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6,175 results for “Composition”
Figure 6 in Catch composition and discard of the boat seine in the İstanbul Strait (Turkey)
Figure 6. The ranges and frequencies of the lengths of the most abundant species.
Figure 2 in Assessment of terrestrial snails' diversity and composition in six forests of west central - Morocco along an altitudinal gradient
Figure 2. Geographical location of the prospected stations.
Figure 3 in Species composition and ecological structure of ground beetle communities (Coleoptera, Carabidae) in reclaimed rock dumps in the south of Western Siberia
Figure 3. Variations in the dynamic density of ground beetles at the study sites (Krb1–Krb5).
COMPOSITES REINFORCED WITH FIBERS OR FIBROUS CRYSTALS
<p><em><span>In this article, the types of composite materials and their development, fields of application, and technological methods of preparation are widely covered.</span></em></p>
THE GENERAL EFFECT OF PROTEINS, THEIR COMPOSITION AND THE ROLE OF THE ORGANISM
<p><em><span>This scientific in the article of proteins common influence , content organism role seeing exit on the subject main concepts light up given</span></em></p>
Score matching for differential abundance testing of compositional high-throughput sequencing data - data repository
<p>Data repository for "Score matching for differential abundance testing<br>of compositional high-throughput sequencing data" (<a href="https://github.com/bio-datascience/cosmoDA">github</a>)</p> <p>To use, clone the repository, then download the zip file and unpack it in the main directory of the repository.</p>
4D Printing of Electro-Activated Thermochromic Composites for Dynamic 3D Displays
<ol> <li>Grasshopper algorithm for 7-segment display + Appendix A</li> <li>MATLAB files for color grading + Appendix B</li> <li>7-Segment display video at 5x speed</li> </ol>
MMETHANE: predicting host status from microbial composition and metabolomics data with interpretable AI
<p>Data to reproduce figures:</p> <ol> <li>"real_data.xlsx" contains data to reproduce Figure 2.</li> <li>"semisyn_data.xlsx" contains data to reproduce Figure 3.</li> <li>"fingerprint_data.xlsx" contains data to reproduce Supplementary Figure 1.</li> </ol>
Data from: Site-specific group selection drives locally adapted group compositions
Group selection may be defined as selection caused by the differential extinction or proliferation of groups. The socially polymorphic spider Anelosimus studiosus exhibits a behavioral polymorphism where females exhibit either a "docile" or "aggressive" behavioral phenotype. Natural colonies are composed of a mixture of related docile and aggressive individuals, and populations differ in colonies' characteristic docile:aggressive ratios. Using experimentally-constructed colonies of known composition, we demonstrate that population-level divergence in docile:aggressive ratios is driven by site-specific selection at the group level: certain ratios yield high survivorship at some sites but not others. Our data also suggest that colonies responded to the risk of extinction: perturbed colonies tended to adjust their composition over 2 generations to match the ratio characteristic of their native site, thus promoting their long-term survival in their natal habitat. However, colonies of displaced individuals continued to shift their compositions towards mixtures that would have promoted their survival, had they remained at their home sites, regardless of their contemporary environment. Thus, the regulatory mechanisms that colonies use to adjust their composition appear to be locally-adapted. Our data provide the best experimental evidence of group selection driving collective traits in wild populations.
Antibacterial and osteogenic activities of clindamycin-releasing mesoporous ailica/carboxymethyl chitosan composite hydrogels
<p>Conventional treatment of jaw bone infection is often ineffective to control bacterial infection and enhance bone regeneration. Biodegradable composite hydrogels comprised carboxymethyl chitosan (CMCS) and clindamycin (CDM)-loaded mesoporous silica nanoparticles (MCM-41), possessing dual antibacterial activity and osteogenic potency, were developed in the present study. CDM was successfully loaded into both untreated and plasma-treated MCM-41 nanoparticles, denoted as (p)-MCM-41, followed by the incorporation of each of CDM-loaded (p)-MCM-41 into CMCS. The resulting CDM-loaded composite hydrogels, (p)-MCM-41-CDM-CMCS, demonstrated slow degradation rates (about 70% remaining weight after 14-day immersion), while the CDM-free composite hydrogel entirely disintegrated after 4-day immersion. The plasma treatment was found to improve drug loading capacity and slow down initial drug burst effect. The prolonged releases of CDM from both (p)-MCM-41-CDM-CMCS retained their antibacterial effect against <i>Streptococcus sanguinis</i> for at least 14 days<i> in vitro.</i> <i>In vitro</i> assessment of osteogenic activity showed that the CDM-incorporated composite hydrogel was cytocompatible to human mesenchymal stem cells (hMSCs) and induced hMSC mineralization via p38-dependent upregulated alkaline phosphatase activity. In conclusion, novel (p)-MCM-41-CDM-CMCS hydrogels with combined controlled release of CDM and osteogenic potency were successfully developed for the first time, suggesting their potential clinical benefit for treatments of intraoral bone infection.</p>
