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3,339 results for “heterogeneities”
Expression Profile of CD157 Reveals Functional Heterogeneity of Capillaries in Human Dermal Skin
<p>CD157 acts as a receptor, regulating leukocyte trafficking and the binding of extracellular matrix components. However, the expression pattern and the role of CD157 in human blood (BEC) and the lymphatic endothelial cells (LEC) of human dermal microvascular cells (HDMEC), remain elusive. We demonstrated constitutive expression of CD157 on BEC and LEC, in fetal and juvenile/adult skin, in situ, as well as in isolated HDMEC. Interestingly, CD157 epitopes were mostly localized on BEC, co-expressing high levels of CD31 (CD31<sup>High</sup>), as compared to CD31<sup>Low</sup> BEC, whereas the podoplanin expression level on LEC did not affect CD157. Cultured HDMEC exhibited significantly higher numbers of CD157-positive LEC, as compared to BEC. Interestingly, separated CD157<sup>-</sup> and CD157<sup>+</sup> HDMEC demonstrated no significant differences in clonal expansion in vitro, but they showed distinct expression levels of cell adhesion molecules, before and after cytokine stimulation in vitro. In particular, we proved the enhanced and specific adherence of CD11b-expressing human blood myeloid cells to CD157<sup>+</sup> HDMEC fraction, using an in vitro immune-binding assay. Indeed, CD157 was also involved in chemotaxis and adhesion of CD11b/c monocytes/neutrophils in prevascularized dermo-epidermal skin substitutes (vascDESS) in vivo. Thus, our data attribute specific roles to endothelial CD157, in the regulation of innate immunity during inflammation.</p> <p> </p>
High heterogeneity in genomic differentiation between phenotypically divergent songbirds: A test of mitonuclear co-introgression
<p>Comparisons of genomic variation among closely related species often show more differentiation in mitochondrial DNA (mtDNA) and sex chromosomes than in autosomes, a pattern expected due to the differing effective population sizes and evolutionary dynamics of these genomic components. Yet, introgression can cause species pairs to deviate dramatically from general differentiation trends. The yellowhammer (<em>Emberiza</em> <em>citrinella</em>) and pine bunting (<em>E</em>. <em>leucocephalos</em>) are hybridizing avian sister species that differ greatly in appearance and moderately in nuclear DNA, but that show no mtDNA differentiation. This discordance is best explained by adaptive mtDNA introgression—a process that can select for co-introgression at nuclear genes with mitochondrial functions (mitonuclear genes). To better understand these discordant differentiation patterns and characterize nuclear differentiation in this system, we investigated genome-wide differentiation between allopatric yellowhammers and pine buntings and compared it to what was seen previously in mtDNA. We found significant nuclear differentiation that was highly heterogeneous across the genome, with a particularly wide differentiation peak on the sex chromosome Z. We further investigated mitonuclear gene co-introgression between yellowhammers and pine buntings and found support for this process in the direction of pine buntings into yellowhammers. Genomic signals indicative of co-introgression were common in mitonuclear genes coding for subunits of the mitoribosome and electron transport chain complexes. Such introgression of mitochondrial DNA and mitonuclear genes provides a possible explanation for the patterns of high genomic heterogeneity in genomic differentiation seen among some species groups.</p>
Individual variation in the avian gut microbiota: The influence of host state and environmental heterogeneity
<div class="abstract-group metis-abstract"> <div class="article-section__content en main"> <p>The gut microbiota have important consequences for host biological processes and there is some evidence that they also affect fitness. However, the complex, interactive nature of ecological factors that influence the gut microbiota has scarcely been investigated in natural populations. We sampled the gut microbiota of wild great tits (<em>Parus major</em>) at different life stages allowing us to evaluate how microbiota varied with respect to a diverse range of key ecological factors of two broad types: (1) host state, namely age and sex, and the life history variables, timing of breeding, fecundity and reproductive success; and (2) the environment, including habitat type, the distance of the nest to the woodland edge, and the general nest and woodland site environments. The gut microbiota varied with life history and the environment in many ways that were largely dependent on age. Nestlings were far more sensitive to environmental variation than adults, pointing to a high degree of flexibility at an important time in development. As nestlings developed their microbiota from one to two weeks of life, they retained consistent (i.e., repeatable) among-individual differences. However these apparent individual differences were driven entirely by the effect of sharing the same nest. Our findings point to important early windows during development in which the gut microbiota are most sensitive to a variety of environmental drivers at multiple scales, and suggest reproductive timing, and hence potentially parental quality or food availability, are linked with the microbiota. Identifying and explicating the various ecological sources that shape an individual's gut bacteria is of vital importance for understanding the gut microbiota's role in animal fitness.</p> </div> </div> <div class="pb-dropzone"> </div>
Inferring and comparing metabolisms across heterogeneous sets of annotated genomes using AuCoMe
<p>CONTENT OF THIS ARCHIVE</p> <p>The Zenodo archive is composed of one file and four main directories:<br> * <strong>analyses</strong> gathers three subdirectories: algae, bacteria, and fungi. It includes all files used to create the figures, supplemental figures, and results of the paper.</p> <p>* <strong>code</strong> contains all AuCoMe and PADMET codes.</p> <p> – <strong>aucome_v0.5.1</strong> this directory gathers the code of AuCoMe used to run the three datasets.</p> <p> – <strong>padmet_v5.0.1</strong> this directory contains the code of PADMET used to run AuCoMe.</p> <p>* <strong>datasets</strong> this directory gathers all datasets on which AuCoMe was run: the bacterial, fungal, and algal datasets, and the 32 synthetic datasets, which contain an <em>E. coli</em> K–12 MG1655 genome to which various degradations were applied, together with 28 other bacterial genomes. It also encompasses the version 23.5 of MetaCyc database.</p> <p>* <strong>scripts_analyses</strong> this directory contains several scripts to generate the figures, supplemental figures and a script to degrade the <em>E. coli</em> K–12 MG1655 genome.