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880 results for “fecal”
Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.
To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).
Mink fecal microbiomes are influenced by sex, temperature and time post-defecation
<p>Mink metadata, QIIME2 artifacts and demultiplexed EMP-paired end sequences from Argonne National laboratory, R code for statistical analyses and figure generation, and QIIME2 pipeline for Lafferty et al. 2021.</p>
KMA Mapping and alignment statistics : livestock fecal metagenomes against ResFinder and genomes
<p>Three zip archives are included used in the analysis of the European livestock resistome.</p> <p>Two of them contain 'mapstat' files produced by the KMA software using the 'extended features' flag.<br> Each mapstat file thus summarize the mapping and alignment statistics when using KMA on a metagenome against a database.</p> <p>The last archive contains the 'refdata' file used to annotate the genomic mapstat hits. It encodes the taxonomic affilication of sequences hit by one or more samples.<br> </p>
Data for the publication "Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer"
<p>This dataset encompasses all data needed to reproduce the analyses presented in <a href="https://www.nature.com/articles/s41591-019-0406-6">Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer</a></p> <p>You can also check the <a href="https://github.com/zellerlab/crc_meta">GitHub repository</a></p>
Sediment trap fecal pellets enumerations collected aboard CCE LTER process cruises in the California Current system, 2007, 2008 and 2016.
The collection and enumeration of sinking fecal pellets on CCE LTER Process cruises has been led by Mike Stukel since 2007. Sinking particles are collected in VERTEX-style particle interceptor traps (PIT) with an 8:1 aspect ratio, 70-mm diameter, and a baffle on top comprised of 13 smaller beveled tubes with a similar 8:1 aspect ratio. Tubes are deployed with a formalin-brine for a duration of 2-5 days. After recovery, samples are gently split on a Folsom splitter and typically 3/8 to 1/2 of two separate tubes are utilized for fecal pellet enumeration. After the cruise, samples for fecal pellet enumeration are placed in a settling chamber to allow fecal pellets to settle out. Overlying water is then strained through a 60-um filter to collect any pellets that may have remained in the water. Pellets were then placed on a gridded Petri dish and analyzed using a Zeiss Discovery stereomicroscope. Pellets were separated from other particles and photographed with a dedicated camera. Image processing was then conducted using either Image J or Image Pro to extract area and maximum feret length for each fecal pellet. Pellets were classified by shape and shape-appropriate equations were used to determine the volume of each fecal pellet. Volume was converted to mass using the equations in Stukel et al. (2013). ‘Sample’ refers to which of two samples the fecal pellet was contained within. ‘PelletID’ is the identifier for each fecal pellet in a sample. ‘Conversion Factor’ accounts for the proportion of a sample that was sorted for fecal pellets, as well as the deployment duration and cross-sectional area of the sediment trap. To determine the mass flux of fecal pellets of a certain type: 1) Sum the pellet mass for all fecal pellets of that type in a given sample and 2) multiply by the Conversion factor for that sample. ‘Shape’ is an identifier for the shape of a fecal pellet: 1) ovoid, 2) cylindrical, 3) spherical, 4) tabular, 5) amorphous, 6) ellipsoidal, 7) degraded fecal mater
Taxonomic Composition of Red Knot Fecal Samples on the Virginia Coast
Taxonomic Composition of Red Knot Fecal Samples on the Virginia Coast Understanding which prey birds use and how prey selection is related to prey availability is important to understanding avian ecology and for conservation planning. Abundant prey at stopovers during migration is a key to shorebird survival and breeding success. We determined which prey were available to foraging red knots (Calidris canutus rufa) using Virginia's barrier islands during spring migration by collecting substrate core samples containing prey on sand and peat substrates in May 2017 - 2019. We also collected red knot feces during the same period and used fecal DNA metabarcoding to determine which invertebrates red knots consumed. We used compositional analysis to determine which prey red knots selected on these islands. Crustaceans (Orders Amphipoda and Calanoida) were the most abundant prey on both sand and peat. Red knots consumed bivalves (Orders Venerida and Mytiloida), crustaceans (Orders Amphipoda and Calanoida), and insect larvae (Order Diptera). Red knots selected bivalves over non-bivalve prey, though non-bivalve prey may still be an important portion of the total caloric intake on Virginia's stopover, given their abundance and use. It is important that coastal conservation practices in the Western Mid-Atlantic stopover region continue to be designed to promote natural barrier island movement which leads to the formation of the peat banks used by many prey.
