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Data used for Zaya et al. (2018), published in Invasive Plant Science and Management DOI 10.1017/inp.2017.37, are made available here. There are three spreadsheet files (CSV) available, as well as a text file that has detailed descriptions for each file ("readme.txt"). One spreadsheet file ("prices.csv") gives pricing information, associated with Figure 3 in Zaya et al. (2018). The other two spreadsheet files are associated with the genetic analysis, where one file contains raw data for biallelic microsatellite loci ("genotypes.csv") and the other ("structureResults.csv") contains the results of Bayesian clustering analysis with the program STRUCTURE. The genetic data may be especially useful for future researchers. The genetic data contain the genotypes of the horticultural samples that were the focus of the published article, and also genotypes of nearly 400 wild plants. More information on the location of the wild plant collections can be found in the Supplemental information for Zaya et al. (2015) Biological Invasions 17:2975–2988 DOI 10.1007/s10530-015-0926-z. See "readme.txt" for more information.

Genotype calls are provided for a collection of 583 Miscanthus sinensis clones across 1,108,836 loci mapped to version 7 of the Miscanthus sinensis reference genome. Sequence and alignment information for all unique RAD tags is also provided to facilitate cross-referencing to other genomes.

Acepromazine was administered to healthy adult horses to induce transient anemia secondary to splenic sequestration. Data was collected at baseline (T0), 1 hour (T1) and 12 hours (T2) post acepromazine administration. Data collection included PCV, TP, CBC, fibrinogen, PT, PTT and viscoelastic coagulation profiles (VCM Vet) as well as ultrasonographic measurements of the spleen at all 3 time points.

Dataset associated with "Hiding in plain sight: genetic confirmation of putative Louisiana Fatmucket Lampsilis hydiana in Illinois" as submitted to Freshwater Mollusk Biology and Conservation by Stodola et al. Images are from cataloged specimens from the Illinois Natural History Survey (INHS) Mollusk Collection in Champaign, Illinois that were used for genetic research. File names indicate the species as confirmed in Stodola et al. (i.e., Lampsilis siliquoidea or Lampsilis hydiana) followed by the INHS Mollusk Collection catalog number, followed by the individual specimen number, followed by shell view (interior or exterior). If no specimen number is noted in the file name, there is only one specimen for that catalog number. For example: Lsiliquoidea_46515_1_2_3_exterior. Images were created by photographing specimens on a metric grid in an OrTech Photo-e-Box Plus with a Nikon D610 single lens reflex camera using a 60mm lens. Post-processing of images (cropping, image rotation, and auto contrast) occurred in Adobe Photoshop and saved as TIFF files using no image compression, interleaved pixel order, and IBM PC Byte Order. One additional partial lot, INHS Mollusk Catalog No. 37059 (shown with both interior and exterior view in one image), is included for reference but was not genetically sequenced. A .csv file contains an index of all specimens photographed. SPECIES: species confirmed using genetic analyses GENE: cox1 or nad1 mitochondrial gene ACCESSION: GenBank accession number INHS CATALOG NO: Illinois Natural History Survey Mollusk Collection Catalog number WATERBODY: waterbody where specimen was collected PUTATIVE SPECIES: species determination based on morphological characters prior to genetic analysis Phylogenetic sequence data (.nex files) were aligned using BioEdit (Hall, T.A. 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series 41:95-98.). Pertinent methodology for the analysis are contained within the manuscript submittal for Stodola et al. to Freshwater Mollusk Biology and Conservation. In these files, "N" is a standard symbol for an unknown base.

The included files were used to reconstruct the phylogeny of Coelidiinae using combined morphological and molecular data, estimate divergence times and reconstruct ancestral biogeographic areas as described in the manuscript submitted for publication. The file “Coelidiinae_dna_morph_combined.nex” is a text file in standard NEXUS format used by various phylogenetic analysis programs. This file includes the aligned and concatenated nucleotide sequences or five gene regions (mitochondrial COI and 16S, and nuclear 28S D-2, histone H3, histone H2A and wingless) indicated by standard “ACGT” nucleotide symbols with missing data indicated by “?”, and morphological character data as defined in Table S3 used in the analyses. The data partitions are indicated toward the end of the file by ranges of numbers (“charset Subset 1 – 4” for the DNA data and “charset morph” for the morphological characters) followed by commands for the phylogenetic analysis program MrBayes that specify the model settings for each data partition. Detailed data on species included (as rows) in the dataset, including collection localities and GenBank accession numbers are provided in the Table_S1_Specimen_information.csv file. The file "TablesS2-S4.pdf" lists the primers used for polymerase chain reaction amplification, the list of morphological character definitions, and the morphological character matrix. The file “RASP_Distribution.csv” contains a list of the species included in the phylogenetic dataset (first column) and a code (second column) indicating their distributions as follows: (A) Oriental, (B) Palaearctic, (C) Australian, (D) Afrotropical, (E) Neotropical, and (F) Nearctic. More than one letter indicates that the species occurs in more than one region. The file "infile_for_BEAST.txt" is the input file in XML format used for the molecular divergence time analysis using the program BEAST (Bayesian Evolutionary Analysis by Sampling Trees) as described in the Methods section of the manuscript. This file includes comments that document the steps of the analysis.