</p> <p> </p> <p> </p> <p>1/ Content of the <strong>analyses</strong> repertory<br> It is composed of three subdirectories: <strong>algae</strong>, <strong>bacteria</strong>, and <strong>fungi</strong>.</p> <p>1.1/ Content of the <strong>algae</strong> subdirectory<br> It encompasses 9 files.</p> <p>* <strong>Figure_2_algal_nb_reactions.tsv</strong> for each species of the algal dataset, this file gives the number of reactions at each AuCoMe step. It was used to create figure 2D.</p> <p>* <strong>Figure_S10_Deepec_algal.tsv</strong> for each species of the algal dataset, at each AuCoMe step (robust orthology, non-robust orthology, and annotation or orthology), several measures were computed, i.e.: the number of reactions, the number of ECs, the number of ECs validated by DeepEC, and the ratio number of ECs valided by DeepEC / number of ECs. It was used to design figure S10(b).</p> <p>* <strong>Table_S6_50_random_reactions_found.xlsx</strong> contains manual validation of 50 randomly chosen reactions found in any of the species (is the Supplemental Table S6).</p> <p>* <strong>Table_S7_50_random reactions absent.xlsx</strong> includes manual validation of 50 reactions absent from a species and randomly chosen (is the Supplemental Table S7).</p> <p>* <strong>Table_S8_reactions_common_only_Cokamuranus_Sjaponica.xlsx</strong> encompasses reactions common to <em>Saccharina japonica</em> and <em>Cladosiphon okamuranus</em> but not found in other brown algae (is the Supplemental Table S8).</p> <p>* <strong>Table_S9_homologues_Esiliculosus_Sjaponica.xlsx</strong> contains additional homologs in <em>E. siliculosus</em> found by BLASTP searches for sequences inferred to be present only in <em>C. okamuranus</em> and <em>S. japonica</em> (is the Supplemental Table S9).</p> <p>* <strong>Table_S10_o-aminophenol_Esiliculosus_holomogues.xlsx</strong> includes additional o-aminophenol oxidases from <em>E. siliculosus</em> and their homologs in other stramenopiles. It is the Supplemental Table S10 with more detail (like the amino acid sequences).</p> <p>* <strong>Table_S11_reactions_cryptophytes_haptophytes_stramenopiles_archeplastida.xlsx</strong> encompasses reactions distinguishing the cryptophyte, haptophyte, stramenopile, and archeplastida groups (is the Supplemental Table S11).</p> <p>* <strong>Table_S12_pathways_cryptophytes_haptophytes_stramenopiles_archeplastida.xlsx</strong> contains shared metabolic pathways as well as the absence of pathways between chryptophytes, haptophytes, stramenopiles, and archaeplastida (is the Supplemental Table S12).</p> <p> </p> <p>1.2/ Content of the <strong>bacteria</strong> subdirectory<br> It gathers 12 files and 9 repertories.</p> <p>* <strong>aucome_final.tsv</strong> output file of the figure S4 comparison bacteria.py script, for each of the 29 bacterial metabolic networks produced with AuCoMe, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>carveme_stat.tsv</strong> output file of the figure S4 comparison bacteria.py script, for each of the 29 bacterial metabolic networks produced with CarveMe, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>ecocyc.padmet</strong> contains the EcoCyc database version 23.5 at the PADMet, is used to generate the Supplemental Fig. S5.</p> <p>* <strong>Figure_2_bacterial_nb_reactions.tsv</strong> for each species of the bacterial dataset, this file gives the number of reactions at each AuCoMe step. It was used to create figure 2B.</p> <p>* <strong>Figure_3_nb_reactions_step.tsv</strong> for each dataset of the 32 synthetic bacterial datasets, this file enumerates the number of reactions at each AuCoMe step. It was used to create figure 3A.</p> <p>* <strong>Figure_3_fmeasure_steps.tsv</strong> for each dataset of the 32 synthetic bacterial datasets, this file indicates the values of the F-measures resulting of the comparison of the GSMNs recovered for each <em>E. coli</em> K–12 MG1655 genome replicate with the gold-standard network EcoCyc. It was used to create figure 3B.</p> <p>* <strong>Figure_S4_output</strong> contains 3 output files of the figure_S4_comparison_bacteria.py script:</p> <p> – <strong>Figure_S4_boxplot_networks.svg</strong> is the Supplemental Figure S4 in high resolution.</p> <p> – <strong>Figure_S4_boxplot_networks.tsv</strong> contains the number of reactions, the type of reactions (All, Reactions with genes, ...), and the used software. Thes data were produced and used in the figure_S4_comparison_bacteria.py script.</p> <p> – <strong>Figure_S4_barplot_time_networks.svg</strong> for each software, shows the required time in seconds used to reconstruct these bacterial metabolic networks.</p> <p>* <strong>Figure_S5_output</strong> encompasses 3 output files of figure S5 reference catalog.py script:</p> <p> – <strong>Figure_S5_ec_union.svg</strong> is the Supplemental Figure S5 in high resolution.</p> <p> – <strong>Figure_S5_ec_union_venn.svg</strong> another visualisation of presenting the results of the Supplemental Fig. S5.</p> <p> – <strong>Figure_S5_refence_ec_catalog_K12MG1655.tsv</strong> contains an EC catalog to <em>E. coli</em> K-12 MG1655 from the BIGG, EcoCyc, KEGG, and ModelSEED databases. This file is used to produce the Supplemental Figure S5.</p> <p>* <strong>Figure_S6_output</strong> includes 2 output files of the figure_S6.py script:</p> <p> – <strong>Figure_S6_comparison_all.svg</strong> is the Supplemental Figure S6 in high resolution.</p> <p> – <strong>Figure_S6_comparison_all.tsv</strong> contains data used to produce the Supplemental Figure S6.</p> <p>* <strong>gapseq_stat.tsv</strong> output file of the figure_S4_comparison_bacteria.py script, for each of the 29 bacterial metabolic networks produced with gapseq, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>jsons_bigg</strong> todate contains the five metabolic networks of <em>E. coli</em> K–12 MG1655 that can find in BIGG at JSON format. These files correspond to the BIGG reference metabolic network on the Supplemental Figure S5.</p> <p>* <strong>jsons_modelseed</strong> todate includes the metabolic network of <em>E. coli</em> K–12 MG1655 that can find in ModelSEED at JSON format. It is the ModelSEED reference metabolic network on the Supplemental Figure S5.</p> <p>* <strong>kegg_ecs.txt</strong> input file of the figure_S5_reference_catalog.py script, it contains matches between EC numbers and all the entries of <em>E. coli</em> K–12 MG1655 in the KEGG database.</p> <p>* <strong>mapping_modelseed_ec.tsv</strong> input file of the figure_S4_comparison_bacteria.py script, it encompasses matches between ModelSEED reactions and EC numbers.</p> <p>* <strong>modelseed_stat.tsv</strong> output file of the figure_S4_comparison_bacteria.py script, for each of the 29 bacterial metabolic networks produced with ModelSEED, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>networks_aucome</strong> for each of the 29 bacteria, contains a metabolic networks at the PADMet format obtained with AuCoMe.