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2,212 results for “virality”
Fig. 4 in Refining the Occurrence of Viral Encephalopathy and Retinopathy, Photobacteriosis, and Vibriosis in Connection with Seawater Physicochemical Parameters: A Five-Year Case Study Abstract
Fig. 4: Estimated time series of the probability of absence/presence (0 to 1) based on the model (depicted by a line), alongside on-site records of absence/presence (represented by dots) for three diseases at Fish Farm A.
Fig. 2 in Refining the Occurrence of Viral Encephalopathy and Retinopathy, Photobacteriosis, and Vibriosis in Connection with Seawater Physicochemical Parameters: A Five-Year Case Study Abstract
Fig. 2: A - Annual variation (HREG) of physico-chemical parameters based on mean value parameters (T=temperature °C), TS- D=standard deviation of T, S=salinity (psu), SSD=standard deviation of salinity, O=oxygen (mg/ml), OSD=standard deviation of oxygen, pH, pHSD=standard deviation of pH). B - Presence/absence of diseases (t0= 1/1, step 1 day) (VAO1=Vibriosis, PHDP=- Photobacteriosis, VER=viral encephalopathy & retinopathy).
Fig. 3 in Refining the Occurrence of Viral Encephalopathy and Retinopathy, Photobacteriosis, and Vibriosis in Connection with Seawater Physicochemical Parameters: A Five-Year Case Study Abstract
Fig. 3: Presence/absence of fish diseases in the years 2011-2015. (A: VER - Viral Encephalopathy & Retinopathy, B: PHDP - Photobacteriosis, C: VAO1 - Vibriosis). For the presence of disease, the value was [1], and for absence, it was [0].
Supplementary Data for 'Crossing host boundaries: the evolutionary drivers and correlates of viral host jumps'
<p>This version provides the raw maximum likelihood trees with ancestral host states annotated, as described in Tan et al. 2024 (https://doi.org/10.1038/s41559-024-02353-4). </p> <p> </p> <p>Tip labels are formatted as {genbank accession}|{host}|{collection_date}|{country}.</p> <p>Nodel labels are formatted as follows:</p> <ul> <li>rooted_trees_simplified: {node name}|{host}, where {host} is the most likely ancestral state (i.e., highest ancestral state likelihood)</li> </ul> <ul> <li>rooted_trees: {node name}|{host1}:{likelihood1}|{host2}:{likelihood2}...|{hostn}:{likelihoodn}, which provides the raw ancestral state likelihoods for each host state. </li> <li>All node names correspond to those provided in the Supplementary Tables in Tan et al. 2024.</li> </ul> <p> </p> <p> </p>
Data Pack fro VirSorter: mining viral signal from microbial genomic data
<p>This is the data pack for VirSorter, the publication of which by Roux et al. titled "<strong>VirSorter: mining viral signal from microbial genomic data</strong>" appeared in PeerJ on 2015-05-28 (<a href="https://doi.org/10.7717/peerj.985">doi:10.7717/peerj.985</a>).</p> <p>Most up-to-date tutorials and the code for VirSorter can be found at the GitHub repository <a href="https://github.com/simroux/VirSorter">https://github.com/simroux/VirSorter</a>.</p> <p>The original source of this data pack was here (last accessed 2018-02-03): <a href="http://datacommons.cyverse.org/browse/iplant/home/shared/imicrobe/VirSorter/virsorter-data.tar.gz">http://datacommons.cyverse.org/browse/iplant/home/shared/imicrobe/VirSorter/virsorter-data.tar.gz</a></p>
Viral reference data for PathoLive
<p>Viral reference data for PathoLive including GI numbers and taxonomic information per sequence. Data taken from the viral part of the NCBI RefSeq downloaded on 2016-07-06.</p>
Communication virale dans la publicité au sein des espaces numériques : jeux de données
