96 datasets found
  1. f

    Relative abundances and temporal profile classification of CCMV proteins.

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Jan 4, 2022
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    Landthaler, Markus; Wiebusch, Lüder; Hagemeier, Christian; Phan, Quang Vinh; Wyler, Emanuel; Bogdanow, Boris; Liu, Fan (2022). Relative abundances and temporal profile classification of CCMV proteins. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000225734
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    Dataset updated
    Jan 4, 2022
    Authors
    Landthaler, Markus; Wiebusch, Lüder; Hagemeier, Christian; Phan, Quang Vinh; Wyler, Emanuel; Bogdanow, Boris; Liu, Fan
    Description

    LFQ values were the basis for the calculation of expression profiles. The maximum LFQ for each gene across time points was set to 1. The remaining time points were calculated relative to this maximum. A value of 0 indicates that the respective protein could not be quantified at the respective time point. (XLSX)

  2. f

    The temporal profile of activity-dependent presynaptic phospho-signalling...

    • plos.figshare.com
    pdf
    Updated Jun 3, 2023
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    Kasper Engholm-Keller; Ashley J. Waardenberg; Johannes A. Müller; Jesse R. Wark; Rowena N. Fernando; Jonathan W. Arthur; Phillip J. Robinson; Dirk Dietrich; Susanne Schoch; Mark E. Graham (2023). The temporal profile of activity-dependent presynaptic phospho-signalling reveals long-lasting patterns of poststimulus regulation [Dataset]. http://doi.org/10.1371/journal.pbio.3000170
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    pdfAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS Biology
    Authors
    Kasper Engholm-Keller; Ashley J. Waardenberg; Johannes A. Müller; Jesse R. Wark; Rowena N. Fernando; Jonathan W. Arthur; Phillip J. Robinson; Dirk Dietrich; Susanne Schoch; Mark E. Graham
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Depolarization of presynaptic terminals stimulates calcium influx, which evokes neurotransmitter release and activates phosphorylation-based signalling. Here, we present the first global temporal profile of presynaptic activity-dependent phospho-signalling, which includes two KCl stimulation levels and analysis of the poststimulus period. We profiled 1,917 regulated phosphopeptides and bioinformatically identified six temporal patterns of co-regulated proteins. The presynaptic proteins with large changes in phospho-status were again prominently regulated in the analysis of 7,070 activity-dependent phosphopeptides from KCl-stimulated cultured hippocampal neurons. Active zone scaffold proteins showed a high level of activity-dependent phospho-regulation that far exceeded the response from postsynaptic density scaffold proteins. Accordingly, bassoon was identified as the major target of neuronal phospho-signalling. We developed a probabilistic computational method, KinSwing, which matched protein kinase substrate motifs to regulated phosphorylation sites to reveal underlying protein kinase activity. This approach allowed us to link protein kinases to profiles of co-regulated presynaptic protein networks. Ca2+- and calmodulin-dependent protein kinase IIα (CaMKIIα) responded rapidly, scaled with stimulus strength, and had long-lasting activity. Mitogen-activated protein kinase (MAPK)/extracellular signal–regulated kinase (ERK) was the main protein kinase predicted to control a distinct and significant pattern of poststimulus up-regulation of phosphorylation. This work provides a unique resource of activity-dependent phosphorylation sites of synaptosomes and neurons, the vast majority of which have not been investigated with regard to their functional impact. This resource will enable detailed characterization of the phospho-regulated mechanisms impacting the plasticity of neurotransmitter release.

  3. r

    Temporal Profiles Laboratory Data

    • researchdata.edu.au
    • figshare.com
    Updated Oct 9, 2020
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    Jessica E. Manousakis; Clare Anderson; Jessica E. Manousakis; Clare Anderson; Katherine Jeppe; Nikita Mann (2020). Temporal Profiles Laboratory Data [Dataset]. http://doi.org/10.26180/5f56bbc66b534
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    Dataset updated
    Oct 9, 2020
    Dataset provided by
    Monash University
    Authors
    Jessica E. Manousakis; Clare Anderson; Jessica E. Manousakis; Clare Anderson; Katherine Jeppe; Nikita Mann
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    Subjective sleepiness, sustained attention lapses, and physiological drowsiness for N=18 participants across 40-hours of sleep deprivation in controlled laboratory conditions

