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123 results for “disease vectors”
Data from: Disentangling the drivers of invasion spread in a vector-borne tree disease
1. Pine wilt disease (PWD) invaded southern Japan in the early 1900's and has gradually expanded its range to northern Honshu (Japanese mainland). The disease is caused by a pathogenic North American nematode, which is transmitted by native pine sawyer beetles. Recently the disease has invaded other portions of East Asia and Europe where extensive mortality of host pines is anticipated to resemble historical patterns seen in Japan. 2. There is a critical need to identify the main drivers of PWD invasion spread so as to predict future spread and evaluate containment strategies in newly invaded world regions. But the coupling of pathogen and vector population dynamics introduces considerable complexity that is important for understanding this and other plant disease invasions. 3. In this study, we analysed historical (1980-2011) records of PWD infection and vector abundance, which were spatially extensive but recorded at coarse categorical levels (none, low and high) across 403 municipalities in northern Honshu. We employed a multistate occupancy model that accounted both for demographic stochasticity and observation errors in categorical data. 4. Analysis revealed that sparse sawyer populations had lower probabilities of transition to high abundance than did more abundant populations even when regional abundance stayed the same, suggesting the existence of positive density dependence, i.e. an Allee effect, in sawyer dynamics. Climatic conditions (average accumulated degree days) substantially limited invasion spread in northern regions, but this climatic influence on sawyer dynamics was generally weaker than the Allee effect. 5. Our results suggest that tactics (e.g., sanitation logging of infected pines) which strengthen Allee effects in sawyer dynamics may be effective strategies for slowing the spread of PWD.
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>
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>
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>
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>
Data from: Automated identification of insect vectors of Chagas disease in Brazil and Mexico: the Virtual Vector Lab
Identification of arthropods important in disease transmission is a crucial, yet difficult, task that can demand considerable training and experience. An important case in point is that of the 150+ species of Triatominae, vectors of Trypanosoma cruzi, causative agent of Chagas disease across the Americas. We present a fully automated system that is able to identify triatomine bugs from Mexico and Brazil with an accuracy consistently above 80%, and with considerable potential for further improvement. The system processes digital photographs from a photo apparatus into landmarks, and uses ratios of measurements among those landmarks, as well as (in a preliminary exploration) two measurements that approximate aspects of coloration, as the basis for classification. This project has thus produced a working prototype that achieves reasonably robust correct identification rates, although many more developments can and will be added, and—more broadly—the project illustrates the value of multidisciplinary collaborations in resolving difficult and complex challenges.
FIGURE 9 in The Triatoma phyllosoma species group (Hemiptera: Reduviidae: Triatominae), vectors of Chagas disease: Diagnoses and a key to the species
FIGURE 9. Pygophore of Triatoma spp., lateral view A, T. bassolsae; B, T. longipennis; C, T. mazzottii; D, T. pallidipennis; E, T. phyllosoma; F, T. picturata.
FIGURE 8 in The Triatoma phyllosoma species group (Hemiptera: Reduviidae: Triatominae), vectors of Chagas disease: Diagnoses and a key to the species
FIGURE 8. Pygophore of Triatoma spp., ventral view (A, T. bassolsae; B, T. longipennis; C, T. mazzottii; D, T. pallidipennis; E, T. phyllosoma; F, T. picturata.
FIGURE 7 in The Triatoma phyllosoma species group (Hemiptera: Reduviidae: Triatominae), vectors of Chagas disease: Diagnoses and a key to the species
FIGURE 7. Pygophore of Triatoma spp., ventral view. A, T. dimidiata; B, T. huehuetenanguensis; C, T. mopan. Credits of B, Lima-Cordón et al. 2019; C, Dorn et al. 2018.
FIGURE 5. Triatoma spp. A, T in The Triatoma phyllosoma species group (Hemiptera: Reduviidae: Triatominae), vectors of Chagas disease: Diagnoses and a key to the species
FIGURE 5. Triatoma spp. A, T. longipennis; B, T. mazzottii; C, D, E. T. mexicana. F, T. pallidipennis.
FIGURE 4. Triatoma spp. A, B, T in The Triatoma phyllosoma species group (Hemiptera: Reduviidae: Triatominae), vectors of Chagas disease: Diagnoses and a key to the species
FIGURE 4. Triatoma spp. A, B, T. gerstaeckeri; C, T. gomeznunezi; D, T. hegneri; E, F, T. indictiva (4C and D courtesy of C. Dale, 4E, F courtesy of E. Barrera-Vargas).
FIGURE 3. Triatoma spp. A, B, T in The Triatoma phyllosoma species group (Hemiptera: Reduviidae: Triatominae), vectors of Chagas disease: Diagnoses and a key to the species
FIGURE 3. Triatoma spp. A, B, T. bassolsae; C, T. brailovskyi; D, E, F T. dimidiata (3B courtesy of E. Barrera-Vargas and 3C courtesy of C. Dale).
FIGURE 2 in The Triatoma phyllosoma species group (Hemiptera: Reduviidae: Triatominae), vectors of Chagas disease: Diagnoses and a key to the species
FIGURE 2. Morphology of Triatoma. A, male of T. dimidiata, ventral view; B, corium of T. longipennis, dorsal view; C, corium of T. mazzottii, dorsal view; D, pronotum of T. mazzottii, lateral view; E, spongy fossulae of T. dimidiata.
FIGURE 6. Triatoma spp. A, T in The Triatoma phyllosoma species group (Hemiptera: Reduviidae: Triatominae), vectors of Chagas disease: Diagnoses and a key to the species
FIGURE 6. Triatoma spp. A, T. phyllosoma; B, T. picturata; C, T. recurva; D, T. sanguisuga (6D courtesy of C. Dale).
FIGURE 1 in The Triatoma phyllosoma species group (Hemiptera: Reduviidae: Triatominae), vectors of Chagas disease: Diagnoses and a key to the species
FIGURE 1. Morphology of Triatoma. A, Head of T. mopan, dorsal view (based on Dorn et al., 2018); B, head of T. huehuetenanguensis, ventral view (based on Lima-Cordón et al., 2019); C, pronotum and scutellum of Triatoma, dorsal view; D, abdomen of Triatoma, dorsal view; E, abdomen of T. mopan, ventral view.
Field Evaluations of Innovative Tools for Vector-borne Disease Control in Conflict-affected Communities
ClinicalTrials.gov study NCT06179732. IPD Sharing: NO. Countries: 1. Publications: 1.
Phase I Trial of Gene Vector to Patients With Retinal Disease Due to RPE65 Mutations
ClinicalTrials.gov study NCT00481546. IPD Sharing: Not stated. Countries: 1. Publications: 17.
Study of Gene Therapy Using a Lentiviral Vector to Treat X-linked Chronic Granulomatous Disease
ClinicalTrials.gov study NCT02234934. IPD Sharing: NO. Countries: 1. Publications: 2.
A Live Recombinant Newcastle Disease Virus-vectored COVID-19 Vaccine Phase 1 Study.
ClinicalTrials.gov study NCT05181709. IPD Sharing: YES. Countries: 1. Publications: 2.
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
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DANDI Archive for NWB datasets
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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.