<p>Les jeux de données ici présentés ont été récoltés pour les besoins de nos différents travaux (Roux, 2016, 2018). Le fichier qui les accompagne contient des ressources complémentaires. Il s’agit des outils de récolte et de mesure de ces données.</p> <p> </p> <p><strong>Bibliographie</strong></p> <p>Roux, U. (2016). <em>Communication virale dans la publicité au sein des espaces numériques : approche critique et expérimentale du phénomène </em>(thèse de doctorat, Université de Toulon, Toulon). <a href="https://tel.archives-ouvertes.fr/tel-01368883">https://tel.archives-ouvertes.fr/tel-01368883</a></p> <p>Roux, U. (2018). Communication virale dans les espaces numériques : effet de la définition de l’image sur la diffusion d’une vidéo publicitaire en ligne. <em>¿ Interrogations ?</em>, 26. <a href="http://www.revue-interrogations.org/Communication-virale-dans-les">http://www.revue-interrogations.org/Communication-virale-dans-les</a></p> <p>Roux, U. (2019). Communication virale dans la publicité au sein des espaces numériques : jeux de données. <em>Revue française des sciences de l'information et de la communication</em>, 18. <a href="https://journals.openedition.org/rfsic/7784">https://journals.openedition.org/rfsic/7784</a></p>
Figure 2 in Peptidoglycan from Immunobiotic Lactobacillus rhamnosus Improves Resistance of Infant Mice to Respiratory Syncytial Viral Infection and Secondary Pneumococcal Pneumonia
Figure 2. – Photography of a juvenile lemon shark identified as Negaprion acutidens with an estimated total length of 70 cm (Photo MI).
Figure 1 in Peptidoglycan from Immunobiotic Lactobacillus rhamnosus Improves Resistance of Infant Mice to Respiratory Syncytial Viral Infection and Secondary Pneumococcal Pneumonia
Figure 1. – The Chesterfield islands (A) lie in the Coral Sea with New Caledonia (NC) to the east and Australia (AUS) to the west. The atoll structure of the Chesterfield (B) includes a V shaped barrier reef in the South (C) where the juvenile lemon sharks were observed (arrow).
HIV-1 control in vivo is related to the number but not the fraction of infected cells with viral unspliced RNA
<p>In the absence of antiretroviral therapy (ART), a subset of individuals, termed HIV controllers, have levels of plasma viremia that are orders of magnitude lower than non-controllers who are at higher risk for HIV disease progression. In addition to having fewer infected cells resulting in fewer cells with HIV RNA, it is possible that lower levels of plasma viremia in controllers is due to a lower fraction of the infected cells having HIV-1 unspliced RNA (HIV usRNA) compared with non-controllers. To directly test this possibility, we used sensitive and quantitative single cell sequencing methods to compare the fraction of infected cells that contain one or more copies of HIV usRNA in peripheral blood mononuclear cells (PBMC) obtained from controllers and non-controllers. The fraction of infected cells containing HIV usRNA did not differ between the two groups. Rather, the levels of viremia were strongly associated with the total number of infected cells that had HIV usRNA, as reported by others, with controllers having 34-fold fewer infected cells per million PBMC. These results reveal for the first time that viremic control is not associated with a lower fraction of proviruses expressing HIV usRNA, unlike what is reported for elite controllers, but is only related to having fewer infected cells overall, maybe reflecting greater immune clearance of infected cells. Our findings show that proviral silencing is not a key mechanism for viremic control and will help to refine strategies towards achieving HIV remission without ART.</p>
Data From: Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping
<p>This is the data set from all figures and tables from the manuscript "Clinical evaluation of patterned dried plasma spot cards to support quantification of HIV viral load and reflexive genotyping", which is posted to the ChemRxiv preprint server (10.26434/chemrxiv-2024-5bqm7) and currently in consideration for peer-reviewed publication elsewhere.</p>
Supplemental data for: Evaluation of SARS-CoV-2 response at the University of North Carolina (UNC) at Charlotte using percent positivity data and viral genomic sequence data.
