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115 results for “Chemical Profiles”
Chemical structures, Cell Painting and transcriptional profiles for compound bioactivity prediction.
<p>This is the related data, both input and produced for the paper <a href="https://doi.org/10.1101/2020.12.15.422887">"Predicting compound activity from phenotypic profiles and chemical structures"</a>.</p> <p>This data can be merged with <a href="https://github.com/CaicedoLab/2023_Moshkov_NatComm">paper's GitHub repository</a> for reproduction.</p> <p>Folders and files and are described below:</p> <pre><code>├── assay_data ├── assay_matrix_discrete_270_assays.csv Assay matrix with hits for assays (270) and compounds (16170). Note that this is the final file that we used to produce splits. ├── assay_metadata.csv Assay metadata ├── broad_ids.txt List of broad ids used in this study. That is an unfiltered list of compounds required by some analysis scripts. ├── smiles.txt Same as broad_ids.txt, but SMILES strings. ├── feature_data (for 16978 compounds, can be masked with ./misc/compounds16978to16170.npy) ├── cp.npz Classical chemical features ├── ge.npz Gene expression features ├── ge_scale.npz Gene expression scaled features ├── mo.npz Morphology features (not batch corrected) ├── mobc.npz Morphology features (batch corrected) ├── misc ├── compound_analysis.npz Compounds in the dataset identified as PAINS ├── compounds16978to16170.npy Used to filter features from the bigger set of compounds to the final one ├── fingerprints.npz Calculated fingerprints of compounds, those were then used to calculate similarity ├── similarity_fingerprints.npz Similarity matrix for compounds (16978) ├── population_normalized.csv.gz Well-level morphological profiles that were used for batch-correction ├── Table for PUMA Excel file with additional data and plots ├── predictions ├── scaffold_median(mean)_AUC.csv Aggregated median(mean) AUC scores over scaffold-based cross-validation splits. In the paper, median results were reported. ├── scaffold_median(mean)_EF.csv Aggregated median(mean) enrichment factor (EF) over scaffold-based cross-validation splits. In the paper, median results were reported. ├── toprank_chemical_cv{}_hitsnorm.csv Those files are needed to create enrichment plots and contain hit rate and top rank hit rate. ├── Each folder here stands for an experiment type, the number in the folder name is a number of the split. Inside each folder there are the following elements: ├── predictions Folder with predictions for each assay-compound pair for each modality ├── 2022_01_evaluation_all_data.csv File with AUC scores for each assay for the test set in the split ├── 2022_01_evaluation_all_data_EF.csv File with enrichment factor (EF) values for each assay for the test set in the split. Those files exist only for *chemical* folders. ├── assay_matrix_discrete_train(test)_old_scaff.csv Training and test subsets of data for the split. The first column contains broad_id. ├── assay_matrix_discrete_train(test)_old_scaff.csv Same, but SMILES strings in the first column. Those files are used as input to ChemProp! Experiments in this folder are the following: - chemical Scaffold-based 5-fold cross-validation splits, the main results in the paper are reported with this series of experiments. - chemical_bal Same splits as in chemical, but training were run with ChemProp built-in data balancing. - chemical_st Same splits as in chemical, but separate models were trained for each assay. - CV Random 5-fold cross-validation splits. - GE 5-fold cross-validation splits based on same-size clustering of gene expression features. - MOBC 5-fold cross-validation splits based on same-size clustering of batch-corrected morphology features. - random 10 random splits, ~80% of compounds in the training set and the rest in the test set. ├── splitting This folder contains numpy files which help to match compounds and features to create training and test sets for a split, which can be reused in the analysis notebook for data preparation. ├── scaffold_based_split.npz Splitting for scaffold-based splits. ├── random_split_{}.npz Random split indices of test set compounds (10 files). ├── cross_validation_indicies.npz Indices for random cross-validation splits ├── GE_clusters_size_constrained.npz Indicies of clusters of same-size clustering for gene-expression features. ├── MOBC_clusters_size_constrained.npz Indices of clusters of same-size clustering for batch-corrected morphology features.</code></pre> <p> </p>
Chemical profiles of Crescent Stream, McMurdo Dry Valleys, Antarctica (2021-2023).
