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1,705 results for “Vector”
FIGURE 1. Nesoclutha species. A–J, N in Taxonomy of the vector leafhopper genus Nesoclutha Evans (Hemiptera: Cicadellidae: Deltocephalinae) in China, with description of a new species
FIGURE 1. Nesoclutha species. A–J, N. concavipenis sp. nov. A. Head and thorax, dorsal view; B. face; C. head and thorax, left lateral view; D. pygophore, valve and subgenital plate, lateral view; E. enlarged posterior margin of pygophore; F. forewing; G. hindwing; H. aedeagus and connective, lateral view; I. valve, subgenital plates and style, ventral view; J. aedeagus and connective, ventral view; K. N. erythrocephala, aedeagus, lateral view; L, N. phryne, aedeagus, lateral view.
Plastisphere as a vector for pathogens
<p><span>There is growing evidence that plastic particles can accumulate microorganisms that are pathogenic to humans or animals.</span><span> In the current study, the composition of the plastispheres that accumulated on polypropylene (PP), polyvinyl chloride (PVC), and high-density polyethylene (HDPE) pieces submerged in a river in the southeast Norway was characterized by 16S rRNA amplicon sequencing. Seasonal and geographical effects on the bacterial composition of the plastispheres were identified, in addition to the detection of potential foodborne pathogenic bacteria and viruses as part of the plastisphere. The diversity and taxonomic composition of the plastispheres were influenced by the number of weeks in the river, the season, and the location. The bacterial diversity differed significantly in the plastispheres from June and September, with a generally higher diversity in June. Also, the community composition of the plastispheres was significantly influenced by the geographical location, while the type of plastic had less impact. Plastics submerged in river water assembled a variety of microorganisms including potentially pathogenic bacteria and viruses (noro- and adenovirus) detected by qPCR. Cultivation methods detected viable bacteria such as <em>E. coli </em>and<em> L. monocytogenes</em>. The results highlight the need for additional research on the risk of contaminating food with plastic particles colonized with human pathogens through irrigation water. </span></p>
A wideband, high-resolution vector spectrum analyzer for integrated photonics
<p>This data contains the raw data of the experiment and the code and corresponding data of the figures in the artical.</p>
Concatenated SBWT bit vectors
<p><span>The concatenated bit vectors from the "plain matrix" of the SBWT pr</span><span>oposed by Alanko, Puglisi and Vuohtoniemi[1]</span><span>. The SBWT in question is built based </span><span>on a set of 17,336,887 Illumina HiSeq 2500 reads of length 502 sampled from the human </span><span>gut (SRAidentifier ERR5035349) in a study on irritable bowel syndrome and bile acid </span><span>malabsorption[2] with k-mer size 31.</span></p> <p><span><span>[1] Jarno N. Alanko, Simon J. Puglisi, and Jaakko Vuohtoniemi.</span><span> </span><span>Small searchable</span><span> </span><span>κ</span><span>-spectra </span><span>via subset rank queries on the spectral burrows-wheeler transform. In Jonathan W. Berry, </span><span>David B. Shmoys, Lenore Cowen, and Uwe Naumann, editors,</span><span> </span><span>SIAM Conference on Applied </span><span>and Computational Discrete Algorithms, ACDA 2023, Seattle, WA, USA, May 31 - June 2, </span><span>2023</span><span>, pages 225–236. SIAM, 2023.</span><span> </span><span>doi:10.1137/1.9781611977714.20</span><span>.</span></span></p> <p><span><span><span>Ian B Jeffery, Anubhav Das, Eileen O’Herlihy, Simone Coughlan, Katryna Cisek, Michael </span><span>Moore, Fintan Bradley, Tom Carty, Meenakshi Pradhan, Chinmay Dwibedi, et al. Differences </span><span>in fecal microbiomes and metabolomes of people with vs without irritable bowel syndrome </span><span>and bile acid malabsorption.</span><span> </span><span>Gastroenterology</span><span>, 158(4):1016–1028, 2020.</span></span></span></p>
An Empirical Comparative Study of Convolutional Neural Network and Support Vector Machine in Digital Signature for Digital Document Authentication
<p>Dataset dan figure of the research</p>
PIV vector fields from: Boundary layer hydrodynamics of patchy biofilms
