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Fig. 4 in Pseudoleucochloridium ainohelicis nom. nov. (Trematoda: Panopistidae), a Replacement for Glaphyrostomum soricis Found from Long-Clawed Shrews in Hokkaido, Japan, with New Data on its Intermediate Hosts
Fig. 4. The cercaria and metacercaria of Pseudoleucochloridium ainohelicis nom. nov. from Ainohelix editha. Both of the drawings are in ventral view. A) Cercaria. Scale bar 100 µm; B) Metacercaria. Scale bar 500 µm.
Fig. 1 in Pseudoleucochloridium ainohelicis nom. nov. (Trematoda: Panopistidae), a Replacement for Glaphyrostomum soricis Found from Long-Clawed Shrews in Hokkaido, Japan, with New Data on its Intermediate Hosts
Fig. 1. Frequencies of cox1 haplotypes and their statistical parsimony network in Pseudoleucochloridium ainohelicis nom. nov. All of the twelve isolates were collected in Asahikawa. The size of circles indicates the frequency of the haplotypes. Small circles show hypothetical haplotypes. The shaded circle represents the hypothetical ancestor.
Fig. 5 in Pseudoleucochloridium ainohelicis nom. nov. (Trematoda: Panopistidae), a Replacement for Glaphyrostomum soricis Found from Long-Clawed Shrews in Hokkaido, Japan, with New Data on its Intermediate Hosts
Fig. 5. The adult of Pseudoleucochloridium ainohelicis nom. nov. from Sorex unguiculatus. The drawing is in ventral view. The large suckers, M-shaped configuration of uterus, and terminally-positioned genital pore are characteristic of the genus. Scale bar 500 µm.
Data from: Virus infection and host plant suitability affect feeding behaviors of cannabis aphid (Hemiptera: Aphididae), a newly described vector of potato virus Y
<p>Aphids are the most prolific vectors of plant viruses resulting in significant yield losses to crops worldwide. P<span>otato virus Y (PVY) </span>is transmitted in a non-persistent manner by 65 species of aphids. <span>With the increasing acreage of hemp </span>(<i>Cannabis sativa</i> L.) (Rosales: Cannabaceae) <span>in the U.S, we were interested to know if the cannabis aphid (<i>Phorodon cannabis</i> Passerini) </span><span>(Hemiptera: Aphididae) </span><span>is a potential vector of PVY.</span> Here, we conduct transmission assays and utilize the electrical penetration graph (EPG) technique to determine whether cannabis aphids can transmit PVY to hemp (host) and potato (non-host) (<i>Solanum tuberosum</i> L.) (Solanales: Solanaceace). We show for the first time that the cannabis aphid is an efficient vector of PVY to hemp (96%) and potato (91%) using cohorts of aphids. In contrast, individual aphids transmitted the virus more efficiently to hemp (63%) compared to potato (19%). During the initial 15 minutes of EPG recordings, aphids demonstrated lower number and time spent performing intracellular punctures on potato compared to hemp, which may in part explain low virus transmission to potato using individual aphids. During the entire 8-hour recording, viruliferous aphids spent less time ingesting phloem compared to non-viruliferous aphids on hemp. This reduced host suitability could potentially cause aphids to disperse to more suitable hosts thereby increasing virus transmission. Overall, our study shows that cannabis aphid is an efficient vector of PVY, and that virus infection and host plant suitability affect feeding behaviors of the cannabis aphid in ways which may increase virus transmission.</p>
Data analysis pipeline for investigating drug-host-microbiome relationships in cardiometabolic disease (MetaCardis cohort).
