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123 results for “disease vectors”
Figure 2. Box-plot head centroid size. A. Rhodnius prolixus instars. B in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 2. Box-plot head centroid size. A. Rhodnius prolixus instars. B. Triatoma maculata instars. Abbreviation: I—First instar; II— Second instar; III—Third instar; IV—Fourth instar; V—Fifth instar; F—Adult female; M—Adult male.
Figure 4. Canonical Variates Analysis head conformation diagram for 136 in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 4. Canonical Variates Analysis head conformation diagram for 136 Triatoma maculata specimens and thin-plate deformation grids. A. V instar–Adults. B. I instar–Adults. C. II instar–III instar.
Figure 3. Canonical Variates Analysis head conformation diagram for 140 in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 3. Canonical Variates Analysis head conformation diagram for 140 Rhodnius prolixus specimens and thin-plate deformation grids. A. V instar–Adults. B. I instar–Adults. C. II instar–III instar.
Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling
<p>A necessary component of understanding vector-borne disease risk is the accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data-rich species but may produce inaccurate results for sparsely-documented vectors. In light of global change, vectors that are currently not well-documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a<strong> </strong>sparsely-documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely-documented species. To demonstrate this approach, we evaluated the ability of twelve models—which differed in their pooling of data from multiple vector species and inclusion of disease data—to improve distribution estimates of sparsely-documented vectors. We assessed our models on two simulated data sets, which allowed us to generalize our results and examine their mechanisms. We found that when the focal species is sparsely documented, incorporating data on related vector species reduces uncertainty and improves accuracy by reducing overfitting. When data on vector species are already incorporated, disease data only marginally improve model performance. However, when data on other vectors are not available, disease data can improve model accuracy and reduce overfitting and uncertainty. We then assessed the approach on empirical data on ticks and tick-borne diseases in Florida and found that incorporating data on other vector species improved model performance. This study illustrates the value of exploiting correlated data via joint modeling to improve distribution models of data-limited species.</p>
Climatic niche conservatism in a clade of disease vectors (Diptera: Phlebotominae)
<p>Sandflies of the family Psychodidae show notable diversity in both disease vector status and climatic niche. Psychodid species' ranges can be solely tropical, confined to the temperate zones, or span both. We obtained observation site data, and associated climate data, for 223 psychodid species to understand which aspects of climate most closely predict distribution. Temperature and seasonality are strong determinants of species occurrence within the clade. We built a mitochondrial DNA phylogeny of Psychodidae, and found a positive relationship between pairwise genetic distance and climate niche differentiation, which indicates strong niche conservatism. This result is also supported by strong phylogenetic signals of metrics of climate differentiation. Finally, we used ancestral trait reconstruction to infer the tropicality (i.e., proportion of latitudinal range in the tropics minus the proportion of the latitudinal range in temperate areas) of ancestral species, and counted transitions to and from tropicality states, finding that tropical and temperate species respectively produced almost entirely tropical and temperate descendant species, a result consistent for vector and non-vector species. Taken together, these results imply that while vectors of Leishmania can survive in a variety of climates, their climate niches are strongly predicted by phylogeny.</p>
High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling - Datasets
<p>Datasets and notebooks used in the publication High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling</p>
Fig. 1 in Effects of relative humidity on the vector of rose rosette disease, Phyllocoptes fructiphilus (Eriophyidae), and incidence of disease symptoms
Fig. 1. Mean (± SE) number of Phyllocoptes fructiphilus under various relative humidity regimes (A) by wk and (B) for the duration of the experiment. The same letters within a wk afer infestation or bars are not significantly different (ANOVA followed by Tukey's HSD test; α = 0.05). Where no differences were observed, no letters are included.
Fig. 2 in Effects of relative humidity on the vector of rose rosette disease, Phyllocoptes fructiphilus (Eriophyidae), and incidence of disease symptoms
Fig. 2. Mean (± SE) (A) proportion of rose rosette disease symptomatic terminals and (B) value of the Horsfall-Barratt scale on the severity of rose rosette disease. The same letters within a wk afer infestation are not significantly different (ANOVA followed by Tukey's HSD test; α = 0.05). Where no differences were observed, no letters are included.
