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955 results for “APS”

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zenodo40/100

AP-132

<p>This is a proteomics dataset using a novel photocatalytic probe complexed with HDL, applied to special cells.</p>

opencc-by-4.0Apr 2023View details →
dryad40/100

Self-assembly and structure of a clathrin-independent AP-1:Arf1 tubular membrane coat

Open the record for dataset details and reuse information.

publicMay 2023View details →
zenodo36/100

List of specimens, collection numbers, localities, and GenBank accessions of sequences. The neotype of Scinax x‑signatus is underlined. New sequences produced for this study are in bold. Abbreviations are as follow. Countries: ARG = Argentina, BOL = Bolivia, BRA = Brazil, GUF = French Guiana, GUY = Guyana, MTQ = Martinique, PER = Peru, SUR = Suriname; Brazilian states: AP = Amapá, BA = Bahia, CE = Ceará, ES = Espírito Santo, MA = Maranhão, MG = Minas Gerais, PE = Pernambuco, RJ = Rio de Janeiro, RS = Rio Grande do Sul, SP = São Paulo. An asterisk (*) indicates approximate coordinates taken from Google Earth. in A neotype for Hyla x-signata Spix, 1824 (Amphibia, Anura, Hylidae)

List of specimens, collection numbers, localities, and GenBank accessions of sequences. The neotype of Scinax x‑signatus is underlined. New sequences produced for this study are in bold. Abbreviations are as follow. Countries: ARG = Argentina, BOL = Bolivia, BRA = Brazil, GUF = French Guiana, GUY = Guyana, MTQ = Martinique, PER = Peru, SUR = Suriname; Brazilian states: AP = Amapá, BA = Bahia, CE = Ceará, ES = Espírito Santo, MA = Maranhão, MG = Minas Gerais, PE = Pernambuco, RJ = Rio de Janeiro, RS = Rio Grande do Sul, SP = São Paulo. An asterisk (*) indicates approximate coordinates taken from Google Earth.

opencc-by-nc-4.0Nov 2020View details →
zenodo36/100

Figure 1. - Sample sites of Palaemoncarteri, Palaemonivonicus and Palaemonyuna sp. n. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas; AC-Acre; AM-Amazonas; AP-Amapá; MS-Mato Grosso do Sul; MT-Mato Grosso; PA-Pará and RO-Rondônia.

Figure 1. - Sample sites of Palaemoncarteri, Palaemonivonicus and Palaemonyuna sp. n. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas; AC-Acre; AM-Amazonas; AP-Amapá; MS-Mato Grosso do Sul; MT-Mato Grosso; PA-Pará and RO-Rondônia.

opencc-by-4.0Feb 2017View details →
dryad36/100

Data from: Clathrin-independent endocytic retrieval of SV proteins mediated by the clathrin adaptor AP-2 at mammalian central synapses

<p><span>Neurotransmission is based on the exocytic fusion of synaptic vesicles (SVs) followed by endocytic membrane retrieval and the reformation of SVs. Conflicting models have been proposed regarding the mechanisms of SV endocytosis, most notably clathrin/ AP-2-mediated endocytosis and clathrin-independent ultrafast endocytosis. Partitioning between these pathways has been suggested to be controlled by temperature and stimulus paradigm. We report on the comprehensive survey of six major SV proteins to show that SV endocytosis in mouse hippocampal neurons at physiological temperature occurs independent of clathrin while the endocytic retrieval of a subset of SV proteins including the vesicular transporters for glutamate and GABA depend on sorting by the clathrin adaptor AP-2. Our findings highlight a clathrin-independent role of the clathrin adaptor AP-2 in the endocytic retrieval of select SV cargos from the presynaptic cell surface and suggest a revised model for the endocytosis of SV membranes at mammalian central synapses.</span></p>

opencc-zeroJan 2022View details →
zenodo36/100

OGG1 in AP

<p>The file contains data supporting our findings reported in the manuscript titled &quot;OGG1 inhibition reduces acinar cell injury in a mouse model of acute pancreatitis&quot; accepted for publication in &quot;Biomedicines&quot;..</p>