<p>*******************************************************************<br> MetaDrugs workflow<br> *******************************************************************</p> <p>Data analysis pipeline for investigating drug-host-microbiome relationships in cardiometabolic disease (MetaCardis cohort).</p> <p>For questions and requests, please contact:<br> Sofia K. Forslund (sofia.forslund@mdc-berlin.de)<br> and Till Birkner (till.birkner@mdc-berlin.de)</p> <p>*******************************************************************<br> Contents:<br> -------------------------------------------------------------------<br> Data files:<br> metadata.tar.gz - archived cohort metadata files*<br> input_features.tar.gz - archived preprocessed serum and urine metabolome and gut microbiome features<br> output_complete.tar.gz - archived example analysis output files for each of the input feature file<br> output_rerun.tar.gz - archived empty directory for generating test output files as described in this document<br> <br> *Please note: Due to conflicts with Danish Data Protection laws, metadata from the Danish subset of the cohort were removed in this repository. Please reach out for a potential case-by-case access request for access to the complete set of metadata.<br> -------------------------------------------------------------------<br> Text files:<br> archived in feature_names.tar.gz:<br> atcs_names - full names for atcs drug compounds<br> contrast_names - full names for disease comparison groups<br> file_names - brief description of the files in input_features folder<br> gmm_names - full names of GMM modules<br> kegg_names - full names of KEGG modules<br> ko_names - full names of KO modules<br> metadata_names - full names of metadata features<br> mOTU_names - species names for metagenomics data<br> taxon_names - taxon names for metagenomics data<br> -------------------------------------------------------------------<br> Scripts:<br> -------------------------------------------------------------------<br> runFrame.r - main wrapper script envoking the analysis pipeline<br> -------------------------------------------------------------------<br> runFrame_rel_comb.r - script calculating drug combination effects<br> runFrame_rel.r - script calculating dosage effects<br> testCombPresenceSeparate.r - testing of significant drug combination effects beyond single drug effects<br> testDosagePresenceSeparate.pl - testing of significant drug dosage effects beyond single drug effects<br> testDosagePresenceSeparateNegative.pl - testing of unique drug dosage effects beyond single drug effects<br> -------------------------------------------------------------------<br> prettifyResults_uncollapsed.pl - wrapper scripts to create and format a single analysis output file<br> makeTables.r - wrapper script to make excel tables with analysis results<br> -------------------------------------------------------------------<br> Example output file:<br> -------------------------------------------------------------------<br> output_all_formatted_noc_uncollapsed_complete.tsv - contains all disease-drug-host-microbiome feature analysis results in one place.<br> *******************************************************************</p>
Data for Corre et al., Bacterial matrix metalloproteases and serine proteases contribute to the extra-host inactivation of enterovirus in lake water, ISMEJ 2022
<p>Data for Corre et al., <em>Bacterial matrix metalloproteases and serine proteases contribute to the extra-host inactivation of enterovirus in lake water</em>, ISMEJ 2022</p> <p>The first file contains all data pertaining to experiments with isolates: collection date, isolation temperature, protease activity measured by 4 different approaches, antiviral effect on E11 and CVA9 (three replicates each); this table corresponds to the data shown in Supplementary Table 2.</p> <p>The second file contains the raw data for all lake water experiments (Figures 1 and 6): information on sample type, antiviral effect (measured in triplicate), presence of a protease inhibitor.</p>
Extended Data Figure 7 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 7 | Global distribution of viral and host species richness for primates (order Primates). a, Observed total viral richness (for n = 71 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 73);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3e); g, global host species richness for Primates (n = 400); h, host species richness for Primates in our database (n = 98);i, primate species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in primates. Red/pink colours in panel i highlight areas with poor viral surveillance in primate species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 4 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 4 | Global distribution of viral and host species richness for wild carnivores (order Carnivora). a, Observed total viral richness (for n = 55 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 55); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3b); g, global host species richness for Carnivora (n = 276);h, host species richness for Carnivora in our database (n = 79); i, species of the order Carnivora with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in carnivores. Red/pink colours in panel i highlight areas with poor viral surveillance in carnivore species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 3 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 3 | Global distribution of viral and host species richness for all wild mammals. a, Observed total viral richness (for n = 576 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 584);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3a); g, global mammal species richness (n = 5,290); h, mammal richness for species in our database (n = 753);i, mammal species with no described viruses in the literature. Warmer colours (larger values) in panels c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in mammals. Red/pink colours in panel i highlight areas with poor viral surveillance in mammal species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 2 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 2 | Heat map of observed total viral richness by mammalian order and viral family. Dataset includes 754 mammalian species and 586 unique ICTV recognized viral species. Heat map aggregated by rows and columns to group taxa with similar levels of observed viral richness.
