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4,389 results for “Intestine”
Human intestinal Bacteria Collection (HiBC): Isolates and genomes metadata
<p>The <a href="https://hibc.rwth-aachen.de/" target="_blank" rel="noopener">Human intestinal Bacteria Collection (HiBC)</a> is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly.</p> <p>This dataset includes the taxonomy of the isolates, as well as metadata regarding their cultivation and isolation. We also provide metadata regarding the sequencing, genome assembly process and the biological sequences.</p> <p><strong>UPDATE v7</strong>: INSDC accession for <em>Segatella sinensis</em> CLA-AA-H117 was a missing value and is now the correct value of GCA_040324585.2.</p> <p><strong>UPDATE v6: </strong>The growth atmosphere is now indicated by anaerobic or aerobic instead of "Anaerobe/Aerobe" that was a misleading term. The risk group of these two isolates went from 1 to 2:</p> <ul> <li>CLA-AA-H205: <em>Anaerostipes caccae </em></li> <li>CLA-AA-H83: <em>Bacteroides fragilis</em></li> </ul> <p>The risk group of the following isolates has been updated (usually from unknown to 1, or from 2 to 1):</p> <ul> <li>CLA-SR-H026: <em>Aedoeadaptatus acetigenes</em></li> <li>CLA-KB-H139:<em> Bacteroides xylanisolvens</em></li> <li>CLA-SR-H015: <em>Bacteroides xylanisolvens</em></li> <li>CLA-AA-H187: <em>Blautia fusiformis</em></li> <li>CLA-AA-H274: <em>Brotaphodocola catenula</em></li> <li>CLA-AA-H286: <em>Butyricimonas faecihominis</em></li> <li>CLA-AA-H278:<em> Clostridium fessum</em></li> <li>CLA-AA-H147: <em>Dorea ammoniilytica</em></li> <li>CLA-SR-H027: D<em>orea formicigenerans</em></li> <li>CLA-KB-H89: <em>Dorea longicatena</em></li> <li>CLA-KB-H94: <em>Dorea longicatena</em></li> <li>CLA-SR-H022: <em>Enterococcus lactis</em></li> <li>CLA-AA-H250: <em>Hominenteromicrobium mulieris</em></li> <li>CLA-AA-H232: H<em>ominilimicola fabiformis</em></li> <li>CLA-AA-H246: <em>Hominisplanchenecus faecis</em></li> <li>CLA-AA-H276:<em> Hominiventricola filiformis</em></li> <li>CLA-AA-H213:<em> Oliverpabstia intestinalis</em></li> <li>CLA-AA-H241: <em>Oliverpabstia intestinalis</em></li> <li>CLA-AA-H58: <em>Pilosibacter fragilis</em></li> <li>CLA-KB-H110: <em>Ruthenibacterium lactatiformans</em></li> <li>CLA-AA-H174: <em>Segatella sinensis</em></li> <li>CLA-AA-H2: <em>Veillonella parvula</em></li> <li>CLA-AA-H273: <em>Waltera acetigignens</em></li> </ul> <p>Typos in media list have been fixed. </p> <p><strong>UPDATE v5</strong>: The accessions number for the genomes on INSDC databases are added under the column Accession. Plus two typos in the risk group column have been corrected as follow:</p> <ul> <li>CLA-AA-H173: from Risk Group 4 (!) to 2 like the other strain of <em>Sutterella wadsworthensis</em></li> <li>CLA-AA-H198: from Risk Group 4 (!) to 1 like the other <em>Bifidobacterium </em>species.</li> </ul> <p><strong>UPDATE v4</strong>: Only the taxonomy of a couple of isolates has been changed, as follow:</p> <ul> <li>CLA-ER-H4: <em>Collinsella sp900547855</em> instead of <em>Collinsella sp900544645</em></li> <li>CLA-AA-H142: <em>Pilosibacter fragilis</em> (<em>f__Clostridiaceae</em>) instead of <em>Sakamotonia hominis gen. nov.</em> (<em>f__Lachnospiraceae</em>)</li> <li>CLA-AA-H58: <em>Pilosibacter fragilis </em>(<em>f__Clostridiaceae</em>) instead of <em>Sakamotonia hominis gen. nov. </em>(<em>f__Lachnospiraceae</em>)</li> <li>CLA-AA-H89B: <em>Lachnospira intestinalis sp. nov.</em> instead of <em>Lachnospira hominis sp. nov.</em></li> <li>CLA-JM-H10: <em>Lachnospira hominis sp. nov.</em> instead of <em>Lachnospira intestinalis sp. nov.</em></li> <li>CLA-JM-H7B: <em>Faecalibacterium taiwanense</em> instead of <em>Faecalibacterium faecis sp. nov.</em></li> <li>CLA-JM-H45: <em>Merdimmobilis hominis</em> instead of <em>Hominicola intestinalis gen. nov.</em></li> </ul> <p><strong>UPDATE v3</strong>: The genome of one of our isolate had been unfortunately swapped. This mistake has been now corrected on Zenodo and Coscine. The genome of <em>Segatella sinensis</em> CLA-AA-H117 should be considered correct with 103 contigs and 3 671 232 nt. Please note that the genome available at the NCBI is the correct one (GCA_040324585.2). Two typos regarding taxonomy have been corrected as well: <em>Maccoya intestinihominis</em> has been corrected to <em>Maccoyia intestinihominis</em> and <em>Faecousia faecis</em> to <em>Faecousia intestinalis</em>.</p>