</p> <p>* <strong>networks_carveme</strong> for each of the 29 bacteria, contains a metabolic networks at the SBML format got to CarveMe.</p> <p>* <strong>networks_gapseq</strong> composes of 29 subdirectories (one for each bacterium). All these subdirectories contain 10 files about the metabolic networks a obtained with gapseq:</p> <p> – <strong>species-all-Pathways.tbl</strong> encompasses data on pathways at TBL format.</p> <p> – <strong>species-all-Reactions.tbl</strong> includes data on reactions at TBL format.</p> <p> – <strong>species-draft.RDS</strong> is a draft metabolic network at RDS (R Data Format).</p> <p> – <strong>species-draft.xml</strong> is a draft metabolic network at SBML format.</p> <p> – <strong>species-medium.csv</strong> encompasses all the metabolites allow the default medium.</p> <p> – <strong>species.RDS</strong> is the final metabolic network at RDS (R Data Format).</p> <p> – <strong>species-rxnWeights.RDS</strong> is a temporary file nedeed to gapseq fill at RDS (R Data Format).</p> <p> – <strong>species-rxnXgenes.RDS</strong> is a temporary file nedeed to gapseq fill at RDS (R Data Format).</p> <p> – <strong>species-Transporter.tbl</strong> includes data on transporters at TBL format.</p> <p> – <strong>species.xml</strong> is the final metabolic network at SBML format.</p> <p>* <strong>networks_modelseed</strong> includes two subdirectories:</p> <p> – <strong>sbml</strong> for each of the 29 bacteria, encompasses a metabolic networks at the SBML format got to ModelSEED.</p> <p> – <strong>tsv</strong> for each of the 29 bacteria, contains two TSV files:</p> <p> – <strong>genomeset__species.gbk_genome.fbamodel-compounds.tsv</strong> includes data on compounds at TSV format.</p> <p> – <strong>genomeset__species.gbk_genome.fbamodel-reactions.tsv</strong> encompasses data on reactions at TSV format.</p> <p>* <strong>time_carveme.txt</strong> input file of the figure S4 comparison bacteria.py script, for each of the 29 bacteria it stores the running time of CarveMe (in seconds) to reconstruct a metabolic network.</p> <p>* <strong>time_gapseq.txt</strong> input file of the figure S4 comparison bacteria.py script, for each of the 29 bacteria it stores the running time of gapseq (in seconds) to reconstruct a metabolic network.</p> <p> </p> <p>1.3/ Content of the <strong>fungi</strong> repertory<br> It contains three files and five directories.</p> <p>* <strong>All-pathways-of-S.-cerevisiae-S288c.txt</strong> encompasses all the YeastCyc pathways.</p> <p>* <strong>Figure_2_fungal_nb_reactions.tsv</strong> for each species of the fungal dataset, this file gives the number of reactions at each AuCoMe step. It was used to create figure 2C.</p> <p>* <strong>Figure_S7_output</strong> contains 11 output files of the figure S7 comparison pathway fungi.py script:</p> <p> – <strong>completion_pathway_species.svg</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), contains a subfigure of the Supplemental Fig. S7.</p> <p> – <strong>fungi_stats.tsv</strong> is the Supplemental Table S5.</p> <p> – <strong>pathway_venn_species.png</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), includes a Venn diagram about all the pathways found with the 3 software (AuCoMe, gapseq, and ModelSEED).</p> <p>* <strong>Figures_S8_S9_output</strong> contains 11 files, in all these files, a comparison of all pathways of metabolic networks of <em>S. cerevisiae</em> S288C obtained with AuCoMe and gapseq to those of YeastCyc was released.</p> <p> – <strong>comparison_yeastcyc.png</strong> is a picture about number of pathways true positive, false positive, and false negative are found, according the used method (AuCoMe and gapseq).</p> <p> – <strong>completion_pathway_gapseq.svg</strong> includes the number of pathways common or specific to YeastCyc and gapseq with their completeness ratio predicted by gapseq.</p> <p> – <strong>Figure_S8_completion_pathway_aucome.svg</strong> contanis the number of pathways common or specific to YeastCyc and AuCoMe with their completeness ratio predicted by AuCoMe, is the Supplemental Figure S8.</p> <p> – <strong>Figure_S9_venn_diagram_70_100.svg</strong> is the Supplemental Figure S9. All pathways of AuCoMe, gapseq and YeastCyc with a completion rate between 50% and 70% are compared.</p> <p> – <strong>venn_diagram.svg</strong> in this picture, all pathways are compared.</p> <p> – <strong>venn_diagram 50.svg</strong> all pathways of AuCoMe, gapseq and YeastCyc with a completion rate less than 50% are compared.</p> <p> – <strong>venn_diagram_50_gapseq.svg</strong> all pathways of gapseq whatever their completion rate are compared to the AuCoMe and YeastCyc pathways with a completion rate less than 50%.</p> <p> – <strong>venn_diagram_50_70.svg</strong> all pathways of AuCoMe, gapseq, and YeastCyc with a completion rate between 50% and 70% are compared.</p> <p> – <strong>venn_diagram_50_70_gapseq.svg</strong> all pathways of gapseq whatever their completion rate are compared to the AuCoMe and YeastCyc pathways with a completion rate between 50% and 70%.</p> <p> – <strong>venn diagram_70_100_gapseq.svg</strong> all pathways of gapseq whatever their completion rate are compared to the AuCoMe and YeastCyc pathways with a completion rate between 70% and 100%.</p> <p> – <strong>yeast_cyc_comparison.tsv</strong> contains the number of pathways true positive, false positive, and false negative are found, according the used method (AuCoMe and gapseq).</p> <p>* <strong>Figure_S10_Deepec_fungal.tsv</strong> for each species of the fungal dataset, at each AuCoMe step (robust orthology, non-robust orthology, and annotation or orthology), several measures were computed, i.e.: the number of reactions, the number of ECs, the number of ECs valided by DeepEC, and ratio number of ECs validated by DeepEC / number of ECs. It was used to design figure S10(a).</p> <p>* <strong>networks_aucome</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), contains a metabolic networks at the PADMet format obtained with AuCoMe.</p> <p>* <strong>networks_gapseq</strong> is composed of 5 subdirectories (one for each fungus). All these subdirectories contain two files about the metabolic networks a obtained with gapseq:</p> <p> – <strong>species-all-Pathways.tbl</strong> encompasses data on pathways at TBL format.</p> <p> – <strong>species-all-Reactions.tbl</strong> includes data on reactions at TBL format.</p> <p>* <strong>networks_modelseed</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), contains two TSV files:</p> <p> – <strong>species.gbk_genome.draftModel-compounds.tsv</strong> includes data on compounds at TSV format.</p> <p> – <strong>species.gbk_genome.draftModel-reactions.tsv</strong> encompasses data on reactions at TSV format.</p> <p> </p> <p> </p> <p>2/ Content of the <strong>code</strong> repertory<br> It gathers two directories <strong>aucome v0.5.1</strong> and <strong>padmet_v5.0.1</strong>.