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6,175 results for “Composition”
Figure 2 from: Iosypenko OO, Kyslychenko VS, Omelchenko ZI, Burlaka IS (2019) Fatty acid composition of vegetable marrows and zucchini leaves. Pharmacia 66(4): 201-207. https://doi.org/10.3897/pharmacia.66.e37893
Figure 2 Scheme of chromatography of fatty acid composition of yellow zucchini leaves.
Figure 3 from: Iosypenko OO, Kyslychenko VS, Omelchenko ZI, Burlaka IS (2019) Fatty acid composition of vegetable marrows and zucchini leaves. Pharmacia 66(4): 201-207. https://doi.org/10.3897/pharmacia.66.e37893
Figure 3 Scheme of chromatography of fatty acid composition of green zucchini leaves.
Figure 1 from: Iosypenko OO, Kyslychenko VS, Omelchenko ZI, Burlaka IS (2019) Fatty acid composition of vegetable marrows and zucchini leaves. Pharmacia 66(4): 201-207. https://doi.org/10.3897/pharmacia.66.e37893
Figure 1 Scheme of chromatography of fatty acid composition of vegetable marrows leaves.
Data for publication "Does browning-induced light limitation reduce fish body growth through shifts in prey composition or reduced foraging rates?"
<p>Data for the publication "Does browning-induced light limitation reduce fish body growth through shifts in prey composition or reduced foraging rates?" in Freshwater Biology (<a href="https://doi.org/10.1111/fwb.13481"> https://doi.org/10.1111/fwb.13481)</a></p> <p>.</p>
Figure 2 in Winter species composition, diversity and abundance of macrozoobenthos in Kuwait's waters, Arabian Gulf
Figure 2. Th e percentage composition of species number of the different taxonomic groups.
Figure 7 from: Fiorillo BF, Silva BR, Menezes FA, Marques OAV, Martins M (2020) Composition and Natural History of Snakes from Etá Farm region, Sete Barras, south-eastern Brazil. ZooKeys 931: 115-153. https://doi.org/10.3897/zookeys.931.46882
Figure 7 ABothrops jararaca (photo: Rafael Menegucci) BB. jararacussu.
Figure 1 from: Fiorillo BF, Silva BR, Menezes FA, Marques OAV, Martins M (2020) Composition and Natural History of Snakes from Etá Farm region, Sete Barras, south-eastern Brazil. ZooKeys 931: 115-153. https://doi.org/10.3897/zookeys.931.46882
Figure 1 Topographic map of the region where the Etá Farm is located.
Characterization of the Fiber Orientations in Non-Crimp Glass Fiber Reinforced Composites using Structure Tensor
<p>This dataset contains data, notebooks and code used in the publication:</p> <p>[1] Jeppesen, N., V.A. Dahl, A.N. Christensen, A.B. Dahl, L.P. Mikkelsen, Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor. <em>IOP Conf. Ser.: Mater. Sci. Eng.</em> <strong>942</strong>, 012037, <a href="https://doi.org/10.1088/1757-899X/942/1/012037">https://doi.org/10.1088/1757-899X/942/1/012037</a>, 2020</p> <p>If you reference this dataset, please also consider referencing the paper above.</p> <p>HF401TT-13_FoV16.5_Stitch.zip contains an X-ray CT scan of a non-crimp glass fiber composite sample saved in the TXM file format.</p> <p>HF401TT-13_FoV16.5_Stitch.txm.nii contains a cut-out of the TXM data, where air around the sample has been removed and the result has been saved in the NIfTI file format.</p> <p>The two notebooks, <em>StructureTensorFiberAnalysisDemo</em> and <em>StructureTensorFiberAnalysisAdvancedDemo</em> rely on the NIfTI scan data, HF401TT-13_FoV16.5_Stitch.txm.nii. They demonstrate how to do structure tensor orientation analysis on the data.</p> <p>The HF401TT-13_FoV16.5_Stitch notebook use the TXM scan data, HF401TT-13_FoV16.5_Stitch.zip. It can be used to recreate the results of the published experimental results.</p> <p>To run the notebooks, the Python file, structure_tensor_workers.py, must be in the same directory as the notebook.</p> <p>By default, the notebooks expect the following folder structure:</p> <p>/notebooks: Folder with the notebooks and Python files.<br> /originals: Folder with the data (TXM/NIfTI files).<br> /tmp: Folder for temporary files and output generated running the notebooks.<br> /notebooks/figures: Folder for exporting figures as files (only needed if you want to save figures as files).</p>
