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386 results for “dissemination”
Fig. 2 in Disseminated protozoal infection in a wild feathertail glider (Acrobates pygmaeus) in Australia
Fig. 2. Relationship of the novel apicomplexan taxon (in bold) from the liver of the feathertail glider with representative sequences from members of the Eimeriidae, Lankesterellidae and the Sarcocystidae, established based on a phylogenetic analysis of small subunit of nuclear ribosomal RNA gene (SSU) sequence data employing distance and Bayesian methods. Branch supports include neighbor-joining bootstrap percentages followed by Bayesian posterior probabilities. Members of the genus Goussia were used as outgroups.
Fig. 1 in Disseminated protozoal infection in a wild feathertail glider (Acrobates pygmaeus) in Australia
Fig. 1. Histological image of clusters of zoites (arrow) in the liver (A) and lung (B) of a feathertail glider. Haematoxylin and eosin stain; 1000-times magnification.
Fig. 1 in First record of Liriomyza huidobrensis (Diptera: Agromyzidae) disseminating Alternaria solani (Pleosporaceae) in potato crops in Brazil
Fig. 1. (a) Presence of Liriomyza huidobrensis mines on a Solanum tuberosum leaf infected with Alternaria solani; (b) diagram showing representative distribution of mines and fungal lesions.
Dissemination of mass and volume by double weighing in air - measurement data
<p>All data related to the measurement.</p> <ul> <li>Files "1kg - ...", "500 g - ..." etc. include measurement reports from the mass comparators</li> <li>Files "podminky_1kg" etc. include dtaa for temperature, pressure and humidity from dataloggers</li> <li>File "Inputs.ods" - all input data including measurements with AT1006</li> <li>File "Inputs-noat1006.xlsx" - all input data without measurements from AT1006</li> <li>File "MCOutput.csv" - Output data from Monte Carlo simulation</li> <li>File "Output.xlsx" - Output data with covariances and correlations</li> <li>File "Output_withAT1006.xlsx" - Output data where AT1006 measurements were used</li> </ul>
Radio Interview: dissemination of the project
<p>Radio Interview at "Live Social", Radio Roma Capitale. Recorded on May 16th 2021, on air in June 28th 2021.</p>
The MASTRO Video presenting the project´s dissemination material
<p>Take a look the our video presenting the dissemination material designed during the project´s implementation. </p>
Data set from "A free-space interferometer design for optical frequency dissemination and out-of-loop characterization below the 10^{-21}-level"
<p>The data set contains the data underlying the improved out-of-loop interferometer layout performance evaluation published in Photonics Research (<a href="https://doi.org/10.1364/PRJ.485899">https://doi.org/10.1364/PRJ.485899</a>). The experimental setup and the methodology used is explained in that publication.</p> <p>The data is stored in the Matlab(R)-native file format. This proprietary file format is also readable by other numerical computing environments.</p> <p><br> The files 'data_i_*_S*.mat' contain the timeseries of the analysed continuous measurement runs in configuration S*. Each of these files includes the following variables:</p> <p>year, month, day, hour, minute, second: date at which the measurment point was acquired<br> rem_float, rem: observed 1s Lambda-averaged out-of-loop frequency deviation in Hz as float and string, respectively<br> p: measured air pressure in hPa<br> T: measured laboratory temperature inside the cover close to the interferometer in °C<br> pressure_phase_OOL: phase variations of the out-of-loop signal estimated from the measured pressure variations<br> temp_phase_OOL: phase variations of the out-of-loop signal estimated from the measured temperature variations</p> <p> </p> <p>Different barometers have been used for the pressure mesaurements. In the measurement runs for the S1 and S2PM configurations, a barometer placed in a neighboring laboratory in the same building at PTB was used to characterize pressure fluctuations. For the measurement in the S2 configuration, we used air pressure data from the climate station of the department of Hydrology and River Basin Management of the Technical University Braunschweig, which is ≈6km apart. At times of overlapping operation, we have observed matching pressure instabilities of both barometers for averaging times 𝜏>1000s, which shows that on these averaging times the exact placement of the barometer is of lesser importance.</p>
Revisiting the historical scenario of a disease dissemination using genetic data and Approximate Bayesian Computation methodology: the case of Pseudocercospora fijiensis invasion in Africa
<p class="MsoNormal"><span>The reconstruction of geographic and demographic scenarios of dissemination for invasive pathogens of crops is a key step towards improving the management of emerging infectious diseases. Nowadays, the reconstruction of biological invasions typically uses the information of both genetic and historical information to test for different hypotheses of colonization. The Approximate Bayesian Computation framework and its recent Random Forest development (ABC-RF) have been successfully used in evolutionary biology to decipher multiple histories of biological invasions. Yet, for some organisms, typically plant pathogens, historical data may not be reliable notably because of the difficulty to identify the organism and the delay between the introduction and the first mention. We investigated the history of the invasion of Africa by the fungal pathogen of banana, <em>Pseudocercospora fijiensis</em>, by testing the historical hypothesis against other plausible hypotheses. We analysed the genetic structure of eight populations from six eastern and western African countries, using 20 microsatellite markers, and tested competing scenarios of population foundation using the ABC-RF methodology. We do find evidence for an invasion front consistent with the historical hypothesis, but also for the existence of another front never mentioned in historical records. We question the historical introduction point of the disease on the continent. Crucially, our results illustrate that even if ABC-RF inferences may sometimes fail to infer a single, well-supported scenario of invasion, they can be helpful in rejecting unlikely scenarios, which can prove much useful to shed light on disease dissemination routes.</span></p>
One Health EJP 4th Dissemination Workshop: Communication, education and training, and science to policy translation. Lessons learnt and legacy of the One Health EJP
