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407 results for “Biological Activity”
Physical soil characteristics, microbial community composition, extracellular enzymatic activity, biologically based phosphorus (BBP) pools, and available phosphorus from two soil depths, four microhabitats, and four landforms at the Jornada Experimental Range, 2021.
This dataset contains physical soil characteristics, PLFA based microbial community composition, extracellular enzymatic activity, nitrate and ammonium activity, and phosphorus availability in various phosphorus pools (Biologically Based Phosphorus, potassium sulfate, Olsen-P). Soils were collected from two depths (0-2cm, 2-30 cm), four microhabitats (grass, shrub, biocrust, interspace), and four landforms (alluvial flat, alluvial fan remnant, erosional scarplet, fan piedmont – see coordinates) within the Jornada Experimental Range in July 2021 to answer questions about how these variables change across these spatial scales in drylands. This project was a collaboration between researchers at New Mexico State University and The University of Texas at El Paso as part of the Drylands Critical Zone Thematic Cluster within the Critical Zone Network. This dataset is complete.
Accompanying dataset; 'Agroforestry enhances biological activity, diversity and soil-based ecosystem functions in mountain agroecosystems of Latin America: A meta-analysis.'
<p>The database created as part of the meta-analysis is designed to facilitate the comparison of biological activity, diversity (BIAD), and ecosystem functions (EFs) between agroforestry systems (AFS) and other land-use types. It incorporates data extracted from selected studies, each record comprising a mean value, sample size, and a variance measure to compute standard deviation. The database also categorizes data according to 22 explanatory variables, including geographical coordinates, climate classification, soil type, AFS classification, and more, to characterize the sites and management systems involved. This detailed classification enables a nuanced analysis of how different factors might influence the BIAD and EFs in the context of AFS. The database supports the meta-analysis by allowing for the estimation of effect sizes using response ratios, which compare the relative difference in BIAD and EFs between AFS and other land uses. Data extraction from primary studies was meticulous, employing both direct and indirect methods such as graph digitizing software, and missing data were supplemented using reliable sources or direct communication with the original study authors. The comprehensive nature of this database ensures that the analysis can account for a wide range of variables that may affect the outcomes of interest in the meta-analysis. </p><p>For an in-depth exploration of the study's findings and methodology, refer to the comprehensive meta-analysis available in Global Change Biology (2024), entitled "<i>Agroforestry Enhances Biological Activity, Diversity, and Soil-Based Ecosystem Functions in Mountain Agroecosystems of Latin America: A Meta-Analysis</i>."</p>
Biological data science courses at UMONS, Belgium: student's activity for 2019-2020
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2019-2020.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2020-2021
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2020-2021.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2018-2019
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2018-2019.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
FIGURE 3 in A new solar powered species of the genus Phyllodesmium Ehrenberg, 1831 (Mollusca: Nudibranchia: Aeolidoidea) from Indonesia with analysis of its photosynthetic activity and notes on biology
FIGURE 3: Phyllodesmium jakobsenae, hard structures in digestive system: A: Right jaw seen from the outside. B: Left jaw from the inside, denticles at the masticatory border seen from the inside. C: Distal part of radula of specimen No. 5. D: Closeup of distal part of radula of specimen No. 5; note the worn denticles on the edge of the rhachidian teeth .. E: Distal part of radula of specimen No. 3. F: Different angle of view of rhachidian cusps from No. 3. G: Part of the radula of specimen No. 3.
FIGURE 4 in A new solar powered species of the genus Phyllodesmium Ehrenberg, 1831 (Mollusca: Nudibranchia: Aeolidoidea) from Indonesia with analysis of its photosynthetic activity and notes on biology
FIGURE 4: Phyllodesmium jakobsenae, histology: A: View of ceras from side orientated to light. Note the dense branches along the whole ceras. B: View of ceras from side orientated away from light. Note the main branch of digestive gland and the fewer ramifications. C: Cnidosac of large ceras with no nematocysts. D: Digestive glandular branches beneath epidermis in large ceras. Note the many zooxanthellae especially in the digestive glandular tissue. E: Longitudinal section of small ceras with cnidosac. The digestive glandular duct is not branching, and shows glandular cells. The cnidosac is filled with many tiny nematocysts. The epidermis shows many glandular cells, beneath the epidermis a layer of “ cellules spéciales ” can be seen. F: Section trough eye and statocyst with one statolith. G: Small part of the oral gland with outleading duct. Abbreviations: cn cnidosac, cs “ cellules spéciales ”, e eye, mc mucous cells, st statocyst.
FIGURE 1 in A new solar powered species of the genus Phyllodesmium Ehrenberg, 1831 (Mollusca: Nudibranchia: Aeolidoidea) from Indonesia with analysis of its photosynthetic activity and notes on biology
FIGURE 1: Phyllodesmium jakobsenae, living animals from North Sulawesi: A: Specimen laying eggs in an aquarium. B: Two specimens sitting in their food coral Xenia: on the right side a specimen with more brownish cerata and to the left a bigger specimen with more whitish cerata. Polyps of Xenia surround both individuals. C: Bigger specimen from B; please note the smaller cerata in the anterior part of body and the oral tentacles stretched to the lateral sides. D: Animal sitting inactive and mimicking Xenia polyps. E: Specimen starting to crawl.
