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Fig. 3 in Distribution, adult phenology and life history traits of potential insect vectors of Xylella fastidiosa in Belgium

Fig. 3. Immature development of Philaenus spumarius under outdoor conditions (Ixelles, Belgium, 2016).

Fig. 2 in Distribution, adult phenology and life history traits of potential insect vectors of Xylella fastidiosa in Belgium

Fig. 2. Phenology of adult Philaenus spumarius in Belgium: Data: RBINS; Observations.be (2005-2017); our own samplings (2016-2017).

Fig. 6A in Distribution, adult phenology and life history traits of potential insect vectors of Xylella fastidiosa in Belgium

Fig. 6A. Immature development of Cicadella viridis under outdoor conditions (Ixelles, Belgium, 2016. Fig. 6B. Immature development of Cicadella viridis under controlled conditions (21.5°C; D:L= 9:15).

Fig. 9 in Distribution, adult phenology and life history traits of potential insect vectors of Xylella fastidiosa in Belgium

Fig. 9. Distribution map for Aphrophora alni in Belgium. Data: RBINS; Observations.be (2010-2016); our own samplings (2016-2017).

Fig. 12 in Distribution, adult phenology and life history traits of potential insect vectors of Xylella fastidiosa in Belgium

Fig. 12. Phenology of adult Aphrophora salicina in Belgium. Data: RBINS; Observations.be (2005-2017); our own samplings (2016-2017).

Fig. 10 in Distribution, adult phenology and life history traits of potential insect vectors of Xylella fastidiosa in Belgium

Fig. 10. Phenology of adult Aphrophora alni in Belgium. Data: RBINS; Observations.be (2005-2017); our own samplings (2016-2017).

Fig. 13 in Distribution, adult phenology and life history traits of potential insect vectors of Xylella fastidiosa in Belgium

Fig. 13. Immature development of Aphrophora salicina under outdoor conditions (Ixelles, Belgium, 2016.

Fig. 5 in Distribution, adult phenology and life history traits of potential insect vectors of Xylella fastidiosa in Belgium

Fig. 5. Phenology of adult Cicadella viridis in Belgium. Data: RBINS; Observations.be (2005-2017); our own samplings (2016-2017).

opencc-by-4.0Sep 2024View details →

Rapid evolution of reproduction without blood feeding in an invasive vector mosquito

The file contains the Rproject with all the data and scripts to replicate all the analysis.

opencc-by-4.0Sep 2024View details →

2021_SpatialModel_Vector_AedesAlbopictus

Abstract: An ensembled spatial models were produced for the Dengue and Chikungunya Vector  Aedes albopictus ( albovenmalertensrfbrtvar19m1rclpa.zip) by combining Random Forest and Boosted Regression Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population, and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (<a href="https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net" target="_blank" rel="noopener">https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net</a>), from the Global Biodiversity Information Facility (<a href="http://www.gbif.org/" target="_blank" rel="noopener">www.gbif.org</a>) and a series of national databases from the UK, Spain and Finland,  and the Citizen Science project Mosquito Alert (<a href="https://mosquitoalert.com/" target="_blank" rel="noopener">https://mosquitoalert.com</a>).  This output has been converted from the original predicted probability of presence to a simple binary presence/absence)   File naming scheme:  Model output: albovenmalertensrfbrtvar19m1rclpa. TIF  Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent:     Extent   -32.0000000000000000,10.0000000000000000 : 69.0000000000000000,82.0000000000000000 Spatial resolution: 0.0083333 deg (approx. 1000 m)   Pixel values: Unit: Presence=1, absence=0 Source:  The VectorNet project (<a href="https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net" target="_blank" rel="noopener">https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net</a>) ,from the Global Biodiversity Information Facility (<a href="http://www.gbif.org/" target="_blank" rel="noopener">www.gbif.org</a>) and a series of national databases from the UK, Spain and Finland,  and the Citizen Science project Mosquito Alert (<a href="https://mosquitoalert.com/" target="_blank" rel="noopener">https://mosquitoalert.com</a>) Software used: VECMAP License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) <a href="https://ergoonline.co.uk/" target="_blank" rel="noopener">https://ergoonline.co.uk/</a> for the H2020 MOOD project

opencc-by-4.0Oct 2024View details →

A Deep Learning Approach to Automated Bug Triaging: Investigating the Impact of Text Vectorization Methods on Effectiveness of CNN-LSTM

The dataset used in the article 'A Deep Learning Approach to Automated Bug Triaging: Investigating the Impact of Text Vectorization Methods on Effectiveness of CNN-LSTM.'

opencc-zeroDec 2017View details →

Data from: Genetic diversity and population structure of the tsetse fly Glossina fuscipes fuscipes (Diptera: Glossinidae) in Northern Uganda: implications for vector control