Data from: Organic carbon, grain size, elemental/isotopic composition
<p>Salt marshes and seagrass meadows can sequester and store high quantities of organic carbon (OC) in their sediments relative to other marine and terrestrial habitats. Assessing carbon stocks, carbon sources, and the transfer of carbon between habitats within coastal seascapes are each integral in identifying the role of blue carbon habitats in coastal carbon cycling. Here, we quantified carbon stocks, sources, and exchanges in seagrass meadows, salt marshes, and unvegetated sediments in six bays along the Pacific coast of California. The salt marshes studied here contained approximately twice as much OC as did seagrass meadows, 23.51 ± 1.77 kg OC m<sup>-3</sup> compared to 11.01 ± 1.18 kg OC m<sup>-3</sup>, respectively. Both seagrass and salt marsh sediment carbon stocks were higher than previous estimates from this region but lower than global and U.S.-wide averages, respectively. Seagrass-derived carbon was deposited annually into adjacent marshes during fall seagrass senescence. However, isotope mixing models estimate that negligible amounts of this seagrass material were ultimately buried in underlying sediment. Rather, the vast majority of OC in sediment across sites was likely derived from planktonic/benthic diatoms and C3 salt marsh plants.</p>
Figure 2 from: Fiorillo BF, Maciel JH, Martins M (2021) Composition and natural history of a snake community from the southern Cerrado, southeastern Brazil. ZooKeys 1056: 95-147. https://doi.org/10.3897/zookeys.1056.63733
Figure 2 Land use types in the region of the Santa Bárbara Ecological Station.
Figure 1 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 1 - Map of the study area in Piedmont, North Carolina, with 33 sample sites indicated.
Figure 1 from: Esqueda-González M, Ríos-Jara E, Galván-Villa C, Rodríguez-Zaragoza F (2014) Species composition, richness, and distribution of marine bivalve molluscs in Bahía de Mazatlán, México. ZooKeys 399: 43-69. https://doi.org/10.3897/zookeys.399.6256
Figure 1 - Study area and sampling sites at Bahía de Mazatlán, México.
Figure 3 from: Marques R, Mebert K, Fonseca É, Rödder D, Solé M, Tinôco MS (2016) Composition and natural history notes of the coastal snake assemblage from Northern Bahia, Brazil. ZooKeys 611: 93-142. https://doi.org/10.3897/zookeys.611.9529
Figure 3 - Activity of snakes recorded during the day.
Eq 1 from: Pellegrini TG, Sales LP, Aguiar P, Ferreira RL (2016) Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition. Subterranean Biology 18: 17-38. https://doi.org/10.3897/subtbiol.18.8335
Eq 1 -
The fatigue damage evolution in the load-carrying composite laminates of wind turbine blades
<p>Video and data-set behind the figures used in the book chapter:</p> <p>Mikkelsen, LP, The fatigue Damage evolution in the load-carrying composite laminates of wind turbine blades, chapter 16 in “Fatigue life prediction of composites and composite structures”, Edited by A.P. Vassilopoulos, Second edition, Elsevier Ltd, To be published in 2019. <a href="https://doi.org/10.1016/B978-0-08-102575-8.00016-4">https://doi.org/10.1016/B978-0-08-102575-8.00016-4</a>.</p> <p>The files are organised based on the first characters in the filename with fist two numbers and than a "a" for video and "b,c.." for data behind that video. The video have the following link to the figures in the book chapter:</p> <p>Figure 16.19: 03a-Eps05-2-Zoom.mp4 , <a href="https://youtu.be/PynQ0IEyne8">https://youtu.be/PynQ0IEyne8</a><br> Figure 16.21A: 01a-2Dprojection.avi , <a href="https://youtu.be/sr1J_5oc2EE">https://youtu.be/sr1J_5oc2EE</a><br> Figure 16.21B: 02a-Video 3D 360.mpg , <a href="https://youtu.be/riqtI6fARko">https://youtu.be/riqtI6fARko</a><br> Figure 16.25: 11a-Eps05-2.mp4 , <a href="https://youtu.be/RJwfpiiLws0">https://youtu.be/RJwfpiiLws0</a><br> Figure 16.28A: 21a-LFoVexsitu.mp4 , <a href="https://youtu.be/PmUWAI9qfMQ">https://youtu.be/PmUWAI9qfMQ</a><br> Figure 16.28B: 22a-SFoV1G.mp4 ,<a href="https://youtu.be/sFBgVyF1Bfw"> https://youtu.be/sFBgVyF1Bfw</a><br> Figure 16.29: 23a-SFoVGreen.mp4 ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/wJMV9bP5ntw">https://youtu.be/wJMV9bP5ntw</a><br> Figure 16.30: 24a-SFoVRed.mp4 ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/9m1Q0MrzJrY">https://youtu.be/9m1Q0MrzJrY</a><br> Figure 16.31: 25a-RotatingSegmentedDamage.mpg ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/OHPkwwaTUSM">https://youtu.be/OHPkwwaTUSM</a><br> Figure 16.34: 31a-Stained.mpg ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/Sddg0sbzw6w">https://youtu.be/Sddg0sbzw6w</a><br> Figure 16.35: 41a-RLmovie.mpg ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/H7aZrIYjKeQ">https://youtu.be/H7aZrIYjKeQ</a><br> Figure 16.36A: 51a-SFoV-Tension-Compression-T.mpg ,<a href="https://youtu.be/AdsjDkq8QE8"> https://youtu.be/AdsjDkq8QE8</a><br> Figure 16.36B: 52a-SFoV-Tension-Compression-W.mpg , <a href="https://youtu.be/ddSqQnW8jMo">https://youtu.be/ddSqQnW8jMo</a></p>
