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DOE OSTI2025

TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.

Data Analysis, Statistics and Probability (physics↗

DOE OSTI2024

Deep unsupervised learning using spike-timing-dependent plasticity

Abstract Spike-timing-dependent plasticity (STDP) is an unsupervised learning mechanism for spiking neural networks that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work, we investigate a Deep-STDP framework where a rate-based convolutional network, that can be deployed in a neuromorphic setting, is trained in tandem with pseudo-labels generated by the STDP clustering process on the network outputs. We achieve 24.56% higher accuracy and 3.5 × faster convergence speed at iso-accuracy on a 10-class subset of the Tiny ImageNet dataset in contrast to a k -means clustering approach.

Lu, Sen↗

DOE OSTI2020

A Systems Biology Approach to Energy Flow in H 2 Producing Microbial Communities: Microbial Mats

This report describes the results of work conducted at NASA Ames Research Center, in collaboration with investigators at Lawrence Livermore National Laboratory, during the time period 12/15/2014 to 12/15/2017. The work was an extension of a collaboration with LLNL that began in 2008. Work this reporting period focused primarily on tasks related to microbial mats from both the Elkhorn Slough and Baja California field sites, including the analysis and publication of data produced prior to 12/15/2014. During this reporting period, in addition to the microbial mat work, we also conducted a series of experiments with a particular algal-bacterial association (C. sorokiniana and A. brasilense). A number of aspects of this well-characterized algal-bacterial association enabled us not only to further our understanding of interactions between these particular organisms, but to apply that knowledge and techniques to our collection of microorganisms isolated from microbial mats. Microbial mat experiments focused both on intact mat communities and on cultures of organisms isolated from the mats (the Microbial Mat Living Library). In a series of co-culture experiments, we documented both positive and negative interactions amongst heterotrophic and autotrophic isolates of microbial mat microorganisms. One particularly important and novel mat cyanobacterium (ESFC-1) was studied in detail. We developed techniques to pursue an investigation of functional redundancy (the idea that closely related organisms perform identical roles in a community) in cyanobacterial autotrophs in the Elkhorn Slough microbial mat. In those experiments, we documented numerous instances of increases in growth rate between co-cultures of two organisms (relative to their growth rate in isolation) performing what would seem to be identical functional roles. Simplified microbial mat communities consisting of organisms from the Microbial Mat Living Library were constructed and studied in laboratory experiments. The “constructed mats” were grown in standard laboratory glassware and plasticware, but cultures were also combined with artificial materials in an effort to re-create the three-dimensional structure of naturally occurring microbial mats. These constructed mats re-created a number of functions attributable to natural microbial mats (N-fixation, H2-production) and simplified their study using molecular ecological techniques due to the smaller number of microbial “species” present. Cyanobacteria which migrated in response to vertical variations in light intensity (irradiance) obtained characteristic positions in constructed mats that were similar to their positions in natural mats. A detailed investigation of nitrogen cycling in both the Baja California and Elkhorn Slough microbial mats was initiated during this reporting period. The nitrogen cycle in the microbial mats has not been studied in depth, despite the fact that nitrogen usually limits growth in marine environments. The organisms conducting nitrogen cycling in both microbial mats were studied using the genomes available from earlier work on these mats, as well as with a targeted approach using molecular ecological methods developed here. Quantitative measurements of rates of nitrogen transformations were made using stable isotopically labeled nitrogen. The processes quantified and examined using molecular ecological methods included: nitrogen fixation, ammonification, ammonium and nitrate assimilation, denitrification, anaerobic oxidation of ammonium, and dissimilatory nitrate reduction to ammonium. In a series of experiments designed to examine a potential application for microbial mats, we demonstrated that both Elkhorn Slough and Baja California microbial mats were very effective in removing large quantities of both nitrate and ammonium from waste streams containing these compounds at levels where they would be considered to be pollutants. Considerable effort was expended during this reporting period to analyze, and prepare for publication, metagenomes and metatranscriptomes collected from two experimental manipulations of microbial mats. The first, a diel study, was conducted in November 2011, while the second, an investigation of the effects of sulfate on community composition, was conducted during this reporting period. For the earlier study, reference-based and reference-free methods were used to assess microbial ecology and genetic partitioning in these complex microbial systems, and that work is available as a pre-print. Four metagenomes from the same experiment were published. In the second, longer (nearly 200 day) manipulation, rRNA iTag libraries for cDNA and DNA were analyzed to determine the effects of sulfate concentration on microbial mat community composition over the course of the experiment. Work on the algal-bacterial association (Chlorella sorokiniana and Azospirillum brasilense) focused initially on demonstrating the transfer of carbon and nitrogen between the partners, using stable isotopic labeling and the LLNL NanoSIMS. Subsequently, the potential role of diazotrophy (nitrogen fixation) by the bacterial partner in supporting the growth of the association was assessed in a series of experiments. It appears to be the case that, although the bacterial partner demonstrably increases the fitness of the algae under nitrogen limitation, and cells of C. sorokiniana receive nitrogen originally fixed from atmospheric nitrogen by A. brasilense, the amount of nitrogen obtained by diazotrophy is too small to explain the increase in fitness.