  4. EDGAR_temporal_profiles_r1: New high resolution temporal profiles in EDGAR

    • data.europa.eu
    excel xlsx
    Updated Aug 2, 2024
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    Joint Research Centre (2024). EDGAR_temporal_profiles_r1: New high resolution temporal profiles in EDGAR [Dataset]. https://data.europa.eu/data/datasets/b18d9435-1666-4c4b-9a0a-bcb6dd0c461d/embed
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    excel xlsxAvailable download formats
    Dataset updated
    Aug 2, 2024
    Dataset authored and provided by
    Joint Research Centrehttps://joint-research-centre.ec.europa.eu/index_en
    License

    http://data.europa.eu/eli/dec/2011/833/ojhttp://data.europa.eu/eli/dec/2011/833/oj

    Description

    Human activities and the corresponding anthropogenic emissions into the atmosphere show marked temporal variations, from inter-annual to hourly levels. Yearly emissions might be unable, for example, to adequately reflect heavy pollution episodes, seasonal trends, or any process which is inherently time-dependant, thus undermining the capability to determine the causes and to take adequate mitigation measure. Moreover, chemical transport models (CTMs) require the use of spatio-temporally distributed emission data, to simulate the dynamics of photochemical compounds in the atmosphere and pollution loadings during different periods of the year and during different hours of the day.

  5. f

    Temporal-integration profiles in Exp. 1, for the different trial durations...

    • plos.figshare.com
    tiff
    Updated May 31, 2023
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    Zohar Z. Bronfman; Noam Brezis; Marius Usher (2023). Temporal-integration profiles in Exp. 1, for the different trial durations (1, 2 and 3 seconds). [Dataset]. http://doi.org/10.1371/journal.pcbi.1004667.g004
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    tiffAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS Computational Biology
    Authors
    Zohar Z. Bronfman; Noam Brezis; Marius Usher
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Y-axis shows the relative influence of the signal-perturbation on accuracy, as compared to baseline (see text);X-axis depicts the 5 temporal-windows each corresponds to 1/5 of a trial); error bars denote 1 within-participant S.E.M [39].

  6. D

    Data from: Temporal and spatial gene expression profile of stroke recovery...

    • data.sfb1451.de
    • doi.gin.g-node.org
    Updated May 17, 2024
    + more versions
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    Markus Aswendt; Jan Götz (2024). Temporal and spatial gene expression profile of stroke recovery genes in mice [Dataset]. http://doi.org/10.12751/g-node.32p3ym
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    Dataset updated
    May 17, 2024
    Authors
    Markus Aswendt; Jan Götz
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Dataset funded by
    Friebe Foundation; T0498/28960/16
    DFG; 431549029 – SFB 1451
    Description

    Raw and processed data used for the publication: Götz et al. 2023 Temporal and spatial gene expression profile of stroke recovery genes in mice.

  7. a

    Sentinel Explorer

    • cotedivoire.africageoportal.com
    • africageoportal.com
    • +2more
    Updated May 22, 2018
    + more versions
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    Esri (2018). Sentinel Explorer [Dataset]. https://cotedivoire.africageoportal.com/app/esri::sentinel-explorer
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    Dataset updated
    May 22, 2018
    Dataset authored and provided by
    Esri
    Area covered
    Description