<p>This dataset contains the instantaneous velocity vector fields from PIV data taken over large acrylic plates fouled with diatomaceous biofilm of varying patchiness. </p> <p>See associated article, Boundary layer hydrodynamics of patchy biofilms, for methods description.</p> <p>Each zip folder contains data for one of the 4 non-uniform biofilms examined, PB-1 (patchy biofilm 1); PB-2 (patchy biofilm 2); SB-1 (sparse biofilm 1); SB-2 (sparse biofilm 2). For each biofilm, the corresponding folder contains 4000 statistically independent instantaneous velocity vector fields. Each vector field is saved in a .mat file, and the workspace variable that contains the data is called ‘vecfield’. </p> <p>Size calibration and water temperature are provided in the spreadsheet ‘experiment_metadata.xlsx’</p> <p> </p> <p>Column 1: X (streamwise distance [pixels]) </p> <p>Column 2: Y (wall-normal distance from bottom of frame [pixels])</p> <p>Column 3: U (streamwise velocity vector [pixels / 250 microseconds]) </p> <p>Column 4: V (vertical velocity vector [pixels / 250 microseconds]) </p> <p>Column 5: CHC (number of tracked particles. A value < 1 gives the location of the biofilm, which was masked out)</p> <p> </p>
Sequencing data and taxonomic assignments from: Biodiversity and vector-borne diseases: host dilution and vector amplification occur simultaneously for Amazonian leishmaniases
<p>This is the sequencing data used in the paper:<strong> "</strong>Biodiversity and vector-borne diseases: host dilution and vector amplification occur simultaneously for Amazonian leishmaniases" by Kocher et al. The study aims at assessing the effects of biodiversity changes on Leishmania transmission using molecular analyses of sand fly pools and blood-fed dipterans. The data is split in three files corresponding to the PCR amplicons used in the study (Ins16S for insect identifications, 12SV5 for vertebrate identifications and leishmini for Leishmania identifications). Each file combines output from different Illumina Miseq and Hiseq runs, after read demultiplexing and adapter trimming, dereplication and removal of reads present in less than 10 copies (but before further read filtering). The data is presented in tabular format (similar to the output of the obitab command from the obitools package), together with information on the corresponging sample, sequencing run, and taxonomic assignments (performed with ecotag from the obitools).</p>
Data, plotting scripts, and figures for "Accelerating Reactive-Flow Simulations using Vectorized Chemistry Integration"
<p>Data, plotting scripts, and figures for the article "Accelerating Reactive-Flow Simulations using Vectorized Chemistry Integration".</p>
Dataset and code for : "Representing Vector Geographic Information As a Tensor for Deep Learning Based Map Generalisation"
<p>Dataset and code supporting the experiment about map generation in the article: "Representing Vector Geographic Information As a Tensor for Deep Learning-Based Map Generalisation"</p>
Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine
<p>This is the relevant data of the article "Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine"</p>
Research data and example scripts for the paper "Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction"
<p><strong>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</strong></p> <p>This publication contains the research data and example scripts for the paper “Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction” [1]. The research data is found in the directory <code>research_data</code>, the example scripts are found in the directory <code>example_scripts</code>.</p> <p>The research data contains all necessary information to be able to reconstruct the figures and values given in the paper, as well as all result figures shown. Where possible, the directories contain the necessary scripts to recreate the results themselves, up to stochastic variations.</p> <p>The example scripts are intended to show how one can (i), perform a least-square type optimization of a model function (here we focus our efforts on the analytical model functions MGH17 and Gauss3, as described in the paper) using various methods (BTVO, LM, BO, L-BFGS-B, NM, including using derivative information when applicable), and (ii), perform Markov chain Monte Carlo (MCMC) sampling around the found maximum likelihood estimate (MLE) to estimate the uncertainties of the MLE parameter (both using a surrogate model of the actual model function, as well as using the actual model function directly).</p> <p> </p> <p><strong>Research data</strong></p> <p>Contained are directories for the experimental problem GIXRF, and the two analytical model functions MGH17 and Gauss3. What follows is a listing of directories and the contents:</p> <ul> <li><code>gauss3_optimization</code>: Optimization logs for the Gauss3 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_optimization</code>: Optimization logs for the MGH17 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_mcmc_analytical</code>: Scripts for the creation of the plots (does not use an optimization log), as well as plots shown in the paper. This uses the model function directly to perform the MCMC sampling.