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Rhodotorula toruloides is a red oleaginous yeast with growing commercial interest because of its hardiness and exceptional lipid production capacity. Because it is a basidiomycete yeast with a complex life cycle, many of the classical breeding methods used with ascomycetes are unavailable for strain improvement. However, we have been able to construct polyploid yeast by fusing protoplasts of parents with the same mating type. Fusing of Y-6985 (A2) and Y-48190 (A2), which had been transformed with complementary antibiotic markers, led to the recovery of two diploids and one triploid. The stability of the fusion yeasts was tested by plating them on non-selective medium after several growth cycles under antibiotics and then testing five colonies per strain for nuclear DNA contents using flow cytometry and standard cell cycle analysis: the triploid and one diploid were stable. Fusants inherited their mitochondria from a single parent, which was demonstrated using restriction fragment length polymorphism (RFLP) of mitochondrial DNA. The phenotypic properties of the parents and fusants were compared in glucose fed-batch bioreactor studies and cellulosic sugar batch cultures. The final lipid titers for the fed-batch cultures were 24.9–39.7 g/L with Y-6985 and the diploid and triploid performing the best and worst, respectively. The fusants demonstrated intermediate hardiness for growth on hydrolysate prepared with dilute-acid pretreated switchgrass and were outperformed by Y-48190. Unlike one of the haploid parents, the fusants grew in 70% v/v concentrated hydrolysate. However, they did not grow as fast as the other haploid. In this study, a modernized protoplast fusion method is resurrected a useful tool for strain development in this yeast, which is complementary with other available methods.

Plant species and floral traits shape arthropod communities in restored prairies more than neonicotinoid contamination. Using a manipulated field experiment in an established prairie in Champaign Co., IL, we compared the importance of clothianidin contamination and floral traits on arthropod feeding guild abundances and community structure. These datasets contain observations of insects and plants collected during all sampling periods throughout the experiment (JT-HT_Insect_Sampling_2022.csv) and combined feeding guild abundances and floral variables by sample (JT-HT_SEM_Data.csv). The columns in the individual observation dataset include: sampling date, plot number, treatment (1 = CLO for Clothianidin; 0 = CONT for Control), flower abundance, order, superfamily, family, genus, species, and assigned feeding guild. There were inconsistencies in insect determinations and taxonomic resolution among the observations. Cells left empty due to undetermined taxonomic resolution are filled with a period. Additional supporting information—such as seed set, aboveground plant biomass, clothianidin tissue levels, sample design, and proposed structural models—can be found through the publisher. The columns for the combined variables by sample include: Plot #, Plant (USDA plant code), Treatment, Treat (binary variable of treatment required by the R package), Average Seed Set, Plant Dry Weight (in grams), Heads (number of individual flower units), Inflorescences (number of grouped flowering units), Herbivore, Ants, Omnivore, Pollinator, Predator, and Omni. R code for running analyses (SEMs, PERMANOVA) and plot visualization are also provided.

Upon treatment removal, spontaneous and random reactivation of latently infected T cells remains a major barrier toward curing HIV. Due to its stochastic nature, fluctuations in gene expression (or “noise”) can bias HIV reactivation from latency, and conventional drug screens for mean gene expression neglect compounds that modulate noise. Here we present a time-lapse fluorescence microscopy image set obtained from a Jurkat T-cell line, infected with a minimal HIV gene circuit, treated with 1,806 small molecule compounds, and imaged for 48 hours. In addition, the single-cell time-dependent reporter dynamics (single-cell gene expression intensity and noise trajectories) extracted from the image dataset are included. Based on this dataset, a total of 5 latency promoting agents of HIV was found through further experimentation in Lu et al., PNAS 2021 (doi: 10.1073/pnas.2012191118). For a detailed description of the dataset, please refer to the readme file.