</p> <p>2.1/ Content of the <strong>aucome v0.5.1</strong> subdirectory<br> This directory contains a copy of the AuCoMe project on the GitHub site: <a href="https://github.com/AuReMe/aucome">https://github.com/AuReMe/aucome</a> (downloaded the 15/11/2022). It is composed of two subdirectories and five files:<br> * <strong>LICENCE</strong> licence of the AuCoMe software.</p> <p>* <strong>README.rst</strong> README of the AuCoMe software.</p> <p>* <strong>requirements.txt</strong> contains the list of requires Python packages.</p> <p>* <strong>setup.cfg</strong> contains metadata about AuCoMe package and is used with setup.py to distribute AuCoMe.</p> <p>* <strong>setup.py</strong> contains various information relevant to the AuCoMe package including options and metadata. Then, it is used to distribute AuCoMe with PyPI. It is also used to create an entrypoint when installing it with pip.</p> <p>* <strong>recipes</strong> this subdirectory contains two files:<br> – <strong>Dockerfile</strong> contains instructions to run AuCoMe in a Docker environment.<br> <br> – <strong>Singularity</strong> contains instructions to run AuCoMe in a Singularity container.</p> <p>* <strong>aucome</strong> this directory contains 11 Python files:<br> – <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>__main__.py</strong> contains the functions implementing the command-line interface of AuCoMe.</p> <p> – <strong>analysis.py</strong> contains the functions to analyse the AuCoMe results.</p> <p> – <strong>check.py</strong> contains the functions to check the input files.</p> <p> – <strong>compare.py</strong> contains the functions to compare the AuCoMe results between two distinct subgroups.</p> <p> – <strong>orthology.py</strong> contains the functions to propagate reaction through orthology.</p> <p> – <strong>reconstruction.py</strong> contains the functions to perform the reconstruction of draft GSMNs by using Pathway Tools in a parallel implementation.<br> <br> – <strong>spontaneous.py</strong> contains the functions to add spontaneous reactions to some GSMNs if it completes MetaCyc metabolic pathway.</p> <p> – <strong>structural.py</strong> contains the functions to check that no reactions are missing due to missing gene structures. A genomic search is performed for all reactions present in one organism but not in another.<br> <br> – <strong>utils.py</strong> contains a function to analyse the configuration file.</p> <p> – <strong>workflow.py</strong> contains functions to run all the steps of AuCoMe.</p> <p> </p> <p>2.2/ Content of the <strong>padmet_v5.0.1</strong> subdirectory<br> This directory contains a copy of the PADMET project on the GitHub site: <a href="https://github.com/AuReMe/padmet/">https://github.com/AuReMe/padmet/</a> (downloaded the 15/11/2022). It is composed of two subdirectories and six files:<br> * <strong>CHANGELOG.md </strong>records of all notable changes made in the PADMET project.</p> <p>* <strong>docs</strong> this repertory contains all the documentation files of PADMET package in the RST format.</p> <p>* <strong>LICENCE</strong> licence of the PADMET package.</p> <p>* <strong>README.md</strong> manual of the PADMET package.</p> <p>* <strong>requirements.txt</strong> contains the list of requires Python packages.</p> <p>* <strong>setup.cfg</strong> contains metadata about PADMET package and is used with setup.py to distribute PADMET.</p> <p>* <strong>setup.py</strong> contains various information relevant to the PADMET package including options and metadata. Then, it is used to distribute PADMET with PyPI. It is also used to create an entrypoint when installing it with pip.<br> <br> * <strong>padmet</strong> this repertory grathers two files and two subdirectories:<br> – <strong>__init__.py</strong> indicates the version of PADMET.</p> <p> – <strong>__main__.py</strong> contains the functions implementing the command-line interface of PADMET.</p> <p> – <strong>classes</strong> contains 7 files.</p> <p> – <strong>utils</strong> contains 4 files and 3 subdirectories.</p> <p><br> 2.2.1/ Content of the <strong>class</strong> subdirectory<br> The class repertory contains 7 files.<br> * <strong>__init__.py </strong>indicates the directory as a python module.</p> <p>* <strong>instantiation.py</strong> contains a function to instantiate padmet object.</p> <p>* <strong>node.py</strong> contains a class defining a Node object which is representing an element in a metabolic network (e.g: compound, reaction).</p> <p>* <strong>padmetRef.py</strong> contains a class defining a PadmetRef object which is representing a database of metabolic network.</p> <p>* <strong>padmetSpec.py</strong> creates a PadmetSpec object which is representing the metabolic network of a species/organism based on a reference database PadmetRef.</p> <p>* <strong>policy.py</strong> contains a class defining a Policy object that is defining the types of Relations and Nodes of a network.</p> <p>* <strong>relation.py</strong> contains a class defining a Relation object which is representing a link between two elements (Node) in a metabolic network.</p> <p><br> 2.2.2/ Content of the <strong>utils</strong> subdirectory<br> The utils directory contains 4 files and 3 subdirectories.<br> * <strong>__init__.py</strong> indicates the directory as a python module.</p> <p>* <strong>gbr.py</strong> implements a lexical analysis to handle genes relationship associated with a reaction, either a complex (with and relation between genes) or isozyme (with or relation between genes).</p> <p>* <strong>sbmlPlugin.py</strong> contains functions to handle SBML element (ex: species or reaction), then it returns all the sections named notes in a dictionary.<br> <br> * <strong>utils.py</strong> contains a function that checks paths of file.</p> <p>* <strong>connection</strong> this subdirectory contains 22 files:<br> - <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>biggAPI_to_padmet.py</strong> allows to extract the BIGG database from the API to create a padmet. An Internet access is required.</p> <p> – <strong>check_orthology_input.py</strong> is written to check if the metabolic network and the proteome of the model organism use the same identifiers for genes (or at least more than a given cutoff), before running orthology based reconstruction.</p> <p> – <strong>enhanced_meneco_output.py</strong> extracts the results from Meneco gap-filling to add more information to the gap-filled reactions. Then it returns a PADMET file with more information for each reaction.</p> <p> – <strong>extract_orthofinder.py</strong> after running Orthofinder on n FASTA files, it reads the output file ’Orthogroups.tsv’ to identify the orthologous genes. It is used by AuCoMe to extract the orthologous genes.<br> <br> – <strong>extract_rxn_with_gene_assoc.py</strong> from a given SBML file, it creates a SBML with only the reactions associated to a gene.<br> <br> – <strong>gbk_to_faa.py</strong> extracts protein sequence from a GenBank into a FASTA file with Biopython package.