</li> <li><code>mgh17_mcmc_surrogate</code>: Optimization log of the MGH17 function used for the creation of the MCMC plots, scripts for the creation of the plots (use the optimization log), as well as plots shown in the paper. This uses a surrogate model to perform the MCMC sampling.</li> <li><code>gixrf_optimization</code>: <code>benchmark.py</code> file to perform the optimization, the optimization logs for the various methods (BTVO, LM, BO, L-BFGS-B, NM), .npy files and scripts used for the creation of the plots, and the plots shown in the paper.</li> <li><code>gixrf_mcmc_supplement</code>: optimization log used for the creation of the plot, pickle file used for the creation of the plot, script to create the MCMC plot.</li> <li><code>gixrf_optimum_difference_supplement</code>: optimization logs of BTVO optimization of the GIXRF problem, scripts to create the difference/error plots shown for the GIXRF problem in the supplement, and the plots themselves.</li> </ul> <p><strong>Employed software for creating the research data</strong></p> <p>The software used in the creation is:</p> <ul> <li>JCMsuite Analysis and Optimization toolkit, development version, commit d55e99b (the closest commercial release is found in JCMsuite version 5.0.2)</li> <li>A list of Python packages installed (excerpt from <code>conda list</code>, name and version) <ul> <li>corner 2.1.0</li> <li>emcee 3.0.2</li> <li>jax 0.2.22</li> <li>jaxlib 0.1.72</li> <li>matplotlib 3.2.1</li> <li>numba 0.40.1</li> <li>numpy 1.18.1</li> <li>pandas 0.24.1</li> <li>python 3.7.11</li> <li>scikit-optimize 0.7.4</li> <li>scipy 1.7.1</li> <li>tikzplotlib 0.9.9</li> </ul> </li> <li>JCMsuite 4.6.3 for the evaluation of the experimental model</li> </ul> <p> </p> <p><strong>Example scripts</strong></p> <p>This directory contains a few sample files that show how parameter reconstructions can be performed using the JCMsuite analysis and optimization toolbox, with a particular focus on the Bayesian target-vector optimization method shown in the paper.</p> <p>It also contains example files that show how an uncertainty quantification can be performed using MCMC, both directly using a model function, as well as using a surrogate model of the model function.</p> <p>What follows is a listing of the contents of the directory:</p> <ul> <li><code>mcmc_mgh17_analytical.py</code>: performs a MCMC analysis of the MGH17 model function directly, without constructing a surrogate model. Uses <code>emcee</code>.</li> <li><code>mcmc_mgh17_surrogate.py</code>: performs a MCMC analysis of the MGH17 model function by constructing a surrogate model of the model function. Uses the JCMsuite analysis and optimization toolbox.</li> <li><code>opt_gauss3.py</code>: performs a parameter reconstruction of the Gauss3 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>opt_mgh17.py</code>: performs a parameter reconstruction of the MGH17 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>util/model_functions.py</code>: contains the MGH17 and Gauss3 model functions, their (automatic) derivatives, and objective functions used in the optimizations.</li> </ul> <p><strong>Requirements to execute the example scripts</strong></p> <p>These scripts have been developed and tested under Linux, Debian 10. We have tried to make sure that they would also work in a Windows environment, but can unfortunately give no guarantees for that.</p> <p>We mainly use Python to run the reconstructions. To execute the files, a few Python packages have to be installed. In addition to the usual scientific Python stack (NumPy, SciPy, matplotlib, pandas, etc.), the packages <code>jax</code> and <code>jaxlib</code> (for automatic differentiation of Python/NumPy functions), <code>emcee</code> and <code>corner</code> (for MCMC sampling and subsequent plotting of the results) have to be installed.</p> <p>This can be achieved for example using pip, e.g.</p> <pre><code>pip install -r requirements.txt</code></pre> <p>Additionally, JCMsuite has to be installed. For this you can visit [2] and download a free trial version.</p> <p>On Linux, the installation has to be added to the PATH, e.g. by adding the following to your <code>.bashrc</code> file:</p> <pre><code>export JCMROOT=/FULL/PATH/TO/BASE/DIRECTORY export PATH=$JCMROOT/bin:$PATH export PYTHONPATH=$JCMROOT/ThirdPartySupport/Python:$PYTHONPATH</code></pre> <p> </p> <p><strong>Bibliography</strong></p> <p>[1] <span>M. Plock</span>, <span> K. Andrle</span>, <span> S. Burger</span>, <span> P.-I. Schneider</span>, <span>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</span>. <em>Adv. Theory Simul.</em> <strong><span>5</span></strong>, 2200112 (2022).</p> <p>[2] https://jcmwave.com/</p>
Supporting data and code for "A new look at the potential role of marine plastic debris as a global vector of toxic benthic algae".
