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704 results for “Interference”
Increasing lexical engagement with motor-enhanced pictures: An online study using the Picture-Word Interference paradigm
<p>data, training materials & code </p>
Research data for: Replicative Acinetobacter baumannii strains interfere with phagosomal maturation by modulating the vacuolar pH
<p>Bacterial pneumonia is a common infection of the lower respiratory tract that can afflict patients of all ages. Multidrug-resistant strains of <em>Acinetobacter</em> <em>baumannii</em> are increasingly responsible for causing nosocomial pneumonias, thus posing an urgent threat. Alveolar macrophages play a critical role in overcoming respiratory infections caused by this pathogen. Recently, we and others have shown that new clinical isolates of <em>A. baumannii</em>, but not the common lab strain ATCC 19606 (19606), can persist and replicate in macrophages within spacious vacuoles that we called Acinetobacter Containing Vacuoles (ACV). In this work, we demonstrate that the modern <em>A. baumannii</em> clinical isolate 398, but not the lab strain 19606, can infect alveolar macrophages and produce ACVs in vivo in a murine pneumonia model. Both strains initially interact with the alveolar macrophage endocytic pathway, as indicated by EEA1 and LAMP1 markers; however, the fate of these strains diverges at a later stage. While 19606 is eliminated in an autophagy pathway, 398 replicates in ACVs and are not degraded. We show that 398 reverts the natural acidification of the phagosome by secreting large amounts of ammonia, a by-product of amino acid catabolism. We propose that this ability to survive within macrophages may be critical for the persistence of clinical <em>A. baumannii </em>isolates in the lung during a respiratory infection.</p>
Identification-of-interference-sources-dataset
<p>This is the air traffic data near a certain airport in China, collected at four sampling points in different directions and at different distances.</p>
Anthropogenic Specular Interference in the Operational GOES-R Fire Product
<p><strong>Dataset for the research paper "Anthropogenic Specular Interference in the Operational GOES-R Fire Product".</strong></p> <p>Large reflective structures like solar power plants and commercial greenhouses sometimes reflect sunlight directly into GOES-R sensors. These anthropogenic specular reflections, or "sparkles", cause commission errors in operational GOES-R ABI products like the Fire Detection and Characterization Algorithm (FDCA). Using the <em>abi-sparkle</em> library for Python (Dove-Robinson, 2023), we generated a dataset containing both detected anthropogenic specular reflection pixels and the coincident FDCA commission errors caused by them for the GOES-16 CONUS domain during the 2020 calendar year.</p> <p>The dataset consists of two exported PostgreSQL tables: <em>sparkle_pixels_g16_abi_conus_2020</em>, which contains the detected anthropogenic specular reflection pixels at 500 m resolution, and <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>, which contains clustered FDCA false alarm fire pixels caused by anthropogenic specular reflection at 2 km resolution. The FDCA pixels were only processed for fire mask codes 10-15 and 30-35; see Table 3.11 in Schmidt et al., 2013 for fire code definitions.</p> <p>Each row in <em>sparkle_pixels_g16_abi_conus_2020 </em>is a detected specular reflection pixel in a GOES-16 CONUS image from the 2020 calendar year with a unique numeric ID <em>sparkle_id </em>and associated metadata from the detection algorithm <em>abi-sparkle</em>. The column <em>sparkle_geom </em>is a PostGIS geometry ST_Point object that can be used to plot the pixels on a map.</p> <p>FDCA fire pixels at 2 km resolution were clustered based on their connectivity in a 3x3 pixel kernel and assigned a UUID <em>fire_cluster_id </em>in the table <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>. Only the fire clusters that overlapped with sparkle pixels in time and space were retained in the table. In this way, each row of <em>fdca_commission_error_clusters_g16_abi_conus_2020 </em>is a unique cluster of errant FDCA fire pixels caused by anthropogenic specular reflection in every available scan start time for the GOES-16 CONUS domain in 2020. Every fire cluster centroid has a PostGIS geometry object <em>fire_cluster_centroid_geom </em>that can be used to plot the errant fire pixel clusters on a map.</p> <p>The two tables relate with the column <em>sparkle_ids </em>in <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>, which is an array of overlapping sparkle IDs from the <em>sparkle_pixels_g16_abi_conus_2020 </em>table. The combined dataset may therefore be generated with a simple SQL INNER JOIN:</p> <p><em>SELECT * FROM fdca_commission_error_clusters_g16_abi_conus_2020 fcecgac<br> INNER JOIN sparkle_pixels_g16_abi_conus_2020 spgac ON spgac.sparkle_id = ANY(fcecgac.sparkle_ids);</em></p> <p>The tables can be imported into a PostgreSQL database version 12 or newer with PostGIS extensions installed. For example, to import the tables into a database in a Linux environment, run the following commands:</p> <p><em>gunzip -c sparkle_pixels_g16_abi_conus_2020.sql.gz | psql -d your_database_name<br> gunzip -c fdca_commission_error_clusters_g16_abi_conus_2020.sql.gz | psql -d your_database_name</em></p>
The Aedes aegypti RNA interference response against Zika virus in the context of co-infection with dengue and chikungunya viruses
