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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bayesian estimation of HIV acquisition dates for prevention trials

Accurate timing estimates of when participants acquire HIV in HIV prevention trials are necessary for determining antibody levels at acquisition. The Antibody-Mediated Prevention (AMP) Studies showed that a passively administered broadly neutralizing antibody can prevent the acquisition of HIV from a neutralization-sensitive virus. We developed a pipeline for estimating the date of detectable HIV acquisition (DDA) in AMP Study participants using diagnostic and viral sequence data. Using a Bayesian strategy that combines three streams of data (REN [rev/vpu/env/Δnef] sequence, GP [gag/Δpol] sequence, and diagnostic) where their 95% credible intervals overlap based on pre-specified criteria and decision rules. We evaluated the performance of our AMP pipeline using PacBio viral sequence data from 41 participants across two prospective acute HIV acquisition cohort studies, FRESH and RV217, with twice-weekly sampling. These cohort studies enrolled young women in South Africa and men and women in Kenya and Thailand, respectively, with a high likelihood of HIV acquisition. In evaluating performance, “true DDA” was the center of bounds between last-negative and first-positive RNA diagnostic tests (median time 4 days, range 2–7 days); bias was the mean difference between estimated and true DDA. Using diagnostic data alone yielded timing estimates with a bias of 2.4 days and root mean square error (RMSE) of 7.9 days. These results were improved using sequence + diagnostic data (bias 1.5 days, RMSE 6.9 days), as well as by restricting sequence-based estimation to samples from ≤5 weeks post-DDA (bias 0.2 days, RMSE 7.8 days).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Retrospective Analysis Reveals That the 2021 Outbreaks of African Swine Fever Virus in Ghana Were Caused by Two Distinct Genotypes

African swine fever virus (ASFV) is the causative agent of African swine fever (ASF), a highly infectious and lethal disease of domesticated swine. Outbreaks of ASF have been mostly restricted to the continent of Africa. The outbreaks that have occurred outside of Africa were controlled by extensive depopulation of the domesticated pig population. However, in 2007, an outbreak occurred in the country of Georgia, where ASFV infected wild pigs and quickly spread across eastern Europe. Since the reintroduction of ASF into Europe, variants of the current pandemic strain, ASFV Georgia 2007/01 (ASFV-G), which is classified as Genotype 2 based on p72 sequencing, have been reported in countries within western Europe, Asia, and the island of Hispaniola. Additionally, isolates collected in 2020 confirmed the presence of variants of ASFV-G in Nigeria. Recently, we reported similar variants of ASFV-G collected from domestic pigs suspected of dying of ASF in Ghana in 2022. Here, we retroactively report, based on full-length sequencing, that similar variants were present in Ghana in 2021. The SNP analysis revealed derivatives of ASFV with distinct genetic markers. Furthermore, we identified three full-length ASFV genomes as Genotype 1, indicating that there were two genotypes circulating in proximity during the 2021 ASF outbreaks in Ghana.

Virology↗
DOE OSTI2024

Energy Saving Estimation of ASHRAE Guideline 36 Supervisory Setpoint Reset Controls in a Commercial Large Office Building

Designing, commissioning, and retrofitting HVAC control systems for energy efficiency is crucial, but the use of ad-hoc control sequences by designers and contractors, based on scattered information, results in diverse and sub-optimal sequences. ASHRAE Guideline 36 (G36) addresses the challenge by providing standardized, rule-based HVAC control sequences that prioritize energy efficiency. However, there is limited evaluation of their energy performance at the building level, with only a few studies primarily focused on HVAC airside systems in small-to-medium-sized commercial buildings. In this study, the energy performance of ASHRAE Guideline 36 control sequences was assessed using a large office building emulator in Chicago. The emulator features a central plant system with multiple chillers and boilers as well as multiple variable air volume (VAV) systems with terminal reheat. To achieve a high-fidelity representation, we developed a Spawn-of-EnergyPlus-based model for the large office building, maintaining the DOE prototype large office building setup but substituting the HVAC system with its Modelica counterpart. This substitution ensures that the building thermal load, HVAC system's dynamics, and detailed control sequences are all accurately represented. The study involved evaluating and implementing control strategies outlined in ASHRAE Guideline 36-2021 to replace conventional controls. These strategies include the demand-based supply air temperature and duct static pressure setpoint reset and the request logic for demand-based reset of chilled/hot water supply temperature setpoints and pipe static pressure setpoints. Energy performance was evaluated under various load conditions, including cooling, heating, and transitional seasons, both for individual control strategies and in combination. The results indicate that the collective control strategies retrofit yield greater energy savings than the sum of individual strategies, highlighting the synergistic benefits of incorporating both airside and plant-side control retrofits. Additionally, energy savings of up to 41% in the heating season, 18% in the shoulder season, and 20 % in the cooling season were observed compared to baseline control while maintaining the thermal comfort level.

