scale dependence, as it does for macrobiological communities. Here, we leverage multiple large-scale soil microbiome surveys to develop predictive models of bacterial and fungal community composition in soil, then test these models against independent soil microbial community surveys from across the continental United States. We find remark- able scale dependence in community predictability. The predictability of bacterial and fungal communities increases with the spatial scale of observation, and fungal predictability increases with taxonomic scale. These patterns suggest that there is an increasing importance of deterministic versus stochastic processes with scale, consistent with findings in plant and animal communities, suggesting a general scaling relationship across biology. Biogeochemical functional groups and high-level taxonomic groups of microorganisms were equally predictable, indicating that traits and taxonomy are both powerful lenses for understanding soil communities. Here, by focusing on out-of-sample prediction, these findings suggest an emerging generality in our understanding of the soil microbiome, and that this understanding is fundamentally scale dependent

Biogeography↗
DOE OSTI2022

Wildfire-dependent changes in soil microbiome diversity and function

Abstract Forest soil microbiomes have crucial roles in carbon storage, biogeochemical cycling and rhizosphere processes. Wildfire season length, and the frequency and size of severe fires have increased owing to climate change. Fires affect ecosystem recovery and modify soil microbiomes and microbially mediated biogeochemical processes. To study wildfire-dependent changes in soil microbiomes, we characterized functional shifts in the soil microbiota (bacteria, fungi and viruses) across burn severity gradients (low, moderate and high severity) 1 yr post fire in coniferous forests in Colorado and Wyoming, USA. We found severity-dependent increases of Actinobacteria encoding genes for heat resistance, fast growth, and pyrogenic carbon utilization that might enhance post-fire survival. We report that increased burn severity led to the loss of ectomycorrhizal fungi and less tolerant microbial taxa. Viruses remained active in post-fire soils and probably influenced carbon cycling and biogeochemistry via turnover of biomass and ecosystem-relevant auxiliary metabolic genes. Our genome-resolved analyses link post-fire soil microbial taxonomy to functions and reveal the complexity of post-fire soil microbiome activity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hierarchical biota-level and taxonomic controls on the chemistry of fossil melanosomes revealed using synchrotron X-ray fluorescence

Fossil melanosomes, micron-sized granules rich in melanin in vivo, provide key information for investigations of the original coloration, taxonomy and internal anatomy of fossil vertebrates. Such studies rely, in part, on analysis of the inorganic chemistry of preserved melanosomes and an understanding of melanosome chemical taphonomy. The extent to which the preserved chemistry of fossil melanosomes is biased by biotic and abiotic factors is, however, unknown. Here we report the discovery of hierarchical controls on the inorganic chemistry of melanosomes from fossil vertebrates from nine biotas. The chemical data are dominated by a strong biota-level signal, indicating that the primary taphonomic control is the diagenetic history of the host sediment. This extrinsic control is superimposed by a biological, tissue-level control; tissue-specific chemical variation is most likely to survive in fossils where the inorganic chemistry of preserved melanosomes is distinct from that of the host sediment. Comparative analysis of our data for fossil and modern amphibians reveals that most fossil specimens show tissue-specific melanosome chemistries that differ from those of extant analogues, strongly suggesting alteration of original melanosome chemistry. Collectively, these findings form a predictive tool for the identification of fossil deposits with well-preserved melanosomes amenable to studies of fossil colour and anatomy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Genomic and morphological characterization of Knufia obscura isolated from the Mars 2020 spacecraft assembly facility