08 HYDROGEN↗

DOE OSTI2020

A data-driven mobility–energy typology framework for New York State

Exploring multidimensional aspects of differences in technology adoption, travel, and vehicle ownership across settlement types can help inform energy-efficient and affordable mobility system goals. Concurrently, mapping key enablers, barriers, and risks for successfully meeting ambitious goals and targets (e.g. by geography, age, income, education, population density) offer important explanatory power as to context-specific challenges and opportunities. This paper explores how a highly geographically resolved understanding of social, economic, techno-infrastructural, environmental, and governance (SET EG) factors shape variations in technology adoption and associated mobility and energy outcomes in diverse communities of New York State, in terms of electric vehicle adoption rates, alternative commute mode choices, vehicles per household, and vehicle fuel economy. Results indicate the range of two to three times higher adoption rates for electric vehicles by more highly educated, wealthier, “Core Urban” populations relative to the other identified typologies, given the labels “Rural”, “Suburban”, and “Urban” populations. Additionally, commute mode choice is closely linked with population and employment density—more than 89% of Core Urbanites use transit or active modes, compared with just 26% and 18% of Suburban and Rural residents, respectively. Household vehicle ownership varies from approximately 1.9 vehicles per household in Rural areas to only 0.6 in Core Urban settings. Findings on differences among the four settlement types, which go beyond simply rural to urban contexts, suggest an important need to explore how best to manage and anticipate very different types of services that may be supportive in achieving energy-efficient and affordable mobility systems statewide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

MISIP: a data standard for the reuse and reproducibility of any stable isotope probing-derived nucleic acid sequence and experiment

DNA/RNA-stable isotope probing (SIP) is a powerful tool to link in situ microbial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, process rates, and population dynamics, offering valuable insights for a wide range of research questions. Data reuse maximizes the information derived from the labor and resource-intensive SIP approaches. Yet, a review of publicly available SIP sequencing metadata showed that critical information necessary for reproducibility and reuse was often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) framework and include examples of MISIP reporting for common SIP experiments. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires 5 metadata fields—isotope, isotopolog, isotopolog label, labeling approach, and gradient position—and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (e.g., gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will improve the reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding of in situ biogeochemical processes and microbial ecology.

Simpson, Abigayle↗

DOE OSTI2022

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

Abstract We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Computer-aided Abnormality Detection in Chest Radiographs in a Clinical Setting via Domain-adaptation

Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. Such models are often trained on a large volume of publicly available labeled radiographs. These pre-trained DL models’ ability to generalize in clinical settings is poor because of the changes in data distributions between publicly available and privately held radiographs. In chest radiographs, the heterogeneity in distributions arises from the diverse conditions in X-ray equipment and their configurations used for generating the images. In the machine learning community, the challenges posed by the heterogeneity in the data generation source is known as domain shift, which is a mode shift in the generative model. In this work, we introduce a domain-shift detection and removal method to overcome this problem. Our experimental results show the proposed method’s effectiveness in deploying a pre-trained DL model for abnormality detection in chest radiographs in a clinical setting.

