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This dataset contains white-tailed deer (Odocoileus virginianus) land cover utility score (deer LCU score) data for every TRS (township, range, and section), township-range, and county in Illinois, USA, based on annual National Land Cover Database (NLCD) data released for all years between 2000 and 2023. LCU data is provided in CSV files for each spatial scale, with TRS data split into 2 CSV files due to size limits. Rasters (TIF) showing all deer habitat in Illinois are also provided to show the location, quality, and quantity of deer habitat. A metadata file is also included for additional information.

Results of RT-LAMP reactions for influenza A virus diagnostic development.

Root exudation is a key process for plant nutrient acquisition, but the controls on root exudation and its relationship to soil C and N processes in agroecosystems are unclear. We hypothesized that root exudation rates would be related to root morphological traits, N fertilization, and soil moisture. We also anticipated that root exudation would be correlated with bulk soil enzyme activity. Root exudation, root traits, and bulk soil extracellular enzyme activity were assessed in maize (Zea mays L.), soybean (Glycine max (L.) Merr.), biomass sorghum (Sorghum bicolor (L.) Moench), giant miscanthus (Miscanthus × giganteus), and switchgrass (Panicum virgatum L.). Measurements were taken in situ during two growing seasons with contrasting precipitation regimes, and N fertilization rate was varied in sorghum during one year. Specific root exudation (per unit root surface area) was negatively related to root diameter and was generally higher in annuals than perennials. Sorghum N fertilization did not affect root exudation rates, and soil moisture regime had no effect on annual root exudation rates within maize, sorghum, and miscanthus. Specific root exudation was negatively related to bulk soil C- and N-degrading soil enzyme activities. Intrinsic plant characteristics appeared more important than environmental variables in controlling in situ root exudation rates. The relationships between root diameter, root exudation, and soil C and N processes link root morphological traits to soil functions and demonstrate the potential tradeoffs among plant nutrient acquisition strategies in agroecosystems.

Shared dataset consists of 16S sequencing data of microbial communities. Each community is composed of heterotrophic bacteria derived from one of two soil samples and the model algae Chlamydomonas reinhardtii. Each comunity was placed in a materially closed environment with an initial supply of carbon in the media and subjected to light-dark cycles. The closed microbial ecosystems (CES) survived via carbon cycling. Each CES was subjected to rounds of dilution, after which the community was sequenced (data provided here). The shared dataset allowed us to conclude that CES consistently self-assembled to cycle carbon (data not provided) via conserved metabolic capabilites (data not provided) dispite differences in taxonomic composition (data provided). --------------------------- Naming convention: [soil sample = A or B][CES replicate = 1,2,3, or 4]_[round number = 1,2,3,or 4]_[reverse read = R or forward read = F]_filt.fastq Example -- A1_r1_F_filt.fastq means soil sample A, CES replicate 1, end of round1, forward read

This dataset contains model code (including input data) to replicate the outcomes for "Assessing the Efficiency Implications of Renewable Fuel Policy Design in the United States". The model consists of: (1) The replication codes and data for the model. To run the model, using GAMS to run the "Models.gms" file.

The availability of economically marginal land for energy crops is identified using the Cropland Data Layer and other soil, wind, climate data resources. All data are recognized on a 30m spatial resolution across the continental United States.

Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation.

β‐Carotene is a natural pigment and health‐promoting metabolite, and has been widely used in the nutraceutical, feed, and cosmetic industries. Here, we engineered a GRAS yeast Saccharomyces cerevisiae to produce β‐carotene from xylose, the second most abundant and inedible sugar component of lignocellulose biomass. Specifically, a β‐carotene biosynthetic pathway containing crtYB, crtI, and crtE from Xanthophyllomyces dendrorhous was introduced into a xylose‐fermenting S. cerevisiae. The resulting strain produced β‐carotene from xylose at a titer threefold higher than from glucose. Interestingly, overexpression of tHMG1, which has been reported as a critical genetic perturbation to enhance metabolic fluxes in the mevalonate pathway and β‐carotene production in yeast when glucose is used, did not further improve the production of β‐carotene from xylose. Through fermentation profiling, metabolites analysis, and transcriptional studies, we found the advantages of using xylose as a carbon source, instead of glucose, for β‐carotene production to be a more respiratory feature of xylose consumption, a larger cytosolic acetyl‐CoA pool, and an upregulated expression level of rate‐limiting genes in the β‐carotene‐producing pathway, including ACS1 and HMG1. As a result, 772.8 mg/L of β‐carotene was obtained in a fed‐batch bioreactor culture with xylose feeding. Considering the inevitable large scale production of xylose when cellulosic biomass‐based bioeconomy is implemented, our results suggest xylose utilization is a promising strategy for overproduction of carotenoids and other isoprenoids in engineered S. cerevisiae.