GBS data from Phaseolus accessions, for a study led by Dr. Glen Hartman, UIUC. <br />The (zipped) fastq file can be processed with the TASSEL GBS pipeline or other pipelines for SNP calling. The related article has been submitted and the methods section describes the data processing in detail.

We measured the effects of fire or drought treatment on plant, microbial and biogeochemical responses in temperate deciduous forests invaded by the annual grass Microstegium vimineum with a history of either frequent fire or fire exclusion. Please note, on Documentation tab / Experimental or Sampling Design, “15 (XVI)” should be “16 (XVI)”.

The success of a bioenergy policy relies largely on the wide adoption of perennial energy crops at the farm scale. This study uses survey data to examine potential adoption decisions by farmers in the U.S. Midwest and the causal effects of various direct and indirect influencing factors, especially heterogeneous preferences of farmers. A Bayesian network (BN) model is developed to delineate the causal relationship between farmers adoption decisions and the influencing factors. We find a dominating role of economic factors and a non-negligible impact of non-economic factors, such as the perceived environmental benefits and the extent of familiarity with perennial energy crops. To examine the effect of heterogeneity in farmer preferences, we classify the surveyed farmers into four categories based on their attitudes toward the economic, social, and environmental dimensions of perennial energy crops. We identified statistically significant between-group differences in the responses of the four types of farmers to the various influencing factors. Our findings contribute to disentangling the complicated motivations that will influence perennial energy crop adoption decisions and provide implications for more targeted policy development that need to consider the heterogeneous drivers of farmer decisions about land use.