Detecting Changes in the Caenorhabditis elegans Intestinal Environment Using an Engineered Bacterial Biosensor
<p>Data for the figures in the manuscript <br> <a href="https://pubs.acs.org/doi/10.1021/acssynbio.9b00166">https://pubs.acs.org/doi/10.1021/acssynbio.9b00166</a></p> <p>Abstract:<br> <em>Caenorhabditis elegans</em> has become a key model organism within biology. In particular, the transparent gut, rapid growing time, and ability to create a defined gut microbiota make it an ideal candidate organism for understanding and engineering the host microbiota. Here we present the development of an experimental model that can be used to characterize whole-cell bacterial biosensors <em>in vivo</em>. A dual-plasmid sensor system responding to isopropyl β-d-1-thiogalactopyranoside was developed and fully characterized <em>in vitro</em>. Subsequently, we show that the sensor was capable of detecting and reporting on changes in the intestinal environment of <em>C. elegans</em> after introducing an exogenous inducer into the environment. The protocols presented here may be used to aid the rational design of engineered bacterial circuits, primarily for diagnostic applications. In addition, the model system may serve to reduce the use of current animal models and aid in the exploration of complex questions within general nematode and host–microbe biology.</p>
Psoriasis is associated with elevated gut IL-1α and intestinal microbiome alterations
<p>Background: Psoriasis is a chronic inflammatory condition that predominantly affects the skin and is associated with extracutaneous disorders, such as inflammatory bowel disease and arthritis. Changes in gut immunology and microbiota are important drivers of proinflammatory disorders and could play a role in the pathogenesis of psoriasis. Therefore, we explored whether psoriasis in a Central Asian cohort is associated with alterations in select immunological markers and/or microbiota of the gut. Methods: We undertook a case-control study of stool samples collected from outpatients, aged 30-45 years, of a dermatology clinic in Kazakhstan presenting with plaque, guttate or palmoplantar psoriasis (n=20), and age-sex matched subjects without psoriasis (n=20). Stool supernatant was subjected to multiplex ELISA to assess the concentration of 47 cytokines and immunoglobulins and to 16S rRNA gene sequencing to characterize microbial diversity in both psoriasis participants and controls. Results: The psoriasis group tended to have higher concentrations of most analytes in stool (29/47=61.7%) and gut IL-1α was significantly elevated (4.19-fold, p=0.007) compared to controls. Levels of gut IL-1α in the psoriasis participants remained significantly unaltered up to three months after the first sampling (p=0.430). Psoriasis was associated with alterations in gut Firmicutes, including elevated Faecalibacterium and decreased Oscillibacter and Roseburia abundance, but no association was observed between gut microbial diversity or Firmicutes/Bacteroidetes ratios and disease status.<br> Conclusions: Psoriasis may be associated with gut inflammation and dysbiosis. Studies are warranted to explore the use of gut microbiome-focused therapies in the management of psoriasis in this under-studied population.</p>
Code and data from "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations"
<p>Includes the microscopy images, cell tracking data and scripts used in the publication Huelsz-Prince, Guizela, et al. "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations." <em>eLife </em> 11:e80682 (2022). <a href="https://doi.org/10.7554/eLife.80682"> https://doi.org/10.7554/eLife.80682</a> .</p>
Human intestinal Bacteria Collection (HiBC): 16S rRNA gene sequences
<p>The <a href="https://hibc.rwth-aachen.de/" target="_blank" rel="noopener">Human intestinal Bacteria Collection (HiBC)</a> is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly.</p> <p>This dataset includes the sequences of the 16S rRNA gene sequences of the isolates in the FASTA nucleotide format. Sequences ending in Sanger were obtained using the Sanger dideoxy sequencing technology. Sequences ending in Genome were obtained from the genome sequence using barrnap.</p>