Data from: Effect of coupled reduced irrigation and nitrogen fertilizer on soil mite community composition in a wheat field
Overuse of groundwater and nitrogen fertilizer constitute a severe threat to crop production, thus, low inputs of irrigation and nitrogen fertilizer were required in current ecological agriculture. The effects of combined reduced irrigation and nitrogen fertilizer addition on soil organism (e.g., mite) community and biodiversity remain poorly understood. We analyzed soil mite community composition, wheat grain yields, and soil characteristics in a ten-year manipulation experiment with two levels of irrigation (reduced irrigation and conventional irrigation) and five levels of nitrogen fertilizer (0, 70, 140, 210, 280 kg N ha-1). The study showed that reduced irrigation (20% reduction, from 280 to 220 mm) and nitrogen fertilizer (25% reduction, from 280 to 210 kg N ha-1) addition did not significantly influence soil mite community and wheat yields. The relative abundances of fungivore and predator showed negative quadratic relationships with wheat yields, while that of plant parasite was positive. The relationships between soil mite trophic groups and wheat yields revealed that we can elevated the impacts of reduced irrigation and nitrogen fertilizer addition from the perspective of soil fauna. The soil mite community composition was altered by soil abiotic factors prior to reduced irrigation and nitrogen fertilizer addition. On the whole, moderate reductions of irrigation and nitrogen fertilizer may not threaten to soil mite community and diversity or decrease crop production, on the contrary, it will benefit to the development of mite community and the sustainable agriculture.
Data of "Accuracy of predicting chemical body composition of growing pigs by dual-energy X-ray absorptiometry"
<p>Data set for article "Accuracy of predicting chemical body composition of growing pigs by dual-energy X-ray absorptiometry" (DOI). Data set of Swiss Large White entire male pigs for nutrient composition (water, lipid, N, ash, Ca and P) determined by wet-chemistry and body composition (lean mass, body mineral content and fat tissue mass) by dual-energy X-ray absorptiometry (DXA) in the empty body of live pigs and (N=61) in pig carcasses (N=68) within a body weight range from 20 to 100 kg.</p> <p><strong>metadata.xlsx</strong>: description of variables in the data sets</p> <p><strong>emptybody.txt</strong>: corresponds to contents of the empty body at slaughter (three days after DXA live scans) and DXA live scans</p> <p><strong>Carcass.txt</strong>: corresponds to carcass contents and DXA carcass scans on the day of slaughter</p> <p> </p>
Figure 2 from: Savych A, Marchyshyn S, Basaraba R (2020) Determination of fatty acid composition content in the herbal antidiabetic collections. Pharmacia 67(3): 153-159. https://doi.org/10.3897/pharmacia.67.e51812
Figure 2 Chomatogram of fatty acids in the herbal antidiabetic collection № 4.
Figure 1 from: Savych A, Marchyshyn S, Basaraba R (2020) Determination of fatty acid composition content in the herbal antidiabetic collections. Pharmacia 67(3): 153-159. https://doi.org/10.3897/pharmacia.67.e51812
Figure 1 Chomatogram of fatty acids in the herbal antidiabetic collection № 3.