</p> <p> – <strong>gene_to_targets.py</strong> from a list of genes, it gets the products associated with the reactions linked to the genes. For example: R1 is linked to G1, R1 produces M1 and M2, this script outputs: M1, M2.</p> <p> – <strong>get_metacyc_ontology.py</strong> from the PadmetRef of MetaCyc, it creates the MetaCyc ontology.</p> <p> – <strong>metexploreviz_export.py</strong> converts a PADMET object representing a metabolic network into a JSON compatible with MetExplore.<br> <br> – <strong>modelSeed_to_padmet.py</strong> from ModelSEED reactions and pathways files, it creates a PADMET.<br> <br> – <strong>network_to_gnn.py</strong> creates input for GNN (Graph Neural Networks) from PADMET or SBML.</p> <p> – <strong>padmet_to_asp.py</strong> converts PADMET to Answer Set Programming.</p> <p> – <strong>padmet_to_matrix.py</strong> creates a stoichiometry matrix from a PADMET file, in which the columns represent the reactions and rows represent metabolites.</p> <p> – <strong>padmet_to_padmet.py</strong> allows to merge 1-n PADMET.<br> <br> – <strong>padmet_to_tsv.py</strong> converts a PADMET representing a database (PadmetRef) and/or a PADMET representing a model (PadmetSpec) to TSV files.</p> <p> – <strong>pgdb_to_padmet.py</strong> reads a PGDB folder (from BIOCYC/Pathway Tools) and creates a PADMET. It is used by AuCoMe to create PADMET files from PGDB in the annotation-based step.</p> <p> – <strong>sbmlGenerator.py</strong> contains functions to generate SBML files from PADMET and TXT files usign the libsbml package. It is used by AuCoMe to create SBML files at the annotation-based, orthology and final steps.</p> <p> – <strong>sbml_to_curation_form.py</strong> extracts one or several reactions from a SBML file to the form used in AuReMe for curation.</p> <p> – <strong>sbml_to_padmet.py</strong> converts a SBML file into a PADMET file (with or without a reference database).</p> <p> – <strong>sbml_to_sbml.py</strong> creates a SBML file from another one. Use it to change the SBML level.</p> <p> – <strong>wikiGenerator.py</strong> contains all necessary functions to generate wiki pages from a PADMET file and update a wiki online. It requires WikiManager module (with wikiMate, Vendor).</p> <p>* <strong>exploration</strong> this subdirectory contains 15 files:<br> - <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>compare_padmet.py</strong> compares 1-n PADMET files, and creates a folder with 4 output files (compounds.tsv, genes.tsv, pathways.tsv and reactions.tsv). It is used by AuCoMe to create these files to analyse the metabolic networks.</p> <p> – <strong>compare_sbml.py</strong> compares 2 or 1-n SBML, then it creates two output files reactions.tsv and metabolites.tsv with the reactions/metabolites in each SBML files.</p> <p> – <strong>compare_sbml_padmet.py</strong> compares reaction identifiers in SBML versus PADMET, then returns the number of reactions in both, and reaction identifiers not in SBML or not in PADMET.</p> <p> – <strong>convert_sbml_db.py</strong> uses the MetaNetX database to check or convert a SBML. Flat files from MetaNetx are required to run this script. They can be found in the AuReMe workflow or from the MetaNetx website.</p> <p> – <strong>dendrogram_reactions_distance.py</strong> uses the reactions.tsv file from compare_padmet.py to create a dendrogram using the R package pvclust. It has been used in the article to create the metabolic dendrogram.</p> <p> – <strong>flux_analysis.py</strong> runs the flux balance analyse with cobra package on an already defined reaction. It needs to set in the SBML the value ’objective_coefficient’ to 1.</p> <p> – <strong>get_pwy_from_rxn.py</strong> from a file containing a list of reaction, it returns the pathways where these reactions are involved.</p> <p> – <strong>padmet_stats.py</strong> creates a PADMET stats file (named padlet_stats.tsv) containing the number of pathways, reactions, genes and compounds inside the one or several PADMET files.</p> <p> – <strong>pathway_production.py</strong> compares 1-n PADMET objects to show the pathway input/output for them.</p> <p> – <strong>prot2genome.py</strong> contains function to search a genome using protein sequences and Gene-Protein-Reaction associations. It is used in the structural search step of AuCoMe.</p> <p> – <strong>report_network.py</strong> creates reports of a PADMET file, and it writes three TSV files (all metabolites.tsv, all_pathways.tsv, and all_reactions.tsv).</p> <p> – <strong>visu_network.py</strong> allows to visualize a metabolic network on a compounds perspectives.</p> <p> – <strong>visu_path.py</strong> allows to visualize a pathway in PADMET network.<br> <br> – <strong>visu_similarity_gsmn.py</strong> visualize similarity between metabolic networks using MDS.</p> <p>* <strong>management</strong> this subdirectory contains 5 files:<br> – <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>manual_curation.py</strong> updates a PadmetSpec object by filling specific forms. It either creates new reaction(s) to PADMET file, or it adds/removes reaction(s) from a PadmetRef.</p> <p> – <strong>padmet_compart.py</strong> for a given PADMET file, it checks and updates compartment.</p> <p> – <strong>padmet_medium.py</strong> for a given set of compounds representing the growth medium (or seeds), it creates two reactions in order to maintain consistency of the network for flux analysis.</p> <p> – <strong>relation_curation.py</strong> for a given PADMET file, it adds or removes relations between nodes.</p> <p> </p> <p> </p> <p>3/ Content of the <strong>datasets</strong> subdirectory<br> It contains a files and four repertories.</p> <p>* <strong>metacyc 23.5.padmet</strong> the version 23.5 of the <a href="https://metacyc.org/">MetaCyc</a> database in the PADMET format. It was used by AuCoMe to reconstruct all the metabolic networks. Hence metacyc 23.5.padmet is required to reproduce the article results.</p> <p>3.1/ Content of the <strong>algal</strong>, <strong>bacterial</strong>, and <strong>fungal</strong> directories<br> These three directories are composed of 8 subdirectories and a Supplemental Table (respectively Table S1, Table S2 and Table S3 in bacterial, fungal and algal directories):<br> * <strong>FASTA</strong> contains the proteome of each species as a FASTA file.</p> <p>* <strong>cleaned_GBKs</strong> for each species, it contains the annotated genome, with the protein sequences in a GenBank format file.</p> <p>* <strong>dictionaries</strong> for some species, genes needed to be renamed for compatibility reasons. This folder contains CSV files with the mapping between the old names of genes and the new ones.</p> <p>* <strong>annotated_DATs</strong> contains a subdirectory per species with all the output files from Pathway Tools v23.5, without any post-treatment, in the DAT format.