The following seven zip files are compressed folders containing the input datasets/trees, main output files and the scripts of the related analyses performed in this study. I. ancestral_microhabitat_reconstruction.zip: contains four files, including two input files (microhabitats.csv, timetree.tre) and a script (simmap_microhabitat.R) for ancestral states reconstruction of microhabitat by make.simmap implemented in the R package phytools v1.5, as well as the main output file (ancestral_microhabitats.csv). 1. ancestral_microhabitats.csv: reconstructed ancestral microhabitats for each node. 2. microhabitats.csv: microhabitats of the studies species. 3. simmap_microhabitat.R: the R script of make.simmap for ancestral microhabitat reconstruction 4. timetree.tre: dated tree used for ancestral state reconstruction for microhabitat and morphological characters II. ancestral_morphology_reconstruction.zip: contains six files, including an input file (morphology.csv) and a script (simmap_morphology.R) for ancestral states reconstruction of morphology by make.simmap implemented in the R package phytools v1.5, as well as four main output files(forewing_ancestral_state.csv, frontal_sutures_ancestral_state.csv, hind_wing_ancestral_state.csv, ocellus_ancestral_state.csv). 1. forewing_ancestral_state.csv: reconstructed ancestral states of the development of the forewing for each node. 2. frontal_sutures_ancestral_state.csv: reconstructed ancestral states of the development of frontal sutures for each node. 3. hind_wing_ancestral_state.csv: reconstructed ancestral states of the development of the hind wing for each node. 4. morphology.csv: the states of the development of ocellus, forewing, hing wing and frontal sutures for each studies species. 5. ocellus_ancestral_state.csv: reconstructed ancestral states of the development of the ocellus for each node. 6. simmap_morphology.R: the R script of make.simmap for ancestral state reconstruction of morphology III. biogeographic_reconstruction.zip: contains four files, including three input files (dispersal_probablity.txt, distributions.csv, timetree_noOutgroup.tre) used for a stratified biogeographic analysis by BioGeoBEARS in RASP v4.2 and the main output file (DIVELIKE_result.txt). 1. dispersal_probablity.txt: relative dispersal probabilities among biogeographical regions at different geological epochs. 2. distributions.csv: current distributions of the studied species. 3. DIVELIKE_result.txt: BioGeoBEARS result of ancestral areas based on the DIVELIKE model. 4. timetree_noOutgroup.tre: the dated tree with the outgroup lineage (Eurymelinae) excluded. IV. coalescent_analysis.zip: contains a folder and two files, including a folder (individual_gene_alignment) of input files used to construct gene trees, an input file (MLtree_BS70.tre) used for the multi-species coalescent analysis by ASTRAL v 4.10.5 and the main output file (coalescent_species_tree.tre). 1. coalescent_species_tree.tre: the species tree generated by the multi-species coalescent analysis with the quartet support, effective number of genes and the local posterior probability indicated. 2. individual_gene_alignment: a folder containing 427 FASTA files, each one represents the nucleotide alignment for a gene. Hyphens are used to represent gaps. These files were used to construct gene trees using IQ-TREE v1.6.12. 3. MLtree_BS70.tre: 165 gene trees with the average SH-aLRT and ultrafast bootstrap values of ≥ 70%. This file was used to estimate the species tree by ASTRAL v 4.10.5. V. divergence_time_estimation.zip: contains five files, including two input files (treefile_rooted_noBranchLength.tre, treefile_rooted.tre) and two control files (baseml.ctl, mcmctree.ctl) used for divergence time estimation by BASEML and MCMCTREE in PAML v4.9, as well as the main output file (timetree_with95%HPD.tre). 1. baseml.ctl: the control file used for the estimation of substitution rates by BASEML in PAML v4.9. 2. mcmctree.ctl: the control file used for the estimation of divergence times by MCMCTREE in PAML v4.9. 3. timetree_with95%HPD.tre: dated tree with the 95% highest posterior density confidence intervals indicated. 4. treefile_rooted_noBranchLength.tre: the maximum likelihood tree based on the concatenated nucleotide dataset with calibrations for the crown and internal nodes. Branch length and support values were not indicated. 5. treefile_rooted.tre: the maximum likelihood tree based on the concatenated nucleotide dataset with a secondary calibration on the root age. Branch support values were not indicated. VI. maximum_likelihood_analysis_aa.zip: contains three files, including two input files (concatenated_aa_partition.nex, concatenated_aa.phy) used for the maximum likelihood analysis by IQ-TREE v1.6.12 and the main output file (MLtree_aa.tre). 1. concatenated_aa_partition.nex: the partitioning schemes for the maximum likelihood analysis using concatenated_aa.phy. This file partitions the 52,024 amino acid positions into 427 character sets. 2. concatenated_aa.phy: a concatenated amino acid dataset with 52,024 amino acid positions. Hyphens are used to represent gaps. This dataset was used for the maximum likelihood analysis. 3. MLtree_aa.tre: the maximum likelihood tree based on the concatenated amino acid dataset, with SH-aLRT values and ultrafast bootstrap values indicated. VII. maximum_likelihood_analysis_nt.zip: contains three files, including two input files (concatenated_nt_partition.nex, concatenated_nt.phy) used for the maximum likelihood analysis by IQ-TREE v1.6.12 and the main output file (MLtree_nt.tre). 1. concatenated_nt_partition.nex: the partitioning schemes for the maximum likelihood analysis using concatenated_nt.phy. This file partitions the 156,072 nucleotide positions into 427 character sets. 2. concatenated_nt.phy: a concatenated nucleotide dataset with 156,072 nucleotide positions. Hyphens are used to represent gaps. This dataset was used for the maximum likelihood analysis as well as divergence time estimation. 3. MLtree_nt.tre: the maximum likelihood tree based on the concatenated nucleotide dataset, with SH-aLRT values and ultrafast bootstrap values indicated. VIII. Taxon_sampling.csv: contains the sample IDs (1st column) which were used in the alignments and the taxonomic information (2nd to 6th columns).

These are the results of an 8 month cohort study in two commercial dairy herds in Northwest Illinois. From each herd, 50 cows were selected at random, stratified over lactations 1 to 3. Serum from these animals was collected every two months and tested for antibodies to Bovine Leukosis Virus, Neospora caninum, and Mycobacterium avium subsp. paratuberculosis. Animals that left the herd during the study were replaced by another animal in the same herd and lactation. At the last sampling, serum neutralization assays were performed for Bovine Herpesvirus type 1 and Bovine Viral Diarrhea virus type 1 and 2. Production data before and after sampling was collected for the entire herd from PCdart.

