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712 results for “tick”
Incidence of Ticks and Tick Bites at Harvard Forest since 2006
In an effort to determine exposure to ticks and to determine appropriate preventative measures to reduce the occurrence of Lyme Disease in summer research interns at Harvard Forest, data are collected about where students work at Harvard Forest; the time spent in the field; the number of ticks students find on themselves after each trip to the field; and the number of ticks that actually bite and embed themselves in the skin. From these data, we estimate areas of high tick densities and estimate time of summer during which ticks are most prevalent. The results are used to develop recommendations for appropriate precautions that students can take to avoid tick-borne diseases.
Predicted occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution
<p>The dataset contains predictions of occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution + all covariate layers used for modeling. Over seven million electronic health records (EHRs), among which 11,741 EHRs reported tick attachment, were used to evaluate climate, environmental and animal host factors affecting the risk of tick attachment in cats and dogs in Great Britain (GB). The tick presence/absence EHRs for dogs and cats were further overlaid with spatiotemporal time-series of climatic, vegetation, human influence, hydrological and terrain variables (slope, wetness index) to produce a spatiotemporal regression matrix; an Ensemble Machine Learning framework was used to fine-tune hyperparameters for Random Forest (classif.ranger), Gradient boosting (classif.xgboost) and GLM-net (classif.glmnet) algorithms, which were then used to produce a final ensemble meta-learner that predicts the probability of occurrence of ticks across GB with monthly intervals.</p> <ul> <li>gb1km_covariates.zip contains ALL covariate layers as GeoTIFFs (time-series) used for modeling ticks dynamics;</li> <li>data_1km_2014_M01.rds = contains all covariates for January 2014 prepared as SpatialGridDataFrame (R data object);</li> </ul> <p>Codes of files indicate e.g.:</p> <ul> <li>"monthly.tick.prob_savsnet.mar_p_1km_s_2014_2021" = monthly occurrence probability for January based on the training data from 2014 to 2021;</li> <li>"monthly.tick.prob_savsnet.oct_md_1km_s_20211001_20211031" = monthly prediction (model) error derived as the standard deviation from multiple base learners;</li> </ul> <p>The dataset is described in detail in the following publication:</p> <ul> <li>Arsevska, E., Hengl, T., Singelton, D. et al. (2023?) <strong>Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021</strong>. Submitted to Parasites & Vectors (in review).</li> </ul> <p>The model summary shows:</p> <pre><code>Call: stats::glm(formula = f, family = "binomial", data = getTaskData(.task, .subset), weights = .weights, model = FALSE) Deviance Residuals: Min 1Q Median 3Q Max -1.4749 -0.0557 -0.0471 -0.0430 3.7611 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.64495 0.02095 -364.957 < 2e-16 *** classif.ranger 4.95061 0.63615 7.782 7.13e-15 *** classif.xgboost 189.75543 5.53109 34.307 < 2e-16 *** classif.glmnet 140.24208 5.05375 27.750 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 170604 on 7303013 degrees of freedom Residual deviance: 162571 on 7303010 degrees of freedom AIC: 162579 Number of Fisher Scoring iterations: 9</code></pre> <p><em>Acknowledgements</em>: We are grateful to data providers in veterinary practice (VetSolutions, Teleos, CVS, and other practitioners). We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale Bioinformatics Facility, doi: <a href="https://entrepot.recherche.data.gouv.fr/dataverse/migale">10.15454/1.5572390655343293E12</a>) for providing computing resources. We are also grateful for<br> the help and support provided by <a href="https://www.liverpool.ac.uk/savsnet/">SAVSNET team members</a> Bethaney Brant, Susan Bolan and Steven Smyth.<br> This study was funded mainly by a grant from the <strong>Biotechnology and Biological Sciences Research Council</strong>,<br> BB/NO19547/1 and <strong>British Small Animal Veterinary Association</strong> (BSAVA). The research was partly funded by the National Institute for <strong>Health Research Health Protection Research Unit</strong> (NIHR HPRU) in Emerging and Zoonotic Infections at the <strong>University of Liverpool</strong> in partnership with <strong>Public Health England</strong> (PHE) and <strong>Liverpool School of Tropical Medicine</strong> (LSTM). This work has been partially funded by the <em>“Monitoring outbreak events for disease surveillance in a data science context"</em> (MOOD) project from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 874850 (<a href="https://mood-h2020.eu/">https://mood-h2020.eu/</a>). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.</p>