Fig. 1 in Reptile vector-borne diseases of zoonotic concern
Fig. 1. Arthropod vectors associated to reptiles represented by a Podarcis siculus lizard and Tarentola mauritanica gecko and zoonotic pathogens they may transmit. a) Ixodes ricinus tick larva, b) Ophionyssus natricis mite, c) Sergentomyia minuta sand fly, d) Aedes albopictus mosquito. Red lines represent high importance role of transmission, orange line represents medium importance role of transmission, gray line represents mechanical vector and green line represents transmission of nonpathogenic zoonotic microorganisms. Dashed lines represent neglectable knowledge on actual role of vector. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Reptile vector-borne diseases of zoonotic concern
Fig. 2. Arthropod vectors that may feed on reptiles. a) Ixodes ricinus larva on Podarcis siculus lizard being collected with tweezers, b) Neotrombicula autumnalis larvae mites on Podarcis siculus lizard, c) female Sergentomyia minuta phlebotomine sand fly, d) Aedes albopictus mosquito.
All data for the preprint Population genetics of Glossina palpalis gambiensis in the sleeping sickness focus of Boffa (Guinea) before and after eight years of vector control: no effect of control despite a significant decrease of human exposure to the disease
<p>Data set for the paper titled "Population genetics of <em>Glossina palpalis gambiensis</em> in the sleeping sickness focus of Boffa (Guinea) before and after eight years of vector control: no effect of control despite a significant decrease of human exposure to the disease"</p>
Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling
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Climatic niche conservatism in a clade of disease vectors (Diptera: Phlebotominae)
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Data and code from: Vector demography, dispersal, and the spread of disease: Experimental epidemics under elevated resource supply
1. The spread of many diseases depends on the demography and dispersal of arthropod vectors. Classic epidemiological theory typically ignores vector dynamics and instead makes the simplifying assumption of frequency-dependent transmission. Yet vector ecology may be critical for understanding the spread of disease over space and time and how disease dynamics respond to environmental change. 2. Here, we ask how environmental change shapes vector demography and dispersal, and how these traits of vectors govern the spatiotemporal spread of disease. 3. We developed disease models parameterized by traits of vectors and fit them to experimental epidemics. The experiment featured a viral pathogen (CYDV-RPV) vectored by aphids (Rhopalosiphum padi) among populations of grass hosts (Avena sativa) under two rates of environmental resource supply (i.e., fertilization of the host). We compared a non-spatial model that ignores vector movement, a lagged dispersal model that emphasizes the delay between vector reproduction and dispersal, and a travelling wave model that generates waves of infections across space and time. 4. Resource supply altered both vector demography and dispersal. The lagged dispersal model fit best, indicating that vectors first reproduced and then dispersed among hosts in the experiment. Elevated resources decreased vector population growth rates, nearly doubled their carrying capacity per host, increased dispersal rates when vectors carried the virus, and homogenized disease risk across space. 5. Together, the models and experiment show how environmental eutrophication can shape spatial disease dynamics – for example, homogenizing disease risk across space – by altering the demography and behavior of vectors.
