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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Impact of short-read sequencing on the misassembly of a plant genome

Abstract Background Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. Results To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Conclusions Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads and the generality of these causes and factors should be tested further in other species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Host phylogeny matters: Examining sources of variation in infection risk by blood parasites across a tropical montane bird community in India

Abstract Background Identifying patterns and drivers of infection risk among host communities is crucial to elucidate disease dynamics and predict infectious disease risk in wildlife populations. Blood parasites of the genera Plasmodium and Haemoproteus are a diverse group of vector-borne protozoan parasites that affect bird populations globally. Despite their widespread distribution and exceptional diversity, factors underlying haemosporidian infection risk in wild bird communities remain poorly understood. While some studies have examined variation in avian haemosporidian risk, researchers have primarily focused on host ecological traits without considering host phylogenetic relationships. In this study, we employ a phylogenetically informed approach to examine the association between host ecological traits and haemosporidian infection risk in endemic bird communities in the Western Ghats Sky Islands. Methods We used parasite sequence data based on partial mitochondrial cytochrome b gene, that was amplified from genomic DNA extracted from 1177 birds (28 species) across the Western Ghats to assess infection of birds with haemosporidian parasites. We employed a Bayesian phylogenetic mixed effect modelling approach to test whether haemosporidian infection risk was affected by seven species-specific and four individual-level ecological predictors. We also examined the effect of host phylogenetic relationships on the observed patterns of variation in haemosporidian infection risk by estimating phylogenetic signal. Results Our study shows that host ecological traits and host phylogeny differentially influence infection risk by Plasmodium (generalist parasite) and Haemoproteus (specialist parasite). For Plasmodium , we found that sociality, sexual dimorphism and foraging strata were important ecological predictors. For Haemoproteus , patterns of infection risk among host species were associated with sociality, species elevation and individual body condition. Interestingly, variance in infection risk explained by host phylogeny was higher for Haemoproteus parasites compared to Plasmodium . Conclusions Our study highlights that while host ecological traits promoting parasite exposure and host susceptibility are important determinants of infection risk, host phylogeny also contributes substantially to predicting patterns of haemosporidian infection risk in multi-host communities. Importantly, infection risk is driven by joint contributions of host ecology and host phylogeny and studying these effects together could increase our ability to better understand the drivers of infection risk and predict future disease threats. Graphical abstract

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Safety Analysis for the Versatile Test Reactor Conceptual Design

Safety analysis simulations have been carried out using the SAS4A/SASSYS-1 safety analysis code system to update the baseline set of results for the Versatile Test Reactor conceptual design. Results from a previous revision of this work provided input for the Conceptual Safety Design Report summarizing the overall safety basis for the VTR. This work will also support future analyses that will be required for Chapter 13, “Accident Analysis,” of a preliminary safety analysis report. A number of transients have been evaluated based on categories of postulated accident sequences suggested for consideration in NUREG-1537. Ultimately, the full spectrum of event initiators and subsequent accident sequences will be defined through a probabilistic risk assessment of the plant design following the implementation of the Licensing Modernization Project guidelines as described in the VTR Safety Design Strategy. Since that process is ongoing, the present analysis addresses several transients that are expected to be bounding accident scenarios that represent the three key ways to perturb a reactor: through changes to the core inlet temperature, mass flow rate, or reactivity. These correspond to a loss of heat sink (LOHS), loss of flow (LOF) or station blackout (SBO), and transient overpower (TOP), respectively. Both Protected and Unprotected versions of these transients have been evaluated, along with additional transients that include transient overpower with one stuck rod, pump coast-down failures, seismic events, and overcooling events. At the current stage of design, transient simulation results for the Versatile Test Reactor indicate that large safety margins exist for many event initiators. However, several “enabling” assumptions have been made, in terms of both design features and design limits, in order to perform the analyses; and these assumptions will need to be revised as the design matures. In addition, a larger spectrum of events needs to be evaluated as part of the on-going probabilistic risk assessment process.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Exploring prokaryotic diversity in permafrost-affected soils of Ladakh’s Changthang region and its geochemical drivers