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

Improving nitrogen (N) efficiency is essential for sustainable high-biomass sorghum (Sorghum bicolor L. Moench) production. This study evaluated leaf and stem N dynamics, canopy N remobilization, and physiological nitrogen use efficiency (pNUE) in two photoperiod-sensitive sorghum hybrids under two N rates (0 and 168 kg-N ha−1) across multiple environments in Texas and Illinois. Leaf N concentrations increased with plant height in the canopy with steeper gradients under low-N conditions, indicating enhanced N remobilization when N is limited. Stem tissue showed less variation in N concentration across canopy nodal positions, with within-plant differences ranging from 1.2 to 7.6 g kg−1, compared to 3.1 to 16.3 g kg−1 in leaves. While pNUE was generally higher under unfertilized conditions, it varied largely by site; however, genotypic differences were minimal within the given year. These results highlight the importance of integrating environmental and management factors into breeding and fertilization strategies to enhance N efficiency in high-biomass sorghum.

This study develops and validates a simplified, fully solvent-free downstream processing (DSP) strategy for high-purity recovery of 3-hydroxypropionic acid (3-HP) from real fermentation broth containing 62.3 g/L of 3-HP. Optimized activated carbon treatment achieved 98% color removal, while Amberlite IRA-67 was operated at pH 4.5 and 30 °C to minimize product loss. This is the first integrated demonstration of a fully solvent-free DSP enabling recovery of bio-based 3-HP as both a solid sodium salt and a concentrated aqueous solution, supported by techno-economic analysis. At lab scale, the process achieved 77.3% recovery of sodium 3-HP with 83.2% (w/w) purity and produced a 30% (w/v) aqueous solution. Techno-economic analysis yielded minimum selling prices of $0.551/kg for the solution and $0.892/kg for the salt, both below target thresholds for cost-competitive bio-acrylic acid production. Overall, these results demonstrate an efficient, scalable, and economically viable industrial pathway for 3-HP recovery.

The price gap between the market and breakeven prices of cellulosic biomass for farmers represents a significant barrier to the development of a low-carbon cellulosic bioeconomy. Using a bottom-up, agent-based modeling tool that replicates the behaviors and interactions of key stakeholders, this study analyzes the emergence of a cellulosic bioeconomy at the local scale through a wedge approach that examines an integrated portfolio of multiple policy options, including subsidies for small-scale bioproducts and environmental credits. The role of collaboration among multiple stakeholders, such as biomass producers (farmers), bio-refinery industry, government, and society, is assessed for filling the price gap. Using the Sangamon River Basin as a case study site, we evaluate the effectiveness of the wedge approach by comparing simulation results from multiple scenarios, each incorporating different combinations of bioeconomy wedges, with and without stakeholder collaboration. Results underscore that active collaboration among stakeholders acts as a catalyst enlarging the effectiveness of bioeconomy wedges. Including the carbon credits and environmental value in the policy portfolio is found to bridge the price gap through collective contributions from diverse stakeholders, where the cellulosic biofuel and bioproduct industry plays a pivotal role. Although this study is conducted at the local watershed scale, the methodology and findings offer valuable insights for market development in other watersheds and the potential scaling of local markets to regional and national levels.

This dataset contains 16S rRNA amplicon sequencing data and associated code from field-collected Culex pipiens complex and Culex restuans mosquitoes sampled across three regions in the Midwestern United States May-September 2023.

The dataset contains unbiased molecular dynamics (MD) trajectories in XTC format for anandamide binding in cannabinoid receptors, along with the files containing corresponding parameter and topology. All simulations employed the CHARMM36m force field for proteins, while endocannabinoids were parameterized using the CGenFF force field. Unbiased simulations were performed with OpenMM v7.7.