<p>R code and dataset for: Leite I.P., Menegotto A., Lana P.C. & Mafra Jr LL. 2022. A new look at the potential role of marine plastic debris as a global vector of toxic benthic algae. Science of the Total Environment, 838, 156262.</p>
No net effect of host density on tick-borne disease hazard due to opposing roles of vector amplification and pathogen dilution
<p>To better understand vector-borne disease dynamics, knowledge of the ecological interactions between animal hosts, vectors and pathogens is needed. The effects of hosts on disease hazard depends on their role in driving vector abundance and their ability to transmit pathogens. Theoretically, a host that cannot transmit a pathogen could dilute pathogen prevalence but increase disease hazard if it increases vector population size. In the case of Lyme disease, caused by <em>Borrelia burgdorferi </em>s.l. and vectored by Ixodid ticks, deer may have dual opposing effects on vectors and pathogen: deer drive tick population densities but do not transmit <em>B. burgdorferi</em> s.l. and could thus decrease or increase disease hazard. We aimed to test for the role of deer in shaping Lyme disease hazard by using a wide range of deer densities while taking transmission host abundance into account. We predicted that deer increase nymphal tick abundance while reducing pathogen prevalence. The resulting impact of deer on disease hazard will depend on the relative strengths of these opposing effects. We conducted a cross-sectional survey across 24 woodlands in Scotland between 2017 and 2019, estimating host (deer, rodents) abundance, questing<em> Ixodes ricinus</em> nymph density and <em>B. burgdorferi</em> s.l. prevalence at each site. As predicted, deer density was positively associated with nymph density and negatively with nymphal infection prevalence. Overall, these two opposite effects cancelled each other out: Lyme disease hazard did not vary with increasing deer density. This demonstrates that, across a wide range of deer and rodent densities, the role of deer in amplifying tick densities cancels their effect of reducing pathogen prevalence. We demonstrate how non-competent host density has little effect on disease hazard even though they reduce pathogen prevalence, because of their role in increasing vector populations. These results have implications for informing disease mitigation strategies, especially through host management.</p>
UFO model for Vector-like Quarks at NLO QCD with five flavor scheme
<p>Vector-like Quark UFO Model at NLO QCD with five flavour scheme</p>
H-2020 MOOD Scoping review of Tularemia on the human, animal, vector and environmental covariates
<div> <p><span><span>The dataset </span><span>contains</span><span> quantitative data on the human, animal</span><span>, </span><span>vector</span><span> and environmental covariates associated with </span><span>Tularemia</span><span> retrieved from scientific papers through a standardized search on PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data on the association between disease and covariates, language (English or other EU languages), </span><span>time frame</span><span> (30 years), geographical location (Europe), and publication type. Studies without data or with non-original or duplicated data (reviews, editorials, letters, model</span><span>l</span><span>ing studies with no data), lacking denominators or reference populations, unavailable </span><span>full-texts</span><span>, referring to data older than 2000 or gathered outside Europe, were excluded. The final </span><span>time frame</span><span> covered a period from 2000 to 2022.</span></span><span> </span></p> </div> <div> <p><span><span>The important </span><span>human, animal</span><span>, </span><span>vector</span><span> and environmental</span><span> covariates were extracted with the associated quantitative information, and the related information on the diseases. </span><span>The covariates were </span><span>submitted</span><span> to a revision process </span><span>and </span><span>label</span><span>led</span><span> according to a </span><span>labelling</span><span> system</span><span> agreed upon among a group of experts within the MOOD (grant agreement No 874850; </span></span><a href="https://mood-h2020.eu/" target="_blank" rel="noreferrer noopener"><span><span>https://mood-h2020.eu/</span></span></a><span><span>) project consortium.</span></span><span> </span></p> </div>
H-2020 MOOD Scoping review of Leptospirosis, Influenza A and Chikungunya on the human, animal, vector and environmental covariates