<p>This is the corresponding data of the Publication in Plos Neglected tropical diseases.</p> <p>Zika virus (ZIKV) is a mosquito-borne human-pathogenic arbovirus of the <em>Flaviviridae</em> family, genus <em>Flavivirus</em>. Other arboviruses, including dengue (DENV) or chikungunya (CHIKV) virus, can occur in the same regions as ZIKV and are also transmitted by <em>Aedes aegypti</em>. Notably, it has been shown that these viruses can co-infect this mosquito, andco-transmission occurs. Such processes may add to the serious public health issues already linked to those pathogens. Arbovirus infections in mosquitoes are controlled through an immune response called RNA interference (RNAi). It is however unknown whether immune responses changs when a mosquito is exposed to a co-infection of ZIKV with either DENV or CHIKV. In this study, we provide evidence that ZIKV co-infections with CHIKV or DENV are similarly well controlled by RNAi as single infections. These findings give new insights into the dynamics of arboviral co-infections in mosquito vectors that increase our understanding of co-infection scenarios during arbovirus outbreaks.</p>
Foundress number in Local ecological factors, not interference competition, drive the foundress number of two species of fig wasp sharing Ficus septica figs
<p><span>Recent studies have challenged assumptions about the classic fig-fig wasp pollination mutualism model, suggesting that further investigation into the receptive phase of fig development is needed. This study assessed the pollination mechanisms of <em>Ficus septica</em> in southern Taiwan and identified two species of wasps as the primary pollinators. Machine learning was used to identify and rank the factors that explain the relative abundance of these wasps. The two wasp species showed the highest level of cohabitation ever reported in the literature, with three-quarters of the figs containing multiple foundresses. The study also reported re-emerged foundresses and a 10% ratio of pollinated figs without foundresses. Local factors, such as the sampling period and tree identity, were the best predictors of the presence and number of each foundress species, with fig size also affecting the number of foundresses. The study highlights the variability in pollinator abundance between figs, crops, and trees. It also shows that the local environment of the trees and the availability of figs are crucial factors in determining which figs the pollinator wasps choose. These findings challenge assumptions about the classic mutualism model and suggest that long-term surveys are needed to estimate the relative contributions of each partner and provide data for evolutionary and ecological models. This study also provides valuable insights into the factors that affect the abundance and interactions of pollinator wasps during the receptive phase of fig development, with implications for understanding the behaviour of pollinating wasps and advancing our knowledge of population dynamics in <em>Ficus </em>species.</span></p>
Motor sources of dual-task interference: Evidence for effector-based prioritization in dual-task control
<p>Data of "Motor sources of dual-task interference: Evidence for effector-based prioritization in dual-task control", Hoffmann, Pieczykolan, Koch, & Huestegge.</p> <p>Dual-task costs in error rates and reaction times in six pairwise combination groups of effector systems.</p>
Bacterial Interference for Prevention of Catheter-Associated UTI: Geriatric Pilot Study
ClinicalTrials.gov study NCT00554996. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Effectiveness of Online ACT for Pain Interference in Cancer Survivors With Chronic Painful CIPN
ClinicalTrials.gov study NCT05371158. IPD Sharing: YES. Countries: 1. Publications: 2.
Temporal Interference Neurostimulation and Addiction
ClinicalTrials.gov study NCT04432064. IPD Sharing: YES. Countries: 1. Publications: 10.
Study to Assess Potential Immune Interference When GlaxoSmithKline (GSK) Biologicals' MenABCWY Vaccine is Administered to Healthy Subjects Aged 10-25 Years
ClinicalTrials.gov study NCT03587207. IPD Sharing: YES. Countries: 1. Publications: 1.
Rapamycin as a Means of Interference With Reconsolidation of Posttraumatic Stress Disorder-related Traumatic Memory
ClinicalTrials.gov study NCT01449955. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Effect of Tetrabenazine on Stroop Interference in HD
ClinicalTrials.gov study NCT01834911. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Application of an improved calibration flight scheme in aeromagnetic interference compensation
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Data from: What is regulating chironomid populations? The influence of food supply and interference competition on development and mortality in <em>Chironomus riparius</em>
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Data from: Alternative food sources interfere with removal of a fungal amphibian pathogen by zooplankton
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Foundress number in local ecological factors, not interference competition, drive the foundress number of two species of fig wasp sharing Ficus septica figs
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Mechanisms of male-male interference during dispersal of orchid pollen
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Data from: RNA interference reveals that male nuptial gift proteins affect female behavior to increase male paternity share in decorated crickets
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Data from: Sympatry and interference of divergent Microbotryum pathogen species.
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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)
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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.