Plants can influence soil microbes through resource acquisition and interference competition, with consequences for ecosystem function such as nitrification. However, how plants alter soil conditions to influence nitrifiers and nitrification rates remains poorly understood, especially in the subsoil. Here, coupling the 15N isotopic pool dilution technique, high throughput sequencing and in situ soil O2 monitoring, we investigated how a deep-rooted perennial grass, miscanthus, versus an adjacent shallow-rooted turfgrass reference shapes nitrifier assembly and function along 1 m soil profiles. In topsoil, the suppression of ammonia (NH3) oxidizing archaea (AOA) and gross nitrification rates in miscanthus relative to the reference likely resulted from nitrifiers being outcompeted by plant roots and heterotrophic bacteria for ammonium (NH4+). The stronger tripartite competition under miscanthus may have been caused in part by the lower soil organic matter (SOM) content, which supported lower gross nitrogen (N) mineralization, the major soil process that produces NH4+. In contrast, below 10 cm soil depth, significantly greater gross nitrification rates were observed in miscanthus compared to the reference. This was likely driven by the significantly lower oxygen (O2) in miscanthus than reference subsoil, which selected against aerobic heterotrophic bacteria but in favor of AOA. Overall, we found that plants can regulate AOA community structure and function through different mechanisms in topsoil and subsoil, with suppression of nitrification in topsoil and enhancement of nitrification in subsoil.

Data and code provided for Wildlife Biology publication "Integrating multiple surveys using state-space models improves inference of population trends for Illinois furbearers". We used four datasets collected by the Illinois Department of Natural Resources and the Illinois Natural History Survey to assess abundance trends in Illinois between 1979-2023 for three species: raccoon (Procyon lotor), opossum (Didelphis virginiana), and striped skunk (Mephitis mephitis). We used Bayesian state-space models to examine population growth for each dataset individually, and compared these to trends derived from using all four datasets in one integrated model. Two datasets are included here; please contact Dr. Craig Miller (<a href="mailto:craigm@illinois.edu">craigm@illinois.edu</a>) for access to the two datasets with containing human subject data.

Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum’s 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about stem cell-type gene expression and regulation was available to enable engineering. To obtain this information, Laser Capture Microdissection (LCM) was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type specific gene regulatory networks (GRNs) revealed that unique TF families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with a stem developmental transcriptome dataset to identify the GRN that differentially activates the secondary cell wall (SCW) formation in stem xylem sclerenchyma and epidermal cells. The cell-type transcriptomic dataset provides a valuable source of information about the function of sorghum stem cell types and GRNs that will enable the engineering of bioenergy sorghum stems.

Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.

Seeds of Col-0 wild type, sig6 T-DNA mutants (CS877785, ABRC), PRL-1-OE, and sig6 T-DNA mutants transfected with PRL-1 (sig6::PRL-1) were planted in 1/2 MS media. Seedlings growth including chlorophyll development defects were investigated across the genotypes. Four-days-old-post-light exposure seedlings were harvested and performed RNAseq analysis with four biological replicates.

This dataset contains simulation results from PartMC-MOSAIC and WRF-PartMC that used in the journal article: Quantifying the Impact of Surfactants on Cloud Condensation Nuclei Activity Using a Particle-Resolved Model. Two compressed folder are uploaded here, one is for the data that used in this article, the other folder is the python scripts to process the data. For more details of the uploaded files, please check the README file.

Arecibo ISR CLP/ULP/LULP ion-line spectra obtained from USRP receiver with 500 kHz bandwidth and 120-1400 km altitude range, experiment dates September 23-26, 2016. Used for Joint inversions of coded and uncoded long pulse1 F-region ISR returns measured at Arecibo.

Includes three different types of stacks, in two folders: "REC_RAW_STACKS.zip" contains: (1) Raw gray-scale reconstructed microCT x-ray scans, in the form of individual stacks per sample. "ML_STACKS.zip" contains: (2) Stacks that have been labeled using a machine-learning mask-RCNN pipeline identifying epidermis, mesophyll, airspace, vascular bundle, and background. (3) Stacks that have stomata locations labeled using a small point. The three types of stacks were used to calculate a variety of anatomical and physiological traits, using ImageJ Macros provided on Github: https://github.com/leakey-lab/microCT-Macros-Fischer. Young leaves from WT and transgenic Sorghum plants. CSV "microCT_REC2_META.csv" contains metadata, including sample/sub-sample labels.