Dubey, Abhishek↗

DOE OSTI2023

Development and implementation of an in situ high-resolution isotopic microscope for measuring metabolic interactions in soil mesocosms (Final Report)

Many of our planet’s ecosystems rely on the activities of soil microbial communities. These microbes have significant and wide-ranging effects: they metabolize carbon and other nutrients, interact with plants and fungi, and perform other processes important to soil health. However, our ability to directly observe the enzymatic and metabolic activities of microbes within soil is currently limited, not only by the complexity of soil microbial communities themselves, but also by the lack of experimental tools to study them and their molecular interactions in situ. These challenges hinder our understanding of the life-sustaining processes of biomass decomposition and the manner in which it contributes to the movement of freed carbon within soil ecosystems. Here we aimed to develop a novel ultrahigh-resolution isotopic microscope that combines complementary imaging modalities to gain insights into metabolic cycling in soil. This final report covers both the portion of this work that was initially completed at the University of North Carolina at Chapel Hill from 2018 to 2019 (as award DE-SC0019012) and then from 2020 to 2022 (2023 in NCE) at the University of Massachusetts Chan Medical School. This goal was to build an integrated platform consisting of fluorescence microscopy, Raman microspectroscopy, and nanospray desorption electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (nanoDESI-FTICR-MS) to directly investigate microbial activities and molecular transformations occurring in soil by exploiting the use of both fluorescent labels and stable isotope probing. Specifically, our instrument was conceived as integrating: (A) fluorescence detection to localize soil microbes, identify bacteria taking up polysaccharides, and monitor gene expression of enzymes involved in decomposition; (B) Raman microspectroscopy to determine which microbes incorporate decomposition products into their biomass, as well as which decomposers are metabolically active; and (C) nanoDESI-FTICR-MS imaging to spatially probe, in real-time, the metabolites in the surrounding area, which we expect will reveal the distributions of the products of the enzymatic breakdown of polysaccharides, as well as specialized metabolites acting as cell-cell signals between decomposers. To enable these measurements, we exploited fluorescence-based probes to map the microbes that are enzymatically active (per A) and utilized stable isotope-labeled substrates to visualize both the microbial and molecular fate of decomposed biomass (B and C, respectively). To accomplish our goal, we combined the expertise of a multidisciplinary group of scientists to pursue the construction of this microscope and to investigate scientific questions that would be facilitated by such a capability. We aimed to employ this spatially informative, high-resolution isotopic microscope to visualize the critical steps of biomass degradation and the molecular fate of other environmentally relevant substrates within soil mesocosms. Accordingly, this technology will enhance our understanding of the microbial and metabolic interactions occurring within soil communities that are relevant to carbon degradation and other soil processes. The instrument has been developed and housed at EMSL, where it will be available to the entire EMSL User Base, enabling a variety of related DOE-relevant systems to be interrogated in the future by diverse scientific research groups.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Neutrons in Structural Biology: Challenges and Opportunities (Workshop Report)