Global warming due to climate change has substantial impact on high-altitude permafrost affected soils. This raises a serious concern that the microbial degradation of sequestered carbon can result in alteration of the biogeochemical cycles. Therefore, the characterization of permafrost affected soil microbiomes, especially of unexplored high-altitude, low oxygen arid region, is important for predicting their response to climate change. This study presents the first report of the bacterial diversity of permafrost-affected soils in the Changthang region of Ladakh. The relationship between soil pH, organic carbon, electrical conductivity, and available micronutrients with the microbial diversity was investigated. Amplicon sequencing of permafrost affected soil samples from Jukti and Tsokar showed that Proteobacteria and Actinobacteria were the dominant phyla in all samples. The genera Brevitalea, Chthoniobacter, Sphingomonas, Hydrogenispora, Clostridium, Gaiella, Gemmatimonas were relatively abundant in the Jukti samples whereas the genera Thiocapsa, Actinotalea, Syntrophotalea, Antracticibcterium, Luteolibacter, Nitrospirillum dominated the Tsokar sample. Correlation analyses highlighted the influence of soil geochemical parameters on the bacterial community structure. PCoA analyses showed that the bacterial beta diversity varied significantly between the sampling locations (PERMANOVA test (F-value: 2.3316; R 2 = 0.466, p = 0.001) and similar results were also obtained while comparing genus abundance data using the ANOSIM test (R = 0.345, p = 0.007).

16S rRNA↗
DOE OSTI2026

Additively Reinforced Thermoformable PETG Composite Sheets for Improved Structural Efficiency

Thermoforming of short-fiber reinforced thermoplastic sheets offers a viable pathway for producing lightweight composite components; however, inherent anisotropy in fiber-reinforced sheets can limit structural performance under multidirectional loading. In this work, short carbon fiber, glass fiber, and hybrid fiber–reinforced PETG sheets were evaluated as candidate feedstock materials for thermoforming, with flexural and tensile testing performed both along the primary fiber direction and in the off-axis orientation to establish baseline stiffness, strength, and anisotropy. As expected, short carbon fiber PETG exhibited the highest stiffness and strength in the primary fiber direction, while all systems showed reduced performance in the off-axis direction. This off-axis performance reduction provides clear justification for the use of additive reinforcement when such thermoformed sheets are intended for structural applications. The intended manufacturing sequence involves thermoforming the reinforced sheet first, followed by the application of additively manufactured lattice reinforcement; therefore, the reinforcement strategy does not impose limitations on sheet formability during thermoforming. Post-forming lattice reinforcement significantly reduced load-normalized displacement by approximately 95–99% relative to non-reinforced sheets and improved weight-normalized stiffness by ~70%. These findings demonstrate that geometry-driven additive reinforcement can effectively compensate for off-axis property reductions in thermoformed PETG composites, enabling enhanced multidirectional structural performance without compromising manufacturability.

Talabi, Isaac [ORNL] (ORCID:0000000340215594)↗
DOE OSTI2021

efam: an e xpanded, metaproteome-supported HMM profile database of viral protein fam ilies

Viruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases. Here, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, pan-ecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from ‘conservative’ to ‘eXtremely Conservative’ resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by ~24% on average (up to ~42%) and especially improved the recovery of often-missed shorter contigs (<5 kb). Additionally, to help elucidate lesser-known viral protein functions, we annotated the profiles using multiple databases from the DRAM pipeline and virion-associated metaproteomic data, which doubled the number of annotations obtainable by standard, single-database annotation approaches. Together, these marine resources (efam and efam-XC) are provided as searchable, compressed HMM databases that will be updated bi-annually to help maximize viral sequence discovery and study from any ecosystem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Inverse Reinforcement Learning based Bayesian Goal Inference Method for Early Nuclear Proliferation Detection