opencc-by-4.0May 2022View details →
zenodo36/100

(Stechkin pistol) APS | Free game ready asset

The Stechkin automatic pistol or APS (Avtomaticheskiy Pistolet Stechkina = Автоматический Пистолет Стечкина) is a Soviet selective fire machine pistol chambered in 9×18mm Makarov and 9×19mm Parabellum introduced into service in 1951 for use with artillery and mortar crews, tank crews and aircraft personnel, where a cumbersome assault rifle was deemed unnecessary. Seeing service in a number of wars such as the Vietnam War, War in Donbas and Syrian Civil War. The APS was praised for its innovative concept and good controllability for its size. However, the high cost of the weapon, complex and time-consuming machining, combined with a limited effective range, large size and weight for a pistol, and fragile buttstock have been mentioned as a reason to phase it out of active service in favour of assault rifles such as the AKS-74U. The pistol bears the name of its developer, Igor Stechkin. Source: Objaverse 1.0 / Sketchfab

opencc-byFeb 2022View details →
zenodo36/100

Three-Point Correlation Multipoles with AP

<p>This repository contains the backbones for the computation of 3PCF theoretical predictions.</p> <p>See attached code</p> <p>&nbsp;</p>

opencc-by-4.0Apr 2024View details →
zenodo36/100

Degree of alternans depends on AP morphology and pacing frequency.

<p>Dataset to determine how degree of alternans depends on AP morphology and pacing frequency.</p> <p>Clampfit files</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

SR load modulation by AP morphology

<p>Data in this data set was used to determine SR Ca load during cell pacing with different AP waveforms.</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

L-type Ca channel kinetics vs AP morphology

<p>Data in this data set was used to determine L-type Ca current kinetics and net Ca entry during pacing of atrial myocyte with different AP waveforms. Raw data is in pClamp format.</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

Training and Testing Data for AP-SVM

<p>The files in here contain training and testing data for the AP-SVM data cleaning model, including datasets curated for leakage and sacrifice studies. Raw and digital signal processed files are included</p>

