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1,923 results for “compounds”
Figure 4 in Phenolic compound and fatty acid properties of some microalgae species isolated from Erbil City
Figure 4. Chemical composition of fatty acid in algae obtained by HPLC.
Purification of ACVR1 for co-crystallisation with LDN-193189 and other compounds
<p>Purification of constitutively active ACVR1 for co-crystallisation with LDN-193189 and three other compounds.</p>
Dataset on the characterization of the flavor of two red wine varieties using sensory descriptive analysis, volatile organic compounds quantitative analysis by GC-MS and odorant composition by GC-MS-O
<p>The dataset contains data that were collected on 2 sets of 8 French red wines from two grape varieties, Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Gas Chromatography–Mass Spectrometry (GC-MS) and (iii) odorant composition obtained by Gas Chromatography–Mass Spectrometry–Olfactometry (GC-MS-O).</p> <p> </p> <p>The dataset is a Microsoft Excel Worksheet containing 8 sheets.</p> <p>- Sheet 1: Information</p> <p>Gives information about the sheets contained in this .xlsx file</p> <p>- Sheet 2: Experimental_factors</p> <p>Each row represents a wine</p> <p>Each column corresponds to an experimental factors of the wines (Grape variety, Vintage and Protected Designation of Origin)</p> <p>- Sheet 3: List_sensory_descriptors</p> <p>Lists the 33 sensory descriptors used for the sensory descriptive analysis of the wines</p> <p>- Sheet 4: Sensory_descriptive_analysis</p> <p>Each row represents a wine</p> <p>Each column corresponds to a condition (2640 columns)</p> <p>Senso_(ortho or retro)_(Panelist1 to Panelist 16)_(1 to 33 Sensory descriptors)_(1 to 3 repetitions for ortho and 1 to 2 repetitions for retro)</p> <p>For the ortho (orthonasal) measurements, there is 16 panelists, 33 sensory descriptors and 3 repetitions = 1584 columns</p> <p>For the retro (retronasal) measurements, there is 16 panelists, 33 sensory descriptors and 2 repetitions = 1056 columns</p> <p>Each cell contains a sensory measurement for the corresponding condition in the corresponding wine</p> <p>- Sheet 5: List_VOC</p> <p>Lists the 45 VOC quantified in the wines with their corresponding CAS number</p> <p>VOC: Volatil Organic Compounds</p> <p>- Sheet 6: VOC_quantification</p> <p>Each row represents a wine</p> <p>Each column corresponds to a VOC (45 columns)</p> <p>Each cell contains the quantification of the corresponding VOC in the corresponding wine</p> <p>- Sheet 7: List_GC-MS-O</p> <p>Lists the 49 odor-active compounds identified with their corresponding CAS number and the 34 compounds identified by their apex indice</p> <p>- Sheet 8: GC-MS-O</p> <p>Each row represents a wine</p> <p>Each column corresponds to an odor-active compound identified by its CAS number or by its Apex indice if the compound was not identify (81 odor-active compounds) + the number of judges who smelled the compound and its description (by 8 judges) = 9 columns per odor-active compound for a total of 729 columns</p>
Compounds of interest identified by screening a focused library against USP5 Zf-UBD with a 19F NMR assay
<p>Screening a set of commercial compounds selected from computational docking studies against USP5 zinc finger ubiquitin binding domain (Zf-UBD) using <sup>19</sup>F NMR spectroscopy. You can find details of preliminary <sup>19</sup>F NMR experiments <a href="https://zenodo.org/record/1246807#.WxV8HkgvzIV">here</a>.</p>
Screening compounds of interest against USP5 Zf-UBD with a surface plasmon resonance assay
<p>The determination of binding affinities of compounds of interest against USP5 zinc finger ubiquitin binding domain (Zf-UBD) with a surface plasmon resonance (SPR) assay. </p>
Co-crystal structures of USP5 Zf-UBD and weak binding compounds
<p>Determination of crystallization conditions which allow growth of well-diffracting co-crystals of USP5 zinc finger ubiquitin binding domain (Zf-UBD) and compounds shown to bind weakly by <a href="https://zenodo.org/record/1283325#.Wz5VXtJKjIV">19F NMR</a> and <a href="https://zenodo.org/record/1311686#.W0il_tJKjIU">SPR assays</a> & to solve the co-crystal structures to determine if electron density of ligands can be seen in the binding pocket of the protein domain.</p>