Extended Data Figure 6 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 6 | Global distribution of viral and host species richness for bats (order Chiroptera). a, Observed total viral richness (for n = 156 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 157);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3d); g, global host species richness for Chiroptera (n = 1117);h, host species richness for Chiroptera in our database (n = 192);i, species of the order Chiroptera with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in bats. Red/pink colours in panel i highlight areas with poor viral surveillance in bat species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 1 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 1 | Conceptual model of zoonotic spillover, viral richness, and summary of models. a, Conceptual model of zoonotic spillover showing primary risk factors examined, colour-coded according to generalized additive models used. b, Conceptual model of observed, predicted, and actual viral richness in mammals. c, GAMs used in our study to address specific components of a and b, colour-coded by model. Variables listed with 'or' under each GAM covaried and were provided as competing terms in model selection, and those in bold were included in the best-fit model using all host–virus associations. Significant variables from each best-fit GAM are noted with an asterisk. Zoonotic viral spillover first depends on the underlying total viral richness in mammal populations and the ecological, taxonomic, and life-history traits that govern this diversity (GAM 1). Second, host- and virus-specific factors may facilitate viral spillover. We examine the relative importance of host phylogenetic distance to humans, ecological opportunity for contact, or other species-specific life-history and taxonomic traits (GAM 2), and identify viral traits associated with a higher likelihood of an observed virus being zoonotic (GAM 3). We estimate the total and zoonotic viral richness per host species using GAMs 1 and 2, and calculate the missing viruses and missing zoonoses under a scenario of increased research effort (b, Methods). Owing to imperfect surveillance in both humans and wildlife and biases in viral detection, there may be uncertainty in the exact proportion of viruses that are zoonotic (b, light grey), and also between the actual, or true, viral richness (dotted lines) and the predicted maximum viral richness per host (dashed line).
Extended Data Figure 9 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 9 | Order-level phylogenies showing residuals from zoonoses model. a–e, Subtrees from cytochrome b maximum likelihood phylogeny for 558 mammal species (constrained to order-level topology of mammal supertree) for bats (a), carnivores (b), even-toed ungulates (c), rodents (d) and primates (e). Species included have at least one described virus association and available genetic data. Wildlife species names and terminal branches are colour-coded by the residuals (predicted minus observed) from the best-fit GAM to predict the number of zoonotic viruses using all data. Species with residual values between −1 and 1 (black) are accurately predicted within one virus. Warm colours represent species with positive residuals (orange>1 to 3; red>3). Cool colours represent species with negative residuals (green <−1 to−3; blue<−3). Marine mammals, domestic animals, and species with missing data and not included in the best-fit models are shown in grey.
Extended Data Figure 8 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 8 | Global distribution of viral and host species richness for rodents (order Rodentia). a, Observed total viral richness (for n = 178 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 183);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3f); g, global host species richness for Rodentia (n = 2206);h, host species richness for Rodentia in our database (n = 221); i, rodent species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in wild rodents. Red/pink colours in panel i highlight areas with poor viral surveillance in rodent species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 5 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 5 | Global distribution of viral and host species richness for wild even-toed ungulates (order Cetartiodactyla). a, Observed total viral richness (for n = 70 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 70);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3c); g, global host species richness for Cetartiodactyla (n = 229);h, host species richness for Cetartiodactyla in our database (n = 105);i, species of the order Cetartiodactyla with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in even-toed ungulates. Red/pink colours in panel i highlight areas with poor viral surveillance in even-toed ungulates species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Data from: Speciation in Nearctic oak gall wasps is frequently correlated with changes in host plant, host organ, or both
<p>Quantifying the frequency of shifts to new host plants within diverse clades of specialist herbivorous insects is critically important to understand whether and how host shifts contribute to the origin of species. Oak gall wasps (Hymenoptera: Cynipidae: Cynipini) comprise a tribe of ~1000 species of phytophagous insects that induce gall formation on various organs of trees in the family Fagacae —primarily the oaks (genus <em>Quercus</em>; ~435 sp). The association of oak gall wasps with oaks is ancient (~50 my), and most oak species are galled by one or more gall wasp species. Despite the diversity of both gall wasp species and their plant associations, previous phylogenetic work has not identified the strong signal of host plant shifting among oak gall wasps that has been found in other phytophagous insect systems. However, most emphasis has been on the Western Palearctic and not the Nearctic where both oaks and oak gall wasps are considerably more species rich. We collected 