Gaining a thorough understanding of biological systems requires building our knowledge about biological processes from the level of atoms and electrons, and up to whole organisms. Such comprehensive knowledge will allow for a predictive understanding of complex biological systems behavior. It will guide us in the design and development of novel therapeutics and vaccines to tackle existing health threats and to prepare for future pandemics, and it will provide information necessary to create new biomaterials and bio-inspired technologies through manipulation of biological macromolecules, their assemblies, single cells and even microorganisms. Reaching these goals will require a synergistic combination of multiple experimental techniques with molecular calculations and predictive simulations, and the design and development of new techniques and capabilities that bridge current knowledge and technology gaps. Neutron scattering provides unique information about the biomacromolecular structure and function and can play a major role in achieving these goals. A workshop was held to engage the scientific community in identifying pressing challenges in biochemistry, structural biology, enzymology and structure-guided drug design not solved with the current neutron scattering technologies or utilizing other structural biology techniques such as X-ray crystallography, NMR, and cryo-EM. The workshop brought together structural biology, biochemistry and computational experts, as well as early career researchers and students, creating a forum for discussing scientific advancement and collaboration. The workshop included a one-day satellite training workshop where graduate students and postdoctoral researchers were educated in the application of neutron crystallography and small-angle scattering in structural biology. Furthermore, the Instrument Scientific Advisory Board (ISAB) for the development of a macromolecular neutron diffractometer at ORNL’s Second Target Station was introduced at the workshop. The major outcome was that neutrons can provide atomic-level understanding of biomacromolecular structure, function and dynamics which is of paramount importance for addressing the identified challenges. Neutron crystallography, in particular, can resolve long-standing biochemical issues regarding enzyme function by delineating the underlying chemistry and can have a major impact on the design of small-molecule therapeutics, especially in combination with molecular computation (quantum chemistry and molecular dynamics simulations) and the emerging artificial intelligence (AI)-assisted drug design technologies. The unique properties of neutrons, including their high sensitivity to hydrogen and their non-destructive nature, make them ideal probes of biological matter. There is a palpable need in the scientific community to expand and enhance the impact of neutron sciences on biology. Neutron crystallography is the only structural biology method capable of determining positions of all hydrogen atoms in proteins, nucleic acids and their complexes at near-physiological temperatures and of unstable species at cryogenic temperatures. Moreover, neutron analysis is non-ionizing, non-destructive and does not perturb the structure or redox chemistry of active site metal centers and clusters in proteins, which can be invaluable for studying radiation-sensitive metalloprotein complexes. Further, neutron energies used in scattering applications are similar to atomic motions, permitting neutron spectroscopies to characterize the dynamics of biomacromolecules on the picosecond to microsecond timescales. The different sensitivities of neutrons to protium (H) and deuterium (D) isotopes of hydrogen allow enhanced visibility of specific parts of biological complexes through isotopic labeling. The impact of neutrons will be most powerful when neutron scattering is combined with complementary experimental techniques that use photons and electrons, and with high-performance computing. The interconnection and mutuality of the experimental and theoretical capabilities will drive discoveries in biological and health sciences to generate more complete picture of complex biological systems. The major limitation in the field of biological neutron crystallography has been signal-to-noise, demanding large samples that are difficult to produce for the majority of biomacromolecules and limiting the applicability of this technique in biological sciences. A neutron crystallography instrument at the Second Target Station will revolutionize biological science with neutrons by engaging a large scientific community of structural biologists, enabling successful neutron diffraction experiments from radically smaller biomacromolecular crystals, resolving unanswered biochemical questions, and meaningfully contributing to rational drug design. The meeting highlighted 10 grand challenges that will be addressed with this advanced capability over the next decade and beyond, and the recommendations required to help address them are given below.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Belowground plant carbon and nitrogen exchange: plant-derived carbon inputs and pore structure formation

Belowground plant transfer of carbon (C) and nitrogen (N) can benefit soil ecosystems, increasing soil C gains and plant N availability, while improving soil pore structure. We explored such transfers among three plant species of North American prairie, where C and N were transferred from a grass (Panicum virgatum L., switchgrass (Sgrass)) to either a legume (Lespedeza capitata Michx., bush clover (Bclover)), a forb (Rudbeckia hirta L., black-eyed Susan (BSforb)), or a mixture of the two. The plants were grown either with/out direct root contact, thus allowing assessment of the relative contributions of fungal- and root-based transfer pathways. The Sgrass was labeled with 13 C and 15 N, and C and N transfers were assessed by measuring isotope enrichment of roots and aboveground biomass of neighboring plants. Soil inputs of plant-derived C and N were assessed by isotope analyses of the rhizosphere soil. X-ray computed tomography was used for pore structure analyses. Carbon transfer was much higher in the presence of direct/close root contact between source and recipient plants, yet N transfers appeared to be mainly fungal driven. Here, while C and N were readily transferred from Sgrass to other Sgrass and Bclover neighbors, transfers to BSforb were negligible. However, in a three species system, the presence of the legume enhanced C and N transfers to BSforb, suggesting non-additive influences of diverse plant community composition. The more plant-derived C and N was found in the rhizosphere of recipient plants, the greater C and N transfers through roots. Greater C and N transfers were associated with increases in 8–30 μm diameter pores and decreases in >150 μm pores. Summarily, diverse plant communities, especially those with legumes, increase C and N transfers, which then benefit soil C inputs and its protection via changes in pore structure.