Flow cytometry of mesenteric lymph nodes, small and large intestinal lamina propria, and spinal cord cells from fibre-rich and fiber-free diet-fed gnotobiotic mice at baseline and after experimental autoimmune encephalomyelitis (EAE) induction
<p>We perform profiling of different immune cell populations in the small (SILP) and large intestine lamina propria (CLP), mesenteric lymph nodes (MLN) and spinal cords (SC). We are specifically interested to evaluate the impact of dietary fiber deprivation followed by mucus erosion on the immune cell profiles of T helper cells (Th cells, T cell population) of gnotobiotic mice fed a fiber-rich (FR) or fiber-free (FF) diet. This dataset aims to assess the impact of microbiome and diet on disease course in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) via T cell populations. Mice are either germ-free or colonized by intragastric gavage with a defined variation of a 14-member synthetic human gut microbiome (doi: 10.1016/j.cell.2016.10.043 and 10.1016/j.xpro.2021.100607): SM01 (Akkermansia muciniphila monocolonisation), SM03 (Bacteroides caccae, Bacteroides thetaiotaomicron, Barnesiella intestinihominis), SM04 (B. caccae, B. thetaiotaomicron, B. intestinihominis, A. muciniphila), SM12 (full community except mucin-specialists B. intestinihominis and A. muciniphila), SM13 (full community except mucin specialist A. muciniphila), or SM14 (full community: Roseburia intestinalis, Faecalibacterium prausnitzii, Marvinbryantia formatexigens, Collinsella aerofaciens, Desulfovibrio piger, B. caccae, B. thetaiotaomicron, Bacteroides ovatus, Bacteroides uniformis, B. intestinihominis, Eubacterium rectale, Clostridium symbiosum, Escherichia coli, and A. muciniphila). At age 5 to 8 weeks, mice were colonized with SM combinations while fed an FR diet. Mice were either maintained on an FR diet or switched to an FF diet at 5 days after initial colonization, until the end of experiment. Baseline samples were collected 20 days following the diet switch. Otherwise, EAE induction was performed 15 days after the diet switch and samples were collected 30 days after the induction.</p>
Genetic and epigenetic regulation of zebrafish intestinal development
<p>This dataset contains zebrafish (<em>Danio rerio</em>) raw RNA and ChIP (paired-end) sequencing data:</p> <ul> <li>RNA-seq <ul> <li>lane1_BSwt5dpf*: 3 biological replicates of RNA-seq data from 5dpf wild-type (AB background) pooled intestines</li> <li>lane1_BSwt7dpf*: 3 biological replicates of RNA-seq data from 7dpf wild-type (AB background) pooled intestines</li> <li>lane1_BSwt9dpf*: 3 biological replicates of RNA-seq data from 9dpf wild-type (AB background) pooled intestines</li> </ul> </li> <li>ChIP-seq <ul> <li>Cldn-wt-int-5dpf-H3K27me3*: 2 biological replicates of H3K27me3 ChIP-seq data from 5dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-5dpf-H3K4me3*: 2 biological replicates of H3K4me3 ChIP-seq data from 5dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-5dpf-input-12727_R[12].fastq.gz: 1 sample of input ChIP-seq data from 5dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-7dpf-H3K27me3*: 2 biological replicates of H3K27me3 ChIP-seq data from 7dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-7dpf-H3K4me3*: 2 biological replicates of H3K4me3 ChIP-seq data from 7dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-7dpf-input-12727_R[12].fastq.gz: 1 sample of input ChIP-seq data from 7dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-9dpf-H3K27me3*: 2 biological replicates of H3K27me3 ChIP-seq data from 9dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-9dpf-H3K4me3*: 2 biological replicates of H3K4me3 ChIP-seq data from 9dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-9dpf-input-12727_R[12].fastq.gz: 1 sample of input ChIP-seq data from 9dpf wild-type (AB background) pooled intestines</li> </ul> </li> </ul>
raw data of Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice
<p>Microbiome dataset for "Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice" publication</p> <p>https://doi.org/10.1016/j.jcmgh.2023.02.013</p> <p> </p>
Chicken intestinal development is affected by different dietary interventions