</p> <p>* <strong>annotated_PADMETs</strong> for each species, it contains a metabolic network of the draft reconstruction step of AuCoMe, in the PADMET format.<br> <br> * <strong>final_PADMETs</strong> for each species, it contains a metabolic network generated by the AuCoMe workflow, at the PADMET format.</p> <p>* <strong>final_SBMLs</strong> for each species, it contains a metabolic network generated by the AuCoMe workflow, in the SBML format.</p> <p>* <strong>panmetabolism</strong> is composed of 7 files describing the final metabolic networks:<br> – <strong>genes.tsv</strong> contains, for each organism, the list of genes and the associated reactions.</p> <p> – <strong>metabolites.tsv</strong> contains the list of metabolites present in the panmetabolism. Then, for each metabolite and for each organism, it lists the reactions that produced this compound and the reactions that consumed it.<br> <br> – <strong>pathways.tsv</strong> contains the list of pathways present in the panmetabolism. For each pathway and for each organism, it indicates the number of reactions present in this pathway, and the names of these reactions.<br> <br> – <strong>reactions.tsv</strong> contains the list of reactions present in the panmetabolism. Then for each reaction, it indicates whether or not it belongs to an organism. If a reaction is found in a species, the genes associated with the reaction are also listed.</p> <p> – <strong>pvclust_reaction_dendrogram.png</strong> based on the presence/absence matrix of reactions in different species of the dataset, it computes the Jaccard distances between these species, and it applies a hierarchical clustering on these data with a complete linkage to create a dendrogram. The R package pvclust is used to create the dendrogram, with bootstrap resampling. For each node, a p-value indicates how strong the cluster is supported by data. This dendrogram is provided as a PNG picture.</p> <p><br> 3.2/ Content of the <strong>synthetic_bacterial</strong> repertory<br> The synthetic_bacterial repertory contains the Supplemental Table S4 and 32 subdirectories named Run_00, Run_01, . . . , etc, Run 31. Each subdirectory is composed of 9 files:<br> * <strong>K_12_MG1655.gbk</strong> the annotated genome of <em>E. coli K–12 MG1655</em> to which degradation of the functional and/or structural annotations was applied.</p> <p>* <strong>annotated_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the draft reconstruction step of AuCoMe in the SBML format.</p> <p>* <strong>annotated_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the draft reconstruction step of AuCoMe in the PADMET format.</p> <p>* <strong>orthology_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the orthology propagation step of AuCoMe in the SBML format.</p> <p>* <strong>orthology_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the orthology propagation step of AuCoMe in the PADMET format.<br> <br> * <strong>structural_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the structural verification step of AuCoMe in the SBML format.<br> <br> * <strong>structural_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the structural verification step of AuCoMe in the PADMET format.</p> <p>* <strong>final_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the AuCoMe workflow in the SBML format.</p> <p>* <strong>final_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the AuCoMe worflow in the PADMET format.</p> <p> </p> <p> </p> <p>4/ Content of the <strong>scripts_analyses</strong> subdirectory<br> The scripts repertory contains 12 files:</p> <p>* <strong>bacteria_random_degradation.py</strong> was used to degrade the <em>E. coli</em> K–12 MG1655 genome. The procedure for the genome degradation is described in the algorithm 1.</p> <p>* <strong>figure_2_algal_dataset.py</strong> for each species of the algal dataset, and at each AuCoMe step. This script allows to generate the figure 2D.</p> <p>* <strong>figure_2_bacterial_dataset.py</strong> for each species of the bacterial dataset, and at each AuCoMe step. This script allows to generate the figure 2B.</p> <p>* <strong>figure_2_fungal_dataset.py</strong> for each species of the fungal dataset, and at each AuCoMe step. This script allows to generate the figure 2C.</p> <p>* <strong>figure_3_degradation.py</strong> allows to generate the figure 3B from the figure_3_fmeasure_steps.tsv file (described above).</p> <p>* <strong>figure_5_mds.py</strong> allows to generate the figure 5A from two reactions.tsv files of the algal dataset (annotation-based and final).</p> <p>* <strong>figure_S4_comparison_bacteria.py</strong> computes statistics on all the 29 bacterial metabolic networks reconstructed with AuCoMe, CarveMe, gapseq and ModelSEED, it uses the mapping_modelseed_ec.tsv, soft_stat.tsv files and bacteria/networks soft directories, then it creates the Supplemental Fig. S4 and files inside the analyses/bacteria/Figure_S4_output repertory.</p> <p>* <strong>figure_S5_reference_catalog.py</strong> reads the ecocyc.padmet, kegg ecs.txt files, and the jsons bigg/, jsons modelseed/ directories, then it creates the Figure_S5_refence_ec_catalog_K12MG1655.tsv and the Supplemental Fig. S5.</p> <p>* <strong>figure_S6.py</strong> reads the Figure_S5_refence_ec_catalog_K12MG1655.tsv file and those inside the analyses/bacteria/networks_soft directories about <em>E. coli</em> K–12 MG1655, it generates the Supplemental Fig. S6.</p> <p>* <strong>figure_S7_comparison_pathway_fungi.py</strong> reads metacyc_23.5.padmet file and those inside the analyses/fungi/networks_soft directories, then it create five completion pathway species.svg pictures which composed the Supplemental Fig. S7.</p> <p>* <strong>figures_S8_S9.py</strong> reads the metacyc_23.5.padmet, and metabolic networks of <em>S. cerevisiae</em> S288 reconstructed with AuCoMe, gapseq, and YeastCyc. For all pathways of both AuCoMe and gapseq networks, it also computes their completion rates. Then it compares the results obtained with AuCoMe and gapseq on <em>S. cerevisiae</em> S288C to YeastCyc according to the completion rates of their pathways. It generates Supplemental Fig. S8 and S9.</p> <p>* <strong>figure_S12_supervenn.py</strong> allows to generate the figure S12, it reads the reactions.tsv file of the algal dataset at the final AuCoMe step, and another tabular file that contains abbreviated names of species.</p>
NTDs2RDF: A heterogeneous and integrated knowledge graph for the exploration of neglected tropical diseases