    This web application highlights some of the capabilities for accessing Sentinel-2 imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Sentinel-2 images on a daily basis.Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Sentinel-2 Explorer app provides the power of Sentinel-2 satellites, which gather data beyond what the eye can see. Use this app to draw on Sentinel's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the Sentinel-2 archive to visualize how the Earth's surface has changed over the last fourteen monthsQuick access to the following band combinations and indices is provided:BandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020Agriculture : Highlights vigorous vegetation in bright green, stressed vegetation dull green and bare areas brown; Bands 11, 8, 2Natural Color : Bands 4, 3, 2Color Infrared : Healthy vegetation is bright red while stressed vegetation is dull red; Bands 8, 4 ,3 SWIR (Short-wave Infrared) : Highlights rock formations; Bands 12, 11, 4Geology : Highlights geologic features; Bands 12, 11, 2Bathymetric : Highlights underwater features; Bands 4, 3, 1Vegetation Index : Normalized Difference Vegetation Index(NDVI) with Colormap ; (Band 8 - Band 4)/(Band 8 + Band 4)Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 8 - Band 11)/(Band 8 + Band 11)Normalized Burn Ratio : (Band 8 - Band 12)/(Band 8 + Band 12)Built-Up Index : (Band 11 - Band 8)/(Band 11 + Band 8)NDVI Raw : Normalized Difference Vegetation Index(NDVI); (Band 8 - Band 4)/(Band 8 + Band 4)NDVI - VRE only Raw : NDVI with VRE bands only; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - VRE only Colorized : NDVI with VRE bands only with Colormap; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - with VRE Raw : Also known as NDRE. NDVI with VRE band 5 and NIR band 8; (Band 8 - Band 5)/(Band 8 + Band 5)NDVI - with VRE Colorized : Also known as NDRE with Colormap; (Band 8 - Band 5)/(Band 8 + Band 5)NDWI Raw : Normalized Difference Water index with Green band and NIR band; (Band 3 - Band 8)/(Band 3 + Band 8)NDWI - with VRE Raw : Normalized Difference Water index with VRE band 5 and Green band 3; (Band 3 - Band 5)/(Band 3 + Band 5)NDWI - with VRE Colorized : NDWI index with VRE band 5 and Green band 3 with Colormap; (Band 3 - Band 5)/(Band 3 + Band 5)Custom SAVI : (Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 8 - Band 4)/(Band 8 + Band 4 + 0.5))Custom Water Index : Offset + Scale*(Band 3 - Band 12)/(Band 3 + Band 12)Custom Burn Index : Offset + Scale*(Band 8 - Band 13)/(Band 8 + Band 13)Urban Index : Offset + Scale*(Band 8 - Band 12)/(Band 8 + Band 12)Optionally, you can also choose the "Custom Bands" or "Custom Index" option to create your own band combinationsThe Time tool enables access to a temporal time slider and a temporal profile of different indices for a selected point. The Time tool is only accessible at larger zoom scales. It provides temporal profiles for indices like NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index) and Urban Index. The Identify tool enables access to information on the images, and can also provide a spectral profile for a selected point. The Bookmark tool will direct you to pre-selected interesting locations.NOTE: Using the Time tool to access imagery in the Sentinel-2 archive requires an ArcGIS account.The application is written using Web AppBuilder for ArcGIS accessing imagery layers using ArcGIS API for JavaScript.The following Imagery Layer are being accessed : Sentinel-2 - Provides access to 10, 20, and 60m 13-band multispectral imagery and a range of functions that provide different band combinations and indices.

  8. d

    Data from: The peripheral olfactory code in Drosophila larvae contains...

    • search.dataone.org
    • data.niaid.nih.gov
    • +1more
    Updated Jun 12, 2025
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    Micheline Grillet; Dario Campagner; Rasmus Petersen; Catherine McCrohan; Matthew Cobb (2025). The peripheral olfactory code in Drosophila larvae contains temporal information and is robust over multiple timescales [Dataset]. http://doi.org/10.5061/dryad.2rb57
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    Dataset updated
    Jun 12, 2025
    Dataset provided by
    Dryad Digital Repository
    Authors
    Micheline Grillet; Dario Campagner; Rasmus Petersen; Catherine McCrohan; Matthew Cobb
    Time period covered
    Jul 13, 2020
    Description

    We studied the electrophysiological activity of two classes of Drosophila melanogaster larval olfactory sensory neurons (OSNs), Or24a and Or74a, in response to 1 s stimulation with butanol, octanol, 2-heptanone, and propyl acetate. Each odour/OSN combination produced unique responses in terms of spike count and temporal profile. We used a classifier algorithm to explore the information content of OSN activity, and showed that as well as spike count, the activity of these OSNs included temporal information that enabled the classifier to accurately identify odours. The responses of OSNs during continuous odour exposure (5 and 20 min) showed that both types of neuron continued to respond, with no complete adaptation, and with no change to their ability to encode temporal information. Finally, we exposed larvae to octanol for 3 days and found only minor quantitative changes in OSN response to odours, indicating that the larval peripheral code is robust when faced with long-term exposure to ...

  9. d

    Mate-finding dispersal reduces local mate limitation and sex bias in...

