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Fig. 1 in Predicting Baylisascaris procyonis roundworm prevalence, presence and abundance in raccoons (Procyon lotor) of southwestern Ohio using landscape features
Fig. 1. Map of the townships of Greene and Clark Counties Ohio. The data represent the proportion of raccoons from an individual township that had raccoon roundworms when necropsied. This map also demonstrates the mean patch size, proportion of landscape modified by urbanization and the proportion of landscape modified by agriculture for the nine townships.
Fig. 5 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 5. Scanning electron microscopic images of E. horridus attached to seal skin and fur. A: E. horridus utilizing a hair follicle of harbour seal skin. B: E. horridus attached to seal hair with the head pointing towards seal skin. C: E. horridus with six claws attached to seal fur. D: Close up of an unattached claw of E. horridus. Asterisks positioned on nits of E. horridus. Sale bars: A 200 μm, B 400 μm, C 400 μm, D 100 μm.
Fig. 4 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 4. Histological sections and staining of E. horridus revealing filarial stages in E. horridus. A: Filarial stages (arrowheads) in the pharynx. bar = 20 μm. B: Filarial stage (arrow) in the mouth region. bar = 40 μm. C: Filarial stage (arrowhead) in the intestine (in) surrounded by erythrocytes (e). bar = 15 μm. D: Filarial stage in the haemocoel (hc) of the abdomen of E. horridus (square). E: Close up of filarial stage. mp = mouthparts, mo = mouth, cu = cuticula. A–C: Haematoxylin - Eosin stain, D: Giemsa stain.
Fig. 3. A in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 3. A: Prevalence of A. spirocauda and E. horridus in harbour seals in the North and Baltic Sea from 1996 to 2021, data from 1996 to 2013 according to Lehnert et al. (2016). B: Prevalence of A. spirocauda and E. horridus in harbour seals during the seasons in the North and Baltic Sea from 2014 to 2021.
Fig. 2 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 2. Sampling routine of E. horridus infected seal skin for histological and bacteriological examinations. A: Mild E. horridus infection of a harbour seal yearling, asterisk pointing at E. horridus. B: Close up of E. horridus C: Removing of E. horridus D: Cutting and removing of the infected skin with a sterile forceps for further investigations. Scale bars: A-D 1 cm.
Fig. 1 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 1. Levels of infection with E. horridus in P. vitulina. A: Mild E. horridus infection of a harbour seal yearling, asterisk pointing at E. horridus B: Close up of E. horridus in the head area of a harbour seal C: Severe E. horridus infection of a harbour seal D: Close up of severe E. horridus infection. Scale bars: A-D 1 cm.
Fig. 2 in Prevalence and geographical distribution of amphistomes of African wild ruminants: A scoping review
Fig. 2. Map showing geographical distribution of amphistomes in wild ruminants in Africa (1900–2022).
Fig. 2. Neighbor-joining phylogenetic tree for a 219 in Prevalence of filarioid nematodes and trypanosomes in American robins and house sparrows, Chicago USA
Fig. 2. Neighbor-joining phylogenetic tree for a 219 bp region of the trypanosome 18s rRNA gene. Underlined sequences are from this study. Sequences for additional Trypanosoma spp. were downloaded from NCBI Genbank for comparison and Bodo caudatus was used as an outgroup. Numbers by branches indicate statistical bootstrap support of À50%.
Fig. 1. Neighbor-joining phylogenetic trees for a 475 in Prevalence of filarioid nematodes and trypanosomes in American robins and house sparrows, Chicago USA
Fig. 1. Neighbor-joining phylogenetic trees for a 475 bp region of the 18S rRNA gene for filarioid nematodes (A) and a 529 bp region of the filarial nematode mitochondrial cytochrome c oxidase subunit I gene (B). Sequences were obtained from bird blood clots, bird tissues, or adult nematodes recovered from birds. Underlined sequences are from this study. Additional sequences for filarial nematode species were downloaded from NCBI Genbank for comparison and Thelazia lacrimalis and Caenorhabditis elegans were used as outgroups. Numbers by branches indicate statistical bootstrap support of À50%.
Fig. 1 in High Trypanosoma cruzi infection prevalence associated with minimal cardiac pathology among wild carnivores in central Texas
Fig. 1. Spatial occurrence and distribution of T. cruzi infected, hunter-harvested wildlife, 2014. Number of infected over total number of that species tested are shown by county.
Fig. 1 in Manifold habitat effects on the prevalence and diversity of avian blood parasites
Fig. 1. Diagram illustrating how conditions of the vector, parasite, host and habitat must all be permissive for pathogen transmission to occur. The outer layer depicts some factors that are currently causing rapid environmental change, which will affect host‾parasite dynamics.