<p>Neglected tropical diseases (NTDs) are a heterogeneous group of 20 bacterial, viral, parasitic, and fungal conditions that generally occur in developing tropical countries in the Americas, Africa, and Asia. NTDs mainly affect poor populations that do not have access to safe water, sanitation, and high-quality healthcare. Because of the severe effects of NTDs (i.e., they can cause long-lasting disabilities), they reinforce the cycle of poverty in vulnerable communities.</p> <p>The <strong>NTDs2RDF</strong> project aimed to create a knowledge graph (KG) of genes, proteins, metabolic pathways, gene ontologies, single nucleotide variants, drugs, and other relevant data for three NTDs (Chagas disease, leishmaniasis, and African trypanosomiasis), integrating all the information in a single data structure that can be explored through a query interface implemented with a Streamlit web application. This software provides a user-friendly platform to extract information from the KG using SPARQL queries.</p> <p>The project represents an initial step towards the creation of a heterogeneous database for different NTDs with several potential applications in advancing the understanding of NTDs biology and providing insights that cannot be obtained through alternative resources.</p>
Cascadia Subduction Zone Fault Heterogeneities from Newly Detected Small Earthquakes - Datasets
<p>Datasets associated with manuscript "Cascadia Subduction Zone Fault Heterogeneities from Newly Detected Small Earthquakes" by Morton et al. (2023), submitted to the <em>Journal of Geophysical Research: Solid Earth</em>. Three datasets are present in this upload:</p> <p><strong>ds01.xlsx</strong>: Catalog of 5,282 detected earthquakes along the Cascadia subduction margin, ordered by time. Events were located using Hypoinverse (Klein, 2002). Columns of the catalog are: Cascadia Initiative deployment year, Origin Time String (YYYYMMDDhhmmss), Origin Time (Year, Month, Day, Hour, Minute, Second), Latitude, Longitude, Event Depth (km), Duration Magnitude (Md), Number of P and S arrival picks with weights > 0.1, Maximum Azimuthal Gap (deg.), Distance to the Nearest Station (km), Travel Time Residual RMS (s), Horizontal Location Error (ERH; km), Vertical Location Error (ERZ; km), Focal Mechanism if applicable (Strike, Dip, Rake; deg.), Plate Designation (Slab, Interface, or Upper Plate), and Previous Existence in Regional Catalogs. Duration magnitudes that could not be constrained are listed as -9. Detected earthquakes that had previously been reported in other catalogs are listed as "Catalog" or "Stone" form the regional or Stone et al. (2018) catalogs, respectively; Those used as template events are marked as "T" or "ST", for those from regional catalogs or the Stone et al. (2018) catalog, respectively, in the last column.</p> <p><strong>ds02.xlsx</strong>: Table of earthquakes chosen as template events for subspace scanning from regional (NEIC, ANF, PNSN, CNDC) and Stone et al. (2018) catalogs. Columns of the template event table are: Catalog Source (T for regional, ST for Stone et al. 2018), Cascadia Initiative (CI) Deployment Year, Template Cluster ID, Date, Time (UTC), Catalog Location (Latitude, Longitude, Depth), Catalog Magnitude, and Whether the Template Event was Detected. Some of the templates were detected but were not included in the final catalog because the travel time residual RMS was greater than 1s and are noted in the table as "poorly located".</p> <p><strong>ds03.xlsx</strong>: Table of seismic stations used in subspace detection scanning, identified by the CI deployment year, Template Cluster ID, Station SEED, and Network Codes. Stations are listed with the corresponding high-pass (HP) or band-pass (BP) filter applied before scanning to maximize the signal-to-noise ratio.</p> <p>References</p> <p>Klein, F. W. (2002). <em>User’s Guide to HYPOINVERSE-2000, a Fortran Program to Solve for Earthquake Locations and Magnitudes</em> (Open File Report 02-171). U.S Geological Survey. https://doi.org/10.3133/ofr02171</p> <p>Stone, I., Vidale, J. E., Han, S., & Roland, E. (2018). Catalog of off-shore seismicity in Cascadia: Insights into the regional distribution of microseismicity and its relation to subduction processes. <em>Journal of Geophysical Research: Solid Earth, 123</em>, 1–12. https://doi.org/10.1002/2017JB014966</p>
Data for Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties
<p>Data for the manuscript "Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties". The data set contains all the generated mechanisms, DFT data used for the construction of the RMG library, a backup of the RMG-database, all results and scripts for the evaluation of the results. </p>
Data from: Evaluating the importance of individual heterogeneity in reproduction to Weddell seal population dynamics using integral projection models
<ol> <li>Identifying and accounting for unobserved individual heterogeneity in vital rates in demographic models is important for estimating population-level vital rates and identifying diverse life-history strategies, but much less is known about how this individual heterogeneity influences population dynamics.</li> <li>We aimed to understand how the distribution of individual heterogeneity in reproductive and survival rates influenced population dynamics using vital rates from a Weddell seal population by altering the distribution of individual heterogeneity in reproduction, which also altered the distribution of individual survival rates through the incorporation of our estimate of the correlation between the two rates and assessing resulting changes in population growth.</li> <li>We constructed an integral projection model (IPM) structured by age and reproductive state using estimates of vital rates for a long-lived mammal that has recently been shown to exhibit large individual heterogeneity in reproduction. Using output from the IPM, we evaluated how population dynamics changed with different underlying distributions of unobserved individual heterogeneity in reproduction.</li> <li>Results indicate that the changes to the underlying distribution of individual heterogeneity in reproduction cause very small changes in the population growth rate and other population metrics. The largest difference in the estimated population growth rate resulting from changes to the underlying distribution of individual heterogeneity was less than 1%.</li> <li>Our work highlights the differing importance of individual heterogeneity at the population level compared to the individual level. Although individual heterogeneity in reproduction may result in large differences in the lifetime fitness of individuals, changing the proportion of above- or below-average breeders in the population results in much smaller differences in annual population growth rate. For a long-lived mammal with stable and high adult-survival that gives birth to a single offspring, individual heterogeneity in reproduction has a limited effect on population dynamics. We posit that the limited effect of individual heterogeneity on population dynamics may be due to canalization of life-history traits.</li> </ol>
Heterogeneous Receptor Expression Underlies Non-uniform Peptidergic Modulation of Olfaction in Drosophila