    • datadryad.org
    • data.niaid.nih.gov
    zip
    Updated Jun 24, 2020
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    Abhishek Mishra; Sudipta Tung; V.R. Shree Sruti; Sahana Srivathsa; Sutirth Dey (2020). Mate-finding dispersal reduces local mate limitation and sex bias in dispersal [Dataset]. http://doi.org/10.5061/dryad.qjq2bvqcz
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    zipAvailable download formats
    Dataset updated
    Jun 24, 2020
    Dataset provided by
    Dryad
    Authors
    Abhishek Mishra; Sudipta Tung; V.R. Shree Sruti; Sahana Srivathsa; Sutirth Dey
    Time period covered
    May 28, 2020
    Description

    No missing values. No outlier rejection.

  10. f

    EDGAR temporal profiles (r1)

    • springernature.figshare.com
    xlsx
    Updated May 30, 2023
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    Greet J. Maenhout; Monica Crippa; Efisio Solazzo; Diego Guizzardi; Ernest Koffi; Marilena muntean; Ganlin Huang; Christian Schieberle; Rainer Friedrich (2023). EDGAR temporal profiles (r1) [Dataset]. http://doi.org/10.6084/m9.figshare.11363852.v1
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    xlsxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    figshare
    Authors
    Greet J. Maenhout; Monica Crippa; Efisio Solazzo; Diego Guizzardi; Ernest Koffi; Marilena muntean; Ganlin Huang; Christian Schieberle; Rainer Friedrich
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The file 'EDGAR_temporal_profiles_r1.xls' contains the EDGAR temporal profiles developed in this work to disaggregate annual emissions into monthly data.

  11. Sentinel Explorer Classic (Mature Support)

    • sdgs-amerigeoss.opendata.arcgis.com
    • caribbeangeoportal.com
    • +18more
    Updated May 23, 2018
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    Esri (2018). Sentinel Explorer Classic (Mature Support) [Dataset]. https://sdgs-amerigeoss.opendata.arcgis.com/items/93ba20b268fb426c9c665a6bcd816da8
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    Dataset updated
    May 23, 2018
    Dataset authored and provided by
    Esrihttp://esri.com/
    Description

    Important Note: This item is in mature support as of February 2025 and is no longer being updated. A new version of this item is available for your use.This web application highlights some of the capabilities for accessing Sentinel-2 imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Sentinel-2 images on a daily basis.Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Sentinel-2 Explorer app provides the power of Sentinel-2 satellites, which gather data beyond what the eye can see. Use this app to draw on Sentinel's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the Sentinel-2 archive to visualize how the Earth's surface has changed over the last fourteen monthsQuick access to the following band combinations and indices is provided:BandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020Agriculture : Highlights vigorous vegetation in bright green, stressed vegetation dull green and bare areas brown; Bands 11, 8, 2Natural Color : Bands 4, 3, 2Color Infrared : Healthy vegetation is bright red while stressed vegetation is dull red; Bands 8, 4 ,3 SWIR (Short-wave Infrared) : Highlights rock formations; Bands 12, 11, 4Geology : Highlights geologic features; Bands 12, 11, 2Bathymetric : Highlights underwater features; Bands 4, 3, 1Vegetation Index : Normalized Difference Vegetation Index(NDVI) with Colormap ; (Band 8 - Band 4)/(Band 8 + Band 4)Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 8 - Band 11)/(Band 8 + Band 11)Normalized Burn Ratio : (Band 8 - Band 12)/(Band 8 + Band 12)Built-Up Index : (Band 11 - Band 8)/(Band 11 + Band 8)NDVI Raw : Normalized Difference Vegetation Index(NDVI); (Band 8 - Band 4)/(Band 8 + Band 4)NDVI - VRE only Raw : NDVI with VRE bands only; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - VRE only Colorized : NDVI with VRE bands only with Colormap; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - with VRE Raw : Also known as NDRE. NDVI with VRE band 5 and NIR band 8; (Band 8 - Band 5)/(Band 8 + Band 5)NDVI - with VRE Colorized : Also known as NDRE with Colormap; (Band 8 - Band 5)/(Band 8 + Band 5)NDWI Raw : Normalized Difference Water index with Green band and NIR band; (Band 3 - Band 8)/(Band 3 + Band 8)NDWI - with VRE Raw : Normalized Difference Water index with VRE band 5 and Green band 3; (Band 3 - Band 5)/(Band 3 + Band 5)NDWI - with VRE Colorized : NDWI index with VRE band 5 and Green band 3 with Colormap; (Band 3 - Band 5)/(Band 3 + Band 5)Custom SAVI : (Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 8 - Band 4)/(Band 8 + Band 4 + 0.5))Custom Water Index : Offset + Scale*(Band 3 - Band 12)/(Band 3 + Band 12)Custom Burn Index : Offset + Scale*(Band 8 - Band 13)/(Band 8 + Band 13)Urban Index : Offset + Scale*(Band 8 - Band 12)/(Band 8 + Band 12)Optionally, you can also choose the "Custom Bands" or "Custom Index" option to create your own band combinationsThe Time tool enables access to a temporal time slider and a temporal profile of different indices for a selected point. The Time tool is only accessible at larger zoom scales. It provides temporal profiles for indices like NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index) and Urban Index. The Identify tool enables access to information on the images, and can also provide a spectral profile for a selected point. The Bookmark tool will direct you to pre-selected interesting locations.NOTE: Using the Time tool to access imagery in the Sentinel-2 archive requires an ArcGIS account.The application is written using Web AppBuilder for ArcGIS accessing imagery layers using ArcGIS API for JavaScript.The following Imagery Layer are being accessed : Sentinel-2 - Provides access to 10, 20, and 60m 13-band multispectral imagery and a range of functions that provide different band combinations and indices.