Quantifying species traits related to oviposition behavior and offspring survival in two important disease vectors
<p>Animals with complex life cycles have traits related to oviposition and juvenile survival that can respond to environmental factors in similar or dissimilar ways. We examined the preference-performance hypothesis (PPH), which states that females lacking parental care select juvenile habitats that maximize fitness, for two ubiquitous mosquito species, <i>Aedes albopictus</i> and <i>Culex quinquefasciatus</i>. Specifically, we examined if environmental factors known to affect larval abundance patterns in the field played a role in the PPH for these species. We first identified important environmental factors from a field survey that predicted larvae across different spatial scales. We then performed two experiments, the first testing the independent responses of oviposition and larval survival to these environmental factors, followed by a combined experiment where initial oviposition decisions were allowed to affect larval life history measures. We used path analysis for this last experiment to determine important links among factors in explaining egg numbers, larval mass, development time, and survival. For separate trials, <i>Aedes albopictus</i> displayed congruence between oviposition and larval survival, however <i>C. quinquefasciatus</i> did not. For the combined experiment path analysis suggested neither species completely fit predictions of the PPH, with density dependent effects of initial egg number on juvenile performance in <i>A. albopictus</i>. For these species the consequences of female oviposition choices on larval performance do not appear to fit expectations of the PPH. </p>
Integrated genome-wide investigations of the housefly, a global vector of diseases reveal unique dispersal patterns and bacterial communities across farms
<p><span><span><span><span><span><span><span><span><span><span><span><b>Background:</b>Houseflies (<i>Musca domestica</i>L.) live in intimate association with numerous microorganisms and is a vector of human pathogens. In temperate areas, houseflies will<span>overwinter in environments constructed by humans and recolonize surrounding areas in early summer. However, the </span>dispersal patterns and associated bacteria across season and location are unclear.We used genotyping-by-sequencing (GBS) for the simultaneous identification and genotyping of thousands of Single Nucleotide Polymorphisms (SNPs) to establish dispersal patterns of houseflies across farms. Secondly, we used16S rRNA gene amplicon sequencing to establish the variation and association between bacterial communities and the housefly across farms. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results: </b>Using GBS we identified 18,000 SNPs across 400 individualssampled within and between 11 dairy farms in Denmark. There was evidence for sub-structuring of Danish housefly populations and with genetic structure that differed across season and sex. Further, there was a strong isolation by distance (IBD) effect, but with large variation suggesting that other hidden geographic barriers are important. Large individual variations were observed in the community structure of the microbiome and it was found to be dependent on location, sex, and collection time. Furthermore, the relative prevalence of putative pathogens was highly dependent on location and collection time.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusion:</b>We were able to identify SNPs for the determination of the spatiotemporal housefly genetic structure, and to establish the variation and association between bacterial communities and the housefly across farms using novel <span>next</span><span>‐</span><span>generation sequencing (NGS)</span>techniques. These results are important for disease prevention given the fine-scale population structure and IBD for the housefly, and that individual houseflies carry location specific bacteria including putative pathogens. </span></span></span></span></span></span></span></span></span></span></span></p>
Figure 1. Landmarks head selection. A. Rhodnius prolixus. B in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 1. Landmarks head selection. A. Rhodnius prolixus. B. Triatoma maculata. Scale bar = 1 mm.
The relationship between vector species richness and the risk of vector-borne infectious diseases
<p>Infectious diseases can impact human welfare and impede wildlife management. Much recent research explores whether biodiversity increases or decreases infectious disease risk. Here we theoretically study the relationship between vector species richness and the risk of vector-borne diseases by an epidemiological model of a single host and multiple vectors. The model considers that vectors are involved in interspecific feeding interference that causes transmission interference and in interspecific recruitment competition that mediates susceptible vector regulation. The model reveals three possible shapes of the vector richness-disease risk relationship: monotonic amplification, hump-shaped, and monotonic dilution patterns. Monotonic amplification pattern occurs across a wide parameter region. Hump-shaped or monotonic dilution patterns are found when transmission interference is strong and recruitment competition is weak. Unexpectedly, susceptible vector regulation does not only promote dilution but can strengthen amplification if coupled with strong transmission interference. Our results suggest that vector richness might be more likely to cause amplification rather than dilution, and shifts in the community mean trait values of vectors could also affect disease risk along the vector richness gradient.</p>