This dataset contains all data used in the paper "Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids". The dataset includes genotypes and phenotypic data from two autotetraploid species Miscanthus sacchariflorus and Vaccinium corymbosum that was used used for genome wide association studies and genomic prediction and the scripts used in the analysis. In this V2, 2 files have the raw data are added: "Miscanthus_sacchariflorus_RADSeq.vcf" is the VCF file with the raw SNP calls of the Miscanthus sacchariflorus data used for genotype calling using the 6 genotype calling methods. "Blueberry_data_read_depths.RData" is the a RData file with the read depth data that was used for genotype calling in the Blueberry dataset.

This repository contains the data and computational analysis notebooks that were used in the following manuscript. For more information on the methods and contributing authors, please refer to the original manuscript. "Beyond A and B Compartments: how major nuclear locales define nuclear genome organization and function Omid Gholamalamdari et al. 2024"

During the transformation of wild-type (WT) Arabidopsis thaliana, a T-DNA containing OLEOSIN-GFP (OLE1-GFP) was inserted by happenstance within the GBSS1 gene, resulting in significant reduction in amylose and increase in leaf oil content in the transgenic line (OG). The synergistic effect on oil accumulation of combining gbss1 with the expression of OLE1-GFP was confirmed by transforming an independent gbss1 mutant (GABI_914G01) with OLE1-GFP. The resulting OLE1-GFP/gbss1 transgenic lines showed higher leaf oil content than the individual OLE1-GFP/WT or single gbss1 mutant lines. Further stacking of the lipogenic factors WRINKLED1, Diacylglycerol O-Acyltransferase (DGAT1), and Cys-OLEOSIN1 (an engineered sesame OLEOSIN1) in OG significantly elevated its oil content in mature leaves to 2.3% of dry weight, which is 15 times higher than that in WT Arabidopsis. Inducible expression of the same lipogenic factors was shown to be an effective strategy for triacylglycerol (TAG) accumulation without incurring growth, development, and yield penalties.

Table S1. Pollen types identified in the BCI and PNSL pollen rain data sets. Pollen types were identified to species when possible and assigned a life form based on descriptions provided in Croat, T.B. (1978). Taxa from BCI and PNSL were assigned a 1 if present in forest census data or a 0 if absent. The relative representation of each taxon has been provided for each extended record and by dry and wet season representation respectively. CA loadings are provided for axes 1 and 2 (Fig. 1).

The file “Fla.fasta”, comprising 10526 positions, is the concatenated amino acid alignments of 51 orthologues of 182 bacterial strains. It was used for the maximum likelihood and maximum parsimony analyses of Flavobacteriales. Bacterial species names and strains were used as the sequence names, host names of insect endosymbionts were shown in brackets. The file “16S.fasta” is the alignment of 233 bacterial 16S rRNA sequences. It contains 1455 positions and was used for the maximum likelihood analysis of flavobacterial insect endosymbionts. The names of endosymbiont strains were replaced by the name of their hosts. In addition to the species names, National Center for Biotechnology Information (NCBI) accession numbers were also indicated in the sequence names (e.g., sequence “Cicadellidae_Deltocephalinae_Macrostelini_Macrosteles_striifrons_AB795320” is the 16S rRNA of Macrosteles striifrons (Cicadellidae: Deltocephalinae: Macrostelini) with a NCBI accession number AB795320). The file “Sulcia_pep.fasta” is the concatenated amino acid alignments of 131 orthologues of “Candidatus Sulcia muelleri” (Sulcia). It contains 41970 positions and presents 101 Sulcia strains and 3 Blattabacterium strains. This file was used for the maximum likelihood analysis of Sulcia. The file “Sulcia_nucleotide.fasta” is the concatenated nucleotide alignment corresponding to the sequences in “Sulcia_pep.fasta” but also comprises the alignment of 16S rRNA. It has 127339 positions and was used for the maximum likelihood and maximum parsimony analyses of Sulcia. Individual gene alignments (16S rRNA and 131 orthologues of Sulcia and Blattabacterium) are deposited in the compressed file “individual_gene_alignments.zip”, which were used to construct gene trees for multispecies coalescent analysis. The names of Sulcia strains were replaced by the name of their hosts in “Sulcia_pep.fasta”, “Sulcia_nucleotide.fasta” and the files in “individual_gene_alignments.zip”. In all the alignment files, gaps are indicated by “-”.