  12. Data from: A New Method for Tracking the Preparatory Activation of Target...

    • beta.ukdataservice.ac.uk
    Updated 2025
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    datacite (2025). A New Method for Tracking the Preparatory Activation of Target Templates for Visual Search with High Temporal Precision, 2022-2024 [Dataset]. http://doi.org/10.5255/ukda-sn-857751
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    Dataset updated
    2025
    Dataset provided by
    DataCitehttps://www.datacite.org/
    UK Data Servicehttps://ukdataservice.ac.uk/
    Description

    Efficiently selecting task-relevant objects during visual search depends on foreknowledge of their defining characteristics, which are represented within attentional templates. These templates bias attentional processing toward template-matching sensory signals and are assumed to become anticipatorily activated prior to search display onset. However, a direct neural signal for such preparatory template activation processes has so far remained elusive. Here, we introduce a new high-definition rapid serial probe presentation paradigm (RSPP–HD), which facilitates high temporal resolution tracking of target template activation processes in real time via monitoring of the N2pc component. In the RSPP–HD procedure, task-irrelevant probe displays are presented in rapid succession throughout the period between task-relevant search displays. The probe and search displays are homologously formed by lateralized “clouds” of colored dots, yielding probes that occur at task-relevant locations without confounding template-guided and salience-driven attentional shifts. Target color probes appearing at times when a corresponding target template is active should attract attention, thereby eliciting an N2pc. In a condition where new probe displays appeared every 50 ms, probe N2pcs were reliably elicited during the final 800 ms prior to search display onset, increasing in amplitude toward the end of this preparation period. Analogous temporal profiles were also observed with longer intervals between probes. These findings show that search template activation processes are transient and that their temporal profile can be reliably monitored at high-sampling frequencies with the RSPP–HD paradigm. This procedure offers a new route to approach various questions regarding the content and temporal dynamics of attentional control processes.

  13. t

    BIOGRID CURATED DATA FOR PUBLICATION: Temporal profile of brain and...

