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599 results for “Volatile”
Supplementary Data for "Earth's Volatile Depletion Trend Consistent with a High-Energy Moon-Forming Impact"
<p>Vaporization statistics for pyrolite melts containing In, Cd, S, Zn and Hg. Please refer to "Computational Methods" of the main text for details of the simulations and Supplementary Table 2 for the stoichiometry of the melts.</p>
Fig. 3 in Variation in the amino acids, volatile organic compounds and terpenes profiles in induced polyploids and in Solanum tuberosum varieties
Fig. 3. Hierarchical cluster analysis (represented by a heat-map) of amino acids content in leaves of potato allo- and autotetraploids and cultivated varieties. Dendrograms were constructed by UPGMA clustering method for 18 amino acids and 10 lines: diploid S. kurtzianum parental line (2xPL), diploid S. tuberosum x S. kurtzianum parental interspecific hybrid (2xPIH), three autotetraploids (4xAuL1, 4xAuL2 and 4xAuL3), two allotetraploids (4xAL2 and 4xAL4) and three cultivated varieties (4xCalen, 4xInnovator and 4xPampeana).
Fig. 1 in Variation in the amino acids, volatile organic compounds and terpenes profiles in induced polyploids and in Solanum tuberosum varieties
Fig. 1. Fold change of compounds content in allotetraploids (a) and autotetraploids (b) relative to their respective diploid parental line. Fold change is expressed as log10(Tetraploid/Diploid). Horizontal lines are the average of the absolute logFC for each evaluated line, letters denote differences by Duncan's multiple range test (P <0.05).
Managing the volatility of the results of the comparative approach in evaluating the market value of Russian companies
<p>Dataset for Master's dissertation 'Managing the volatility of the results of the comparative approach in evaluating the market value of Russian companies'.</p>
Bitcoin volatility in bull vs. bear market - insights from analyzing on-chain metrics and Twitter posts
<p>On-Chain Metrics.xlsx contains a description of the on-chain metrics.<br> Merged_df.xlsx is the main data source containing the BTC prices, the on-chain metrics and the sentiment scores.<br> btc_twets_new.csv and training.1600000.processed.noemoticon.csv are the data sources for calculating the sentiment scores.<br> Sentiment_Analysis.py contains the code to calculate the sentiment scores. The scores are in Merged_df.xlsx<br> BTC_Prediction.py contains the implementation of the main approach described in the paper, especially in Fig. 11.</p>
Figure data of "Variation in chemical composition and volatility of oxygenated organic aerosol in different rural, urban, and mountain environments"
<p>This dataset involves the data that is used for the figures in "Variation in chemical composition and volatility of oxygenated organic aerosol in different rural, urban, and mountain environments".</p>
Negligible fractionation between fluorine and chlorine during magma ocean crystallization and implications for the origin of Earth's volatiles
<p>Research data underlying all figures in the main text and supplementary material . </p>
Fig. 2 in Early infestation volatile biomarkers of fruit fly Bactrocera dorsalis (Hendel) ovipositional activity in mango (Mangifera indica L.)
Fig. 2. Principal coordinate analysis (PcoA) of control, mechanically damaged, and B. dorsalis infested mango treatments (yellow squares, gray triangles, red circles, respectively). Treatment is significant in PERMANOVA (F2,14 = 6.20, p <0.001). Blue boxes with an X denote overlay of individual compounds 1–11 (Table 1) which are unique to the infestation treatment and drive the difference among treatments. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Identification of herbivore-induced plant volatiles from selected Rubus species fed upon by raspberry bud moth (Heterocrossa rubophaga) larvae
Fig. 5. Phenology data for Heterocrossa rubophaga on Rubus fruticosus (blackberry) and Rubus cissoides (bush lawyer) from December 2019 to December 2020.
Fig. 13 in Non-volatile constituents from Monimiaceae, Siparunaceae and Atherospermataceae plant species and their bioactivities: An up-date covering 2000-2021
Fig. 13. Distribution of alkaloid-type isolated between 2000 and 2021 from different genera in the Monimiaceae (Hortonia, Mollinedia, Peumus, Tambourissa, Xymalos), Siparunaceae (Siparuna, Glossocalyx) and Atherospermataceae (Doryphora, Laureliopsis).
Fig. 12 in Non-volatile constituents from Monimiaceae, Siparunaceae and Atherospermataceae plant species and their bioactivities: An up-date covering 2000-2021
Fig. 12. Distribution of the non-volatile constituents isolated between 2000 and 2021 from different genera in the Monimiaceae (Hortonia, Mollinedia, Peumus, Tambourissa, Xymalos), Siparunaceae (Siparuna, Glossocalyx) and Atherospermataceae (Doryphora, Laureliopsis).