This dataset accompanies the research paper "The single edge notch fracture test for viscoelastic elastomers" by Kamarei, Sozio, and Lopez-Pamies, published in the Journal of Theoretical, Computational and Applied Mechanics (2026). Making use of the Griffith criticality condition introduced by Shrimali and Lopez-Pamies (Extreme Mechanics Letters 58: 101944, 2023), the paper presents a comprehensive analysis of the single edge notch fracture test for viscoelastic elastomers — combining a parametric study with direct comparisons against experiments — to reveal how non-Gaussian elasticity, nonlinear viscosity, and intrinsic fracture energy interact to govern fracture nucleation from a pre-existing crack. The dataset contains figure data, numerical results, and supporting materials for reproducing the findings of the paper.

This dataset contains the raw Florida bonneted bat echolocation calls recorded in southern Florida, USA from the years 2021 and 2022. This dataset also includes our artificial roost microclimate data (2021 only) and observations of bats recorded in our artificial roosts (2021 and 2022). Lastly, we include the R script required to analyze the Florida bonneted bat echolocation calls and the R script to produce the supplemental table and supplemental figure for our microclimate data.

Data for the publication Protostellar Outflows Shed Light on the Dominant Close Companion Star Formation Pathways (Sponzilli et al). Contains the fits files, data files, and python scripts. The entire analysis is containerized with Docker. The `Dockerfile` in the root folder can be used to build the image. <b>Note:</b> __MACOSX folder or files starting with dot can be safely ignored or removed.

This dataset contains five files. (i) open_citations_jan2024_pub_ids.csv.gz, open_citations_jan2024_iid_el.csv.gz, open_citations_jan2024_el.csv.gz, and open_citation_jan2024_pubs.csv.gz represent a conversion of Open Citations to an edge list using integer ids assigned by us. The integer ids can be mapped to omids, pmids, and dois using the open_citation_jan2024_pubs.csv and open_citations_jan2024_pub_ids.scv files. The network consists of 121,052,490 nodes and 1,962,840,983 edges. Code for generating these data can be found https://github.com/chackoge/ERNIE_Plus/tree/master/OpenCitations. (ii) The fifth file, baseline2024.csv.gz, provides information about the metadata of PubMed papers. A 2024 version of PubMed was downloaded using Entrez and parsed into a table restricted to records that contain a pmid, a doi, and has a title and an abstract. A value of 1 in columns indicates that the information exists in metadata and a zero indicates otherwise. Code for generating this data: https://github.com/illinois-or-research-analytics/pubmed_etl. If you use these data or code in your work, please cite https://doi.org/10.13012/B2IDB-5216575_V1.

Curated networks and clustering output from the manuscript: Well-Connected Communities in Real-World Networks https://arxiv.org/abs/2303.02813

This dataset contains files and relevant metadata for real-world and synthetic LFR networks used in the manuscript "Well-Connectedness and Community Detection (2024) Park et al. presently under review at PLOS Complex Systems. The manuscript is an extended version of Park, M. et al. (2024). Identifying Well-Connected Communities in Real-World and Synthetic Networks. In Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-031-53499-7_1. “The Overview of Real-World Networks image provides high-level information about the seven real-world networks. TSVs of the seven real-world networks are provided as [network-name]_cleaned to indicate that duplicated edges and self-loops were removed, where column 1 is source and column 2 is target. LFR datasets are contained within the zipped file. Real-world networks are labeled _cleaned_ to indicate that duplicate edges and self loops were removed. #LFR datasets for the Connectivity Modifier (CM) paper ### File organization Each directory `[network-name]_[resolution-value]_lfr` includes the following files: * `network.dat`: LFR network edge-list * `community.dat`: LFR ground-truth communities * `time_seed.dat`: time seed used in the LFR software * `statistics.dat`: statistics generated by the LFR software * `cmd.stat`: command used to run the LFR software as well as time and memory usage information