<div> <p><span><span>The dataset </span><span>contains</span><span> quantitative data on the </span><span>human, animal</span><span>, </span><span>vector</span><span> and </span><span>environmental covariates associated with influenza A, Chikungunya, and Leptospirosis retrieved from scientific papers through a standardized search on PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data on the association between disease and covariates, language (English or other EU languages), </span><span>time frame</span><span> (30 years), geographical location (Europe), and publication type. </span><span>Studies without data or with non-original or duplicated data (reviews, editorials, letters, </span><span>modelling</span><span> studies with no data), lacking denominators or reference populations, unavailable </span><span>full-texts</span><span>, referring to data older than 2000 or gathered outside Europe, were excluded.</span><span> The final </span><span>time frame</span><span> covered a period from 2000 to 2022.</span></span><span> </span></p> </div> <div> <p><span><span>The important </span><span>human, animal</span><span>, </span><span>vector</span><span> and environmental</span><span> covariates were extracted with the associated quantitative information, and the related information on the diseases. The covariates were </span><span>submitted</span><span> to a revision process </span><span>and </span><span>label</span><span>led</span><span> according to a </span><span>labelling</span> <span>system agreed upon among a group of experts within the MOOD (grant agreement No 874850; </span></span><a href="https://mood-h2020.eu/" target="_blank" rel="noreferrer noopener"><span><span>https://mood-h2020.eu/</span></span></a><span><span>) project consortium.</span></span><span> </span></p> </div>
Outputs from Vector-borne diseases and climate change (VECLIMIT)- project
<p>Vector-borne diseases (VBDs) pose a significant global public health threat, influenced by intricate interactions between hosts, vectors, pathogens, and the environment. In northern Europe the climate is warming at over twice the rate of the global average, affecting ecosystems and their biota, and consequently drivers of VBD circulation and epidemiology. By integrating long-term disease incidence data, metagenomic analyses, empirical field studies, high-resolution climate data, and predictive spatiotemporal modelling, we approached to set baselines and to understand the main climate-dependencies of the drivers of the complex dynamics governing transmission and distribution of main VBDs in Finland. In addition, by mapping the knowledge, attitudes, and practices among Finnish residents related to VBDs in a changing climate to further understand the possible gaps and misunderstandings associated with VBDs that may hinder the uptake of protective measures. </p>
ROS and SGI data for manuscript "The perception and evolution of flagellin, cold shock protein, and elongation factor Tu from vector-borne bacterial plant pathogens"
<p>This contains raw data for the ROS and seedling growth inhibition (SGI) assays collected for the manuscript "The perception and evolution of flagellin, cold shock protein, and elongation factor Tu from vector-borne bacterial plant pathogens". For a quick reference, there are two spreadsheets listing all the Max RLUs and Z-scores for the experiments, but the actual output of each plate reader is also included. </p>
Interactions between bat species and agricultural pests and disease vectors in northern Madagascar
<p>This table is part of the PhD thesis of Carme Tuneu-Corral, entitled '<strong>Bats and rice: promoting Integrated Pest Management to enhance biodiversity conservation</strong>'. It is the <span>Table A4.4</span> of the supplementary material of the Chapter 5 '<em>Beyond borders: evaluating the role of protected areas in promoting bat-mediated pest suppression in rural areas of northern Madagascar</em>', and illustrates the arthropod species detected in bat faecal sampels and classified as insect pests or disease vectors, and their interactions with bat species. Information on the bold percentage of similarity, study site, habitat type and pest type.</p>
List of agricultural pests and disease vectors detected in the diet of insectivorous bats in northern Madagascar
<p>This table is part of the PhD thesis of Carme Tuneu-Corral, entitled '<strong>Bats and rice: promoting Integrated Pest Management to enhance biodiversity conservation</strong>'. It is the <span>Table A4.3</span> of the supplementary material of the Chapter 5 '<em>Beyond borders: evaluating the role of protected areas in promoting bat-mediated pest suppression in rural areas of northern Madagascar</em>', and shows the list of agricultural pests (known and potential) and disease vectors detected in the diet of insectivorous bats, BOLD ID percentage (similarity), and information on the type of crops attacked or disease transmitted by them in Madagascar and/or continental Africa.</p> <p>Methodology:</p> <p><span>To evaluate whether bats were consuming agricultural pests or disease vectors, we only considered prey identified to species level. Using published scientific literature, we classified each arthropod species in one of the following categories: ‘non-pest prey’, ‘known human-disease vector’ (species confirmed as human-disease vector in Madagascar), ‘known livestock-disease vector’ (species confirmed as livestock-disease vector in Madagascar), ‘potential human-disease vector’ (species not confirmed as human-disease vector in Madagascar, but considered as such in continental Africa), ‘potential livestock-disease vector’ (species not confirmed as livestock-disease vector in Madagascar, but considered as such in continental Africa), ‘known agricultural pest’ (species confirmed as agricultural pest in Madagascar), ‘potential agricultural pest’ (species not confirmed as agricultural pest in Madagascar, but considered as such in continental Africa).</span></p> <p> </p>
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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.