<p>These RDS files contain <strong>DESeqDataSet </strong>objects subset into broiler groups of 4, 12 and 33 days post-hatch.<br> These objects are the result of DESeq2::DESeq( … ,betaPrior=FALSE).The .txt-objects contain the normalized sequencing counts per broiler age group. These objects are the result of DESeq2::counts( … , normalized=TRUE). Data was generated using STAR v2.7.9a and DESeq2 v1.34. Metadata is included as Excel file.</p> <p>Sequencing data is deposited at NCBI-SRA under BioProject: PRJNA949454. </p> <p> </p> <p><strong>Study abstract</strong></p> <p>Dirkjan Schokker, Soumya K. Kar, Paul Stege, Norbert Stockhofe, Alex Bossers, Vera Perricone, Annemarie Rebel, Ingrid de Jong</p> <p>Gut health is a prerequisite for broiler welfare. In this study, we investigated the effect of three dietary interventions on small intestinal development in broilers and special emphasis on immunity. Morphometry and immunostaining showed no significant differences in any of the treatments, however, gene expression revealed significant differences in the LPHF and MCFA treatments. Pathway enrichment analysis showed a few main pathways involved in regulation of cellular processes, cell structural processes, and cell protection, as well as a putative link to inflammatory processes. Taken together, these findings suggest that dietary interventions can modulate gut health and functionality in chicken.</p>
RNA-Seq data from: Hox genes modulate physical forces to differentially shape small and large intestinal epithelia
<p>Hox genes are highly conserved, master regulators of spatial patterning in the embryo, but how these factors trigger regional morphogenesis has largely remained a mystery. In the developing gut, Hox genes help demarcate identities of the small and large intestines early in embryogenesis, which ultimately leads to their specialization in both form and function. While the midgut forms villi, the hindgut develops flat, brain-like sulci that resolve into heterogeneous outgrowths. Combining mechanical measurements and mathematical modeling, we demonstrate that the posterior Hox gene Hoxd13 regulates biophysical phenomena that shape the hindgut lumen. We further show that Hoxd13 acts through the TGFβ pathway to thicken, stiffen, and promote isotropic growth of the subepithelial mesenchyme; together, these features lead to hindgut surface buckling. TGFβ, in turn, promotes collagen deposition to affect mesenchymal geometry and growth. We thus identify a cascade of events downstream of positional genetic identity that direct posterior intestinal morphogenesis. </p> <p>To identify genes and pathways that are directly or indirectly regulated by Hoxd13 to affect posterior gut morphogenesis in the chick, we compared mesodermal transcriptomes of wild-type midgut and hindgut intestinal samples, as well as mesodermal samples from a Hoxd13-overexpressing midgut at E12 and E14. Tissues were dissected and endoderm layers were removed manually before RNA extraction and downstream processing. Unbiased clustering was used to identify genes commonly differentially expressed in the hindgut and Hoxd13-misexpressing midgut. This submission contains bulk RNA-seq raw data (fastq.bz2 files) and processed .txt files with read counts. Experiment information is provided in .xlsx Metadata file used for NCBI GEO submission.</p>
Having the guts to compete: How intestinal plasticity explains costs of inducible defenses.
Predators commonly induce phenotypic changes that make prey better at surviving predation at the cost of reduced growth. While we have a good understanding of how trait changes affect predation risk, we lack a mechanistic understanding of why predatorinduced phenotypes differ in growth. Using two mesocosm experiments, we combined phenotypic plasticity theory with predictions from optimal digestion theory to demonstrate that intra- and interspecific competition induced relatively long guts while predators induced relatively short guts. The longer guts induced by competition appear to be an adaptive response that allows more efficient digestion and more rapid growth whereas the shorter guts induced by predators appear to result from a tradeoff of building larger tails in predator environments at the cost of smaller bodies. By combining these two bodies of theory, we now have a much better understanding of the mechanisms that cause the phenotypic trade-offs that select for inducible defences.