opencc-zeroSep 2024View details →
zenodo36/100

ACT-AP Mobile Monitoring

<h1>1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; About This Document</h1> <p>This directory contains air pollution concentration data from the Adult Changes in Thought &ndash; Traffic-Related Air Pollution (ACT-TRAP) mobile monitoring study (Blanco et al., 2022).</p> <p>&nbsp;</p> <h1>2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Data Source</h1> <p>The ACT-TRAP mobile monitoring study was an air pollution monitoring campaign that was conducted between March 2019 and March 2020 in the greater Seattle area. Briefly, the campaign collected 2-minute pollutant concentrations at 309 stop locations throughout the greater Seattle area. Pollutants were simultaneously measured with high temporal resolution (measurements every 1-60 sec). These included particle number concentration (PNC, an indicator of ultrafine particulates or UFP) from four different instruments, black carbon (BC), nitrogen dioxide (NO<sub>2</sub>), carbon dioxide (CO<sub>2</sub>), and fine particulate matter (PM<sub>2.5</sub>). Each stop location was visited approximately 29 times during all seasons and days of the week between the hours of approximately 5 AM and 11 PM.</p> <p>&nbsp;</p> <h1>3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The Data</h1> <p>We summarized high-resolution instrument data into median (and mean) stop concentrations, and these were used to calculate annual average concentrations for each of the 309 mobile monitoring sites. Using universal kriging-partial least squares (UK-PLS) regression models with hundreds of geographic covariate predictors, we generated out-of-sample TRAP predictions for monitoring locations, cohort locations (for epidemiologic purposes), census block centroids, and a grid (for visual purposes).</p> <p>&nbsp;</p> <p>The relevant data files are:</p> <p>&nbsp;</p> <p>1.&nbsp;&nbsp;&nbsp;&nbsp; Stop-level data (stop_data.csv)</p> <p>2.&nbsp;&nbsp;&nbsp;&nbsp; Annual average estimates and predictions for the mobile monitoring sites (N=309) (monitoring_location_estimates_and_predictions.csv)</p> <p>3.&nbsp;&nbsp;&nbsp;&nbsp; Annual average model predictions for:</p> <p>a.&nbsp;&nbsp;&nbsp;&nbsp; a grid in the region (grid_predictions.csv)</p> <p>b.&nbsp;&nbsp;&nbsp;&nbsp; 2010 Census block centroids for Washington state (census_block_predictions.csv)</p> <p>4.&nbsp;&nbsp;&nbsp;&nbsp; The geographic covariates available for modeling for the: &nbsp;</p> <p>a.&nbsp;&nbsp;&nbsp;&nbsp; monitoring sites (dr0311_mobile_covars.csv)</p> <p>b.&nbsp;&nbsp;&nbsp;&nbsp; grid (dr0311_grid_covars.csv)</p> <p>c.&nbsp;&nbsp;&nbsp;&nbsp; 2010 census block covariates (block10_intpts_wa.csv)</p> <p>5.&nbsp;&nbsp;&nbsp;&nbsp; UK-PLS model performances for annual average TRAP from the mobile monitoring campaign (model_performances.csv). &nbsp;</p> <p>6.&nbsp;&nbsp;&nbsp;&nbsp; Spatial files for</p> <p>a.&nbsp;&nbsp;&nbsp;&nbsp; A polygon encompassing all of the monitoring sites. There are three versions:</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; i.&nbsp;&nbsp;&nbsp;&nbsp; The first includes all land and water areas (monitoring_area.rda) and is the simplest.</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ii.&nbsp;&nbsp;&nbsp;&nbsp; Same as above but excludes major water areas (monitoring_land.rda). This is useful for mapping, for example.</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; iii.&nbsp;&nbsp;&nbsp;&nbsp; Same as above but excludes all water areas (monitoring_land_zero_water.rda). This is the file used to make predictions to ensure that no predictions are made on bodies of water.</p> <p>&nbsp;</p> <p>On-road data were also collected while the vehicle was in motion. Please inquire if you are interested in these data.</p> <p>&nbsp;</p> <h1>4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Data Dictionaries</h1> <p>Data dictionaries for all the campaign data can be found in data_dictionaries.docx.</p> <p>&nbsp;</p> <p>Geographic covariates come from the MESA Air geodatabase. Details on these covariates can be found in MESAAirDOOP_20190501.pdf and here: <a href="https://deohs.washington.edu/sites/default/files/MESAAirDOOP_Rev12.pdf">https://deohs.washington.edu/sites/default/files/MESAAirDOOP_Rev12.pdf</a>.</p> <p>&nbsp;</p> <h1>5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Reference</h1> <p>Blanco MN, Gassett A, Gould T, Doubleday A, Slager DL, Austin E, Seto E, Larson TV, Marshall JD, Sheppard L. Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign. Environ Sci Technol. 2022 Aug 16;56(16):11460-11472. doi: 10.1021/acs.est.2c01077. Epub 2022 Aug 2. PMID: 35917479; PMCID: PMC9396693.</p> <p>&nbsp;</p>

opencc-by-nc-4.0Sep 2024View details →
zenodo36/100

Table 1 in Pseudofilamentous desmids (Zygnematophyceae) from an Amazonian floodplain lake (Macapá, AP, Brazil)