    • thebiogrid.org
    zip
    Updated Jan 1, 2007
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    BioGRID Project (2007). BIOGRID CURATED DATA FOR PUBLICATION: Temporal profile of brain and pituitary GnRHs, GnRH-R and gonadotropin mRNA expression and content during early development in European sea bass (Dicentrarchus labrax L.). [Dataset]. https://thebiogrid.org/177539/publication/temporal-profile-of-brain-and-pituitary-gnrhs-gnrh-r-and-gonadotropin-mrna-expression-and-content-during-early-development-in-european-sea-bass-dicentrarchus-labrax-l.html
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    zipAvailable download formats
    Dataset updated
    Jan 1, 2007
    Dataset authored and provided by
    BioGRID Project
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Protein-Protein, Genetic, and Chemical Interactions for Moles G (2007):Temporal profile of brain and pituitary GnRHs, GnRH-R and gonadotropin mRNA expression and content during early development in European sea bass (Dicentrarchus labrax L.). curated by BioGRID (https://thebiogrid.org); ABSTRACT: A likely endocrine control mechanism for sexual differentiation in size-graded populations of European sea bass (Dicentrarchus labrax) is proposed by evaluating the brain expression and pituitary content of two forms of gonadotropin-releasing hormone (GnRH), namely sea bream (sbGnRH) and salmon (sGnRH), the pituitary expression of one subtype of GnRH receptor (dlGnRH-R-2A) and the three gonadotropin (GtH) subunits, namely glycoprotein alpha (GPalpha), follicle-stimulating hormone beta (FSHbeta) and luteinizing hormone beta (LHbeta), as well as the pituitary and plasma LH levels between 50 and 300 days post-hatching (dph). Four gradings were conducted between 2 and 8 months after hatching, resulting in a population of large and small individuals, having 96.5% females (female-dominant population) and 69.2% males (male-dominant population), respectively, after the last grading. The onset of gonadal differentiation was different in the two sexes, and coincided with a peak of expression of sbGnRH or sGnRH. Furthermore, the expression of these GnRHs was correlated with the expression of dlGnRH-R-2A. Sex-related differences in the brain and pituitary content of sbGnRH were also found at the time of sexual differentiation. Moreover, the observed sexual dimorphism at the transcriptional or synthesis level of these GnRH forms suggests that a different neuro-hormonal regulation is operating according to sex. At the onset of sex differentiation, FSHbeta transcriptional activity reached maximal values, which were maintained until the completion of the process. The present study suggests a role for sbGnRH, sGnRH and the dlGnRH-R-2A during gonadal differentiation, possibly through enhancement of FSHbeta gene expression. In males, a different endocrine regulation seems to exist also during spermiogenesis and spermiation, when gene transcription, peptide synthesis and release of LH are of greater importance.

  14. d

    Data from: Non-monotonic temporal-weighting indicates a dynamically...

    • datadryad.org
    • data.niaid.nih.gov
    • +1more
    zip
    Updated Nov 25, 2016
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    Zohar Z. Bronfman; Noam Brezis; Marius Usher (2016). Non-monotonic temporal-weighting indicates a dynamically modulated evidence-integration mechanism [Dataset]. http://doi.org/10.5061/dryad.46qm6
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    zipAvailable download formats
    Dataset updated
    Nov 25, 2016
    Dataset provided by
    Dryad
    Authors
    Zohar Z. Bronfman; Noam Brezis; Marius Usher
    Time period covered
    Sep 15, 2015
    Description

    AllData_Exp1Behavioral data Exp.1 in .mat formatAllData_Exp2AllData_Exp3AllData_Exp4

  15. f

    Data from: Temporal Profile of the Renal Transcriptome of HIV-1 Transgenic...

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Mar 25, 2014
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    Wei, Chengguo; He, John Cijiang; Fan, Ying; Xiao, Wenzhen; Wang, Niansong; Chuang, Peter Y.; Zhang, Weijia (2014). Temporal Profile of the Renal Transcriptome of HIV-1 Transgenic Mice during Disease Progression [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001232604
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    Dataset updated
    Mar 25, 2014
    Authors
    Wei, Chengguo; He, John Cijiang; Fan, Ying; Xiao, Wenzhen; Wang, Niansong; Chuang, Peter Y.; Zhang, Weijia
    Description

    Profiling of temporal changes of gene expression in the same kidney over the course of renal disease progression is challenging because repeat renal biopsies are rarely indicated in clinical practice. Here, we profiled the temporal change in renal transcriptome of HIV-1 transgenic mice (Tg26), an animal model for human HIV-associated nephropathy (HIVAN), and their littermates at three different time points (4, 8, and 12 weeks of age) representing early, middle, and late stages of renal disease by serial kidney biopsy. We analyzed both static levels of gene expression at three stages of disease and dynamic changes in gene expression between different stages. Analysis of static and dynamic changes in gene expression revealed that up-regulated genes at the early and middle stages are mostly involved in immune response and inflammation, whereas down-regulated genes mostly related to fatty acid and retinoid metabolisms. We validated the expression of a selected panel of genes that are up-regulated at the early stage (CCL2, CCL5, CXCL11, Ubd, Anxa1, and Spon1) by real-time PCR. Among these up-regulated genes, Spon1, which is a previously identified candidate gene for hypertension, was found to be up-regulated in kidney of human with diabetic nephropathy. Immunostaining of human biopsy samples demonstrated that protein expression of Spon1 was also markedly increased in kidneys of patients with both early and late HIVAN and diabetic nephropathy. Our studies suggest that analysis of both static and dynamic changes of gene expression profiles in disease progression avails another layer of information that could be utilized to gain a more comprehensive understanding of disease progression and identify potential biomarkers and drug targets.