Full summary statistics of mixQTL for GTEx v8 Small_Intestine_Terminal_Ileum
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
The impact of Opisthorchis felineus infection and praziquantel treatment on the intestinal microbiome in children
<p>The presence of some species of helminths is associated with changes in host microbiota composition and diversity, which varies widely depending on the infecting helminth species and other factors. We conducted a prospective case-control study to evaluate the gut microbiota in children with Opisthorchis felineus infection (n=50) before and after anthelmintic treatment and in uninfected children (n=49) in the endemic region. A total of 99 children and adolescents aged between 7 and 18 years were enrolled to the study. Helminth infection was assessed before and at 3 months after treatment with praziquantel. A complex examination for each participant was performed in the study, including an assessment of the clinical symptoms and an intestinal microbiota survey by 16S rRNA gene sequencing of stool samples. There was no change in alpha diversity between O. felineus-infected and control groups. We found significant changes in the abundances of bacterial taxa at different taxonomic levels between the infected and uninfected individuals. Enterobacteriaceae family was more abundant in infected participants compared to uninfected children. On the genus level, O. felineus-infected participants’ microbiomes showed higher levels of Lachnospira, Escherichia-Shigella, Bacteroides, Eubacterium eligens group, Ruminiclostridium 6, Barnesiella, Oscillibacter, Faecalitalea and Anaerosporobacter and reduction of Blautia, Lachnospiraceae FCS020 and Eubacterium hallii group in comparison with the uninfected individuals. Following praziquantel therapy, there were significant differences in abundances of some microorganisms, including an increase of Faecalibacterium and decrease of Megasphaera, Roseburia. Enterobacteriaceae and Escherichia abundances were decreased up to the control group values. Our results highlight the importance of the host-parasite-microbiota interactions for the community health in the endemic regions.</p>
Haber2017 (GSE92332) mouse intestine dataset for Besca
<p>Here we used Besca to reprocessed the mouse small intestine single cell transcriptomics dataset <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92332">GSE92332</a> downloaded from GEO. It was originally published by [<a href="https://doi.org/10.1038/nature24489">Haber et al. Nature. 2017</a>] and also used in the best practices paper by [<a href="https://doi.org/10.15252/msb.20188746">Luecken & Theis. Mol Syst Biol. 2019</a>].</p>
FIGURE 2 in A new species of Auriculostoma (Trematoda: Allocreadiidae) from the intestine of Brycon guatemalensis (Characiformes: Bryconidae) from the Usumacinta River Basin, Mexico, based on morphology and 28 S rDNA sequences, with a key to species of the genus
FIGURE 2. Scanning electron micrographs of Auriculostoma lobata n. sp. (A) Anterior end with oral sucker bearing muscular lobe on either side, genital atrium and ventral sucker. (B) Pair of muscular lobes with a short posterior ‘‘ free’ ’ end, and 4 apical dome-like papillae (white arrows). (C) Lateral view of muscular lobe. (D) Distribution of dome-like papillae over oral sucker, 6 anterior (white arrows), 4 on the inner surface (white triangle), and 5 on the outer surface (white arrows).
Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine
<p>Supplementary Table Captions:</p> <p>Supplementary Table S9. Evaluation and parameter determination of host and microbiome RNA read classification using simulation datasets</p> <p>Supplementary Table S10. 40 pathways significantly upregulated in the cecum as compared to the transverse colon</p> <p>Supplementary Table S11. Host-microbiome gene co-expression network edges</p> <p>Supplementary Table S12. Host-host gene co-expression network edges</p> <p>Supplementary Table S13. Microbiome-microbiome gene co-expression network edges</p> <p>Supplementary Table S14. List of genes included in each gene module identified from the gene co-expression network</p> <p>Supplementary Table S15. Results of enrichment analysis for each gene module identified from the gene co-expression network</p> <p>Supplementary Table S16. The top 32 bacterial species in terms of expression abundance based on metatranscriptome profiles</p> <p>Supplementary Table S17. Number of microbiome RNA reads annotated by the KEGG database</p> <p>Supplementary Table S18. Results of enrichment analysis of gene modules for each parameter</p> <p>Supplementary Table S19. Evaluation of modules in each parameter of Newman algorithm</p> <p>Supplementary Table S20. Evaluation of modules in each parameter of Louvain algorithm</p> <p>Supplementary Table S21. Evaluation of modules in each parameter of Leiden algorithm</p> <p>Supplementary Table S22. Evaluation of modules in each parameter of WGCNA</p>
Figs. 7–9. Paraharmotrema karinganiense Dutton & Bullard n in Paraharmotrema karinganiense n. gen., n. sp. (Digenea: Liolopidae) infecting the intestine of serrated hinged terrapin (Pelusios sinuatus), east African black mud turtle (Pelusios subniger), and South African helmeted turtle (Pelomedusa galeata) and a phylogenetic hypothesis for liolopid genera
Figs. 7–9. Paraharmotrema karinganiense Dutton & Bullard n. sp. (Digenea: Liolopidae) from the intestine of the intestine of the serrated hinged terrapin, Pelusios sinuatus (Smith 1838) (Pleurodira: Pelomedusidae). (7) Tegumental scales in antero-dextral ventral body surface, ventral view, light micrograph. (8) Tegumental scales on ventral body surface posterior to oral sucker, ventral view, light micrograph. (9) Tegumental scales in same position as in Fig. 8 (showing exposed tips of scales only), ventral view, scanning electron micrograph.