FIG. 2 in Biological activity of some Romanian and Turkish Trichoderma Pers. strains
FIG. 2. — Trichoderma Pers. hyperparasitic development on various plant pathogens: Alt., Alternaria sp. Nees; B.c., Botrytis cinerea Pers.; F.c., Fusarium culmorum (Wm.G.Sm.) Sacc.; F.g., F. graminearum Schwabe; F.o., F.oxysporum Schltdl.; F.p., F. proliferatum (Matsush.) Nirenberg ex Gerlach & Nirenberg; M.p., Macrophomina phaseolina (Tassi) Goid.; S.s., Sclerotinia sclerotiorum (Lib.) de Bary.
FIG. 1 in Biological activity of some Romanian and Turkish Trichoderma Pers. strains
FIG. 1. — In vitro antifungal activity of tested Trichoderma strains Ș4 (2), Td-Exp1 (3) and Td-Exp2 (4) against various plant pathogens (1), such as Fusarium culmorum (Wm.G.Sm.) Sacc. (A), F. oxysporum Schltdl. (B), Macrophomina phaseolina (Tassi) Goid. (C) and Sclerotinia sclerotiorum (Lib.) de Bary (D). Scale bars: 1 cm.
FIG. 3 in Biological activity of some Romanian and Turkish Trichoderma Pers. strains
FIG. 3. — Mycoparasitic activity of Trichoderma Td-Exp1 strains against plant pathogens:A, Botrytis cinerea Pers.; B, Fusarium graminearum Schwabe;C, Sclerotinia sclerotiorum (Lib.) de Bary. Scale bars: 1 cm.
FIG. 5 in Biological activity of some Romanian and Turkish Trichoderma Pers. strains
FIG. 5. — Metabolic activity in biofilm and antibiofilm effect of Trichoderma spp. against human pathogenic bacterial strains. Symbols: ns, no significant; *, p <0.05; **, p <0.01; ***, p <0.001; ****, p <0.0001.
Dataset from the analysis of biological activity of endophytic strain Serratia quinivorans KP32, the expression of biocontrol-related genes and the activity of antioxidant enzymes in bacterial cells treated with pathogenic fungi filtrates
<p>This dataset contains the data from the analyses published in the article entitled "Genetic Determinants of Antagonistic Interactions and the Response of New Endophytic Strain <i>Serratia quinivorans</i> KP32 to Fungal Phytopathogens" in the International Journal of Molecular Sciences (https://doi.org/10.3390/ijms232415561). The data consist of results collected for studies on the antifungal activity of KP32 strain towards four fungal phytopathogens, results of primer efficiency determination and studies on the expression of genes potentially involved in biocontrol after treatment of KP32 strain with the fungal phytopathogens filtrates. Additionally, absorbances from activity tests for catalase (CAT) and superoxide dismutase (SOD) in the strain treated with fungal pathogens are included.</p>
Fig. 3 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 3. Temporal overlap of Leopardus guttulus (Hensel, 1872) activity during the different seasons: autumn/winter and spring/summer; the gray area represents the overlap between the activity observed in the two periods of the year and the vertical lines represent sunrise and sunset in each period (autumn/winter: 06h 45min sunrise and 18h 05min sunset; spring/summer: 06h 08min sunrise and 19h 33min sunset).
Fig. 4 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 4. Circular graph showing the distribution of Leopardus guttulus (Hensel, 1872) records in BRLJL, Rio Grande do Sul, Brazil, throughout the 12 months sampled. The black lines represent the concentration of records.
Fig. 2 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 2. Circular graphs showing the daily activity of Leopardus guttulus (Hensel, 1872) at the BRLJL, Rio Grande do Sul, Brazil, based on all records obtained for the species (n=25, all seasons), on records obtained during spring/summer (n=15), and on records from autumn/winter (n=10). The arrow on each circular graphs indicates the direction of the angular mean.
Fig. 1 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 1. Location of the study area in South America and the State of Rio Grande do Sul (left panel), with the indication of the geographic range of Leopardus guttulus (Hensel, 1872) (grey area) in Brazil, Paraguay and Argentina (above), and the Biological Reserve Lami JosÉ Lutzenberger – BRLJL in the municipality of Porto Alegre, Rio Grande do Sul, Brazil (below). On the right panel, a detailed map of the study area showing the limits of the BRLJL (black line), the vegetation types occurring in the area, the grid sQuares of 1 x 1 km (grey lines) designed to delimitate the zones where sampling stations (camera stations) were installed (black triangles).
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis</p> <p>2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis</p>
Ir and NMR for article Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (in vitro) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors
<p>this is IR and NMR data for article titled <strong>Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (<em>in vitro</em>) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors </strong></p> <p> </p>
Figure 9 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 9. Means number of deposited eggs by females of tested mites after 4 days post-exposure to α- and γ-Al2O3 NPs at tested concentrations. Different letters denote to significant differences in means at tested concentrations (Duncan test, P ≤ 0.05).
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.