Sugarcane is being enhanced as a bioenergy crop by engineering it to accumulate and store lipids along with polymeric sugars in vegetative tissues. However, there is no existing process that allows for processing this new crop to recover both lipid and cellulosic sugars from the oilcane bagasse. Therefore, a comprehensive investigation of two pretreatment methods—natural deep eutectic solvents (NADES) and chemical-free hydrothermal pretreatment (HT) was conducted to judge their suitability for recovering fermentable sugars, lipids, and lignin from bagasse. Two NADES, i.e., choline chloride: lactic acid (ChCl:LA) and betaine: lactic acid (BT:LA) were prepared using a 1:2 M ratio and were evaluated for pretreatment of oilcane bagasse at 10, 20, and 50 % (w/w) solids, followed by enzymatic hydrolysis at 10 % (w/w) solids. Notably, ChCl:LA NADES treatment at 10 % (w/w) solids at 140 °C for 2 h, solubilized 78.8 % of lignin and 80.4 % of hemicellulose and allowed 82.7 % enzymatic conversion of glucans to glucose. In contrast, HT pretreatment removed approximately 87.6 % of the hemicellulose and provided an enzymatic glucose yield of 69.7 %. Furthermore, ChCl:LA operated at 50 % solids loading the enriched lipids 2.6-fold (9.2 wt%) in recovered solids compared to HT (6.4 %) and BT:LA (5.1 %) pretreatment processes. NMR-HSQC and GPC analysis showed that ChCl:LA also cleaved the most lignin β–O–4 linkages and demonstrated lower molecular weight compared to HT. This study demonstrates that NADES pretreatment is an effective green processing method for recovering lipids, sugars, and lignin from bioenergy crops at high solid loading (50 % w/w) within the context of an integrated biorefinery.

An online knowledge, attitudes, and practices survey on ticks and tick-borne diseases was distributed to medical professionals in Illinois during summer 2020 to fall 2021. These are the raw data associated with that survey and the survey questions used. Age, gender, and county of practice have been removed for identifiability. We have added calculated values (columns 165 to end), including: the tick knowledge score, TBD knowledge score, and total knowledge score, which are the sum of the total number of correct answers in each category, and score percent, which are the proportion of correct answers in each category; region, which is determined from the county of practice; TBD relevant practice, which separates the practice variable into TBD primary, secondary, and non-responders; and several variables which group categories.

Datasets that accompany Beilke, Haulton, and O'Keefe 2022 publication (Title: Foliage-roosting eastern red bats select for features associated with management in a central hardwood forest; Journal: Forest Ecology and Management).

Datasets that accompany Beilke and O'Keefe 2022 publication (Title: Bats reduce insect density and defoliation in temperate forests: an exclusion experiment; Journal: Ecology).

This dataset supports the implementation described in the manuscript "Breaking the Barrier of Human-Annotated Training Data for Machine-Learning-Aided Biological Research Using Aerial Imagery." It comprises UAV aerial imagery used to execute the code available at https://github.com/pixelvar79/GAN-Flowering-Detection-paper. For detailed information on dataset usage and instructions for implementing the code to reproduce the study, please refer to the GitHub repository.

This dataset contains the behavioral, metabolic, and capture data which is reported within the manuscript Data for Capture is predicted by behavior and size, not metabolism, in Muskellunge

Data at the lake summary and individual crayfish level that supports the manuscript Sawyer, E.K., Kreps, T. A., Lodge, D. M. and E.R. Larson. “Long-term declines in body size of the invasive rusty crayfish (Faxonius rusticus) in temperate lakes." Includes size measurements of 69,303 individual rusty crayfish (Faxonius rusticus) for 17 lakes of Vilas County, Wisconsin, United States collected between 1980 and 2020.