This data package contains chemical profiles (anions, cations, nutrients, and dissolved organic carbon (DOC)) from water samples collected during the austral summers of 2021-2022 and 2022-2023 in Crescent Stream, located in the McMurdo Dry Valleys of Antarctica. Samples were collected from the F8 gage and from the east and west branches of Crescent Stream, just above their confluence. Grab samples were analyzed according to the McMurdo Dry Valleys LTER core stream chemistry protocols. In December 2021, permafrost degradation was observed along the banks of the west branch, with a prior degradation event documented at the same location in January 2012. This data package supports a pending publication by Wright et al., examining the impacts of permafrost degradation on stream chemistry.
Electron concentration profiles calculated using different plasma chemical models during solar X-ray flares
<p>The files contain electron concentration <em>Ne</em> profiles during solar X-ray flares that occurred on 24-25 October 2013 and 9-11 June 2014. The altitude range is 50-90 km.</p> <p>Values of electron concentration were calculated using four-, five- and eight-component models of the ionospheric D-region. Results are obtained on four VLF paths: from European transmitters ICV, FTA, GQD, DHO to Mikhnevo geophysical observatory (55°N 38°E).</p> <p>The data is presented as MATLAB files. Each .mat file contains data and variable "description" with data's structure information.</p>
Fig. 2 in Genetic and chemical profiling of Solenopsis spp. (Hymenoptera: Formicidae) intercepted in Hawaii
Fig. 2. The Gas Chromatography-Mass Spectrometry (GC-MS), total ion chro- matogram (TIC) of a methanol extract of a gaster from a single ant intercepted in 2019 at a port in Hawaii. The reported major venom alkaloids from Solenopsis geminata and Solenopsis xyloni are cis-2-methyl-6-undecyl-piperidine (peak 1) and trans-2-methyl-6-undecyl-piperidine (peak 3). This intercepted Solenopsis geminata worker has 2-methyl-6-undecyl-pyridine (peak 2) as its major component. The areas under peaks 1, 2, and 3 are proportional to their quantity. This is expressed on the graph as the percent composition of the 3 identified peaks. This is the first time that the pyridine (peak 2) has been reported as a major venom component of Solenopsis geminata. Components 1 and 3 have been identified in previous literature.
Fig. 1 in Genetic and chemical profiling of Solenopsis spp. (Hymenoptera: Formicidae) intercepted in Hawaii
Fig. 1. Unrooted neighbor-joining tree constructed using the matrix of absolute numbers of pairwise differences among 55 mitochondrial COX1 haplotypes from 6 Solenopsis species: S. geminata, S. xyloni, S. amblychila, S. aurea, S. invicta, and S. saevissima. All positions containing gaps and missing data were eliminated. Bootstrap values greater than 70% are located below or above nodes. The scale bar distance is shown at the top right corner of the tree indicating number of nucleotide differences. Each sequence was identified by their GenBank number and species name, except for the Oahu sample (GenBank OK071254) marked as Oahu 2018* and the haplotype from the 2019 samples (GenBank OK071253) labelled Ltu19_03**.
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
Data from: Ecological factors influence balancing selection on leaf chemical profiles of a wildflower
<div> <div> <div> <div> <p>Balancing selection is frequently invoked as a mechanism to maintain variation within and across populations. However, rigorous tests demonstrating balancing selection operating in nature are scarce, particularly on complex traits, which frequently display high levels of variation. Leveraging a focal polymorphism, leaf chemical profile in a perennial wildflower (<em>Boechera stricta</em>, Brassicaceae), we investigated the ecological and genetic mechanisms that may influence the maintenance of variation in this trait. A suite of common garden and greenhouse experiments showed that the alleles underlying variation in chemical profile have contrasting fitness effects across environments, implicating two ecological drivers of selection on chemical profile: herbivory and drought. Phenotype-environment associations and molecular genetic analyses revealed additional evidence of past selection by these drivers. Together, these data are consistent with balancing selection on chemical profile, likely caused by pleiotropic effects of genes that influence secondary chemical biosynthesis on herbivore defense and drought response.</p> </div> </div> </div> </div>
Figure 5 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 5. Ordering by Detrended Correspondence Analyses (Axes 1 and 2) based on the cuticular hydrocarbon profile of the samples of the 3 species whose colonies were nested in two types of environments. The points on Axis 1, between 0 and 50 on the left, represent compounds more characteristic of samples from more anthropized environments and between 100 and 150 on the right, from less anthropized environments.