Fecal and Blood Metabolites of Pigs
<p>The experimental design of the animal study and the sanitary challenge model used have been described by Van der Meer et al. (2020). Pigs were divided into high sanitary condition (HSC) or to low sanitary condition (LSC), for details please see the original publication by Van der Meer et al. (2020). At the dissection day, three pigs per room were euthanized to collect blood and digesta samples for further analysis. We used colon digesta and blood samples from pigs in this study that received a diet with a basal amino acid (AA) ratios (indicated as “diet AA-B” in the paper of van der Meer et al. 2020) and a protein content of CP 168 g/kg; LSC (n=18) and HSC (n=18). </p> <p>These samples were analyzed by Nuclear Magnetic Resonance (NMR) and by Triple Quad Mass Spectrometry (TQMS). The details of these laboratory analysis are described in the journal article entitled "Sanitary conditions affect the colonic microbiome and the colonic and systemic metabolome of female pigs" (doi: will update accordingly). The data uploaded here are by the format of the Joint Committee on Atomic and Molecular Physical Data (JCAMP). Moreover the metadata file shows the link between the samples and their corresponding group, i.e. HSC or LSC, and other characteristics.</p>
Data from: Non-invasive age estimation based on fecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins
<p class="MsoNormal"><span>Age is necessary information for the study of life history of wild animals. A general method to estimate the age of odontocetes is counting dental growth layer groups (GLGs). However, this method is highly invasive as it requires the capture and handling of individuals to collect their teeth.</span><span> Recently, the development of DNA-based age </span><span>estimation methods has been actively studied as an alternative to such invasive methods, of which many have used biopsy samples. However, if DNA-based age estimation can be developed from fecal samples, age estimation can be performed without touching or disrupting individuals, thus establishing an entirely non-invasive method. </span><span>We developed an age estimation model using the methylation rate of two gene regions, <em>GRIA2</em> and <em>CDKN2A,</em> measured through methylation-sensitive high-resolution melting (MS-HRM) from fecal samples of wild Indo-Pacific bottlenose dolphins (<em>Tursiops aduncus</em>). The age of individuals was known through conducting longitudinal individual identification surveys underwater. Methylation rates were quantified from 36 samples. Both gene regions showed a significant correlation between age and methylation rate. The age estimation model was constructed based on the methylation rates of both genes which achieved sufficient accuracy (after LOOCV: MAE = 5.08, <em>R<sup>2</sup></em> = 0.34) for the ecological studies of the Indo-Pacific bottlenose dolphins, with a lifespan of 40-50 years. This is the first study to report the use of non-invasive fecal samples to estimate the age of marine mammals.</span></p>
Data from: Wildlife fecal microbiota exhibit community stability across a semi-controlled longitudinal non-invasive sampling experiment
<p>Wildlife microbiome studies are being used to assess microbial links with animal health and habitat. The gold standard of sampling microbiomes directly from captured animals is ideal for limiting potential abiotic influences on microbiome composition, yet fails to leverage the many benefits of non-invasive sampling. Application of microbiome-based monitoring for rare, endangered, or elusive species creates a need to non-invasively collect scat samples shed into the environment. Since controlling sample age is not always possible, the potential influence of time-associated abiotic factors was assessed. To accomplish this, we analyzed partial 16S rRNA genes of fecal metagenomic DNA sampled non-invasively from Rocky Mountain elk (<em>Cervus canadensis</em>) near Yellowstone National Park. We sampled pellet piles from four different elk, then aged them in a natural forest plot for 1, 3, 7, and 14 days, with triplicate samples at each time point (i.e., a blocked, repeat measures (longitudinal) study design). We compared microbiomes of each elk through time with point estimates of diversity, bootstrapped hierarchical clustering of samples, and a version of ANOVA–simultaneous