In the repository are example scripts that perform uncertainty injection and propagation to flux balance analysis with outputs for a small sample size (for demonstration purpose only). For proper analysis, user should download the scripts and run for a large sample size (e.g., 10,000 samples). If you use the scripts, please cite the following Metabolic Engineering article: “Quantifying the propagation of parametric uncertainty on flux balance analysis” (https://doi.org/10.1016/j.ymben.2021.10.012) There are two subdirectories: /uncFBA/uncBiom: injection of normally distributed noise to biomass precursor coeffcients and ATP maintenance (growth-associated ATP maintenance (GAM) and non-growth associated ATP maintenance (NGAM)) /uncFBA/uncRHS: departure from steady-state by adding noise drawn from normal distribution to the RHS terms of mass balance constraints

Downed woody debris census data for Trelease Woods collected in the summer of 2022. Dataset contains volume, biomass, decay class, and GPS coordinates for each downed woody debris piece.

Dataset of the standing dead trees at Trelease Woods in 2022. Dataset contains volume, biomass, decay class, and GPS coordinates for each standing dead tree.

Soil data for ten soil cores collected at Trelease Woods in 2022. Soil samples were analyzed with an elemental analyzer via combustion to obtain total carbon (C) and nitrogen. A subset of these samples were analyzed using the Walkley-Black method to obtain organic C. A calibration curve relating organic C and total C was created using these data.

The dataset shows all poison frogs (superfamily Dendrobatoidea) in private U.S. collections during 1990–2020. For each species and color morph, there is a date of arrival, the way it arrived in U.S. collections, and detailed notes related to its presence in the pet trade.

Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models’ effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.

Oilcane is an engineered sugarcane with the ability to hyper-accumulate vegetative lipids. It is processed to obtain juice and bagasse as a potential substrate for the production of biofuels and biochemicals. The juice comprises solid particles that are separated as waste mud before the fermentation of the juice. In this study, the oilcane waste mud (OWM) generated from 1000 liters of oilcane juice was quantified and evaluated as a potential resource for recovering biobased waxes. Hexane and ethyl acetate were evaluated as two different solvents for extracting waxes from OWM followed by its purification using acetone. The extracted biobased wax samples were characterized for their chemical and thermal profiles which were then compared with commercial natural waxes. Detailed mass balance shows that 53.6 ± 2.6 kg (dry basis) of solid OWM gets generated upon processing 1000 L (~1068 kg) of oilcane juice. Hexane and ethyl acetate led to a crude wax yield of 25.6 ± 0.2% and 16.6 ± 0.4% (wt/wt, dry basis) respectively from OWM at the end of 8 h. The relative purification of the wax samples was reported in the range of 58%–65% (wt/wt). The purified OWM wax has a melting point of 74.7°C. The waste mud was valorized as a source of biobased waxes with characteristic chemical and thermal profiles comparable to commercial natural waxes (carnauba and beeswax). Considering the decline in the supply of petroleum wax in the future coupled with the switch to “greener” alternative products by consumers, OWM could be a valuable source of natural wax in the industrial sector reducing the dependence on petroleum waxes. Eventually, recovering biobased wax as a co-product from OWM would bring in an additional stream of revenue leading to the development of a zero-waste biorefinery based on bioenergy crops.

*Updates for this V3: added a few more records and rearranged the sequence of the tables in order to support our new paper "Evaluation of Indirect and Direct Scoring Methods to Relate Biochemical Soil Quality Indicators to Ecosystem Services" accepted by the Soil Science Society of America Journal. We summarize peer reviewed literature reporting associations between for three soil quality indicators (SQIs) (β-glucosidase (BG), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable carbon (POXC)) and crop yield and greenhouse gas emissions. Peer-reviewed articles published between January of 1990 and May 2018 were searched using the Thomas Reuters Web of Science database (Thomas Reuters, Philadelphia, Pennsylvania) and Google Scholar to identify studies reporting results for: “β-glucosidase”, “permanganate oxidizable carbon”, “active carbon”, “readily oxidizable carbon”, or “fluorescein diacetate hydrolysis”, together with one or more of the following: “crop yield”, “productivity”, “greenhouse gas’, “CO2”, “CH4”, or “N2O”. Meta-data for records include the following descriptor variables and covariates useful for scoring function development: 1) identifying factors for the study site (location, duration of the experiment), 2) soil textural class, pH, and SOC, 3) depth of soil sampling, 4) units used in published works (i.e.: equivalent mass, concentration), 5) SQI abundances and measured ecosystem functions, and 6) summary statistics for correlation between SQIs and functions (yield and greenhouse gas emissions). *Note: Blank values in tables are considered unreported data.

The complete data for the publication "RNA helicase MOV10 suppresses fear memory and dendritic arborization and regulates microtubule dynamics in hippocampal neurons," excluding sequencing data deposited in GEO, is provided here.

Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.