13 CO 2 pulse labeling↗

DOE OSTI2025

Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications

A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.

42 ENGINEERING↗

DOE OSTI2025

National User Resource for Biological Accelerator Mass Spectrometry (Final Report)

The National User Resource for Biological Accelerator Mass Spectrometry (User Resource) will provide isotopic analysis (primarily radiocarbon or 14C) by accelerator mass spectrometry (AMS) for NIH- funded researchers across the United States and will be the only User Resource of its type in the United States. The User Resource will provide measurement capability and expertise to a research community that requires highly sensitive, quantitative isotope analyses. Since commissioning a new accelerator mass spectrometer in June 2014, we have measured over 4000 samples a year for collaborators and service users. The User Resource will enable us to continue to meet these research needs, as well as provide for new users whose research programs would benefit from AMS as a measurement tool. The User Resource’s forte will be ultra-high sensitivity quantitation of radiocarbon and selected other radioisotopes for research studies where isotopes are required. Radioisotope labeling studies have been and will continue to be an important tool for addressing many complex biomedical science problems. AMS is a specialized and unique type of mass spectrometry that provides absolute quantitation of radiocarbon and other relevant radioisotopes with extreme sensitivity, having limits of detection in real samples on the order of a few attomol/mg of sample at measurement precisions of ~3%. It is the only instrumental method capable of quantifying radioisotope-labeled agents routinely in real-world samples with such precision and sensitivity. The sensitivity of AMS allows for the quantification of radiolabeled metabolites in extremely complex matrices of cells and organisms at very low concentrations and in small samples. AMS allows studies to be conducted without perturbing metabolism leading to more relevant quantification of metabolic rates and pathways. In addition, it enables quantification of pharmacokinetic and metabolic properties of toxicants at environmentally relevant concentrations in model systems as well as the ability to quantify pharmacokinetics and other molecular endpoints directly in humans. Such quantitative assessments can 1) improve risk assessment for toxicants, 2) address safety and efficacy considerations for therapeutic entities, 3) deepen understanding of xenobiotic and intermediary metabolism, 4) help understand the interactions between critical molecular pathways, and 5) improve efforts to model and predict various metabolic and biological states. These capabilities have been applied in a number of areas including research in carcinogenesis, toxicology, nutrition, pharmacology/drug development and basic biological science. As a NIGMS National Resource the National User Resource for Biological Accelerator Mass Spectrometry will help NIH funded scientists achieve a deeper understanding of the etiology of human health concerns by (1) enabling the quantification of pharmacokinetics and other molecular endpoints directly in humans; (2) offering the ability to conduct quantitative studies using biologics such as proteins or lipids, and thereby reducing the amount of radioisotope usage in biomedical labs; and (3) enabling more relevant studies of metabolic pathways in health and disease through the use of much lower, more biologically-relevant, concentrations of metabolic substrates in cells and intact organisms. Such studies support NIGMS’s basic biomedical research areas that contribute to the understanding of fundamental cellular and physiological principles and enable research supported by the Biophysics, Biomedical Technology, and Computational Biosciences (BBCB); Genetics and Molecular, Cellular, and Developmental Biology (GMCDB); Pharmacology, Physiology, Biological Chemistry (PPBC) and Training, Workforce Development, and Diversity (TWD) Divisions. Over the next five years, our goals are to: 1. Improve the efficiency of operation for AMS measurements through installation of new interfaces to our AMS systems, technical modifications to improve gas accepting ion source efficiency and upgrading our data analysis software for improved ease of use and data reporting. 2. Increase the accessibility and visibility of ultra-sensitive 14C measurements for the biomedical research community by training of new investigators and expanding our national user base. 3. Provide high throughput, ultra-sensitive 14C analysis for the NIGMS and NIH user community.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Arbuscular mycorrhiza convey significant plant carbon to a diverse hyphosphere microbial food web and mineral‐associated organic matter