<p><b>Table 1.</b> Seasonal occurrence of pseudofilamentous desmids at Curralinho Lake (FR: relative frequency; F: frequent; LC: less common; R: rare; BP: both periods, rainy and less rainy; RP: rainy period.</p><table><tbody><tr><th><b>Taxon</b></th><th><b>F.R. (%)</b></th><th><b>Category</b></th><th><b>Seasonal occurrence</b></th></tr></tbody><tbody><tr><th><i>Bambusina borreri</i> var. <i>borreri</i></th><td>58,33</td><td>F</td><td>BP</td></tr><tr><th><i>B. borreri</i> var. <i>brasiliense</i></th><td>50</td><td>F</td><td>BP</td></tr><tr><th><i>B. borreri</i> var. <i>majus</i></th><td>50</td><td>F</td><td>BP</td></tr><tr><th><i>Desmidium baileyi</i> var. <i>baileyi</i> f. <i>baileyi</i></th><td>33.33</td><td>LC</td><td>RP</td></tr><tr><th><i>D. elegans</i></th><td>50</td><td>F</td><td>BP</td></tr><tr><th><i>D. graciliceps</i> var. <i>graciliceps</i></th><td>16.66</td><td>LC</td><td>BP</td></tr><tr><th><i>D. graciliceps</i> var. <i>groenbladii</i></th><td>25</td><td>LC</td><td>BP</td></tr><tr><th><i>D. longatum</i></th><td>33.33</td><td>LC</td><td>BP</td></tr><tr><th><i>D. quadratum</i> var. <i>quadratum</i></th><td>16.66</td><td>LC</td><td>RP</td></tr><tr><th><i>D. quadratum</i> var. <i>constrictum</i></th><td>16.66</td><td>LC</td><td>RP</td></tr><tr><th><i>Groenbladia neglecta</i> var. <i>neglecta</i></th><td>58.33</td><td>F</td><td>BP</td></tr><tr><th><i>G. neglecta</i> var. <i>elongata</i></th><td>58.33</td><td>F</td><td>BP</td></tr><tr><th><i>Haplozyga armata</i> var. <i>armata</i></th><td>41.66</td><td>F</td><td>RP</td></tr><tr><th><i>Hyalotheca dissiliens</i> var. <i>dissiliens</i></th><td>41.66</td><td>F</td><td>BP</td></tr><tr><th><i>H. javanica</i></th><td>58.33</td><td>F</td><td>BP</td></tr><tr><th><i>Mateola curvata</i></th><td>8.33</td><td>R</td><td>RP</td></tr><tr><th><i>Phymatodocis nordstedtiniana</i> var. <i>nordstedtiniana</i> f. <i>minor</i></th><td>25</td><td>LC</td><td>BP</td></tr><tr><th><i>Spondylosium desmidiiforme</i></th><td>8.33</td><td>R</td><td>RP</td></tr><tr><th><i>S. pulchrum</i> var. <i>pulchrum</i></th><td>16.66</td><td>LC</td><td>BP</td></tr><tr><th><i>S. rectangulare</i> var. <i>rectangulare</i></th><td>41.66</td><td>F</td><td>BP</td></tr><tr><th><i>S. rectangulare</i> var. <i>goyazense</i></th><td>25</td><td>LC</td><td>BP</td></tr></tbody></table>

opencc-by-4.0Jul 2020View details →
zenodo36/100

CPSV-AP 2.0 based public service descriptions of the Region of Epirus (Greece) as linked data

<p>It comprises a set of 45 CPSV-AP 2.0 based public service descriptions as linked data. The fields of the included public service descriptions were adapted from a regional public service catalogue, namely &quot;The Citizen&#39;s Guide of the Region of Epirus&quot;, in order to comply with CPSV-AP 2.0 data model. The public service descriptions that are included in the linked dataset are not updated as the intended usage of the linked dataset is for research or educational activities and not for providing valid information to citizens.</p>

opencc-by-4.0Nov 2022View details →
ClinicalTrials.gov36/100

Saline-Controlled Study of nSTRIDE APS for Knee Osteoarthritis

ClinicalTrials.gov study NCT02905240. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

3-AP and Fludarabine in Treating Patients With Myeloproliferative Disorders, Chronic Myelomonocytic Leukemia, or Accelerated Phase or Blastic Phase Chronic Myelogenous Leukemia

ClinicalTrials.gov study NCT00381550. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

LAP-BAND AP Early Experience Trial (APEX)

ClinicalTrials.gov study NCT00501085. IPD Sharing: Not stated. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

Evaluation of Intra-articular Injection of Autologous Protein Solution ("APS(TM)") for the Treatment of Osteoarthritis (OA)

ClinicalTrials.gov study NCT01773226. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Effect of Dalfampridine (4-AP) on Genioglossus Muscle Activity in Healthy Adults

ClinicalTrials.gov study NCT02656160. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record