ASHRAE Guideline 36, Commercial buildings, Control↗
DOE OSTI2026

Genomic-based biosurveillance for avian influenza: whole genome sequencing from wild mallards sampled during autumn migration in 2022–2023 reveals a high co-infection rate on migration stopover site in Georgia

The Caucasus region, including Georgia, is an important intersection for migratory waterbirds, offering potential for avian influenza virus (AIV) transmission between populations from different geographic areas. In 2022 and 2023, wild ducks were sampled during autumn migration events in Georgia to study the genetic relationships and molecular characteristics of influenza strains. Sequencing and phylogenetic analysis were used to compare the sampled strains to reference sequences from Africa, Asia, and Europe, allowing assessment of genetic relationships and virus transmission between migratory birds. Protein language modeling identified potential co-infections. Of 225 duck samples, 128 tested positive for the influenza M gene. 55 influenza-positive samples underwent whole-genome sequencing, revealing significant diversity. Analysis of the hemagglutinin (HA) segment showed notable differences among subtypes. Most samples were H6N1 and H6N6, but co-infections with combinations like H6H3, N8N1, N6H9, N2N6, and H9H6/N1N2 were also identified. These findings demonstrate the high variability of influenza viruses in migratory waterbirds in Georgia, including a notable rate of co-infections. Some samples exhibited uncommon genetic characteristics compared to other strains from the same year, suggesting Georgia’s role as a mixing vessel for influenza viruses. This facilitates reassortment during co-infections and contributes to the genetic diversity observed across flyways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Small Signaling Peptides in Sorghum bicolor : Integrating Phylogeny and Gene Expression to Characterize Roles in Stem Development

Small signaling peptides (SSPs) are important regulators of plant growth, development, and responses to biotic and abiotic stress, yet their role in the C4 grass Sorghum bicolor is largely uncharacterized. To help fill this knowledge gap, 219 sorghum genes that encode SSPs were identified based on SSP sequences previously identified in Arabidopsis thaliana, Zea mays, Oryza sativa, Triticum aestivum , and Brachypodium distachyon . The 219 sorghum SSP-encoding genes were assigned to 19 gene families, analyzed for the presence of motifs, and aligned with genes that encode SSPs in other plants using phylogenetic analysis. Sorghum genes in 12 of the 19 SSP gene families had not been previously characterized. Expression of the 219 SSP-encoding genes in sorghum organs, during stem development, and in stem tissues and cell types revealed distinct spatial, temporal, and developmental patterns of expression. Genes associated with the SbCEP and SbRGF families were preferentially expressed in roots, whereas SbEPF genes were expressed in stem epidermal and pith parenchyma cells and panicles. The expression of genes during bioenergy sorghum stem growth and development was investigated because stems account for ~80% of harvested biomass and serve as conduits for water and nutrient transport between leaves and roots. During stem development, 28 SSP genes in several families ( CLE, EPF, CEP, GASS, PSY, ES, PSK, CAPE, POE ) were expressed at higher levels in zones of cell proliferation. For example, the TDIF homologs SbCLE41 and SbCLE42 were expressed at high levels in nascent stem nodes where they may regulate vascular bundle cambial activity and cell differentiation. A different set of 15 genes in the CIF, POE, CAPE, PSY, CEP, RALF , and CLE families were expressed at higher levels in zones of stem tissue differentiation highlighted by elevated expression of five SbRALFR s in the stem nodal plexus. Cell type–specific expression of many sorghum genes that encode SSPs was observed in fully elongated internodes indicating gene expression is regulated with high spatial resolution. Overall, the results provide a foundation of information for analysis of SSP function in sorghum that can be integrated with knowledge of sorghum gene regulatory networks to modulate traits important for production of sorghum crops.