Figure 4 from: Savych A, Marchyshyn S, Basaraba R (2020) Determination of fatty acid composition content in the herbal antidiabetic collections. Pharmacia 67(3): 153-159. https://doi.org/10.3897/pharmacia.67.e51812
Figure 4 Chomatogram of fatty acids in the herbal antidiabetic collection № 13.
Figure 3 from: Savych A, Marchyshyn S, Basaraba R (2020) Determination of fatty acid composition content in the herbal antidiabetic collections. Pharmacia 67(3): 153-159. https://doi.org/10.3897/pharmacia.67.e51812
Figure 3 Chomatogram of fatty acids in the herbal antidiabetic collection № 7.
Figure 5 from: Savych A, Marchyshyn S, Basaraba R (2020) Determination of fatty acid composition content in the herbal antidiabetic collections. Pharmacia 67(3): 153-159. https://doi.org/10.3897/pharmacia.67.e51812
Figure 5 Chomatogram of fatty acids in the herbal antidiabetic collection № 19.
Kolberger Heide community compositions and machine learning results
<p>Bacteria are ubiquitous and live in complex microbial communities, which can react rapidly to changing environmental conditions. Their physiological variety enables communities to respond in specific ways to environmental drivers, potentially resulting in distinct microbial fingerprints for a given environmental state. Our goal was to assess the opportunities and limitations of machine learning to detect fingerprints indicating the presence of the munition compound 2,4,6-trinitrotoluene (TNT) in southwestern Baltic Sea sediments.</p> <p>Over 40 environmental variables including grain size distribution, elemental composition and concentration of munition compounds (mostly at pmol g<sup>-1</sup> levels) from 150 sediments collected at the near-to-shore munition dumpsite Kolberger Heide by the German city of Kiel were combined with 16S rRNA gene amplicon sequencing libraries. Prediction was achieved using Random Forests; the robustness of predictions was validated using Artificial Neural Networks. To facilitate machine learning with microbiome data we developed the R package phyloseq2ML.</p> <p>Using the most classification-relevant 25 bacterial genera exclusively, potentially representing a TNT-indicative fingerprint, TNT was predicted correctly with up to 81.5 % balanced accuracy. False positive classifications indicated that this approach has also the potential to identify samples where the original TNT contamination was no longer detectable. The sensitivity of this approach can be deduced from the fact that TNT presence was neither identified among the main drivers of the microbial community composition, nor did it correlate with sediment metal content, demonstrated by decreased prediction rates using environmental variables.</p> <p>Our results suggest that microbial communities can predict even minor influencing factors in complex environments, demonstrating the potential of this approach for the discovery of contamination events over an integrated period of time and for environmental monitoring in general.</p>
Major compositions and mode of Kohistan lower crustal cumulates
<p>Arc magmas, a major contributor to continental crust growth, are thought to be more oxidized than mid-ocean ridge basalts, but how arc magmas become oxidized is debated. In particular, fractionation of Fe-rich phases may change the Fe valence and thus the oxidation state of the derivative melt. Here we evaluate the redox effect of Fe-rich amphibole fractionation during arc magma differentiation. We present high precision Fe valence data determined by Mössbauer spectroscopy, and whole-rock Fe<sup>3+</sup>/∑Fe ratios by wet chemistry. Our results show that bulk Fe<sup>3+</sup>/∑Fe ratio of cumulates in mature island arc is mainly controlled by amphibole, and decreases (from 0.4 to 0.2) with decreasing Mg<sup>#</sup> due to decreasing Fe<sup>3+</sup>/∑Fe ratios of amphibole (from 0.35 to 0.2). Our modeling suggests that amphibole fractionation from arc magmas with Fe<sup>3+</sup>/∑Fe significantly above 0.2 can lead to increased Fe<sup>3+</sup>/∑Fe ratios of residual melt (Fe<sup>3+</sup>/∑Fe from 0.5 to 0.7 with Mg<sup>#</sup> from 0.4 to 0.25), and the redox filter capability of amphibole increases with the evolution of arc magmas. However, the formation of the Kohistan cumulates with Fe<sup>3+</sup>/∑Fe of 0.3-0.4 from initial parental magma with Fe<sup>3+</sup>/∑Fe of ~0.2 may require additional mechanism. In addition, although garnet has much lower Fe<sup>3+</sup>/∑Fe ratios, its ability to control the whole-rock Fe<sup>3+</sup>/∑Fe in evolved arc magma in mature island arc is negligible compared with that of amphibole because of the low proportions. Importantly, our findings provide a new perspective for understanding the formation of porphyry Cu deposit and the crustal evolution from the insight of internal petrological processes.</p>
Local fibre volume fraction in non-crimp glass-fibre reinforced composites based on scanning electron microscopy.