Associating Land Cover Changes with Climate Sensitive Infection in Fennoscandia, as part of the CLINF project: Example on Tick-Borne Diseases
<p>The data was used as part of the IJERPH article below. The GeoJSON and shapefile ZIP archive are two versions of the same geometries to represent geographically the districts whole of Fennoscandia and the Russian districts of Leningrad, St Petersburg, Vologda, Arkhangelsk, Nenetsia, Murmansk, Karelia, and Komi, making up 69 districts used for the analysis.</p> <p>Leibovici DG, Bylund H, Björkman C, Tokarevich N, Thierfelder T, Evengård B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive Infections: An Example on Tick-Borne Diseases in the Nordic Area. <strong><em>International Journal of Environmental Research and Public Health, 18(20):10963. <a href="https://doi.org/10.3390/ijerph182010963">doi:10.3390/ijerph182010963</a></em></strong></p> <p>Special Issue: <a href="https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects">https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects</a></p> <p> </p>
Relative abundance of the CHC extracts of each population replicate's of I. uriae ticks from Iceland after log centered ratio transformation
<p>Relative abundance of the CHC extracts of each population replicate’s of I. uriae ticks from Iceland after log centered ratio transformation.</p>
Supplementary dataset for the publication "Prevalence of tick-borne bacterial pathogens in Germany – has the situation changed after a decade?"
<p>The dataset supplements the journal article "Prevalence of tick-borne bacterial pathogens in Germany – has the situation changed after a decade?" published by Katja Mertens-Scholz, Bernd Hoffmann, Jörn M. Gethmann, Hanka Brangsch, Mathias W. Pletz and Christine Klaus in the journal mdpi microorganisms (DOI: <a href="https://doi.org/10.3389/fcimb.2024.1429667">https://doi.org/10.3389/fcimb.2024.1429667 </a><a>)</a></p> <p>The file "Tick samples" contains all data regarding time, location and detected pathogens". The files "Rickettsia sequences" and Borrelia sequences" contains all sequence information of positive samples. The file "Rickettsia reference genes" contains information of used reference genes for phylogenetic analysis.</p>
Valuation of heat related mortality risk and tick-borne diseases
<p>Monetary impacts of premature mortality due to heat waves</p> <p>Preferences for public programmes against spread of ticks due to climate change and a new vaccine against Lyme disease, prevalence of tick-borne diseases and exposure to ticks</p>
High frequency (tick data) of historical FOREX prices
<p>Price tick data for the most liquid Forex assets (AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, USDJPY). The period covered 09 March 2020 to 07, September 2022. </p>
Virome diversity of Hyalomma dromedarii ticks collected from camels in the United Arab Emirates
<p>Viruses are important components of the microbiome of ticks. Ticks are capable of transmitting several serious viral diseases to humans and animals. Hitherto, the composition of viral communities in <em>Hyalomma dromedarii</em> ticks associated with camels in the United Arab Emirates (UAE) remains unexplored. The purpose of this study was to characterize the RNA virome diversity in male and female <em>H. dromedarii</em> ticks collected from camels in Al Ain, UAE.<strong> </strong>We collected ticks, extracted and sequenced RNA, using Illumina (NovaSeq 6000) and Oxford Nanopore (MinION).<strong> </strong>From the total generated sequencing reads, 180,559 (~0.35 %) and 197,801 (~0.34 %) reads were identified as virus-related reads in male and female tick samples respectively. Taxonomic assignment of the viral sequencing reads was accomplished based on bioinformatic analyses. Further, viral reads were classified into 39 viral families. Poxiviridae, Phycodnaviridae, Phenuiviridae, Mimiviridae, and Polydnaviridae were the most abundant families in the tick viromes. Notably, we assembled the genomes of three RNA viruses, which were placed by phylogenetic analyses in clades that included the Bole tick virus.<strong> </strong>Overall, this study attempts to elucidate the RNA virome of ticks associated with camels in the UAE and the results obtained from this study improve the knowledge of the diversity of viruses in <em>H. dromedarii</em> ticks.</p>
Figure 2 in An instance of Boiga dendrophila dendrophila (Boie, 1827) (Reptilia: Colubridae) being parasitized by Amblyomma helvolum Koch, 1844 (Acari: Ixodidae), with comments about the attachment sites of this tick species
Figure 2 An Amblyomma sp. nymph attached under a lateral mid-body scale of theBoiga dendrophila dendrophila(Photo by Jean-Jay Mao).