components analysis (ASCA) with PCA (LiMM-PCA) to assess the variance contributions of time, individual and sample replication. Our results showed community stability through days 0, 1, 3 and 7, with a modest but detectable change in abundance in only 2 genera (<em>Bacteroides</em> and <em>Sporobacter</em>) at day 14. The total variance explained by time in our LiMM-PCA model across the entire 2-week period was not statistically significant (p>0.195) and the overall effect size was small (<10% variance) compared to the variance explained by the individual animal (p<0.0005; 21% var.). We conclude that non-invasive sampling of elk scat collected within one week during winter/early spring provides a reliable approach to characterize microbiome composition in a 16S rDNA survey and that sampled individuals can be directly compared across unknown time points with minimal bias. Further, point estimates of microbiome diversity were not mechanistically affected by sample age. Our assessment of samples using bootstrap hierarchical clustering produced clustering by animal (branches) but not by sample age (nodes). These results support greater use of non-invasive microbiome sampling to assess ecological patterns in animal systems.</p>
Fecal-bbu-genes-quantification-predicts-L-carnitine-mediated-TMAO-production-and-serves-as-a-biomarker-for-precision-nutrition-code-20231201
<p>Custom code related to the original research article "Fecal bbu genes quantification predicts L-carnitine-mediated TMAO production and serves as a biomarker for precision nutrition"</p>
Data From: what mandrills leave behind: using fecal samples to characterize the major histocompatibility complex in a threatened primate
<p>The major histocompatibility complex (MHC) can be useful in guiding conservation planning because of its influence on immunity, fitness, and reproductive ecology in vertebrates. The mandrill (<em>Mandrillus sphinx</em>) is a threatened primate endemic to central Africa. Considerable research in this species has shown that the MHC is important for disease resistance, mate choice, and reproductive success. However, all previous MHC research in mandrills has focused on an inbred semi-captive population, so their genetic diversity may have been underestimated. Here we expand our current knowledge of mandrill MHC variation by performing next-generation sequencing of non-invasively collected fecal samples from a large wild horde in central Gabon. We observe MHC lineages and alleles shared with other primates, and we uncover 45 putative new class II MHC DRB alleles, including representatives of the DRB9 pseudogene, which has not previously been identified in mandrills. We also document methodological challenges associated with fecal samples in NGS-based MHC research. Even with high read depth, the replicability of alleles from fecal samples was lower than that of tissue samples, and allele assignments are inconsistent between sample types. Further, the common assumption that variants with very high read depth should represent true alleles does not appear to be reliable for fecal samples. Nevertheless, the use of degraded DNA in the present study still enabled significant progress in quantifying immunogenetic diversity and its evolution in wild primates.</p>
16S rRNA Sequencing Data of Fecal Microbiota in an Italian Cohort of Patients with CDKL5 Deficiency Disorder
<h3>Summary of the study </h3> <p>CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by global developmental delay, early-onset seizures, intellectual disability, visual and motor impairments, distinct from Rett Syndrome (RTT) due to the absence of a clear regression period. Gastrointestinal (GI) disturbances and signs of subclinical immune dysregulation are common in CDD patients, yet the underlying causes are unknown. Recent studies hint at a possible link between neurological disorders and gut microbiota, an unexplored area in CDD. In this groundbreaking study, we examined fecal microbiota in CDD patients and their healthy relatives, revealing differences in bacterial diversity and composition. We further investigated microbiota changes based on various factors, including the severity of GI issues, seizure frequency, sleep disorders, food intake type, neuro-behavioral features (assessed through the RTT Behaviour Questionnaire – RSBQ), and ambulation capacity. </p> <p>Our findings suggest a potential connection between CDD, microbiota, and symptom severity. This study represents the first exploration of the gut-microbiota-brain axis in CDD patients, contributing to the growing body of research on the role of gut microbiota in neurodevelopmental disorders. It opens doors to potential interventions targeting intestinal microbes to enhance the well-being of individuals with CDD.