Summary Arbuscular mycorrhizal fungi (AMF) transport substantial plant carbon (C) that serves as a substrate for soil organisms, a precursor of soil organic matter (SOM), and a driver of soil microbial dynamics. Using two‐chamber microcosms where an air gap isolated AMF from roots, we 13 CO 2 ‐labeled Avena barbata for 6 wk and measured the C Rhizophagus intraradices transferred to SOM and hyphosphere microorganisms. NanoSIMS imaging revealed hyphae and roots had similar 13 C enrichment. SOM density fractionation, 13 C NMR, and IRMS showed AMF transferred 0.77 mg C g −1 of soil (increasing total C by 2% relative to non‐mycorrhizal controls); 33% was found in occluded or mineral‐associated pools. In the AMF hyphosphere, there was no overall change in community diversity but 36 bacterial ASVs significantly changed in relative abundance. With stable isotope probing (SIP)‐enabled shotgun sequencing, we found taxa from the Solibacterales, Sphingobacteriales, Myxococcales, and Nitrososphaerales (ammonium oxidizing archaea) were highly enriched in AMF‐imported 13 C (> 20 atom%). Mapping sequences from 13 C‐SIP metagenomes to total ASVs showed at least 92 bacteria and archaea were significantly 13 C‐enriched. Our results illustrate the quantitative and ecological impact of hyphal C transport on the formation of potentially protective SOM pools and microbial roles in the AMF hyphosphere soil food web.

13C SIP↗

DOE OSTI2020

Metabolic flux analysis reaching genome wide coverage: lessons learned and future perspectives

13 C-MFA is currently the only technique capable of elucidating intracellular metabolic fluxes. Generally, in 13 C-MFA studies the reactions that can carry flux are mostly pre-specified by only considering canonical pathways and ignoring alternate ones. This may bias flux elucidation and cause labeling data to erroneously confirm implied assumptions. Here, by expanding the scope of the metabolic mapping models to match known genome-scale metabolism such estimation biases can be eliminated. However, this model expansion to genome-scale requires the construction of expanded atom mapping models, more efficient flux estimation algorithms, and formal estimation of confidence levels. Even though significant progress has been made in this direction, a number of challenges remain before widespread adoption by the community.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Quantification of microcystin production and biodegradation rates in the western basin of Lake Erie

Abstract Cyanobacterial biomass forecasts currently cannot predict the concentrations of microcystin, one of the most ubiquitous cyanotoxins that threaten human and wildlife health globally. Mechanistic insights into how microcystin production and biodegradation by heterotrophic bacteria change spatially and throughout the bloom season can aid in toxin concentration forecasts. We quantified microcystin production and biodegradation during two growth seasons in two western Lake Erie sites with different physicochemical properties commonly plagued by summer Microcystis blooms. Microcystin production rates were greater with elevated nutrients than under ambient conditions and were highest nearshore during the initial phases of the bloom, and production rates were lower in later bloom phases. We examined biodegradation rates of the most common and toxic microcystin by adding extracellular stable isotope‐labeled microcystin‐LR (1 μ g L −1 ), which remained stable in the abiotic treatment (without bacteria) with minimal adsorption onto sediment, but strongly decreased in all unaltered biotic treatments, suggesting biodegradation. Greatest biodegradation rates (highest of −8.76 d −1 , equivalent to the removal of 99.98% in 18 h) were observed during peak bloom conditions, while lower rates were observed with lower cyanobacteria biomass. Cell‐specific nitrogen incorporation from microcystin‐LR by nanoscale imaging mass spectrometry showed that a small percentage of the heterotrophic bacterial community actively degraded microcystin‐LR. Microcystin production and biodegradation rates, combined with the microcystin incorporation by single cells, suggest that microcystin predictive models could be improved by incorporating toxin production and biodegradation rates, which are influenced by cyanobacterial bloom stage (early vs. late bloom), nutrient availability, and bacterial community composition.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Probe-Enabled Approaches for Function-Dependent Cell Sorting and Characterization of Microbiome Subpopulations