<p>Supplemental data for:</p> <p>Evaluation of SARS-CoV-2 response at the<br>University of North Carolina (UNC) at Charlotte<br>using percent positivity data and viral genomic<br>sequence data.</p> <p>Submitted to Biocarla 2024</p> <p>https://carla2024.org/portfolios/biocarla/</p> <p>Authors:</p> <p>Daniel Janies 1,2,3 [0000−0002−7890−9906], Shirish Yasa 1,2,3 [0000−0003−3217−4921],<br>Colby T. Ford 1,3,4 [0000−0002−7859−3622] Jannatul Ferdous 2,3 [0000−0003−3053−9616],<br>William Taylor 2,3 [0009−0000−6204−1172], April Harris 2,3 [0009−0009−2557−7926],<br>Sam Kunkleman 2,3 [0000−0002−2309−6418], Juan Bolanos 2,3, Kevin Lambirth 2,3 [0000-0002-6568-543X], Denis<br>Jacob Machado 1,2,3 [0000−0001−9858−4515], Cynthia Gibas 1,2,3 [0000−0002−1288−9543],<br>and Jessica Schlueter 1,2,3 [0000−0002−6490−0580]</p> <p>Affiliations:</p> <p>1) Center for Computational Intelligence to Predict Health and Environmental Risks<br>(CIPHER), University of North Carolina at Charlotte 28223, USA<br>Correspondence to: djanies@charlotte.edu<br>https://cipher.charlotte.edu<br>2) Department of Bioinformatics and Genomics, University of North Carolina at<br>Charlotte 28223, USA https://cci.charlotte.edu/departments/<br>department-of-bioinformatics-and-genomics/<br>3) College of Computing and Informatics, University of North Carolina at Charlotte<br>28223, USA https://cci.charlotte.edu<br>4) School of Data Science, University of North Carolina at Charlotte 28223, USA<br>https://sds.charlotte.edu<br>5) Division of Research, University of North Carolina at Charlotte 28223, USA<br>https://research.charlotte.edu/</p> <p> </p>
Viral catalogue from faecal microbiomes of great tits and blue tits
<h2>Overview:</h2> <p>The vertebrate gut microbiome plays crucial roles in host health and disease. However, there is limited data on the microbiomes of wild birds, most of which is restricted to barcode sequences. We therefore explored the use of shotgun metagenomics on the faecal microbiomes of two wild bird species widely used as model organisms in ecological studies: the great tit (<em>Parus major</em>) and the Eurasian blue tit (<em>Cyanistes caeruleus</em>). High and Medium quality Metagenome Assembled Genomes (MAGs) were assembled from these metagenomes and are made available as a catalogue in this archive.</p> <h2>Methods:</h2> <p>Metagenomic reads were trimmed, and quality controlled using FastP configured to a minimum phred score of 20 and minimum length of 50 bp. The potential viral and plasmidic sequences were retrieved from the assembled contigs using GeNomad v1.8.0 (Camargo et al. 2023) and the quality of these viral sequences was then assessed using CheckV v 1.0.3 (Nayfach et al. 2021) using the checkV database v 1.5. Putative viral sequences longer than 1000bp carrying at least one hallmark viral gene or no detectable cellular genes were considered in this analysis. The viral sequences were clustered into species-level vOTU clusters using mmseqs2 v2.13 using an identity threshold of 95% over 75% of the longest sequence. For viral species identified as Caudoviral, the potential host was inferred using iPhop v1.3.3 (Roux et al 2023).</p> <h2><strong>Files:</strong></h2> <ul> <li>The<strong> vOTU_characteristics.csv </strong>table contains the sequence characteristics from CheckV and the viral taxonomy determined by GeNomad</li> <li>The<strong> vOTU_PredictedHost.csv </strong>table contains the viral sequence inferred host from iPhop</li> <li>The <strong>vOTU_representatives.fasta file </strong>contains the fasta sequence of the vOTU representatives</li> </ul>
Dataset and code for article "Explicit description of viral capsid subunit shapes by unfolding dihedrons"