Members of the family Trichomeriaceae, belonging to the Chaetothyriales order and the Ascomycota phylum, are known for their capability to inhabit hostile environments characterized by extreme temperatures, oligotrophic conditions, drought, or presence of toxic compounds. The genus Knufia encompasses many polyextremophilic species. In this report, the genomic and morphological features of the strain FJI-L2-BK-P2 presented, which was isolated from the Mars 2020 mission spacecraft assembly facility located at the Jet Propulsion Laboratory in Pasadena, California. The identification is based on sequence alignment for marker genes, multi-locus sequence analysis, and whole genome sequence phylogeny. The morphological features were studied using a diverse range of microscopic techniques (bright field, phase contrast, differential interference contrast and scanning electron microscopy). The phylogenetic marker genes of the strain FJI-L2-BK-P2 exhibited highest similarities with type strain of Knufia obscura (CBS 148926 T ) that was isolated from the gas tank of a car in Italy. To validate the species identity, whole genomes of both strains (FJI-L2-BK-P2 and CBS 148926 T ) were sequenced, annotated, and strain FJI-L2-BK-P2 was confirmed as K. obscura. The morphological analysis and description of the genomic characteristics of K. obscura FJI-L2-BK-P2 may contribute to refining the taxonomy of Knufia species. Key morphological features are reported in this K. obscura strain, resembling microsclerotia and chlamydospore-like propagules. These features known to be characteristic features in black fungi which could potentially facilitate their adaptation to harsh environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Correlative SIP-FISH-Raman-SEM-NanoSIMS links identity, morphology, biochemistry, and physiology of environmental microbes

Microscopic and spectroscopic techniques are commonly applied to study microbial cells but are typically used on separate samples, resulting in population-level datasets that are integrated across different cells with little spatial resolution. To address this shortcoming, we developed a workflow that correlates several microscopic and spectroscopic techniques to generate an in depth analysis of individual cells. By combining stable isotope probing (SIP), fluorescence in situ hybridization (FISH), scanning electron microscopy (SEM), confocal Raman microspectroscopy (Raman), and nano-scale secondary ion mass spectrometry (NanoSIMS), we illustrate how individual cells can be thoroughly interrogated to obtain information about their taxonomic identity, structure, physiology, and metabolic activity. Analysis of an artificial community demonstrated that our correlative approach was able to resolve the activity of single cells using heavy water SIP in conjunction with Raman and/or NanoSIMS and establish their taxonomy and morphology using FISH and SEM. We then applied this workflow to a sample of yet uncultured multicellular magnetotactic bacteria. In addition to establishing their identity and activity, backscatter electron microscopy (BSE), NanoSIMS, and energy-dispersive X-ray spectroscopy (EDS) were employed to characterize the magnetosomes within the cells. By integrating these techniques, we demonstrate a cohesive approach to thoroughly study environmental microbes at single cell resolution.

Schaible, George↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
DOE OSTI2023

The smectic Z A phase: Antiferroelectric smectic order as a prelude to the ferroelectric nematic

We have structurally characterized the liquid crystal (LC) phase that can appear as an intermediate state when a dielectric nematic, having polar disorder of its molecular dipoles, transitions to the almost perfectly polar-ordered ferroelectric nematic. This intermediate phase, which fills a 100-y-old void in the taxonomy of smectic LCs and which we term the “smectic Z A ,” is antiferroelectric, with the nematic director and polarization oriented parallel to smectic layer planes, and the polarization alternating in sign from layer to layer with a 180 Å period. A Landau free energy, originally derived from the Ising model of ferromagnetic ordering of spins in the presence of dipole–dipole interactions, and applied to model incommensurate antiferroelectricity in crystals, describes the key features of the nematic–SmZ A –ferroelectric nematic phase sequence.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Research challenges and directions in HVAC fault prevalence

This study provides a review of the current state of knowledge, gaps, and potential value in research on the prevalence of faults in commercial buildings. Two separate efforts were made in this study: (1) we performed a literature review to determine the extent of currently available fault prevalence data for heating, ventilation, and air-conditioning (HVAC) systems, and (2) we conducted dozens of interviews with subject matter experts and stakeholders to determine the HVAC fault data that would be of greatest value. Through the literature review and interviews, we discovered unmet needs for empirical data on the prevalence of faults at the desired level of granularity, consistency, and scale; this lack of data leads us to recommend future work studying commercial buildings’ HVAC fault prevalence, with robust fault taxonomy and a variety of meaningful fault prevalence metrics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Image-based novel fault detection with deep learning classifiers using hierarchical labels

One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Lastly, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods.

42 ENGINEERING↗