Figure 4 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 4. Similarity dendrogram based on the cuticular hydrocarbon profile of the samples of the 3 species whose colonies were nested in two types of environments. Pentagon: more anthropized areas. Star: less anthropized areas.
Figure 3 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 3. Bar charts showing the relative abundance and number of compounds belonging to the different classes of cuticular hydrocarbons present in the samples of Polistes versicolor (A), Polybia paulista (B) and Polybia occidentalis (C) whose colonies were nesting in more anthropized environments (black box) and less anthropized (white box). LA = Linear Alkanes, BA = Branched Alkanes, AK = Alkenes, ALD = Alkadienes.
Figure 2 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 2. Similarity dendrogram generated based on the percentages of different types of land use in the municipalities where the colonies of the 3 species of social wasps were sampled. Pentagon: more anthropized areas. Star: less anthropized areas. DDS1: Dourados point 1; DDS2: Dourados point 2; IVIN: Ivinhema; MN1: Mundo Novo point 1; MN2: Mundo Novo point 2; PP: Ponta Porã.
Figure 1 in Anthropic action affects the cuticular chemical profile of social wasps
Figure 1. Satellite image showing the places in the municipalities where the colonies of the 3 social wasp species were nesting and, the percentages (pie charts) of the different types of land use and occupation (adapted from the IBGE, 2013 definition). Number 1 and 2 indicates the two collection points in the same city.
Data from: The scent of offspring: chemical profiles of larvae change during development and affect parental behavior in a burying beetle
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Data from: Ecological factors influence balancing selection on leaf chemical profiles of a wildflower
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Profiling the REACH-chemical space with Generative Topographic Mapping
<p>The REACH (Registration Evaluation Authorization and restriction of Chemicals) regulation requires from industries the reporting of hazard data for substances placed on the market. Thanks to the registration procedure initiated in 2007, a large REACH database [1] of well defined (eco)toxicological properties has been created. The REACH-chemical space is defined by more than 21’500 compounds registered in the REACH database.</p> <p>Considering the high number of chemicals and endpoints for which experimental data is available, the ability to visualize this chemical space as well as to profile a compound integrating several key properties at once is a growing need.</p> <p>Here, the data distribution in REACH chemical space was visualized and analysed with the help of 2-dimensional Generative Topographic Map (GTM). Similarly to geographical map, on GTM, each object (compound) is visualized as a datapoint. Moreover, compounds possessing similar properties tend to be located in neighbourhood. The third dimension can be added in order to display a distribution of the given (eco)toxicological property (such-called “property landscape”), which can further be used for property assessment of new compounds projected on the map. </p> <p>We report the universal REACH map which accommodates 11 endpoints, covering environmental fate, and (eco)toxicological properties. This map is able to provide predictions for each property, and demonstrates acceptable predictive performance in cross-validation: balanced accuracy ranges from 0.60 to 0.78. Superposition of different property landscapes allows to delineate the “areas of interest” populated by molecules possessing desirable (eco)toxicological profile.</p>
Data from: The influence of slavemaking lifestyle, caste and sex on chemical profiles in Temnothorax ants: insights into the evolution of cuticular hydrocarbons
Chemical communication is central for the formation and maintenance of insect societies. Generally, social insects only allow nest-mates into their colony, which are recognized by their cuticular hydrocarbons (CHCs). Social parasites, which exploit insect societies, are selected to circumvent host recognition. Here, we studied whether chemical strategies to reduce recognition evolved convergently in slavemaking ants, and whether they extend to workers, queens and males alike. We studied CHCs of three social parasites and their related hosts to investigate whether the parasitic lifestyle selects for specific chemical traits that reduce host recognition. Slavemaker profiles were characterized by shorter-chained hydrocarbons and a shift from methyl-branched alkanes to n-alkanes, presumably to reduce recognition cue quantity. These shifts were consistent across independent origins of slavery and were found in isolated ants and those emerging in their mother colony. Lifestyle influenced profiles of workers most profoundly, with little effect on virgin queen profiles. We detected an across-species caste signal, with workers, for which nest-mate recognition is particularly important, carrying more and longer-chained hydrocarbons and males exhibiting a larger fraction of n-alkanes. This comprehensive study of CHCs across castes and species reveals how lifestyle-specific selection can result in convergent evolution of chemical phenotypes.