Screening Compounds against USP5 Zf-UBD with a 19F NMR assay #2
<p>Screening compounds against USP5 zinc finger ubiquitin binding domain (Zf-UBD) using a <sup>19</sup>F NMR spectroscopy. You can find previous <sup>19</sup>F NMR experiments <a href="https://openlabnotebooks.org/co-crystal-structures-of-usp5-zf-ubd-and-weak-binding-compounds/">here</a>.</p>
Screening Compounds of Interest against USP5 Zf-UBD by SPR #2
<p>Determination of binding affinities of compounds of interest previously identified in a <sup>19</sup>F-NMR spectroscopy assay against USP5 zinc finger ubiquitin binding domain (Zf-UBD) with a surface plasmon resonance (SPR) assay </p>
Technical note: Interferences of volatile organic compounds (VOC) on methane concentration measurements - Raw Data
<p>Technical note: Interferences of volatile organic compounds (VOC) on methane concentration measurements - Raw Data</p>
Different methods to evaluate the effects of M4K compounds on DIPG patient-derived cell lines
<p>Aside from determining the optimal numbers of DIPG cells to be seeded for viability assay, it is also important to validate that measurements using CellTiter Glo are in agreement with other methods of determining cell viability.</p> <p>For my blog post about this experiment, please visit my opennotebook page.</p> <p>https://openlabnotebooks.org/different-methods-to-evaluate-the-effects-of-m4k-compounds-on-dipg-patient-derived-cell-lines/</p>
Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks
<p>Data related to publication:</p> <p>Heider et al.: Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks.</p> <p>The files include:</p> <p>1) Output conformations of the QM Tautomer & Conformation Predictor of Maestro (Schrödinger, LLC, New York, NY, 2018) of the compounds <strong>1</strong>, <strong>3a</strong>, <strong>3b</strong>, <strong>3c</strong>, <strong>3d</strong>, <strong>3e</strong>, <strong>3f</strong>, <strong>3g</strong>, <strong>3h</strong>, <strong>3j</strong>, <strong>3k</strong>, <strong>3l</strong>, <strong>3m</strong>, <strong>6f</strong> and <strong>6g</strong> [.mae and .sdf files].</p> <p>2) Movies of the MD simulations of compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> (6GN1) and movies (*_2) of <strong>3a</strong>, <strong>3j</strong>, <strong>6b</strong>, <strong>6g</strong> (4PTC) [.mpg files] .</p> <p>3) Raw Desmond trajectory files of the MD simulations of the compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> [out.cms and the full trj files].</p> <p> </p> <p><br> </p>
Effects of flavoring compounds used in electronic cigarette refill liquids on endothelial and vascular function
<p>raw data of all figures and tables</p>
Compound activity classes from ChEMBL for machine learning analysis
<p>Ten activity classes are provided that were extracted from ChEMBL version 24 for machine learning studies. Compounds are given in SMILES representations. The following selection criteria were applied. Compounds were required to be tested in a direct binding assay against a single human protein with a ChEMBL assay confidence score of 9. In addition, K<sub>i</sub> measurements had to be available. If multiple K<sub>i</sub> values were available for a compound and did not fall within the same order of magnitude, the compound was not selected. Furthermore only compounds with (mean) pK<sub>i</sub> of at least 5 were considered. Moreover, activity classes had to contain at least 200 compounds belonging to at least 50 computationally determined analog series. The 10 deposited classes consist of 243 to 955 compounds and 57 to 216 analog series.</p>
Evaluating the effect of 8 M4K compounds on the viability of DIPG patient-derived cells (SU-DIPG-VI, HSJD-DIPG-007 and SU-DIPG-IV)
<p>Viability assay using Celltiter Glo kit has been chosen as the main method for evaluating the efficacy of ALK2 inhibitors on DIPG patient-derived cells due to its robustness. As a pilot experiment, 8 ALK2 inhibitors with differing potencies were chosen and evaluated in 3 DIPG patient-derived cell lines (one with wild-type ALK2 and two with mutant ALK2).</p>