Traditional methods for detection of nuclear proliferation indicators are usually applied after nuclear proliferation has already occurred. There is a need to advance these methods to perform early detection of nuclear proliferation indicators. In this project, we formulated an early detection problem as a sequential, decision-making, goal inference problem based on research publications of authors, to determine whether it is possible to infer whether an author will publish on a research activity before it has occurred. To develop and test our approach, we selected a civil nuclear activity for our case study. We constructed a state-action-state transition graph from publications of authors associated with the activity and the co-authors of their publications, using titles, abstracts, and author publication sequences. We then used inverse reinforcement learning to model the goal-directed behavior of authors in trajectories that terminate at selected goal states. Using a Bayesian formulation, we computed the probability that authors would reach each selected state from partially observed trajectories of their state transitions in their research topic space. The state with the highest probability was selected as the most probable goal state. Based on our results, we found that 60% of the times we can infer the correct goal state early; sometimes the inference is either delayed, or multiple states could be inferred as goal states. Overall, our results show that it is possible to perform early detection of research activities of authors in a nuclear technology area. Further research is necessary to establish a more accurate understanding of how topic modeling, topic space grid discretization, and the extent of overlap among trajectories of different goal states, affect the goal inference results. The methods developed in this work may be used to enhance data-driven methods for early detection of nuclear proliferation indicators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

SAM Co-Development to Support Fluoride-salt-cooled High-temperature Reactor Design and Licensing (CRADA 2018-18177 Final Report)

This project aims to raise the SAM code's technical and commercial maturity level to enable the Kairos Power ("Kairos") to use SAM to support its KP-FHR design analysis and licensing application. Argonne National Laboratory ("Argonne") has actively developed and maintained SAM, a modern system-level analysis tool for advanced nonlight water reactors safety analysis. Kairos is currently actively pursuing development of an FHR design and associated technology, and consequently requires a pedigreed safety analysis tool. The purpose of this project is to increase the maturity of the SAM code for the modeling and simulation of the KP-FHR design, thus enabling its use in safety analyses that support licensing application. Argonne will primarily be responsible for software design, development, and testing; while Kairos will be responsible for software requirements, assessment of software capabilities, needs, gaps, and priorities, and development of proprietary models. The research and development activities for the joint SAM development project include: a series of identification and prioritization studies on design characteristics, event sequences, relevant phenomena, and software capabilities; SAM capability enhancements for specific KP-FHR systems and components; performing code verification and validations; integrating uncertainty quantification (UQ), model calibration, and sensitivity analysis (SA) techniques in safety analyses; and raising the software quality rigor level for commercial-grade applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

AI-Assisted Conceptual Development of a Pre-Geometric Cosmological Model - An Exercise in AI-Assisted Conceptual Framework Generation, Paper III: Cosmological Structure and Predictions

This paper develops the cosmological consequences of the replication-driven cosmogenesis framework introduced in Paper I and the emergent geometric structure established in Paper II. After the replication epoch freezes out, the coherent sector occupies a finite spectral band and contains a population of excited states. The relaxation of these excited coherent configurations does not produce coherent radiation; instead, all released energy flows into the incoherent substrate, where the randomizer acts as a rapid phase-scrambling mechanism. This process generates an effectively thermal radiation bath, providing a natural reheating mechanism that requires neither inflaton oscillations nor scalar-field potentials, and can be contrasted with standard scenarios of nonperturbative reheating dynamics. Subsequent symmetry-breaking transitions in the coherent vacuum inject additional radiation, yielding a multi-stage thermal history with well-defined energy transfers. We derive the effective equations of state for each component—the cosmological vacuum, the coherent vacuum, and the radiation bath—and show how their interplay produces an FRW-like expansion. The discrete sequence of coherent-state relaxations imprints a distinctive multi-peaked stochastic gravitational-wave background, whose spectral structure reflects the underlying hierarchy of coherent frequencies. Potential observational signatures in the LISA and mid-band frequency ranges are highlighted, providing concrete avenues to test this replication-based cosmological framework in the context of standard cosmological gravitational-wave backgrounds and LISA-oriented forecasts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Metagenome-assembled genomes provide insight into the metabolic potential during early production of Hydraulic Fracturing Test Site 2 in the Delaware Basin