Fig. 3 in Haemosporidian parasite infections in grouse and ptarmigan: Prevalence and genetic diversity of blood parasites in resident Alaskan birds
Fig. 3. Bayesian phylogenetic tree of haemosporidian mtDNA cytochrome b haplotypes isolated from Alaskan grouse and ptarmigan species. Node tips are labeled with abbreviation for parasite genus (Haem = Haemoproteus, Leuc = Leucocytozoon, and Plas = Plasmodium), followed by the lineage name, GenBank accession number for each lineage, and avian (Phas = Phasianidae, Anat = Anatiade, Turd = Turdidae, Paru = Parulidae, Scol = Scolopacidae, Embe = Emberizidae, and Frin = Fringillidae) or invertebrate (Simu = Simuliidae) host family. All haplotypes identified in this study are highlighted in red and asterisks following tip labels indicate a lineage that was isolated from Alaskan bird hosts. Numbers on branches indicate posterior probabilities from our analysis. All reference sequences were obtained from the National Center for Biotechnology Information website or the MalAvi database. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2. Minimum spanning network for haemosporidian mtDNA cytochrome b in Haemosporidian parasite infections in grouse and ptarmigan: Prevalence and genetic diversity of blood parasites in resident Alaskan birds
Fig. 2. Minimum spanning network for haemosporidian mtDNA cytochrome b haplotypes isolated from Alaskan grouse and ptarmigan species. Dark circles represent un-sampled nodes. All circles are proportional to the frequency at which the haplotypes were detected. Lines between nodes are drawn to scale based on the number of nucleotide mutations unless otherwise indicated by hash marks.
Fig. 1 in Haemosporidian parasite infections in grouse and ptarmigan: Prevalence and genetic diversity of blood parasites in resident Alaskan birds
Fig. 1. Map of Alaskan sampling regions assembled from multiple game management units and sub-units. Regions were grouped for analysis of haemosporidian prevalence as follows: southcoastal (Kenai Peninsula and southeastern Alaska; GMUs 1C, 1D, 2, 7, 15A, 15B, and 15C), southcentral (Anchorage area and Matanuska-Susitna Valley; GMUs 13A, 13D, 14A, 14C, 16A, and 16B), southwestern (Bristol Bay, Alaska Peninsula, and eastern Aleutian islands; 9D, 9E, and 17C), southern interior (south side of Alaska Range; GMUs 12, 13B, and 13E), northern interior (north side of Alaska Range; GMUs 20A-20E and 25C), and Seward Peninsula (GMU 22C).
Fig. 3 in Partial molecular characterization of the mitochondrial genome of Baylisascaris columnaris and prevalence of infection in a wild population of Striped skunks
Fig. 3. Single nucleotide polymorphisms in the ND2 gene of B. columnaris, compared to B. procyonis. Nucleotide position numbers are shown at the top of the figure. Speciesspecific SNPs are shown in bold.
Fig. 1 in Partial molecular characterization of the mitochondrial genome of Baylisascaris columnaris and prevalence of infection in a wild population of Striped skunks
Fig. 1. Single nucleotide polymorphisms in the Cox1 gene of B. columnaris, compared to B. procyonis. Nucleotide position numbers are shown at the top of the figure. Italicized numbers represent the position number from a previously published partial sequence of the B. columnaris Cox1 gene (Franssen et al., 2013). Species-specific SNPs are shown in bold.
Fig. 2 in Partial molecular characterization of the mitochondrial genome of Baylisascaris columnaris and prevalence of infection in a wild population of Striped skunks
Fig. 2. Single nucleotide polymorphisms in the Cox2 gene of B. columnaris, compared to B. procyonis. Nucleotide position numbers are shown at the top of the figure. Italicized numbers represent the position number from a previously published partial sequence of the B. columnaris Cox2 gene (Franssen et al., 2013).
Fig. 1. A in Seasonality, richness and prevalence of intestinal parasites of three neotropical primates (Alouatta seniculus, Ateles hybridus and Cebus versicolor) in a fragmented forest in Colombia
Fig. 1. A. Trichuris sp., B. Oxyuridae, C. Ancylostomatidae, D. Strongyloides sp. (larva), E. Ascarididae, F. Gnathostomatidae, G. Trichostrongylidae, H-I. Trematodes, J. Entamoeba sp. (cyst), K. Acanthocephala, L. Balantidiidae.
Fig. 4 in Partial molecular characterization of the mitochondrial genome of Baylisascaris columnaris and prevalence of infection in a wild population of Striped skunks
Fig. 4. Single nucleotide polymorphisms in several tRNA genes of B. columnaris, compared to B. procyonis, B. transfuga and B. schroederi. Nucleotide position numbers are shown at the top of the figure. SNPs which distinguish B. columnaris from other Baylisascaris species are shown in bold.
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. Models of Cyathocotyle bushiensis (Cb) and Sphaeridiotrema spp. (Sg) metacercarial prevalence (prev) and intensity (int) in the waterbodies we studied in northern Minnesota during 2011‾2013; a) East Winnibigoshish index area, b) West Winnibigoshish index area, c) Lower Twin Lake, d) Crow Wing River, e) White Earth ponds, f) Shell River. Depth_cm is water depth at the sampling location. Dist.scaup is the minimum Euclidean distance between a given waypoint and the nearest point sampled under a raft of scaup in either the same season, or up to two seasons prior in that same year. Log.abund is the log transformed snail abundance at a sampling point. Size.mean is the mean snail size at a sampling point. Year2012 and Year2013 are comparisons between samples collected in 2011 vs 2012 and 2011 vs 2013, respectively.
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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.