<p>The Dissemination Workshops inform on One Health EJP solutions created in response to stakeholder needs, fostering the link between stakeholders and the consortium. Previous Dissemination Workshops targeted policy and decision makers at the national, European, and international level (reports available <a href="https://onehealthejp.eu/outcomes/science-to-policy-translation/reports">here</a>). This workshop instead is dedicated to all those who would like to set up One Health initiatives, encouraging them to learn from the experience of the One Health EJP.</p> <p>The 4th virtual One Health EJP Dissemination Workshop focuses on the interaction with stakeholders to translate science into policy, the impact of the education and training activities, and strategies used for effective dissemination of One Health solutions. Online presentations are given by the members of the One Health EJP <a href="https://onehealthejp.eu/outcomes/science-to-policy-translation/reports">Science to Policy Translation</a> (WP5), <a href="https://onehealthejp.eu/community/education-and-training">Education and Training</a> (WP6), and Communications Teams.</p> <p>Through this workshop, One Health EJP consortium members provide examples of successful strategies and lessons learnt, with the objective of inspiring all those working in this field or setting up new One Health initiatives and increasing the impact of One Health activities in Europe. This is an important legacy of the One Health EJP.</p> <p>Sessions include:</p> <ol> <li> Welcome and introduction.</li> <li> Education and training: Training a new generation of One Health scientists.</li> <li> Communication and dissemination: How to reach One Health audiences.</li> <li> Interactions with stakeholders: targeted dissemination, support, and advocacy. The example of the One Health EJP.</li> <li> Final round table discussion.</li> </ol> <p>Full details on the briefing agenda.</p>
Healthy Eating and Active Living Taught at Home (HEALTH) Dissemination & Implementation (D&I)
ClinicalTrials.gov study NCT03758638. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Dissemination and Implementation of a Videoconference Antimicrobial Stewardship Team
ClinicalTrials.gov study NCT05319561. IPD Sharing: NO. Countries: 1. Publications: 1.
Revisiting the historical scenario of a disease dissemination using genetic data and Approximate Bayesian Computation methodology: the case of Pseudocercospora fijiensis invasion in Africa
Open the record for dataset details and reuse information.
Fig. 1 in Publication and dissemination of datasets in taxonomy: ZooKeys working example
Fig. 1. The ZooKeys model for data publication and semantic enhancements workflow in taxonomy
Fig. 1 in Data publication and dissemination of interactive keys under the open access model
Fig. 1. Th e ZooKeys model for data publication and dissemination of interactive keys.
Data for dissemination and communication reporting_v2
<p>These data were collected through the periodic monitoring of the project's miscellaneous dissemination activities, such as publications in relevant journals, posts, etc. The data consist of a list that depicts the number of publications, posts, events organized or attended by the consortium partners, etc. as well as the number of different type of stakeholders reached by the project's dissemination activities. The purpose of collecting this data is to assess the outreach and efficiency of the dissemination activities during the implementation of the project.</p>
CENTRINNO Dissemination - website and social media material
<p>The data bundle contains all supporting visual material for the website and all social media<br>publications deployment, including:<br>- Visual assets, photos, videos, social media posts, newsletters<br>- Pilot contact persons data</p>
Digitizing Los Millares (Santa Fe de Mondujar, Almería, Spain) through geospatial technologies: preserving and disseminating the archaeological heritage
<p>Data originated from the research project "Digitizing Los Millares (Santa Fe de Mondujar, Almería, Spain) through geospatial technologies: preserving and disseminating the archaeological heritage"</p>
WP2 Task 2.3 Dissemination Analysis
<p>ZIP archive of all WP2 Task 2.3 Dissemination Analysis files</p>
Global dissemination of Influenza A virus is driven by wild bird migration through arctic and subarctic zones
<p><span>Influenza A viruses (IAV) circulate endemically among many wild aquatic bird populations that seasonally migrate between wintering grounds in southern latitudes to breeding ranges along the perimeter of the circumpolar arctic. </span>Arctic and subarctic zones are hypothesized to serve as ecologic drivers of the intercontinental movement and reassortment of IAVs <span>due to high densities of disparate populations of long distance migratory and native bird species present during breeding season</span>s. <span>Iceland</span> is a staging ground that connects <span>the East Atlantic and </span><span>North Atlantic</span><span> American flyways, providing a unique study system for characterizing viral flow between eastern and western hemispheres. Using Bayesian phylodynamic analyses, we sought to evaluate the </span><span>viral connectivity of Iceland to proximal regions and how inter-species transmission and reassortment dynamics in this region influence the geographic spread of low and highly pathogenic IAVs. </span><span>Findings demonstrate that IAV movement in the arctic and subarctic follows seabird migration around the perimeter of the circumpolar north, favoring short-distance flights between proximal regions rather than long distance flights over the polar interior. Iceland connects virus movement between mainland Europe and North America, particularly due to the westward migration of wild birds from mainland Europe to Northeastern Canada and Greenland. Though virus diffusion rates were similar among avian taxonomic groups in Iceland, g</span>ulls act as recipients and not sources of IAVs to other avian hosts prior to onward migration. <span>These data identify patterns of virus movement in northern latitudes and inform future surveillance strategies related to seasonal and emergent IAVs with pandemic potential</span>.</p>
Acute Disseminated Encephalomyelitis (ADEM): Case Definition Pictorial Algorithm
<p>This post contains the Pictorial Algorithm for Acute Disseminated Encephalomyelitis (ADEM). They are included in the case definition companion guides, which contain resources and tools specific to events that have a published Brighton Collaboration case definition.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.