Demand for natural gas continues to climb in the United States, having reached a record monthly high of 104.9 billion cubic feet per day (Bcf/d) in November 2023. Hydraulic fracturing, a technique used to extract natural gas and oil from deep underground reservoirs, involves injecting large volumes of fluid, proppant, and chemical additives into shale units. This is followed by a “shut-in” period, during which the fracture fluid remains pressurized in the well for several weeks. The microbial processes that occur within the reservoir during this shut-in period are not well understood; yet, these reactions may significantly impact the structural integrity and overall recovery of oil and gas from the well. To shed light on this critical phase, we conducted an analysis of both pre-shut-in material alongside production fluid collected throughout the initial production phase at the Hydraulic Fracturing Test Site 2 (HFTS 2) located in the prolific Wolfcamp formation within the Permian Delaware Basin of west Texas, USA. Specifically, we aimed to assess the microbial ecology and functional potential of the microbial community during this crucial time frame. Prior analysis of 16S rRNA sequencing data through the first 35 days of production revealed a strong selection for a Clostridia species corresponding to a significant decrease in microbial diversity. Here, we performed a metagenomic analysis of produced water sampled on Day 33 of production. This analysis yielded three high-quality metagenome-assembled genomes (MAGs), one of which was a Clostridia draft genome closely related to the recently classified Petromonas tenebris. This draft genome likely represents the dominant Clostridia species observed in our 16S rRNA profile. Annotation of the MAGs revealed the presence of genes involved in critical metabolic processes, including thiosulfate reduction, mixed acid fermentation, and biofilm formation. These findings suggest that this microbial community has the potential to contribute to well souring, biocorrosion, and biofouling within the reservoir. Our research provides unique insights into the early stages of production in one of the most prolific unconventional plays in the United States, with important implications for well management and energy recovery.

natural gas↗
DOE OSTI2025

AlgaeOrtho, a bioinformatics tool for processing ortholog inference results in algae

Introduction: Microalgae constitute a prominent feedstock for producing biofuels and biochemicals by virtue of their prolific reproduction, high bioproduct accumulation, and the ability to grow in brackish and saline water. However, naturally occurring wild type algal strains are rarely optimal for industrial use; therefore, bioengineering of algae is necessary to generate superior performing strains that can address production challenges in industrial settings, particularly the bioenergy and bioproduct sectors. One of the crucial steps in this process is deciding on a bioengineering target: namely, which gene/protein to differentially express. These targets are often orthologs which are defined as genes/proteins originating from a common ancestor in divergent species. Although bioinformatics tools for the identification of protein orthologs already exist, processing the output from such tools is nontrivial, especially for a researcher with little or no bioinformatics experience. Methods: The present study introduces AlgaeOrtho, a user-friendly tool that builds upon the SonicParanoid orthology inference tool (based on an algorithm that identifies potential protein orthologs based on amino acid sequences) and the PhycoCosm database from JGI (Joint Genome Institute) to help researchers identify orthologs of their proteins of interest in multiple diverse algal species. Results: The output of this application includes a table of the putative orthologs of their protein of interest, a heatmap showing sequence similarity (%), and an unrooted tree of the putative protein orthologs. Notably, the tool would be instrumental in identifying novel bioengineering targets in different algal strains, including targets in not-fully annotated algal species, since it does not depend on existing protein annotations. We tested AlgaeOrtho using three case studies, for which orthologs of proteins relevant to bioengineering targets, were identified from diverse algal species, demonstrating its ease of use and utility for bioengineering researchers. Discussion: This tool is unique in the protein ortholog identification space as it can visualize putative orthologs, as desired by the user, across several algal species.

09 BIOMASS FUELS↗
DOE OSTI2023

Development of a Machine-Learned Cruise Guide Indicator for Rotorcraft

This paper presents a machine-learned virtual cruise guide indicator (vCGI) for Chinook helicopters. Two temporal neural networks were trained and evaluated on measured data from 55 flight tests, one for the fore rotor and another for the aft rotor, to predict a vCGI value, which protects 23 components from fatigue damage during steady-state conditions. Three different classes of machine learning architectures were evaluated for prediction of the vCGI from time sequences: a temporal convolutional neural network with 1D dilated causal convolutions, a long short-term memory recurrent neural network, and an attention-based transformer architecture. The final average model accuracy on unseen flight data is currently greater than 93% for CGI values which could result in fatigue damage and 90% for normal operation CGI values. Model accuracy was improved through a series of advancements in:(1) selection of optimal training data using temporal collective variables and unsupervised learning, (2) dataset augmentation with maximum-entropy temporal collective variables, and (3) implementation of a mixture-of-experts classification- regression approach using an adversarial classification approach to assign maneuver labels. The results are presented for each advancement in model development along with lessons learned in training machine learning models on real- world, time-dependent rotorcraft data.