<p>This repository contains data and codes used in a paper "Explicit description of viral capsid subunit shapes by expanding dihedrons" by Toyooka et al.</p> <p>- [grasshopper/] contains a GH file used in the 3D CAD Rhinoceros/Grasshopper.<br>- [docking_axes_viper/] contains notebooks for aligning the subunit coordinates with the VIPER coordinate system (Z-axis is the 2-fold axis, 3-fold axis is on the X-axis, and 5-fold axis is on the Y-axis)</p> <p>- [docking_pairwise/] contains input scipts and analysis notebooks for pairwise docking simulations for subunits<br> - [1stm_zdock_iter/df.jld2] data containing top docking scores and poses, and screw motion parameters<br> - [1stm_zdock_iter/rmsd.jld2] RMSD data of docking poses with regard to the experimental structure - [1stm_zdock_iter/run.sh] is a batch script for conducting docking simulations with ZDOCK<br> - [1stm_zdock_iter/run_each.sh] is a script for a single docking simulation with a specified random seed. Called from `run.sh`<br> - [1stm_zdock_iter/skrew_parameters.ipynb] analyzes the result of docking simulations. Sort the results accoring to docking scores and find the skrew axis of each docking pose<br> - [1stm_zdock_iter/visualize.ipynb] plots the docking score and the screw axis parameters, and also calculates and plots RMSDs from the reference structure (whole-shell structure)<br> - [1stm_zdock_iter/ref.pdb] is a sympolic link to a refrence structure (whole-shell structure)<br> - [4v4m_zdock_iter/] PDB ID: 4V4M<br> - [6s44_zdock_iter/] PDB ID: 6S44<br> - [7odw_zdock_iter/] PDB ID: 7ODW<br> - [3r0r_zdock_iter/] PDB ID: 3R0R<br> - [5zju_zdock_iter/] PDB ID: 5ZJU<br> - [1vb4_zdock_iter/] PDB ID: 1VB4<br> - [1m1c_zdock_iter/] PDB ID: 1M1C<br> - [6r7m_zdock_iter/] PDB ID: 6R7M<br> - [2m99_zdock_iter/] PDB ID: 2M99</p>
Correlation of the kinetics of viral antigen and genomic RNA with restoration of normal cell homeostasis
<p>The main objective of the studies within COCID Work Package 6 is to understand basic mechanisms by which viral replication machineries are removed from cells after pharmacological interruption of viral replication using functional as well as imaging techniques, including soft X-ray tomography.</p> <p>In this document, we highlight the progress in establishing the HCV replication models (replicons), the antiviral treatment chosen to eliminate viral structures from the host cells and the correlation between elimination of the viral replication machinery and restoration of a normal host cell homeostasis, using markers of HCV-induced stress. These data are essential to frame the experimental setup chosen for the imaging process required in subsequent stps of the project.</p>
The central nervous system's proteogenomic and spatial imprint upon systemic viral infection, like SARS-CoV-2
<p>Data set including image files of histological stainings, immunohistochemistry, MELC, and spatial transcriptomics associated with the study mentioned above.</p>
Data corresponding to: Evaluation of sequencing and PCR-based methods for the quantification of the viral genome formula
<p>Viruses show great diversity in their genome organisation. Multipartite viruses package their genome segments into separate particles, most or all of which are required to initiate infection in the host cell. The benefits of such seemingly inefficient genome organization are not well understood. One hypothesised benefit of multipartition is that it allows for flexible changes in gene expression by altering the frequency of each genome segment in different environments, such as encountering different host species. The ratio of the frequency of segments is termed the genome formula (GF). Thus far, formal studies quantifying the GF have been performed for well-characterised virus-host systems in experimental settings using RT-qPCR. However, to understand GF variation in natural populations or novel virus-host systems, a comparison of several methods for GF estimation including high-throughput sequencing (HTS) based methods is needed. Currently, it is unclear how HTS-methods compare a golden standard, such as RT-qPCR. Here we show a comparison of multiple GF quantification methods (RT-qPCR, RT-digital PCR, Illumina RNAseq and Nanopore direct RNA sequencing) using three host plants (<em>Nicotiana tabacum</em>, <em>Nicotiana benthamiana</em>, and <em>Chenopodium quinoa</em>) infected with cucumber mosaic virus (CMV), a tripartite RNA virus. Our results show that all methods give roughly similar results, though there is a significant method effect on genome formula estimates. While the RT-qPCR and RT-dPCR GF estimates are congruent, the GF estimates from HTS methods deviate from those found with PCR. Our findings emphasise the need to tailor the GF quantification method to the experimental aim, and highlight that it may not be possible to compare HTS and PCR-based methods directly. The difference in results between PCR-based methods and HTS highlights that the choice of quantification technique is not trivial.</p>