Fig. 2 in Non-volatile constituents from Monimiaceae, Siparunaceae and Atherospermataceae plant species and their bioactivities: An up-date covering 2000-2021
Fig. 2. Previously undescribed and known terpenoids isolated from Hortonia genus in the Monimiaceae family.
Fig. 1 in Non-volatile constituents from Monimiaceae, Siparunaceae and Atherospermataceae plant species and their bioactivities: An up-date covering 2000-2021
Fig. 1. Previously undescribed γ-lactone compounds and ring opened derivative 10 isolated from the Monimiaceae family.
Fig. 7 in Non-volatile constituents from Monimiaceae, Siparunaceae and Atherospermataceae plant species and their bioactivities: An up-date covering 2000-2021
Fig. 7. Previously undescribed and known flavonoids isolated from the Siparunaceae family (The substituents of flavonoids are denoted in blue font). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 10. Previously undescribed homogentisic acid derivatives isolated from G in Non-volatile constituents from Monimiaceae, Siparunaceae and Atherospermataceae plant species and their bioactivities: An up-date covering 2000-2021
Fig. 10. Previously undescribed homogentisic acid derivatives isolated from G. Brevipes in the Siparunaceae family.
Fig. 4 in Volatile metabolic profiling and functional characterization of four terpene synthases reveal terpenoid diversity in different tissues of Chrysanthemum indicum L
Fig. 4. Characterization of CiTPS enzymatic activity. (A) TIC of α-pinene produced in the root of C. indicum, the enzymatic products of CiTPS1 (without an MBP tag), the enzymatic products of CiTPS2 (pET-(-tp)-CiTPS2), and the negative control (GPP + boiled protein). (B) TIC of the corresponding metabolites produced in the root, the enzymatic products of CiTPS3 (without an MBP tag), and the negative control (FPP + boiled protein). (C) TIC of the enzymatic products of CiTPS4 (with GPP and FPP as substrate and without an MBP tag) and the negative control (GPP/FPP + boiled protein). TIC, total ion chromatogram.
Fig. 5 in Volatile metabolic profiling and functional characterization of four terpene synthases reveal terpenoid diversity in different tissues of Chrysanthemum indicum L
Fig. 5. Expression pattern analysis of CiTPSs in different tissues of C. indicum. (A–D) Expression pattern analysis of CiTPS1, CiTPS2, CiTPS3, and CiTPS4 in the root, stem, leaf, flower bud, and flower. Error bars represent the standard deviations between the three biological replicates. Different letters represent significant differences at P <0.05. (E) The corresponding products (volatile terpenoids) produced by CiTPS1, CiTPS2, CiTPS3 or CiTPS4 in C. indicum. n. d.: not detected; trace: 0–1 × 10 4 ng/mg, FW; +, 0.1–10 ng/mg, FW; + +, 10–100 ng/mg, FW; +++,>100 ng/mg, FW.
Fig. 3 in Volatile metabolic profiling and functional characterization of four terpene synthases reveal terpenoid diversity in different tissues of Chrysanthemum indicum L
Fig. 3. (A) Amino acid sequence alignments of CiTPS1, CiTPS2, CiTPS3, and CiTPS4 (unigene 0021,699, 0037,767, 0060,549, and 0062,052, respectively) and six TPSs from other plants. (B) Phylogenetic analysis of four TPSs from C. indicum and some TPSs from other plants. Detailed information, including plant names and GenBank identification numbers, are shown in Supplementary Table S3. Phylogenetic analysis was performed using the maximum likelihood method and the MEGA and ITOL tools (http://itol.embl.de/).
Fig. 1 in Volatile metabolic profiling and functional characterization of four terpene synthases reveal terpenoid diversity in different tissues of Chrysanthemum indicum L
Fig. 1. Volatile terpenoids in different tissues of C. indicum. (A) The representative total ion chromatogram of the root, stem, leaf, flower bud, and flower. (B) Heatmap of monoterpenoids and sesquiterpenoids in different tissues of C. indicum.
Fig. 3 in Electrophysiological responses of Philaenus spumarius and Neophilaenus campestris females to plant volatiles
Fig. 3. Results of dual choice Y-tube olfactometer bioassays performed with P. spumarius females to (1R)-(+)-camphor, sabinene, (S)-()-limonene, ()-α-pinene and (+)-α-pinene. N, number of replicates, NC number of individuals that didn't respond.
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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.