bioenergy sorghum↗
DOE OSTI2025

Standardized Residue Numbering and Secondary Structure Nomenclature in the Class D β-Lactamases

Over 1370 class D β-lactamases are currently known, and they pose a serious threat to the effective treatment of many infectious diseases, particularly in some pathogenic bacteria where evolving carbapenemase activity has been reported. Detailed understanding of their molecular biology, enzymology, and structural biology are critically important, but the lack of a standardized residue numbering scheme and inconsistent secondary structure annotation has made comparative analyses sometimes difficult and cumbersome. Compounding this, in the post-AlphaFold world where we currently find ourselves, an extraordinary wealth of detailed structural information on these enzymes is literally at our fingertips; therefore it is vitally important that a standard numbering system is in place to facilitate the accurate and straightforward analysis of their structures. In conclusion, here we present a residue numbering and secondary structure scheme for the class D enzymes based on the sequence and structure of OXA-48 and apply it to test targets to demonstrate the ease with which it can be used.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A family portrait of lanmodulin selectivity for enhanced rare-earth separations

Proteins offer a molecular design space to create bespoke ligands for the separation of critical metals like rare earth elements (REs). However, data-intensive approaches to tune metalloprotein selectivity are constrained by the low-throughput nature of existing characterization methods. Here we invented an assay called ‘SpyTag-Catcher Immobilization of Lanmodulin for Assaying Metal-Binding Selectivity’ (SpyCI-LAMBS) to measure metalloprotein selectivity en masse. This 96-format workflow was used to study the selectivity of 621 lanmodulin (LanM) orthologs for 15 REs, revealing eight distinct selectivity profiles based on sequence-to-function analyses. We discovered >200 LanMs with stronger selectivity against low-value LaIII relative to the prototypical LanM. This includes a LanM that can perform a challenging one-stage separation of PrIII from LaIII with up to >99.9 mol% purity and 83% yield. SpyCI-LAMBS is a powerful tool that can rapidly collect high-fidelity selectivity data to inform metal ion separations and machine-learning-assisted metalloprotein design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Randomized Adiabatic Quantum Linear Solver Algorithm with Optimal Complexity Scaling and Detailed Running Costs

Solving linear systems of equations is a fundamental problem with a wide variety of applications across many fields of science, and there is increasing effort to develop quantum linear solver algorithms. Subaşı et al. [Phys. Rev. Lett. 122, 060504 (2019)] proposed a randomized algorithm inspired by adiabatic quantum computing, based on a sequence of random Hamiltonian simulation steps, with suboptimal scaling in the condition number 𝜅 of the linear system and the target error 𝜖. Here we go beyond these results in several ways. Firstly, using filtering [Lin and Tong, Quantum 4, 361 (2020)] and Poissonization techniques [Cunningham and Roland, ArXiv:2406.03972 (2024)], the algorithm complexity is improved to the optimal scaling 𝑂⁡(𝜅⁢log (1/𝜖))—an exponential improvement in 𝜖, and a shaving of a log 𝜅 scaling factor in 𝜅. Secondly, the algorithm is further modified to achieve constant factor improvements, which are vital as we progress towards hardware implementations on fault-tolerant devices. We introduce a cheaper randomized walk operator method replacing Hamiltonian simulation—which also removes the need for potentially challenging classical precomputations; randomized routines are sampled over optimized random variables; circuit constructions are improved. We obtain a closed formula rigorously upper bounding the expected number of times one needs to apply a block-encoding of the linear system matrix to output a quantum state encoding the solution to the linear system. The upper bound is 837⁢𝜅 at 𝜖 = 10 −10 for Hermitian matrices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Complete genomes of Mucilaginibacter sabulilitoris SNA2 and Mucilaginibacter sp. cycad4: microbes with the potential for plant growth promotion

Mucilaginibacter species have been isolated from various environments, often in association with plants. Here, we report the complete genomes of Mucilaginibacter sabulilitoris SNA2 and Mucilaginibacter sp. cycad4. The former is the first available for that species, and based on 16S sequence analysis, the latter strain is likely a new species.