Figure 2 from: Nasu T, Kitagawa K, Karasawa S (2018) Species compositions of terrestrial isopods in public parks of a commuter town in Japan. In: Hornung E, Taiti S, Szlavecz K (Eds) Isopods in a Changing World. ZooKeys 801: 389-399. https://doi.org/10.3897/zookeys.801.21875
Figure 2 Frequencies of species richness of native, exotic, and total species in parks
Dataset from Makino H. (2022). Arithmetic value representation for hierarchical behavior composition. Nature Neuroscience.
<p>Dataset from the paper:</p> <p>Makino H. (2022). Arithmetic value representation for hierarchical behavior composition. Nature Neuroscience.</p> <p>Each variable is described in Description.pdf.</p> <p>Analysis code is available at <a href="https://github.com/HiroshiMakinoLaboratory/Makino2022NatureNeuroscience">https://github.com/HiroshiMakinoLaboratory/Makino2022NatureNeuroscience</a>.</p>
Solhomfjell forest floor species composition 1988-2018
<p>Økland, R.H. & Eilertsen, O. 1993. Vegetation-environment relationships of boreal coniferous forests in the Solhomfjell area, Gjerstad, S Norway. - Sommerfeltia 16: 1-254. Oslo. ISBN 82-7420-018-7. ISSN 0800-6865.</p> <p>The understory vegetation (vascular plants, bryophytes and lichens) in an area dominated by boreal coniferous forests is subjected to detailed ecological analysis. Two hundred meso sample plots (1 m2) are used as basis for vegetation sampling, and provided with measurements of 33 environmental variables. Species abundance is recorded as frequency in 16 subplots. Parallel DCA and 2-dimensional LNMDS ordinations of meso sample plots were largely identical, both provided two coenocline axes interpretable in ecological terms. The first axis is interpreted as the response to a broad-scale topographical complex-gradient, made up of two independent complex-gradients; (1) a topography-soil depth complex-gradient in the pine forest (running from lichen-rich pine forests to submesic Vaccinium myrtillus-dominated spruce forests), and (2) a complex-gradient in soil nutrient status in the spruce forest. The second axis, mainly affecting the species composition of the bottom layer, is interpreted as a fine-scale paludification gradient. The causes of variation along these gradients are discussed: Desiccation tolerance is considered to act directly on the physiology of vascular plant species, setting their limits towards xeric sites. Similarly, cryptogams with optima in the more mesic sites are considered to be excluded from drier sites by physiological tolerance. Limits of cryptogams towards more mesic sites are, however, considered to be set by competitive ability (growth rates) in accordance with the competitive hierarchy theory. N availability is assumed to be the most important factor for differentiation of vascular plants along the nutrient gradient, while bryophytes are expected to respond to a complex of factors, including structural properties of the humus layer. Increasing N accumulation in the humus towards xeric sites may indicate oversaturation due to deposition of airborne NO3- or NH4+. Fine-scale paludification, mainly of a soligenous type, occurred in sloping terrain with shallow soil. The cryptogams apparently make up a competitive hierarchy also along the paludification gradient. No other coenoclines could be identified by analysis of 0.0625 m2 micro sample plots, most probably because the response of vegetation to micro-scale environmental gradients (probably most important: the variation in microtopography) not essentially different from the meso-scale gradients, and because the importance of random processes increase towards finer scales. Structuring processes are discussed with reference to the observed patterns. The lack of a closed bottom layer in almost all sample plots is considered a strong indication of high importance of fine-scale disturbance and density-independent mortality in the investigated system, while interspecific competition is of lower importance. The methodology in vegetation ecological studies is discussed with particular reference to monitoring. The potential of an integrated concept using permanent plots, parallel investigation of vegetation and environmental parameters, and gradient analysis, is stressed. Several suggestions for future studies, based on this integrated approach, are made.</p>
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.