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163 results for “Protist”
Fig. 2 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 2. Geostatistical analysis of Lohmaniella oviformis (inset in a) abundance (cells ml–1) produces: a) the variogram, b) the kriging map, and c) a map of the coefficient of variation (CV). A spherical model (a, line) is fit to the empirical variogram (a, points); the points account for different number of pairs of abundance averaged on a class distance (lag). Only half of the maximum distance was calculated and represented to avoid the edge effect, where there are fewer sampling points (see text). The model (a, line) is used to predict abundance at unsampled points and to assess characteristics of patches. The model is also used to map patches of L. oviformis abundance (b, grey areas) using the kriging interpolator; a patch is operationally defined as abundance in the upper quartile. On the CV map (c), grey areas (with lower abundance and closer to edges) have the highest coefficient of variation of the estimated distribution.
Fig. 1. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 1. A schematic description of establishing a variogram, modelling a function, and producing maps by kriging. Samples (e.g. to determine ciliate abundance) are collected at points of a sampling grid (a). Variance estimates of ciliate abundances at points separated by a common distance (lag, h) are calculated using the equation (explanations in the text); this is repeated for each lag (three examples of lags are illustrated in a). Each variance estimate is then plotted against its respective lag to produce an empirical variogram (points in b). Then, a model is fit to the variogram data (lines in b), and the model is used to predict abundance at unsampled points and to characterize patches. The parameters of the variogram models are the nugget, the range, and the sill (see text for their interpretation). Three models are the most common: the Gaussian, spherical and exponential (thick, medium, and thin lines, respectively, in b). Models are used to map ciliate abundance by the kriging procedure, with each model producing different predicted distributions (c, d, e): the spherical and exponential produce "fuzzier" images than the Gaussian.
Fig. 6 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions
Fig. 6. Patches of total phytoplankton biomass (ng C ml–1, left) and total ciliate abundance (cells ml–1, right) in the Irminger Sea, North Atlantic. The spatial coincidence indicates a potential prey-predator relationship.
Dataset and codes for: Partitioning the apparent temperature sensitivity between autotrophic and heterotrophic protists
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Data from: Phylogenetic relatedness drives protists assembly in marine and terrestrial environments
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Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protist Tetrahymena thermophila
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Data from: Selection on growth rate and local adaptation drive genomic adaptation during experimental range expansions in the protist Tetrahymena thermophila
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Ecoregional patterns of protist communities in mineral and organic soils: assembly processes, functional traits and diversity of testate amoebae in Northern Eurasia
<p>TA_Traits.csv file includes trait data for testate amoebae. TA_Abundance.csv file includes environmental variables (soil type and region) and relative abundance data of testate amoebae.</p>
Supplementary data 'Mitochondrial genome sequence of the protist Ancyromonas sigmoides Kent, 1881 (Ancyromonadida) from the Sugluk Inlet, Hudson Strait, Nunavik, Québec'
<p>Fasta file with the transcripts obtained from RNAseq sequencing of Ancryomonas sigmoides (kmer 35) and file with datamining results.</p><p>Fasta file of the transcript matching the cox1 gene.</p><p>Scaffolds of the kmer 85 assembly of the genomic data of Ancryomonas sigmoides.</p><p>Databse used for datamining (fasta file)</p>
Protists regulate microbially-mediated organic carbon turnover in soil aggregates
<p>Soil protists, the major predator of bacteria and fungi, shape the taxonomic and functional structure of soil microbiome via trophic regulation. However, how trophic interactions between protists and their prey influence microbially mediated soil organic carbon turnover remains largely unknown. Here, we investigated the protistan communities and microbial trophic interactions across different aggregates-size fractions in agricultural soil with long-term fertilization regimes. Our results showed that aggregate sizes significantly influenced the protistan community and microbial hierarchical interactions. Bacterivores were the predominant protistan functional group and were more abundant in macroaggregates and silt + clay than in microaggregates, while omnivores showed an opposite distribution pattern. Furthermore, partial least square path modeling revealed positive impacts of omnivores on the C-decomposition genes and soil organic matter (SOM) contents, while bacterivores displayed negative impacts. Microbial trophic interactions were intensive in macroaggregates and silt + clay but were restricted in microaggregates, as indicated by the intensity of protistan-bacterial associations and network complexity and connectivity. Cercozoan taxa were consistently identified as the keystone species in SOM degradation-related ecological clusters in macroaggregates and silt + clay, indicating the critical roles of protists in SOM degradation by regulating bacterial and fungal taxa. Chemical fertilization had a positive effect on soil C sequestration through suppressing SOM degradation-related ecological clusters in macroaggregate and silt + clay. Conversely, the associations between the trophic interactions and SOM contents were decoupled in microaggregates, suggesting limited microbial contributions to SOM turnovers. Our study demonstrates the importance of protists-driven trophic interactions on soil C cycling in agricultural ecosystems.</p>
Fig. 3 in The Challenges of Incorporating Realistic Simulations of Marine Protists in Biogeochemically Based Mathematical Models
Fig. 3. The six stages of selective grazing, redrawn from Montagnes et al. (2008b).
Fig. 2 in Changing Views of Arctic Protists (Marine Microbial Eukaryotes) in a Changing Arctic
Fig. 2. Loboea, collected from Northern Baffin Bay. Scale bar: 8 µm.