Figure 1 in An instance of Boiga dendrophila dendrophila (Boie, 1827) (Reptilia: Colubridae) being parasitized by Amblyomma helvolum Koch, 1844 (Acari: Ixodidae), with comments about the attachment sites of this tick species
Figure 1 Two Amblyomma helvolum females attached to the neck of theBoiga dendrophila dendrophila(Photo by Jean-Jay Mao).
Figure 1 in Effects of commercial oils on the camel tick, Hyalomma dromedarii (Acari: Ixodidae) and their enzyme activities
Figure 1. Mortality percentages of Hyalomma dromedarii semi-engorged females treated with different concentrations of four oils at five successive days after treatment – A. Rosemary; B. Garlic; C. Neem; D. Cyperus. a, b, … etc. indicate significant differences between concentrations (%) of each oil for each day according to Tukey test (P <0.001).
Figure 1 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks
Figure 1. Different amount of starting material – A. The whole individual of partially engorged tick; B. Anterior half of fully-engorged tick (above red line); C. Paired DNA extraction – mouthparts (1) and a part of scutum (2).
Figure 2 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks
Figure 2. Results of PCR amplification – A1. Efficient PCR with clear band of 250bp presented as successful amplification, determined by using a positive control; All amplified bands with sizes differing from the positive control were defined as non-specific alleles; Four types of different PCR results: A2. Non-specific alleles; A3. Poor amplification; A4. A combination of poor amplification and non-specific alleles; B. Unsuccessful amplification detected in paired DNA extraction, after using DNA obtained from mouthparts (B1), whereas target region was amplified for the same sample but using DNA obtained from a part of scutum (B2); a 50 bp DNA ladder (BioLabs, New England) (A, B).
Bloodmeal metabarcoding of the argasid tick (Ornithodoros turicata Dugès) reveals extensive vector-host associations
<p>Molecular methods to understand host feeding patterns of arthropod vectors are critical to assess exposure risk to vector-borne disease and unveil complex ecological interactions. We build on our prior work discovering the utility of PCR-Sanger sequencing bloodmeal analysis that work well for soft ticks (<em>Acari: Argasidae</em>), unlike for hard ticks (<em>Acari: Ixodidae</em>), thanks to their unique physiology that retains prior bloodmeals for years. Here, we apply bloodmeal metabarcoding using amplicon deep sequencing to identify multiple host species in individual <em>Ornithodoros turicata</em> soft ticks collected from two natural areas in Texas, United States. Of 788 collected <em>O. turicata</em>, 394 were evaluated for bloodmeal source via metabarcoding, revealing 27 different vertebrate hosts (17 mammals, 5 birds, 1 reptile, and 4 amphibians) fed upon by 274 soft ticks. Information on multiple hosts was derived from 167 individual <em>O. turicata</em> (61%). Metabarcoding revealed mixed vertebrate bloodmeals in <em>O. turicata</em> while same specimens yielded only one vertebrate species using Sanger sequencing. These data reveal wide host range of <em>O. turicata</em> and demonstrate the value of bloodmeal metabarcoding for understanding the ecology for known and potential tick-borne pathogens circulating among humans, domestic animals and wildlife such as relapsing fever caused by <em>Borrelia turicatae</em>. Our results also document evidence of prior feeding on wild pig from an off-host soft tick for the first time in North America; a critical observation in the context of enzootic transmission of African swine fever virus if it were introduced to the US. This research enhances our understanding of vector-host associations and offers a promising perspective for biodiversity monitoring and disease control strategies.</p>
Figures 1-3 in Morphological and molecular identification of the hard ticks parasitizing Tremarctos ornatus (Carnivora: Ursidae) from paramo of Ecuador
Figures 1-3. Ixodes boliviensis (male)— 1. Dorsal view, 2. Ventral view, 3. Ventral view of capitulum and idiosoma.