</p> <h3>Mehods</h3> <p>The Dataset represent the raw data (.fastq) obtained from the sequencing of the fecal samples from 17 Italian Patients with CDD, and 17 Healthy Relatives (i.e. siblings or mother), collected at a single time-point.</p> <p>Samples from Patients affected by CDD are called CDD, samples from Healthy Relatives are called HC-CDD (i.e. healthy controls of patients affected by CDD). For details about the sample names see the “Explanation Table”.</p> <p>Bacterial DNA was extracted from fecal samples using the QIAmp Powerfexal DNA Kit (Qiagen, Germany) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Germany).</p> <p><em>Targeted Library Preparation</em>: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA). </p> <p><em>Sequencing:</em> The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles). </p>
Figure 1. Experimental units for feeding rates tests with terrestrial isopods and fecal pellets from different food sources. A in Coprophagy in detritivores: methodological design for feeding studies in terrestrial isopods (Crustacea, Isopoda, Oniscidea)
Figure 1. Experimental units for feeding rates tests with terrestrial isopods and fecal pellets from different food sources. A) Treatment access; coprophagy is allowed. B) Treatment removal; coprophagy and bacterial activity on feces are avoided. C) Treatment net; coprophagy is avoided and bacterial activity on feces allowed. D) Fecal pellet from carrot (left) and decomposing leaf (right) consumption.
Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation
<p>Inundation of coastal stormwater networks by tides is widespread due to sea-level rise (SLR). The water quality risks posed by tidal water rising up through stormwater infrastructure (pipes and catch basins), out onto roadways, and back out to receiving water bodies are poorly understood but may be substantial given that stormwater networks are a known source of fecal contamination. In this study, we (1) documented temporal variation in concentrations of <em>Enterococcus spp</em>. (ENT), the fecal indicator bacteria standard for marine waters, in a coastal waterway over a two-month period and more intensively during two perigean spring tide periods, (2) measured ENT concentrations in roadway floodwaters during tidal floods, and (3) explained variation in ENT concentrations as a function of tidal inundation, antecedent rainfall, and stormwater infrastructure using a pipe network inundation model and robust linear mixed effect models. We find that ENT concentrations in the receiving water body vary as a function of tidal stage and antecedent rainfall, but also site-specific characteristics of the stormwater network that drains to the waterbody. Tidal variables significantly explain measured ENT variance in the waterway, however, runoff drove higher ENT concentrations in the receiving waterway. Samples of floodwaters on roadways during both perigean spring tide events were limited, but all samples exceed thresholds for safe public use of recreational water. These results indicate that inundation of stormwater networks by tides could pose public health hazards in receiving water bodies and on roadways, which will likely be exacerbated in the future due to continued SLR.</p>
Analysis Data, "Drivers and Determinants of Strain Dynamics Following Fecal Microbiota Transplantation"
<p>This package contains datasets in `Rdata` format that underlie the analyses presented in the study, "Drivers and Determinants of Strain Dynamics Following Fecal Microbiota Transplantation" by Schmidt, Li et al.<br> </p> <p>Corresponding code, referring to these datasets, is available via `github`:</p> <p>https://github.com/grp-bork/fmt_metastudy</p> <p> </p> <p>The study is available as a preprint:</p> <p>https://www.biorxiv.org/content/10.1101/2021.09.30.462010v1</p> <p> </p> <p>The present package contains processed/derived data. Metagenome-Assembled Genomes generated for the same study are available via `Zenodo` under:</p> <p>https://zenodo.org/record/5534163#.YpoRFy8RrzA<br> doi: 10.5281/zenodo.5534163</p>