Diamond NCBI Genbank Viral database for SOVAP
<p><strong>Diamond NCBI Genbank Viral database</strong></p> <p>Database type: Diamond database</p> <p>Database format version: 3</p> <p>Label: 2023-03-18_18-40-17</p> <p>Sequences: 3,191,190</p> <p>Sum length: 824,564,244</p> <p>Assembly summary entries: 58,201</p> <p>--------------------------------------------------------</p> <p><strong>SOVAP v.1.3: </strong><a href="https://github.com/poursalavati/SOVAP">GitHub</a></p> <p><strong><em>Soil Virome Analysis Pipeline</em></strong></p> <p>Description</p> <p>The study of viral communities in complex environmental samples, such as <strong>soil</strong>, can provide valuable insights into the diversity and functions of viral communities in the ecosystem. However, processing and analyzing of virome data can be a challenging task that requires the integration of various computational tools and techniques.</p> <p>To address these challenges, we have developed <strong>SOVAP</strong> pipeline that utilizes a suite of state-of-the-art tools for processing, analysis, and annotation viromics and metagenomics data.</p> <p>It utilizes various tools such as <strong>Fastp</strong> and <strong>Centrifuge</strong> for preprocessing and contamination removal, <strong>geNomad</strong>, <strong>Diamond</strong> and <strong>Megan</strong> for identification and annotation of viral contigs which are assembled and clustered using <strong>Megahit</strong> and <strong>CD-HIT</strong>. Additionally, this pipeline provides an <strong>estimate of the abundance</strong> of viral contigs, allowing for a more comprehensive understanding of the virome within the sample. The integration of these tools offers a reliable and effective means of taxonomy classification and annotation of viral contigs, aiding researchers in gaining insight into the composition and function of the virome within the analyzed sample.</p> <p>By integrating the SOVAP pipeline with <strong>IMG/VR</strong> and <strong>geNomad</strong>, it is possible to identify a wider range of viruses, including those that were previously unknown.</p> <p>The <strong>batch-mode</strong> script allows for the processing of multiple datasets using the SOVAP pipeline. This feature is particularly useful for <strong>large-scale</strong> analyses, such as those involving multiple environmental samples or large sequencing datasets.</p>
The first arriving virus shapes within-host viral diversity during natural epidemics
Viral diversity has been discovered across scales from host individuals to populations. However, the drivers of viral community assembly are still largely unknown. Within-host viral communities are formed through coinfections, where the interval between the arrival times of viruses may vary. Priority effects describe the timing and order in which species arrive in an environment, and how early colonizers impact subsequent community assembly. To study the effect of the first-arriving virus on subsequent infection patterns of five focal viruses, we set up a field experiment using naïve Plantago lanceolata plants as sentinels during a seasonal virus epidemic. Using joint species distribution modelling, we find both positive and negative effects of early season viral infection on late season viral colonization patterns. The direction of the effect depends on both the host genotype and which virus colonized the host early in the season. It is well-established that co-occurring viruses may change the virulence and transmission of viral infections. However, our results show that priority effects may also play an important, previously unquantified role in viral community assembly. The assessment of these temporal dynamics within a community ecological framework will improve our ability to understand and predict viral diversity in natural systems.
Long-Acting Cabotegravir Plus VRC-HIVMAB075-00-AB (VRC07-523LS) for Viral Suppression in Adults Living With HIV-1
ClinicalTrials.gov study NCT03739996. IPD Sharing: YES. Countries: 2. Publications: 1.
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.