Figs 2, 3 in Seasonal variations in ixodid tick populations on a commercial game farm in the Limpopo Province, South Africa
Figs 2, 3. Numbers of Rhipicephalus (Boophilus) decoloratus collected in wetter and drier months (2), and in warmer and cooler months (3).
Figure 1 in A massive infestation of the long-legged buzzard, Buteo rufinus (Cretzschmar), by Hyalomma marginatum Koch (Acari: Ixodidae) ticks in Türkiye
Figure 1. The long-legged buzzard, Buteo rufinus, presents massive infestation by nymphs of Hyalomma marginatum ticks.
Fig. 2 in First molecular detection of Francisella tularensis in turtle (Testudo graeca) and ticks (Hyalomma aegyptium) in Northwest of Iran
Fig. 2. The evolutionary lineage was determined using the Maximum Likelihood method and the Tamura-Nei model. The displayed tree represents the one with the most favorable log likelihood (429.22). Additionally, the branches are accompanied by the percentage denoting how frequently the related taxa formed clusters in the trees. The initial trees for exploratory purposes were automatically created using the Neighbor-Join and BioNJ algorithms. This was accomplished by utilizing a matrix of pairwise distances, which were calculated employing the Tamura-Nei model. From these initial trees, the one with the most favorable log likelihood value was selected. This analysis was conducted on a collection of 31 nucleotide sequences. The encompassed codon positions consisted of 1st+2nd+3rd +Noncoding. The final dataset consisted of a total of 306 positions. The evolutionary analyses were performed utilizing MEGA11.
Fig. 3 in First molecular detection of Francisella tularensis in turtle (Testudo graeca) and ticks (Hyalomma aegyptium) in Northwest of Iran
Fig. 3. The lineage's evolutionary narrative was deduced through the application of the Neighbor-Joining technique. The most advantageous tree configuration is depicted. Adjacent to the branches, the percentages reflect how often the related taxa aggregated within the bootstrap test, comprising 1000 replicates. Evolutionary distances were calculated using the Maximum Composite Likelihood method, expressed as the count of base substitutions per site. In this study, a collective of 32 nucleotide sequences were taken into account. The codon positions covered 1st+2nd+3rd + Noncoding. Ambiguous positions were excluded for each sequence pair, following the pairwise deletion technique. In the culminating dataset, a collective count of 542 positions was encompassed. The evolutionary analyses were executed using MEGA11.
Figure 1. A in Contribution to the fauna of bat-parasitizing ticks in Bosnia and Herzegovina
Figure 1. A. Distribution of haplotypes obtained in this research and earlier studies. L and C markings represent the location and country, respectively. The locations are marked with numbers from 1 to 6, whereby (1) denotes Zvornik, (2) Šipovo, (3) Dimitrov grad, (4) Zlot, (5) Rijeka Crnojevića, and (6) Jama Šutonjića cave; B. Dorsal surface of tick sample; C. Ventral surface of tick sample.
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