Diet composition based on stable isotopic analysis of fecal samples revealed the preference of Black-faced Spoonbill (Platalea minor) for natural wetlands and fishponds
<p><span>Background:</span><span> Black-faced spoonbill (BFS) is a global endangered species, distributed only in the coastal zones of East Asia. Xinghua Bay is one of the main wintering sites and migration stopovers of BFS in mainland China. However, </span><span>with the </span><span>reduction and degradation of natural wetlands, it is uncertain whether the constructed wetland can provide habitat for the endangered BFS. Research on diet of BFS will help to understand their preference between natural and artificial wetlands, and also provide reference for their conservation and habitat restoration. </span></p> <p><span>Results:</span><span> In the early winter, the proportion of Palaemonidae in BFS's food was as high as 74.4%, while that of other food was only 3.0% to 6.0%. In the late winter, the food contribution of BFS was as follow: Portunidae </span><span>39.3% </span><span>> Palaemonidae </span><span>26.1% </span><span>> Cyprinidae </span><span>8.8% </span><span>></span> <span>Mugilidae </span><span>8.5% </span><span>> Gobiidae </span><span>7.3% </span><span>></span> <span>Crucian </span><span>5.1% </span><span>> Whiteshrimp </span><span>4.8%</span><span>. The proportion of Portunidae exceeded that of Palaemonidae, and together with Palaemonidae, it has become the main food of BFS in late winter. </span></p> <p><span>Conclusion: </span><span>The diet composition of BFS between the early and late winter was significantly different, which may be due to seasonal changes in food resources. Natural wetlands are the main feeding grounds of BFS, but artificial wetlands also provide them with supplementary feeding grounds and resting places. Aquaculture ponds play an important ecological function in maintaining the overwintering population of BFS in Xinghua Bay.</span></p>
Association of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model
<p>this dataset are “ssociation of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model” Supplementary Material.</p>
Fig. 1 in An unexpected diversity of trypanosomatids in fecal samples of great apes
Fig. 1. Phylogenetic relationships of the detected trypanosomatids. An SSU rRNA-based Bayesian phylogenetic tree of trypanosomatid sequences (∼2 kb) obtained from gorilla and chimpanzee fecal samples collected in the Dja Faunal Reserve in Cameroon representing the most likely two new Herpetomonas species, one unknown Phytomonas species and two most likely new monoxenous trypanosomatid species of unnamed genera. These possible new species are assigned as new Typing Units (TUs) with numbers TU229–233. Bootstrap values from Bayesian posterior probabilities (MrBayes; 5 million generations) and bootstrap percentages for maximum-likelihood analysis (PhyML; 1000 replicates) are shown at the nodes; dashes indicate <50% bootstrap support or different topology; asterisks mark branches with maximal statistical support. The tree was rooted with Paratrypanosoma; the closest relative of the family Trypanosomatidae. Parasite names or names of strains supplemented with their GenBank accession numbers are given; the branch lengths are drawn proportionally to the amount of changes (scale bar).
Figure 3 in Seasonal variations in immunoreactive cortisol and fecal immunoglobulin levels in Sichuan golden monkey (Rhinopithecus roxellana)
Figure 3. The immunoreactive cortisol concentrations of Sichuan golden monkeys within seasons (ng/g). Sp: Spring; Su: summer; Au: autumn; Wi: winter. FM refers to the mean of nonpregnant females (F1 and F2); MM refers to the mean of males (M1, M2, and M3). *,#, §,﹠: P <0.05, bar with * was significantly higher than bar with #, and bar with § was significantly higher than bar with ﹠.
Figure 5 in Seasonal variations in immunoreactive cortisol and fecal immunoglobulin levels in Sichuan golden monkey (Rhinopithecus roxellana)
Figure 5. The fecal immunoglobulin levels of Sichuan golden monkeys over the year (ng/g). FM refers to mean of nonpregnant females (F1 and F2); MM refers to mean of males (M1, M2, and M3).
ScienceDex guides
Understand access before you commit
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.