<p>Original confocal scans for all light microscopy images in the main and supplementary figures of Sizemore et al., 2023. <strong>If you make use of this data</strong>, please cite:</p> <p>Heterogeneous Receptor Expression Underlies Non-uniform Peptidergic Modulation of Olfaction in <em>Drosophila</em></p> <p>Tyler R. Sizemore, Julius Jonaitis, Andrew M. Dacks</p> <p><strong>doi: 10.1038/s41467-023-41012-3</strong></p> <p> </p>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
Landscape heterogeneity provides co-benefits to predator and prey
<p>Predator populations are imperiled globally, due in part to changing habitat and trophic interactions. Theoretical and laboratory studies suggest that heterogeneous landscapes containing prey refuges acting as source habitats can benefit both predator and prey populations, although the importance of heterogeneity in natural systems is uncertain. Here, we tested the hypothesis that landscape heterogeneity mediates predator-prey interactions between the California spotted owl (<em>Strix occidentalis occidentalis</em>) – a mature forest species – and one of its principal prey, the dusky-footed woodrat (<em>Neotoma</em> <em>fuscipes</em>) – a younger forest species – to the benefit of both. We did so by combining estimates of woodrat density and survival from live-trapping and VHF tracking with direct observations of prey deliveries to dependent young by owls in both heterogeneous and homogeneous home ranges. Woodrat abundance was approximately 2.5x higher in owl home ranges (1,412 hectares) featuring greater heterogeneity in vegetation types (1,805.0 ± 50.2 SE) compared to those dominated by mature forest (727.3 ± 51.9 SE), in large part because of high densities in young forests appearing to act as sources promoting woodrat densities in nearby mature forests. Woodrat mortality rates were low across vegetation types and did not differ between heterogeneous and homogeneous home ranges, yet all observed predation by owls occurred within mature forests, suggesting young forests may act as woodrat refuges. Owls exhibited a type 1 functional response, consuming approximately 2.5x more woodrats in heterogeneous (31.1/month ± 5.2 SE) versus homogeneous (12.7/month ± 3.7 SE) home ranges. While consumption of smaller-bodied alternative prey partially compensated for lower woodrat consumption in homogeneous home ranges, owls nevertheless consumed 30% more biomass in heterogeneous home ranges – approximately equivalent to the energetic needs of producing one additional offspring. Thus, a mosaic of vegetation types including young forest patches increased woodrat abundance and availability that, in turn, provided energetic and potentially reproductive benefits to mature forest-associated spotted owls. More broadly, our findings provide strong empirical evidence that heterogeneous landscapes containing prey refuges can benefit both predator and prey populations. As anthropogenic activities continue to homogenize landscapes globally, promoting heterogeneous systems with prey refuges may benefit imperiled predators.</p>
Raw, processed and merged Data for Swiss Cat+ East A1 project related to the automated and high-throughput Bayesian Optimization of CO2 hydrogenation heterogeneous catalysts
<p> All files generated during the fully digitalized automated and high-throughput experimentally-guided Bayesian Optimization project, which led to the synthesis of 144 heterogeneous catalysts with a Chemspeed unit (6 generations of 24) and their testing under CO2 hydrogenation conditions with Avantium fixed bed units. Below are some indication to understand the naming of the files.</p> <ul> <li>A1 stands for the internal project number.</li> <li>G1 to G5 stands for the catalyst generation number and G2NC for the alternative second generation suggested by the Bayesian Optimizer without considering the cost of catalyst as an objective (No_Cost).</li> <li>Three fixed bed units have been used, named XDB4x (a 4 parallel reactors unit), XDC4x (another 4 parallel reactors unit) and XR16x (a 16 parallel reactors unit).</li> <li>Individual fixed bed testing raw files (FB_RawData) generated by each unit are then processed to extract the mean and standard deviation (std) values (e.g conversion, selectivity) and to compute reactions rates.</li> <li>Then the processed files for each individual reactor (XDB, XDC, XR) are combined into one file (All_FBData), and finally aggregated with the synthesis details, viathe catalyst barcodes (AllData_Processed).</li> <li>Finally, the processed file for each generation are merged together (AllGen_Merged) and a condensed file is generated for a given reaction temperature (AllGen_275CDataProcessed_Merged)</li> </ul>
Heterogenized Molecular Rhodium Phosphine Catalysts within Metal–Organic Frameworks for Alkene Hydroformylation
<p>Supplementary information for associated publication</p>
A monodisperse, end-capped Ru(bda) oligomer with outstanding performance in heterogeneous electrochemical water oxidation
<p>Synthesis of a monodisperse oligomer from Ru(bda) (bda = 2,2’-bipyridine-6,6’-dicarboxylate), 4,4’-bipyridine linkers and 4‑picoline end caps afforded a highly stable water oxidation catalyst. After deposition on multi-walled carbon nanotubes, an active electrode material with outstanding performance in heterogeneous electrochemical water oxidation catalysis is obtained.</p>
Replication data for: The Effect of IS-Innovation Strategy Alignment on Corporate Performance: Investigating the Role of Environmental Uncertainty by Heterogeneity
<p>A base de dados técnico-científica de 856 empresas brasileiras examinou o alinhamento entre sistemas de informação estratégicos (ISS), na abordagem da estratégia como prática, e inovação de exploration e exploitation, e seu impacto no desempenho corporativo (CP) sobre incerteza ambiental. Os resultados mostram que todos os tipos de alinhamento entre ISS e inovação influenciam positivamente o CP. O alinhamento com inovação ambidestra teve um impacto 62% maior no CP do que o alinhamento com inovações incrementais. Além disso, inovações disruptivas tiveram efeitos positivos em ambientes hostis, enquanto inovações exploratórias e ambidestras tiveram impactos fortes em ambientes altamente dinâmicos.</p>
Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance
Open the record for dataset details and reuse information.
The ClaDS rate-heterogeneous birth-death prior for full phylogenetic inference in BEAST2
Open the record for dataset details and reuse information.
Data from: Heterogeneous palaeo-ecogeography of brachiopods during the Late Ordovician mass extinction in South China
Open the record for dataset details and reuse information.
The kinase ERK plays a conserved dominant role in the heterogeneity of epithelial-mesenchymal transition in pancreatic cancer cells
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Data for: Evaluating heterogeneity in household travel response to carbon pricing: a study focusing on small and rural communities
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.