<p>SEM data and segmentation of two non-crimp fabric cases used in the following two publications to where the references should be given. </p> <p>Sørensen, B.F., Goutianos, S., Mikkelsen, L.P., Fæster, S. (2021). Fatigue damage growth and fatigue life of unidirectional composites. Composites Science and Technology, (in press), <a href="https://doi.org/10.1016/j.compscitech.2021.108656">https://doi.org/10.1016/j.compscitech.2021.108656</a></p> <p>Mikkelsen, L.P., Fæster, S., Goutianos, S., Sørensen, B.F. Scanning electron microscopy datasets for local fibre volume fraction determination in non-crimp glass-fibre reinforced composites. <em>Data in Brief</em>, <strong>35</strong>, <a href="https://doi.org/10.1016/j.dib.2021.106868">https://doi.org/10.1016/j.dib.2021.106868</a>, 2021, 106868.</p> <p>Description of the data-files:</p> <p>For each of the two cases, five files are made available. Those two file-set contains:</p> <p>• Tif-file: The stitched SEM scanned image which is used in the fibre volume fraction analysis</p> <p>• Hdr-file: Meta-data about the stitched SEM scanned image</p> <p>• M-file: The Matlab script used for analysing the tif-file</p> <p>• Mat-file: Matlab mask data for a selection of the bundles used in the fibre volume fraction analysis <br> </p>
Assessing Lumbar Paraspinal Muscle CSA and Fat Composition with T1 versus T2-Weighted MRI: Reliability and Concurrent Validity.
<p>Raw measurement data to support manuscript publication</p>
Data from: Composition of gut microbiota in patients with toxigenic Clostridioides (Clostridium) difficile: comparison between subgroups according to clinical criteria and toxin gene load
Data concerning the human microbiota composition during Clostridioides (Clostridium) difficile infection (CDI) using next-generation sequencing are still limited. We aimed to confirm key features indicating tcdB positive patients and compare the microbiota composition between subgroups based on toxin gene load (tcdB gene) and presence of significant diarrhea. Ninety-nine fecal samples from 79 tcdB positive patients and 20 controls were analyzed using 16S rRNA gene sequencing. Chao1 index for alpha diversity were calculated and principal coordinate analysis was performed for beta diversity using Quantitative Insights into Microbial Ecology (QIIME) pipeline. The mean relative abundance in each group was compared at phylum, family, and genus levels. There were significant alterations in alpha and beta diversity in tcdB positive patients (both colonizer and CDI) compared with those in the control. The mean Chao1 index of tcdB positive patients was significantly lower than the control group (P<0.001), whereas there was no significant difference between tcdB groups and between colonizer and CDI. There were significant differences in microbiota compositions between tcdB positive patients and the control at phylum, family, and genus levels. Several genera such as Phascolarctobacterium, Lachnospira, Butyricimonas, Catenibacterium, Paraprevotella, Odoribacter, and Anaerostipes were not detected in most CDI cases. We identified several changes in the microbiota of CDI that could be further evaluated as predictive markers. Microbiota differences between clinical subgroups of CDI need to be further studied in larger controlled studies.
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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.