Data for article "Synthesis of Tetraarylethene Luminogens by C‐H Vinylation of Aromatic Compounds with Triazenes"
<p>This upload contains NMR, UV-vis, fluorescence and X-Ray diffraction data for article "Synthesis of Tetraarylethene Luminogens by C‐H Vinylation of Aromatic Compounds with Triazenes", Suleymanov et al. <em>Angew. Chem. Int. Ed </em><strong>2019 </strong>(DOI: <a href="https://doi.org/10.1002/anie.201908755">10.1002/anie.201908755</a>).</p>
A bioactive compound isolated from Duku (Lansium domesticum Corr) fruit peels exhibits cytotoxicity against T47D cell line
<p><strong>Background: </strong>Breast cancer is a major health problem for women globally. Many attempts have been promoted to cure cancer by finding new anticancer medicines from natural resources. Despite the richness of biodiversity discovered, there are some natural resources that remain unexplored. Fruit peels of Duku (<em>Lansium domesticum</em> Corr.) are rich with compounds that may have the potential to be developed as anticancer drugs. This study aimed to isolate cytotoxic compounds from the fruit peels of <em>L. domesticum</em> and assess their cytotoxic nature against T47D cells.</p> <p><strong>Methods: </strong>Powdered peels were macerated with ethyl acetate and the filtrate was evaporated to give EtOAc extract A. Dried extract A was triturated with n-hexane to give n-hexane soluble fraction B and insoluble fraction C. The cytotoxic nature of these three samples were assessed using MTT assay using T47D cells and doxorubicin as a control</p> <p><strong>Results: </strong>Fraction C that showed the smallest IC<sub>50</sub> (25.56 + 0.64µg/mL) value compared to extract A and fraction BFraction C was further fractionated by vacuum liquid chromatography to give 6 subfractions. Subfraction 2 showed a single compound based on thin layer chromatography, and this compound was identified as Lamesticumin A on the basis of its spectroscopic data. Lamesticumin A demonstrated cytotoxic activity against T47D cell lines with an IC<sub>50</sub> value of 15.68 + 0.30µg/mL.</p>
Promiscuous compounds with activity against different target classes
<p>Reported are 1063 promiscuous compounds with activity against multiple target classes identified in PubChem. The tab-separated file consists of four columns:</p> <p>- cid: compound identifier (CID) from PubChem</p> <p>- NonstereoAromaticSMILES: SMILES representation of a compound</p> <p>- Number_of_tested_targets: total number of targets a compound was tested against in PubChem</p> <p>- PD: promiscuity degree (PD) of a compound, defined as the number of targets it was found active against</p>
Can metal organic frameworks outperform adsorptive removal of harmful phenolic compound 2-chlorophenol by activated carbon?
<p>A more complete version of this dataset, along with publication details, is available from <a href="https://zenodo.org/record/2586955">https://zenodo.org/record/2586955</a></p>
Deliverable D2.1 - Supplementary material : Datasets on the physicochemical properties of PFAS compounds
<p>Additional information on deliverable D2.1. Data sets for Machine Learning models on physicochemical properties of PFAS compounds for endpoints: logP (n-octanol/water), Sw (solubility in water), VP (vapor pressure), BCF (bioconcentration factor), MP (melting point) developped within the project H2020 PROMISCES. </p>
Data from : "The massive 2016 marine heatwave in the Southwest Pacific: an "El Niño - Madden-Julian Oscillation" compound event"
<p>File : PD-JRA-v1-c6_TMLtrend_1993-2022.nc</p> <p>Daily mixed layer temperature budget between 1993 and 2022 from a NEMO simulation.</p> <p>Data were extracted over the Southwest Pacific region [142.5°E-222.5°E;45°S;1°N].</p> <p>These data were used in Dutheil et al., (2024) The massive 2016 marine heatwave in the Southwest Pacific: an “El Niño - Madden-Julian Oscillation” compound event</p>
ScienceDex guides
Understand access before you commit
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.