Figs. 5–6. Paraharmotrema karinganiense Dutton & Bullard n in Paraharmotrema karinganiense n. gen., n. sp. (Digenea: Liolopidae) infecting the intestine of serrated hinged terrapin (Pelusios sinuatus), east African black mud turtle (Pelusios subniger), and South African helmeted turtle (Pelomedusa galeata) and a phylogenetic hypothesis for liolopid genera
Figs. 5–6. Paraharmotrema karinganiense Dutton & Bullard n. sp. (Digenea: Liolopidae) from intestine of serrated hinged terrapin, Pelusios sinuatus (Smith 1838) (Pleurodira: Pelomedusidae). (5) Ventral sucker, ventral view, scanning electron micrograph. (6) Ventral sucker, ventral view, light micrograph.
Figs. 1–2. Paraharmotrema karinganiense Dutton & Bullard n in Paraharmotrema karinganiense n. gen., n. sp. (Digenea: Liolopidae) infecting the intestine of serrated hinged terrapin (Pelusios sinuatus), east African black mud turtle (Pelusios subniger), and South African helmeted turtle (Pelomedusa galeata) and a phylogenetic hypothesis for liolopid genera
Figs. 1–2. Paraharmotrema karinganiense Dutton & Bullard n. sp. (Digenea: Liolopidae). (1) Body of adult (holotype, USNM No. 1659278) from intestine of serrated hinged terrapin, Pelusios sinuatus (Smith 1838) (Pleurodira: Pelomedusidae), ventral view. (2) Body of juvenile (paratype, USNM No. 1659285) from intestine of east African black mud turtle, Pelusios subniger (Bonnaterre, 1789) (Pleurodira: Pelomedusidae), dorsal view. Oral sucker (os), pharynx (ph), nerve commissure (nc), excretory system (es), sinistral caecum (sc), ventral sucker (vs), vitellarium (vr), cirrus sac (cs), metraterm (m), vas deferens (vd), anterior vas efferens (ave), anterior testis (at), uterus (u), posterior vas efferens (pve), ovary (o), posterior testis (pt), and excretory pore (ep).
Figs. 3–4. Paraharmotrema karinganiense Dutton & Bullard n in Paraharmotrema karinganiense n. gen., n. sp. (Digenea: Liolopidae) infecting the intestine of serrated hinged terrapin (Pelusios sinuatus), east African black mud turtle (Pelusios subniger), and South African helmeted turtle (Pelomedusa galeata) and a phylogenetic hypothesis for liolopid genera
Figs. 3–4. Paraharmotrema karinganiense Dutton & Bullard n. sp. (Digenea: Liolopidae) from intestine of serrated hinged terrapin, Pelusios sinuatus (Smith 1838) (Pleurodira: Pelomedusidae). (3) Female genitalia (holotype, USNM No. 1659278), ventral view. (4) Male genitalia (holotype, USNM No. 1659278), ventral view. Egg (e), ovary (o), oviduct (ov), ootype (oo), uterus (u), primary vitelline reservoir (pvr), dextral caecum (dc), transverse vitelline duct (tvd), sinistral caecum (sc), dextral excretory branch (deb), posterior vas efferens (pve), sinistral excretory branch (seb), posterior testis (pt), cirrus sac (cs), pars prostatica (pp), secondary bipartite internal seminal vesicle (sbisv), cirrus (c), initial bipartite internal seminal vesicle (ibisv), common genital pore (cgp), metraterm (m), vitellarium (vr), and vas deferens (vd).
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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.