The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with CMAQ 5.0.2
<p>All input and model configuration data necessary to reproduce the results as well as all output data discussed in the article</p>
Chemical profiling of milkweed and monarch butterfly wing extracts via mass spectrometry
<p>Herbivores that sequester toxins are thought to have cracked the code of plant defenses. Nonetheless, coevolutionary theory predicts that plants should evolve toxic variants that also negatively impact specialists. We propose and test the selective sequestration hypothesis, that specialists preferentially sequester compounds that are less toxic to themselves, while maintaining toxicity to enemies. Using chemically distinct plants, we show that monarch butterflies sequester only a subset of cardenolides from milkweed leaves that are less potent against their target enzyme (Na+/K+-ATPase) compared to several dominant cardenolides from leaves. However, sequestered compounds remain highly potent against sensitive Na+/K+-ATPases found in most predators. We confirmed this differential toxicity with mixtures of purified cardenolides from leaves and butterflies. The genetic basis of monarch adaptation to sequestered cardenolides was also confirmed with transgenic <em>Drosophila</em> that were CRISPR-edited with the monarch's Na+/K+-ATPase. Thus, the monarch's selective sequestration appears to reduce self-harm while maintaining protection from enemies.</p>
Morphological profiling data resource enables prediction of chemical compound properties
<p><span><span>Morphological profiling with the Cell Painting assay has </span><span>emerged</span><span> as a promising method in drug discovery research. The assay captures morphological changes across various cellular compartments enabling the rapid prediction of compound </span></span><span><span><span>bioactivity</span></span></span><span><span>. We present a comprehensive morphological profiling resource using the carefully curated and well-annotated EU-OPENSCREEN Bioactive Compounds. </span><span>The data was generated across four imaging sites with high-throughput confocal microscopes using the </span><span>Hep</span><span> G2 as well as the U2 OS cell line.</span><span> We employed an extensive assay optimization process to achieve high data quality across the different sites. An analysis of the extracted profiles </span><span>validates</span><span> the robustness of the generated data. <span>We used this resource to compare the morphological features of the different cell lines. By correlating the profiles with overall activity, cellular toxicity, several specific mechanisms of action (MOAs), and protein targets, we </span><span>demonstrate</span><span> the dataset's potential for </span><span>facilitating</span><span> more extensive exploration of mechanisms of action.</span> </span></span></p>
Supplementary material 1 from: Vaníčková L, Břízová R, Pompeiano A, Ferreira LL, de Aquino NC, Tavares RF, Rodriguez LD, Mendonça AL, Canal NA, do Nascimento RR (2015) Characterisation of the chemical profiles of Brazilian and Andean morphotypes belonging to the Anastrepha fraterculus complex (Diptera, Tephritidae). In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 193-209. https://doi.org/10.3897/zookeys.540.9649
Table S1. Anastrepha fraterculus male and female characteristic.: Explanation note: Anastrepha fraterculus male (m) and female (f) characteristic cuticular hydrocarbons identified by principal component analyses.
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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.