Mucilaginibacter↗
DOE OSTI2024

Sponge exhalent metabolites influence coral reef picoplankton dynamics

Coral reef sponges efficiently take up particulate and dissolved organic matter (DOM) from the water column and release compounds such as nucleosides, amino acids, and other dissolved metabolites to the surrounding reef via their exhalent seawater, but the influence of this process on reef picoplankton and nutrient processing is relatively unexplored. Here we examined the impact of sponge exhalent on the reef picoplankon community and subsequent alterations to the reef dissolved metabolite pool. We exposed reef picoplankton communities to a sponge exhalent water mixture (Niphates digitalis and Xestospongia muta) or filtered reef seawater (control) in closed, container-based dark incubations. We used 16S rRNA gene sequencing and flow cytometry-based cell counts to examine the picoplankton community and metabolomics and other analyses to examine the dissolved metabolite pool. The initial sponge exhalent was enriched in adenosine, inosine, chorismate, humic-like and amino acid-like components, and ammonium. Following 48 h of exposure to sponge exhalent, the picoplankton differed in composition, were reduced in diversity, showed doubled (or higher) growth efficiencies, and harbored increased copiotrophic and denitrifying taxa (Marinomonas, Pontibacterium, Aliiroseovarius) compared to control, reef-water based incubations. Alongside these picoplankton alterations, the sponge treatments, relative to seawater controls, had decreased adenosine, inosine, tryptophan, and ammonium, metabolites that may support the observed higher picoplankton growth efficiencies. Sponge treatments also had a net increase in several monosaccharides and other metabolites including anthranilate, riboflavin, nitrite, and nitrate. Our work demonstrates a link between sponge exhalent-associated metabolites and the picoplankton community, with exhalent water supporting an increased abundance of efficient, copiotrophic taxa that catabolize complex nutrients. The copiotrophic taxa were often different from those observed in previous algae and coral studies. These results have implications for better understanding the multifaceted role of sponges on picoplankton biomass with subsequent potential impacts to coral and other planktonic feeders in oligotrophic reef environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for FUN-PROSE: A Deep Learning Approach to Predict Condition-Specific Gene Expression in Fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Genomics↗
DOE OSTI2025

GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability

Aptamers are oligonucleotide receptors that bind to their targets with high affinity. Here, we consider aptamers comprised of single-stranded DNA that undergo target-binding-induced conformational changes, giving rise to unique secondary and tertiary structures. Given a specific aptamer primary sequence, there are well-established computational tools (notably mfold) to predict the secondary structure via free energy minimization algorithms. While mfold generates secondary structures for individual sequences, there is a need for a high-throughput process whereby thousands of DNA structures can be predicted in real-time for use in an interactive setting, when combined with aptamer selections that generate candidate pools that are too large to be experimentally interrogated. We developed a new Python code for high-throughput aptamer secondary structure determination (GMfold). GMfold uses subgraph matching methods to group aptamer candidates by secondary structure similarities. We also improve an open-source code, SeqFold, to incorporate subgraph matching concepts. We represent each secondary structure as a lowest-energy bipartite subgraph matching of the DNA graph to itself. These new tools enable thousands of DNA sequences to be compared based on their secondary structures, using machine-learning algorithms. This process is advantageous when analyzing sequences that arise from aptamer selections via systematic evolution of ligands by exponential enrichment (SELEX). This work is a building block for future machine-learning-informed DNA-aptamer selection processes to identify aptamers with improved target affinity and selectivity and advance aptamer biosensors and therapeutics.