  16. Temporal weighting profile for the 3-sec perceptual decisions in...

    • plos.figshare.com
    • figshare.com
    tiff
    Updated May 31, 2023
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    Zohar Z. Bronfman; Noam Brezis; Marius Usher (2023). Temporal weighting profile for the 3-sec perceptual decisions in experiment-2 and 3 (data collapsed). [Dataset]. http://doi.org/10.1371/journal.pcbi.1004667.g005
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    tiffAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Zohar Z. Bronfman; Noam Brezis; Marius Usher
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Error bars denote 1 within-participant S.E.M [39].

  17. f

    Temporal profile of intracranial pressure and cerebrovascular reactivity in...

    • figshare.com
    pdf
    Updated May 30, 2023
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    Hadie Adams; Joseph Donnelly; Marek Czosnyka; Angelos G. Kolias; Adel Helmy; David K. Menon; Peter Smielewski; Peter J. Hutchinson (2023). Temporal profile of intracranial pressure and cerebrovascular reactivity in severe traumatic brain injury and association with fatal outcome: An observational study [Dataset]. http://doi.org/10.1371/journal.pmed.1002353
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    pdfAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS Medicine
    Authors
    Hadie Adams; Joseph Donnelly; Marek Czosnyka; Angelos G. Kolias; Adel Helmy; David K. Menon; Peter Smielewski; Peter J. Hutchinson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundBoth intracranial pressure (ICP) and the cerebrovascular pressure reactivity represent the dysregulation of pathways directly involved in traumatic brain injury (TBI) pathogenesis and have been used to inform clinical management. However, how these parameters evolve over time following injury and whether this evolution has any prognostic importance have not been studied.Methods and findingsWe analysed the temporal profile of ICP and pressure reactivity index (PRx), examined their relation to TBI-specific mortality, and determined if the prognostic relevance of these parameters was affected by their temporal profile using mixed models for repeated measures of ICP and PRx for the first 240 hours from the time of injury. A total of 601 adults with TBI, admitted between September 2002 to January 2016, and with high-resolution continuous monitoring from a single centre, were studied. At 6 months postinjury, 133 (19%) patients had a fatal outcome; of those, 88 (78%) died from nonsurvivable TBI or brain death. The difference in mean ICP between those with a fatal outcome and functional survivors was only significant for the first 168 hours after injury (all p < 0.05). For PRx, those patients with a fatal outcome also had a higher (more impaired) PRx throughout the first 120 hours after injury (all p < 0.05). The separation of ICP and PRx was greatest in the first 72 hours after injury. Mixed models demonstrated that the explanatory power of the PRx decreases over time; therefore, the prognostic weight assigned to PRx should similarly decrease. However, the ability of ICP to predict a fatal outcome remained relatively stable over time. As control of ICP is the central purpose of TBI management, it is likely that some of the information that is reflected in the natural history of ICP changes is no longer apparent because of therapeutic intervention.ConclusionsWe demonstrated the temporal evolution of ICP and PRx and their relationship with fatal outcome, indicating a potential early prognostic and therapeutic window. The combination of dynamic monitoring variables and their time profile improved prediction of outcome. Therefore, time-driven dynamic modelling of outcome in patients with severe TBI may allow for more accurate and clinically useful prediction models. Further research is needed to confirm and expand on these findings.