Dataset associated with Jones et al. GCB-23-1273.R1 submission: Phenotypic signatures of urbanization? Resident, but not migratory, songbird eye size varies with urban-associated light pollution levels. Excel CSV file with all of the data used in analyses and file with descriptions of each column.

We conducted long-term capture-mark-recapture surveys on two isolated ornate box turtle (Terrapene ornata) populations in northern Illinois, USA. This dataset provides the capture history strings and additional demographic information used for estimating population vital rates with robust design capture-mark-recapture models. The vital rates were then used in a stage-based population projection matrix model for each population.

Raw arthroscopic scores, histologic scores, cytokine measurements, and performance data for the study cohort described in the accompanying publication.

This dataset includes image stacks, annotated counts, and ground-truth masks from two high-resolution sediment cores extracted from Laguna Pallcacocha, in El Cajas National Park, Ecuadorian Andes by Moy et al. (2002) and Hagemans et al. (2021). The first core (PAL 1999, from Moy et al. (2002)) extends through the Holocene (11,600 cal. yr. BP - present). There are a total of 900 annotated image stacks and masks in the PAL 1999 domain. The second core (PAL IV, from Hagemans et al. (2021)) captures the 20th century. There are 2986 annotated image stacks and masks in the PAL IV domain. Different microscopes and annotations tools were used to image and annotate each core and there are corresponding differences in naming conventions and file formats. Thus, we organized our data separately for the PAL 1999 and the PAL IV domains. The three letter codes used to label our pollen annotations are in the file: “Pollen_Identification_Codes.xlsx”. Both domain directories contain: • Image stacks organized by subdirectory • Annotations within each image stack directory, containing specimen identifications using a three letter code and coordinates defining bounding boxes or circles • Ground-truth distance-transform masks for each image stack The zip file "bestValModel_encoder.paramOnly.zip" is the trained pollen detection model produced from the images and annotations in this dataset. Please cite this dataset as: Feng, Jennifer T.; van den Berg, Thya; Donders, Timme H.; Kong, Shu; Puthanveetil Satheesan, Sandeep; Punyasena, Surangi W. (2025): Slide scans, annotated pollen counts, and trained pollen detection models for fossil pollen samples from Laguna Pallcacocha, El Cajas National Park, Ecuador . University of Illinois Urbana-Champaign. https://doi.org/10.13012/B2IDB-4207757_V1 Please also include citations of the original publications from which these data are taken: Feng, Jennifer T., Sandeep Puthanveetil Satheesan, Shu Kong, Timme H. Donders, and Surangi W. Punyasena. “Addressing the ‘Open World’: Detecting and Segmenting Pollen on Palynological Slides with Deep Learning.” bioRxiv, January 1, 2025. https://doi.org/10.1101/2025.01.05.631390. Feng, Jennifer T., Sandeep Puthanveetil Satheesan, Shu Kong, Timme H. Donders, and Surangi W. Punyasena. “Addressing the ‘Open World’: Detecting and Segmenting Pollen on Palynological Slides with Deep Learning.” Paleobiology, 2025 [in press]. Feng, J. T. (2023). Open-world deep learning applied to pollen detection (MS thesis, University of Illinois at Urbana-Champaign). https://hdl.handle.net/2142/120168

Raw data associated with PMID: 38925247

This is a research data for a manuscript - A Framework of Simulating Structural Sediment Perimeter Barriers using VFSMOD.

This dataset contain information for the paper "Changes in neuropeptide prohormone genes among Cetartio-dactyla livestock and wild species associated with evolution and domestication" Veterinary Sciences, MDPI. Protein sequences were predicted using GeneWise for 98 neuropeptide prohormone genes from publicly available genomes of 118 Cetartiodactyla species. All predictions (CetartiodactylaSequences2022.zip) were manually verified. Sequences were aligned within each prohormone using MAFFT (MDPImultalign2022.zip includes multiple sequence alignment of all species available for each prohormone). Phylogenetic gene trees were constructed using PhyML and the species tree was constructed using ASTRAL (MDPItree2022.zip). The data is released under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

Table S2. Raw pollen counts and climatic data for each seasonal sampling period. Climatic data reflects the average daily conditions observed over the duration samples were collected (˚C/day, mm/day, MJ/m2/day). Lycopodium counts and counts for each pollen taxon reflect the aggregated pollen sum from four sampling heights.