Aptamer↗
DOE OSTI2024

Development of near-optimal advanced control sequences for chiller plants with water-side economizers in U.S. Climates (ASHRAE RP-1661)

Various advanced control sequences for chiller plants with water-side economizers (WSE) have been proposed in literature, but the evaluation and optimization of those controls is limited. It is possible to maximize energy savings by selecting different sequences and related parameters based on the plant configuration, load, and climate. This paper addresses this gap by developing near-optimal advanced control sequences for chiller plants with WSEs. First, advanced control sequences for chiller plants with WSEs are categorized into condenser water, chilled water, and hybrid controls and representative sequences from each category are identified. Next, 504 different scenarios are optimized. These scenarios represent all possible combinations of two plant configurations, a constant or variable load profile, three advanced control sequences, and seven optimization parameter combinations in six climate zones. The results show the recommended near-optimal sequences can reduce energy consumption by up to 15% relative to the baseline depending on the configuration, load profile, and climate. Specifically, the CW-CHW sequence is recommended for the majority of systems because it is often the most energy efficient and/or reduces the runtime of chillers. The methodology in this paper provides practical guidance for achieving energy savings through near-optimal control of chiller plants with WSEs.

42 ENGINEERING↗
DOE OSTI2024

Impedance Scan of Inverter-Based Resources and Diesel Generator for Stability Analysis: Preprint

Impedance-based methods are widely used for power system stability analysis with inverter-based resources (IBRs), e.g., assessing dynamic interactions between the power grid and an IBR, control interactions between multiple IBRs, and the sub-synchronous oscillation and damping phenomenon. Since it is difficult to get a numerical model 100% matching with the hardware IBR, using the hardware inverter directly to obtain its output impedance has become a prominent approach nowadays. Therefore, this article presents the impedance scan using hardware IBRs, and also a hardware diesel generator as it still stays with the grid before the grid completely goes to renewable. The devices under test (DuTs) for the impedance scan includes two 3-..phi.., 480 V, 60 Hz commercial grid-forming IBRs (one of 250 kVA and another of 125 kVA rating) in series with ..delta..-Y transformers, one 3-..phi.., 480 V, 60 Hz commercial grid-following IBR (of 125 kVA rating), and a 3-..phi.., 480 V, 60 Hz commercial diesel generator (of 187.5 kVA rating). Using voltage signals perturbed with sub-, inter-, and higher harmonic components, and measuring the current response, the positive-sequence impedances are computed via an offline- based post-analysis. Moreover, best-fit transfer functions are estimated that closely resemble the measured data points of the positive-sequence impedances. Based on the observations from various outcomes of the hardware experiments, this article also provides some fundamental insights on the equivalent positive- sequence impedance of a combination of multiple hardware components by comparing the estimated and the empirically computed impedances. A comparative insight on the damping capability of the DuTs using the positive-sequence impedances of the hardware is also discussed.

grid following inverter↗
DOE OSTI2026

Data for Comparison of Genotyping Assays for Detection of Targeted CRISPR/Cas Mutagenesis in Highly Polyploid Sugarcane

Sugarcane ( Saccharum spp.) is an important biofuel feedstock and a leading source of global table sugar. Saccharum hybrid cultivars are highly polyploid (2n = 100–130), containing large numbers of functionally redundant hom(e)ologs in their genomes. Genome editing with sequence-specific nucleases holds tremendous promise for sugarcane breeding. However, identification of plants with the desired level of co-editing within a pool of primary transformants can be difficult. While DNA sequencing provides direct evidence of targeted mutagenesis, it is cost-prohibitive as a primary screening method in sugarcane and most other methods of identifying mutant lines have not been optimized for use in highly polyploid species. In this study, non-sequencing methods of mutant screening, including capillary electrophoresis (CE), Cas9 RNP assay, and high-resolution melt analysis (HRMA), were compared to assess their potential for CRISPR/Cas9-mediated mutant screening in sugarcane. These assays were used to analyze sugarcane lines containing mutations at one or more of six sgRNA target sites. All three methods distinguished edited lines from wild type, with co-mutation frequencies ranging from 2% to 100%. Cas9 RNP assays were able to identify mutant sugarcane lines with as low as 3.2% co-mutation frequency, and samples could be scored based on undigested band intensity. CE was highlighted as the most comprehensive assay, delivering precise information on both mutagenesis frequency and indel size to a 1 bp resolution across all six targets. This represents an economical and comprehensive alternative to sequencing-based genotyping methods which could be applied in other polyploid species.