  18. TRMM Microwave Imager Hydrometeor Profile L2 1.5 hours V7 (TRMM_2A12) at GES...

    • catalog.data.gov
    • s.cnmilf.com
    • +1more
    Updated Apr 10, 2025
    + more versions
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    NASA/GSFC/SED/ESD/GCDC/GESDISC (2025). TRMM Microwave Imager Hydrometeor Profile L2 1.5 hours V7 (TRMM_2A12) at GES DISC [Dataset]. https://catalog.data.gov/dataset/trmm-microwave-imager-hydrometeor-profile-l2-1-5-hours-v7-trmm-2a12-at-ges-disc
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    Dataset updated
    Apr 10, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    The new version of these data is in GPM-like format and can be found under the name GPM_2AGPROFTRMMTMI_CLIM. This dataset, 2A12, ”TMI Profiling”, generates surface rainfall and vertical hydrometeor profiles on a pixel by pixel basis from the TRMM Microwave Imager (TMI) brightness temperature data using the Goddard Profiling algorithm GPROF2010. Because the vertical information comes from a radiometer, it is not written out in independent vertical layers like the TRMM Precipitation Radar. Instead, the output is referenced to one of 100 typical structures for each hydrometeor or heating profile. These vertical structures are referenced as clusters in the output structure. Vertical hydrometeor profiles can be reconstructed to 28 layers by knowing the cluster number (i.e. shape) of the profile and a scale factor that is written for each pixel. This product contains hydrometeor profiles of cloud liquid water, precipitation water, cloud ice water, precipitation ice, rainfall type, and latent heating in 28 atmospheric layers. Changes in horizontal resolution resulting from the TRMM boost that occurred on 24 August 2001: Pre-Boost (before 7 August 2001): Temporal Resolution: 91.5 min/orbit ~ 16 orbits/day; Swath Width: 760 km; Horizontal Resolution: 4.4 km Post-Boost (after 24 August 2001): Temporal Resolution: 92.5 min/orbit ~ 16 orbits/day; Swath Width: 878 km; Horizontal Resolution: 5.1 km

  19. e

    Temporal analysis of GRB precursors in Swift-BAT cat. - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Apr 17, 2024
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    (2024). Temporal analysis of GRB precursors in Swift-BAT cat. - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/a0b4d84c-765a-5d2e-b9c2-814e490a9eb6
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    Dataset updated
    Apr 17, 2024
    Description

    We select 52 long gamma-ray bursts (GRBs) that have precursor activity in the third Swift-BAT catalog. Each episode shown in both the precursors and the main bursts is fitted by the Norris function. We systematically analyze the temporal properties for both the precursors and the main bursts. We do not find any significant difference between the temporal profile of the precursor and that of the main burst. The photon count of the precursor is related to that of the main burst. It is indicated that the precursor and the main burst might have the same physical origin, as the precursor and the main burst follow the same {tau}p-{omega} relation. However, we do not find the explicit relation between the energy release of the precursor and the quiescent time. Some theoretical models, such as the fallback collapsar scenario and the jet-cocoon scenario, may be helpful to explain the GRB-precursor phenomena. Cone search capability for table J/ApJ/928/152/table3 (The quiescent time and the peak time)

  20. Data from: DED Bead Geometry and Profile Prediction with Multimodal...

    • zenodo.org
    Updated Feb 4, 2025
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    Yichen Wang; Wenze Zhang; Yuanzhi Chen; Kai Tang; Yichen Wang; Wenze Zhang; Yuanzhi Chen; Kai Tang (2025). DED Bead Geometry and Profile Prediction with Multimodal Spatio-temporal Neural Networks [Dataset]. http://doi.org/10.5281/zenodo.14799986
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    Dataset updated
    Feb 4, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Yichen Wang; Wenze Zhang; Yuanzhi Chen; Kai Tang; Yichen Wang; Wenze Zhang; Yuanzhi Chen; Kai Tang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is pre-processed and in binary format . Please contact the author for raw data like melt pool images in png format, or processing data in csv format.

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Landthaler, Markus; Wiebusch, Lüder; Hagemeier, Christian; Phan, Quang Vinh; Wyler, Emanuel; Bogdanow, Boris; Liu, Fan (2022). Relative abundances and temporal profile classification of CCMV proteins. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000225734

Relative abundances and temporal profile classification of CCMV proteins.

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Dataset updated
Jan 4, 2022
Authors
Landthaler, Markus; Wiebusch, Lüder; Hagemeier, Christian; Phan, Quang Vinh; Wyler, Emanuel; Bogdanow, Boris; Liu, Fan
Description

LFQ values were the basis for the calculation of expression profiles. The maximum LFQ for each gene across time points was set to 1. The remaining time points were calculated relative to this maximum. A value of 0 indicates that the respective protein could not be quantified at the respective time point. (XLSX)

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