Understanding the roles that individual species or communities play within a microbiome is a significant challenge. The complexity and heterogeneity of microbiomes presents a challenge to researchers looking to unravel the function that microbiomes serve within larger environments. While identification of the species and proteins present in a microbiome can be accomplished through genomics approaches, strategies that report on enzyme activity are limited. In this chapter, we describe the application of small molecule chemical probes in the isolation and subsequent characterization of microbiome subpopulations based on enzymatic function. We will cover protocols for labeling microbes with appropriate probes, microbiome sample preparation, and using fluorescence-activated cell sorting to isolate subpopulations based on function. We hope that the strategies outlined here will serve as a resource for researchers studying the functional role that microbiomes play in the gut and soil.

Steiger, Andrea K.↗

DOE OSTI2020

Sequential Ammonia and Carbon Dioxide Adsorption on Pyrolyzed Biomass to Recover Waste Stream Nutrients

Untreated fecal waste in unsewered communities and reactive nitrogen (N) loading from wastewater and urine disposal are important environmental challenges. Through pyrolysis, untreated fecal waste can be converted into amine-rich carbon dioxide (CO 2 ) sorbents, the chemisorption of which tunes the surface chemistry toward ammonia (NH 3 ) uptake. We compared the N-loading potential within amine-rich pyrolyzed human solid waste through exposure to CO 2 followed by NH 3 with the N-loading potential on carboxyl-rich, pyrolyzed oxidized wood using sequential adsorption of isotopically labeled CO 2 and NH 3 . The total N content of pyrolyzed oxidized wood exposed once to NH3 was similar to native, pyrolyzed human solid waste (3.7% compared to 4.6%). However, sorption kinetics and spectroscopic evidence revealed distinct N forms between the two materials affecting subsequent CO 2 and NH 3 uptake. Tertiary amines comprised 70% of N in pyrolyzed waste, compared to only 53% in pyrolyzed oxidized wood. The high heat of adsorption of CO 2 on pyrolyzed human solid waste (50 kJ mol-1) at low concentrations (0.05 mmol CO 2 g -1 ) was indicative of CO 2 reactivity with tertiary amines, consistent with the formation of carbamic acid (Figure S1). Conversely, after exposure to NH 3 , pyrolyzed oxidized wood chemisorbed CO 2 at much higher concentrations (0.7 mmol CO 2 g-1), pointing to primary amines as the active functional moieties. Adsorption kinetics of pyrolyzed oxidized wood revealed continued, albeit diminishing NH 3 uptake with each consecutive CO 2 treatment, averaging 5.9 mmol NH 3 g -1 for the first NH 3 exposure event, and still 3.5 and 2.9 mmol NH 3 g -1 for the second and third. Penetration of 15 NH 3 and 13 CO 2 measured by NanoSIMS reached over 7 µm deep into both materials, explaining the large NH 3 capture. The significantly lower NH 3 adsorption compared to pyrolyzed oxidized wood may be a result of potassium (K) carbonate precipitation precluding a Lewis base interaction between NH 3 and chemisorbed CO 2 , suggested by a 2-fold increase in surface K concentrations after exposure of pyrolyzed human solid waste to CO 2 +NH 3 detected by XPS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

National User Resource for Biological Accelerator Mass Spectrometry Annual Report

59 BASIC BIOLOGICAL SCIENCES↗