Data from: Effects of pesticides on soil bacterial, fungal and protist communities, soil functions and crop quality in vineyards
<p>Pesticides can have unintentional effects on non-target organisms and change biotic communities. Such changes might be particularly important in soil microbial communities which drive many ecosystem functions and may affect crop quality. Here, we investigated, in a 3-year study, how vegetation control (by herbicide application) and soil copper content (from long-term copper-based fungicide application), affect biodiversity and the community structure of soil bacteria, fungi and protists and associated soil functions (respiration, decomposition) in Swiss vineyards. Furthermore, we determined the effects of these two management practices on grape quality as the most direct ecosystem service to farmers. Across all study years, the community composition of microorganisms was affected by herbicide application, however, a significant loss of operational taxonomic units (OTUs) was only observed in fungi and protists. Soil copper content reduced OTU richness of bacteria and protists in some years but had no significant effect on fungal richness. Copper changed the community composition in all three groups of soil microorganisms. While we found no effect of copper on soil functions, herbicide application reduced microbial respiration and biomass by about 39% and 45% respectively. However, decomposition rates remained virtually unchanged by any pesticide. Yeast assimilable nitrogen (YAN) levels in grape must were below the critical threshold of 140 mg/L in 40% of the vineyards without herbicide application and the variety Chasselas , whereas in vineyards with herbicide application it was only 20%. Synthesis and applications: Application of pesticides led to changes in richness and composition of soil microbial communities and directly reduced some soil functions (microbial biomass and respiration), but not all (decomposition). Some grape quality parameters can be indirectly enhanced by pesticide application, highlighting the trade-off between the interests of nature conservation and the interests of the farmer. Balancing these two diverging interests requires the establishment of alternative vineyard management allowing reduced pesticide application.</p>
mTagBFP2-expressing vectors for electroporation of marine protists
<p>Plasmid maps and genbank sequence files for the plasmids used for electroporation of model organism <em>Nannochloropsis oceanica</em> and environmental samples following the protocol <a href="https://www.protocols.io/view/fabrication-of-dna-constructs-by-gibson-assembly-a-7r8hm9w">Matute et al.</a></p> <p>EMS initiative from Gordon and Betty Moore foundation.</p>
Catalogue of Life: Catalog of Life Protists
CoL Ciliophora, Oomycota, Polycystina, Eccrinida, Microsporidia, Mycetozoa, Chaetocerotaceae, Naviculaceae branches. Data sets for the assembly of the EOL dynamic hierarchy.
Catalogue of Life: Catalog of Life Protists (20 Feb 2019 dump)
<p></p>https://eol-jira.bibalex.org/browse/TRAM-803 Data sets for the assembly of the EOL dynamic hierarchy.
Arctic Biodiversity: Arctic Protists
Biogeography and other attributes for Arctic organisms, various sources.<p></p>Meltofte, H. (ed.) 2013. Arctic Biodiversity Assessment. Status and trends in Arctic biodiversity. Conservation of Arctic Flora and Fauna, Akureyri. <p></p>https://arcticbiodiversity.is/index.php/the-report/chapters/microorganisms
Protists in Singapore
This is an illustrated guide to protists (protozoa and algae) from Singapore, especially from freshwater habitats. Everything you see here has been sampled from ponds, drains, and other places in the city and around the island. You don__t have to look very far to find microscopic life: some of those shown here are from parks and gardens, while others are from urban roadside drains. <p></p>https://sgprotist.wordpress.com/
Soil protist functional composition shifts with atmospheric nitrogen deposition in subtropical forests
<p><span>1. </span><span>Soil protist plays a key role in ecological functions through predation and parasitism. However, little is known about how nitrogen (N) deposition and seasonal variations influence soil protist function in forest soils. </span></p> <p><span>2. </span><span>Here, we assessed firstly the impacts of N deposition (control, 50 kg N ha<sup>-1</sup> yr<sup>-1</sup>, 100 kg </span><span>N ha<sup>-1</sup> yr<sup>-1</sup></span><span>, and 150 kg </span><span>N ha<sup>-1</sup> yr<sup>-1</sup></span><span>) on the functional composition of the soil protist community in summer and winter, using amplicon sequencing of environmental DNA from a subtropical natural forest. </span></p> <p><span>3. </span><span>We found that soil protists were dominated by consumers (42.6–51.6%), followed by parasites (32.9–40.9%) and phototrophs (3.2–13.1%), implying a predominant role of consumers and potential top-down effects on the other trophic groups in subtropical forest soils. The functional composition of soil protists was greatly influenced by N deposition, but these responses were dependent on seasonal variations. The diversity of phototrophs was lower in summer than in winter. Instead, an opposite pattern was observed for consumers, resulting in a significantly higher protist diversity in summer than in winter, which indicates a greater sensitivity of soil protists to seasonal variations. Furthermore, low and high N deposition simplified the structural complexity of soil protist communities, suggesting a nonlinear response of the protist structural stability to N deposition.</span></p> <p><span>4. <em>Synthesis and applications.</em></span><span><em> </em>This study provides unprecedented evidence that season variation plays an important role in regulating responses of soil protist functional composition to N deposition, and highlights the nonlinear effects of rising N deposition levels on the soil food web. </span></p>
Data from: Effects of pesticides on soil bacterial, fungal and protist communities, soil functions and crop quality in vineyards
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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.