86 species of Nearctic oak gall wasps from 10 of the 14 major clades of Nearctic oaks and sequenced >1000 Ultra Conserved Elements (UCEs) and flanking sequences to infer wasp phylogenies. We assessed the relationships of Nearctic gall wasps to one another and, by leveraging previously published UCE data, to the Palearctic fauna. We then used phylogenies to infer historical patterns of shifts among host tree species and tree organs. Our results indicate that oak gall wasps have moved between the Palearctic and Nearctic at least four times, that some Palearctic wasp clades have their proximate origin in the Nearctic, and that gall wasps have shifted within and between oak tree sections, subsections, and organs considerably more often than previous data have suggested. Given that host shifts have been demonstrated to drive reproductive isolation between host-associated populations in other phytophagous insects, our analyses of Nearctic gall wasps suggest that host shifts are key drivers of speciation in this clade, especially in hotspots of oak diversity. Though formal assessment of this hypothesis requires further study, two putatively oligophagous gall wasp species in our dataset show signals of host-associated genetic differentiation unconfounded by geographic distance, suggestive of barriers to gene flow associated with the use of alternative host plants.</p>
Data from: Adaptive division of growth and development between hosts in helminths with two-host life cycles
<p>Parasitic worms (helminths) with complex life cycles divide growth and development between successive hosts. Using data from 597 species of acanthocephalans, cestodes, and nematodes with two-host life cycles, we found that helminths with larger intermediate hosts were more likely to infect larger, endothermic definitive hosts, although some evolutionarily shifts in definitive host mass occurred without changes in intermediate host mass. Life-history theory predicts parasites to shift growth to hosts in which they can grow rapidly and/or safely. Accordingly, helminth species grew relatively less as larvae and more as adults if they infected smaller intermediate hosts and/or larger, endothermic definitive hosts. Growing larger than expected in one host, relative to host mass/endothermy, was not associated with growing less in the other host, implying a lack of cross-host tradeoffs. Rather, some helminth orders had both large larvae and large adults. Within these taxa, though, size at maturity in the definitive host was unaffected by changes to larval growth, as predicted by optimality models. Parasite life-history strategies were mostly (though not entirely) consistent with theoretical expectations, suggesting that helminths adaptively divide growth and development between the multiple hosts in their complex life cycles.</p>
Data from: Plant host traits mediated by foliar fungal symbionts and secondary metabolites
<p>Fungal symbionts living inside plant leaves ("endophytes") can vary from beneficial to parasitic, but the mechanisms by which the fungi affect the plant host phenotype remain poorly understood. Chemical interactions are likely the proximal mechanism of interaction between foliar endophytes and the plant, as individual fungal strains are often exploited for their diverse secondary metabolite production. Here, we go beyond single strains to examine commonalities in how 16 fungal endophytes shift plant phenotypic traits such as growth and physiology, and how those relate to plant metabolomics profiles. We inoculated individual fungi on switchgrass, <em>Panicum virgatum</em> L. This created a limited range of plant growth and physiology (2–370% of fungus-free controls on average), but effects of most fungi overlapped, indicating functional similarities in unstressed conditions. Overall plant metabolomics profiles included almost 2000 metabolites, which were broadly correlated with plant traits across all the fungal treatments. Terpenoid-rich samples were associated with larger, more physiologically active plants and phenolic-rich samples were associated with smaller, less active plants. Only 47 metabolites were enriched in plants inoculated with fungi relative to fungus-free controls, and of these, LASSO regression identified 12 metabolites that explained from 14–43% of plant trait variation. Fungal long-chain fatty acids and sterol precursors were positively associated with plant photosynthesis, conductance, and shoot biomass, but negatively associated with survival. The phytohormone gibberellin, in contrast, was negatively associated with plant physiology and biomass. These results can inform ongoing efforts to develop metabolites as crop management tools, either by direct application or via breeding, by identifying how associations with more beneficial components of the microbiome may be affected.</p>
data for "Plant genetic effects on microbial hubs impact host fitness in repeated field trials"
<p>These are data tables required for the analysis of the paper "Plant genetic effects on microbial hubs impact host fitness in repeated field trials". <br> All scripts are available at https://forgemia.inra.fr/bbrachi/microbiota_paper.git</p> <p>The folder architecture in the zip files is the same as in the repository: https://forgemia.inra.fr/bbrachi/microbiota_paper.git</p> <p>The dataset includes: </p> <p>- OTU count tables for 16S and ITS</p> <p>- taxonomic assignation</p> <p>- plant seed-set estimates</p> <p>- plant growth data from the B38 experiment. </p> <p>- Metabolomics datasets</p>
Figure 1 in Terrestrial Parasitengona mites (Trombidiformes) of Denmark - new data on parasite-host associations and new country records
Figure 1 Erythraeid larvae (Parasitengona: Erythraeidae) parasitizing various hosts: A – Charletonia cardinalis* on Stiroma affinis(Hemiptera: Delphacidae); B – [?] Leptus mariae* on Brachysomus echinatus(Coleoptera: Curculionidae). Not to scale. *Larvae depigmented due to preservation in EtOH.
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.