Uganda is the only country where the chronic and acute forms of human African Trypanosomiasis (HAT) or sleeping sickness both occur and are separated by < 100 km in areas north of Lake Kyoga. In Uganda, Glossina fuscipes fuscipes is the main vector of the Trypanosoma parasites responsible for these diseases as well for the animal African Trypanosomiasis (AAT), or Nagana. We used highly polymorphic microsatellite loci and a mitochondrial DNA (mtDNA) marker to provide fine scale spatial resolution of genetic structure of G. f. fuscipes from 42 sampling sites from the northern region of Uganda where a merger of the two disease belts is feared. Based on microsatellite analyses, we found that G. f. fuscipes in northern Uganda are structured into three distinct genetic clusters with varying degrees of interconnectivity among them. Based on genetic assignment and spatial location, we grouped the sampling sites into four genetic units corresponding to northwestern Uganda in the Albert Nile drainage, northeastern Uganda in the Lake Kyoga drainage, western Uganda in the Victoria Nile drainage, and a transition zone between the two northern genetic clusters characterized by high level of genetic admixture. An analysis using HYBRIDLAB supported a hybrid swarm model as most consistent with tsetse genotypes in these admixed samples. Results of mtDNA analyses revealed the presence of 30 haplotypes representing three main haplogroups, whose location broadly overlaps with the microsatellite defined clusters. Migration analyses based on microsatellites point to moderate migration among the northern units located in the Albert Nile, Achwa River, Okole River, and Lake Kyoga drainages, but not between the northern units and the Victoria Nile drainage in the west. Effective population size estimates were variable with low to moderate sizes in most populations and with evidence of recent population bottlenecks, especially in the northeast unit of the Lake Kyoga drainage. Our microsatellite and mtDNA based analyses indicate that G. f. fuscipes movement along the Achwa and Okole rivers may facilitate northwest expansion of the Rhodesiense disease belt in Uganda. We identified tsetse migration corridors and recommend a rolling carpet approach from south of Lake Kyoga northward to minimize disease dispersal and prevent vector re-colonization. Additionally, our findings highlight the need for continuing tsetse monitoring efforts during and after control.

opencc-zeroDec 2018View details →

Data from: Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors

The impact of insecticide resistance in malaria vectors is poorly understood and quantified. Here a series of geospatial datasets for insecticide resistance in malaria vectors are provided so that trends in resistance in time and space can be quantified and the impact of resistance found in wild populations on malaria transmission in Africa can be assessed. Data are also provided for common genetic markers of resistance to support analyses of whether these genetic data can improve the ability to monitor resistance in low resource settings. Specifically, data have been collated and geopositioned for the prevalence of insecticide resistance, as measured by standard bioassays, in representative samples of individual species or species complexes. Data are provided for the Anopheles gambiae species complex, the Anopheles funestus subgroup, and for nine individual vector species. In addition, allele frequencies for known resistance associated markers in the Voltage-gated sodium channel (Vgsc) are provided. In total, eight analysis-ready, standardised, geopositioned datasets encompassing over 20,000 African mosquito collections between 1957 and 2017 are provided.

opencc-zeroDec 2012View details →

Data from: Sympatric diversification vs. immigration: deciphering host-plant specialization in a polyphagous insect, the stolbur phytoplasma vector Hyalesthes obsoletus (Cixiidae)

The epidemiology of vector transmitted plant diseases is highly influenced by dispersal and the host-plant range of the vector. Widening the vector's host range may increase transmission potential, whereas specialization may induce specific disease cycles. The process leading to a vector's host shift and its epidemiological outcome is therefore embedded in the frameworks of sympatric evolution vs. immigration of preadapted populations. In this study, we analyse whether a host shift of the stolbur phytoplasma vector, Hyalesthes obsoletus from field bindweed to stinging nettle in its northern distribution range evolved sympatrically or by immigration. The exploitation of stinging nettle has led to outbreaks of the grapevine disease bois noir caused by a stinging nettle-specific phytoplasma strain. Microsatellite data from populations from northern and ancestral ranges provide strong evidence for sympatric host-race evolution in the northern range: Host-plant associated populations were significantly differentiated among syntopic sites (0.054 < FHT < 0.098) and constant over 5 years. While gene flow was asymmetric from the old into the predicted new host race, which had significantly reduced genetic diversity, the genetic identity between syntopic host-race populations in the northern range was higher than between these populations and syntopic populations in ancestral ranges, where there was no evidence for genetic host races. Although immigration was detected in the northern field bindweed population, it cannot explain host-race diversification but suggests the introduction of a stinging nettle-specific phytoplasma strain by plant-unspecific vectors. The evolution of host races in the northern range has led to specific vector-based bois noir disease cycles.

opencc-zeroDec 2016View details →

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).