Genomics↗
DOE OSTI2024

Comparison of genotyping assays for detection of targeted CRISPR/Cas mutagenesis in highly polyploid sugarcane

Sugarcane (Saccharum spp.) is an important biofuel feedstock and a leading source of global table sugar. Saccharum hybrid cultivars are highly polyploid (2n = 100–130), containing large numbers of functionally redundant hom(e)ologs in their genomes. Genome editing with sequence-specific nucleases holds tremendous promise for sugarcane breeding. However, identification of plants with the desired level of co-editing within a pool of primary transformants can be difficult. While DNA sequencing provides direct evidence of targeted mutagenesis, it is cost-prohibitive as a primary screening method in sugarcane and most other methods of identifying mutant lines have not been optimized for use in highly polyploid species. In this study, non-sequencing methods of mutant screening, including capillary electrophoresis (CE), Cas9 RNP assay, and high-resolution melt analysis (HRMA), were compared to assess their potential for CRISPR/Cas9-mediated mutant screening in sugarcane. These assays were used to analyze sugarcane lines containing mutations at one or more of six sgRNA target sites. All three methods distinguished edited lines from wild type, with co-mutation frequencies ranging from 2% to 100%. Cas9 RNP assays were able to identify mutant sugarcane lines with as low as 3.2% co-mutation frequency, and samples could be scored based on undigested band intensity. CE was highlighted as the most comprehensive assay, delivering precise information on both mutagenesis frequency and indel size to a 1 bp resolution across all six targets. This represents an economical and comprehensive alternative to sequencing-based genotyping methods which could be applied in other polyploid species.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Time-series metagenomics reveals changing protistan ecology of a temperate dimictic lake

Abstract Background Protists, single-celled eukaryotic organisms, are critical to food web ecology, contributing to primary productivity and connecting small bacteria and archaea to higher trophic levels. Lake Mendota is a large, eutrophic natural lake that is a Long-Term Ecological Research site and among the world’s best-studied freshwater systems. Metagenomic samples have been collected and shotgun sequenced from Lake Mendota for the last 20 years. Here, we analyze this comprehensive time series to infer changes to the structure and function of the protistan community and to hypothesize about their interactions with bacteria. Results Based on small subunit rRNA genes extracted from the metagenomes and metagenome-assembled genomes of microeukaryotes, we identify shifts in the eukaryotic phytoplankton community over time, which we predict to be a consequence of reduced zooplankton grazing pressures after the invasion of a invasive predator (the spiny water flea) to the lake. The metagenomic data also reveal the presence of the spiny water flea and the zebra mussel, a second invasive species to Lake Mendota, prior to their visual identification during routine monitoring. Furthermore, we use species co-occurrence and co-abundance analysis to connect the protistan community with bacterial taxa. Correlation analysis suggests that protists and bacteria may interact or respond similarly to environmental conditions. Cryptophytes declined in the second decade of the timeseries, while many alveolate groups (e.g., ciliates and dinoflagellates) and diatoms increased in abundance, changes that have implications for food web efficiency in Lake Mendota. Conclusions We demonstrate that metagenomic sequence-based community analysis can complement existing efforts to monitor protists in Lake Mendota based on microscopy-based count surveys. We observed patterns of seasonal abundance in microeukaryotes in Lake Mendota that corroborated expectations from other systems, including high abundance of cryptophytes in winter and diatoms in fall and spring, but with much higher resolution than previous surveys. Our study identified long-term changes in the abundance of eukaryotic microbes and provided context for the known establishment of an invasive species that catalyzes a trophic cascade involving protists. Our findings are important for decoding potential long-term consequences of human interventions, including invasive species introduction.

59 BASIC BIOLOGICAL SCIENCES↗