Boyer, Mathew↗
DOE OSTI2026

Populus_trichocarpa_Breeding_Population_SNPs

These data are from the manuscript “Application of Genomic Prediction in a Populus trichocarpa Breeding Program”, by Brian J. Stanton, David Macaya-Sanz, Chanaka Roshan Abeyratne, David Kainer, Kathy Haiby, Austin Himes, Carlos Gantz, Gerald A. Tuskan, and Stephen P. DiFazio. The data are based on genome resequencing to approximately 10X depth on two collections of Populus trichocarpa trees from Oregon, Washington, California, and British Columbia. The first collection consists of 293 genets collected by Poplar Innovations LLC for a breeding program. The second collection consists of 961 trees collected for the purpose of genome-wide association studies. These genets were sequenced using short, paired-end Illumina sequence reads (Chhetri et al. 2019). Reads were aligned to the P. trichocarpa ′Stettler-14′ reference (Hofmeister et al. 2020), with minor modifications to correct mis-assemblies (Zhou et al. 2020), and variants were called as per methods described in (Abeyratne et al. 2023). Identified variants were filtered using GATK’s VariantFiltration tool (DePristo et al. 2011), with filter expression flag set to “AF < 0.01 || AF > 0.99 || QD < 10.0 || ExcessHet > 20.0 || FS > 10.0 || MQ < 58.0”. SNPs with severe departures from Hardy−Weinberg expectations (exact-test p< 0.01) were also removed using vcftools --hwe flag (Danecek et al. 2011), resulting in 15,627,211 bi-allelic SNPs. The data included here consist of 141,903 high quality bi-allelic genome-wide SNPs obtained by further filtering the original SNP dataset using vcftools with flags --maf 0.05, --max-maf 0.95, --max-missing 0.95, --min-meanDP 10.75, --max-meanDP 43.00, --thin 2000. Collectively, these filtering parameters removed SNPs with 1) a minor allele frequency ≤ 0.05; 2) proportion of missing data for individual loci exceeding 5%; 3) sequencing depth more than 2X mean-depth or less than 0.5X mean-depth; or 4) a distance of

09 BIOMASS FUELS↗
DOE OSTI2022

Resilient Inverter-Driven Black Start with Collective Parallel Grid-Forming Operation: Preprint

As the modern power systems are experiencing exceptional changes with increasing penetration of inverter-based resources (IBRs), system restoration using IBRs has received attention. Using local grid-forming (GFM) assets near consumers, engineered to establish grid voltages in the absence of a stiff grid, i.e., bottom-up restoration, a distribution system would obtain high system resilience, by not relying on the bulk power system restoration requiring significant human intervention and procedure to restore. This paper studies the technical feasibility of the novel approach with detailed electromagnetic transient (EMT) simulations. To thoroughly evaluate the potential of GFM inverters and technical challenges in IBR-driven black start, a detailed three-phase inverter model is developed, including negative-sequence control for voltage balance and a phase-by-phase current limiter to sustain momentary overloading during the black start. To examine dynamic aspects of the black start process, the EMT simulation also models transformer and motor dynamics to emulate their inrush and start-up behaviors as well as network dynamics. In addition, active involvement of grid-following distributed energy resources (DER) is also studied to facilitate the black start process. It is shown that, by allowing multiple GFM inverters to collectively black start without master-slave coordination, a system can achieve high resilience even with a fraction of assets lost. Two test cases of inverter-driven black start using two and one GFM inverters, respectively, for a heavily unbalanced 2-MVA distribution feeder are demonstrated. Takeaways for further study and field deployment are provided.

grid-forming inverter↗
DOE OSTI2024

Geochemical assessment of mineral sequestration of carbon dioxide in the midcontinent rift

Abstract This study examines the potential of Midcontinent Rift rocks to facilitate long‐term CO 2 sequestration by providing the necessary Ca and Mg for carbonate mineralization. Surface samples were collected from the Oronto and Bayfield‐Jacobsville Groups around Lake Superior and used for petrography and X‐ray diffraction to determine their mineral composition. Also, X‐ray fluorescence was also used to assess their bulk chemical composition. The samples were then exposed to CO 2 and deionized water in Teflon‐lined vessels at 90°C, and the resulting leachate fluids were analyzed for the cation released during the testing. SEM microscopy was used to examine the samples for potential mineralization of carbonate minerals. The Oronto Group sediments consist primarily of feldspathic to feldspathic lithic arenites with a chlorite‐dominated matrix, and the primary porosity is blocked by calcite and hematite cement. The Bayfield–Jacobsville sequences are porous quartz arenites to feldspathic quartz arenites that do not contain significant accumulation of Ca‐, Mg‐, and Fe‐bearing minerals. The leachate fluids obtained from Oronto Group samples exhibit a maximum Ca release rate (5.2 × 10 −4 mole/cm 2 .day), indicating rapid calcite cement dissolution and increased porosity and permeability. SEM/EDS microanalysis revealed areas where pore‐filling calcite was preferentially dissolved. Longer‐term rock‐water reactions resulted in induced carbonate mineralization, as evidenced by calcite crystals observed in a sample reacted for 102 days. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Energy & Fuels↗
DOE OSTI2023

Resilient Inverter-Driven Black Start with Collective Parallel Grid-Forming Operation

As modern power systems are experiencing exceptional changes with increasing penetrations of inverter-based resources (IBRs), system restoration using IBRs has received attention. Using local grid-forming (GFM) assets near consumers, engineered to establish grid voltages in the absence of a stiff grid, i.e., bottom-up restoration, a distribution system could obtain high system resilience by not relying on the bulk power system restoration, which requires significant human intervention and procedure. This paper studies the technical feasibility of the novel approach with detailed electromagnetic transient (EMT) simulations. To thoroughly evaluate the potential of GFM inverters and the technical challenges in IBR-driven black start, a detailed three-phase inverter model is developed, including negative-sequence control for voltage balance and a phase-by- phase current limiter to sustain momentary overloading during the black start. To examine dynamic aspects of the black-start process, the EMT simulation also models transformer and motor dynamics to emulate their inrush and startup behaviors as well as network dynamics. In addition, active involvement of grid- following distributed energy resources is also studied to facilitate the black-start process. By allowing multiple GFM inverters to collectively black start without leader-follower coordination, we demonstrate that a system can achieve high resilience even with a fraction of assets lost. Two test cases of inverter-driven black start, using two and one GFM inverters, respectively, for a heavily unbalanced 2-MVA distribution feeder are demonstrated. Takeaways for further study and field deployment are provided.

grid-forming inverter↗
DOE OSTI2022

Soil compartments (bulk soil, litter, root and rhizosphere) as main drivers of soil protistan communities distribution in forests with different nitrogen deposition

Protists, in particular bacterivores, are essential players in the rhizosphere; thus, how their interactions with bacteria and fungi affect plant productivity and soil nutrient cycles warrants more attention. Using next-generation sequencing of the 18 S rRNA gene, we investigated the distribution of two major protistan phyla, Cercozoa and Endomyxa, across four seasons, and four soil compartments - rhizosphere, root, soil and litter. The sampling was replicated in two forests in Norway and the Czech Republic, in order to test our results across biogeographic scales. Compartment had a major influence in shaping protistan communities, over and above spatial distance and seasonal variation. Protistan diversity was highest in the bulk soil while lowest in the roots, suggesting that the plants select for restricted assemblages of protists. Accordingly, only the root compartment harboured a subset of the bulk soil protistan diversity. In addition, protistan communities showed markedly different distributions according to their feeding modes, with opposite patterns for bacterivores versus omnivores and eukaryvores. The small bacterivorous flagellates (mostly Glissomonadida) were more abundant in roots, while the larger amoeboid eukaryvores (e.g. some of the Cryomonadida and vampyrellids) dominated in soil and in the rhizosphere, and the omnivores (e.g. Euglyphida and part of the Cercomonadida), also large and mostly amoeboid, were more abundant in litter. The current view of the soil microbiome is mostly focused on bacteria and fungi: this detailed study on the community distribution of protists according to their feeding modes reveals the essential role they play in each of the soil compartments, an essential precondition for a detailed understanding of the soil food web and nutrient cycling in forest.

59 BASIC BIOLOGICAL SCIENCES↗