Hematopoietic Tumors in a Mouse Model of X-linked Chronic Granulomatous Disease after Lentiviral Vector-Mediated Gene Therapy
<p>Chronic granulomatous disease (CGD) is a rare inherited disorder due to loss-of-function mutations in genes encoding the NADPH oxidase subunits. Hematopoietic stem and progenitor cell (HSPC) gene therapy (GT) using regulated lentiviral vectors (LVs) has emerged as a promising therapeutic option for CGD patients. We performed non-clinical Good Laboratory Practice (GLP) and laboratory-grade studies to assess the safety and genotoxicity of LV targeting myeloid specific Gp91phox expression in X-linked chronic granulomatous disease (XCGD) mice. We found persistence of gene-corrected cells for up to 1 year, restoration of Gp91phox expression and NADPH oxidase activity in XCGD phagocytes, and reduced tissue inflammation after LV-mediated HSPC GT.<br> Although most of the mice showed no hematological or biochemical toxicity, a small subset of XCGD GT mice developed<br> T cell lymphoblastic lymphoma (2.94%) and myeloid leukemia (5.88%). No hematological malignancies were identified in C57BL/6 mice transplanted with transduced XCGD HSPCs. Integration pattern analysis revealed an oligoclonal composition with rare dominant clones harboring vector insertions near oncogenes in mice with tumors. Collectively, our data support the long-term efficacy of LV-mediated HSPC GT in XCGD mice and provide a safety warning because the chronic inflammatory XCGD background may contribute to oncogenesis.</p>
Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient
<b>Description: </b><p>Mosquito larval survey and thermotolerance data</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/172"><b>Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=82">here</a></p><p><b>Data worksheets: </b>There are 4 data worksheets in this dataset:</p><ol><li><p><b>Field microclimate data</b> (Worksheet Microclimate)</p><p>Dimensions: 6917 rows by 11 columns</p><p>Description: Data was recorded using EasyLog USB dataloggers. They were put out either hung on a tree, or suspended off the ground (in the absense of trees) and covered to avoid direct sunlight.</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample location (Field type: ID)</li><li><b>DATE</b>: Date of the measurement (Field type: Date)</li><li><b>TIME</b>: Time of the measurement (Field type: Time)</li><li><b>Temp</b>: Air temperature (Field type: Numeric)</li><li><b>RelHumid</b>: Relative humidity (Field type: Numeric)</li><li><b>DewPoint</b>: The temperature at which the air would condense and dew would form (Field type: Numeric)</li><li><b>SerialNumber</b>: Serial number of the microclimate datalogger used to collect the data (Field type: ID)</li></ul><br></li><li><p><b>Thermal tolerance</b> (Worksheet CTmax)</p><p>Dimensions: 317 rows by 6 columns</p><p>Description: Thermal tolerance experiments on mosquito larvae</p><p>Fields: </p><ul><li><b>Date</b>: The Date the CT max value was taken (Field type: Date)</li><li><b>VialNumber</b>: the vial that the individual came from, and how it is referred to in my field notebook (Field type: ID)</li><li><b>LandType</b>: Habitat type from which the individual was collected (Field type: Categorical)</li><li><b>CriticalMax</b>: The temperature in celcius that the individual became unresponsive to stimulus (Field type: Numeric)</li><li><b>GivenSpecies</b>: Identity of the individual being tested (Field type: Taxa)</li></ul><br></li><li><p><b>Site x species data</b> (Worksheet SpeciesData)</p><p>Dimensions: 192 rows by 9 columns</p><p>Description: Field observations of field mosquito communities</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Replicate</b>: is the replicate of sampling each data collection took place. There were three weeks of sampling so the only values are 1, 2, or 3. (Field type: Replicate)</li><li><b>CollectionType</b>: Method of collection (Field type: Categorical)</li><li><b>Count</b>: Total number of individuals sampled (Field type: Abundance)</li><li><b>GivenSpecies</b>: Identity of the individual(s) (Field type: Taxa)</li></ul><br></li><li><p><b>Forest canopy measurements</b> (Worksheet Densiometer)</p><p>Dimensions: 36 rows by 12 columns</p><p>Description: Densiometer estimates of tree canopy cover</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Val1</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val2</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val3</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val4</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>AveVal</b>: Average densiometer reading (max = 96) (Field type: Numeric)</li><li><b>AdjustedVal</b>: Average canopy openness (Field type: Numeric)</li><li><b>CanopyCover</b>: Average canopy cover (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2017-03-20 to 2017-09-04</p><p><b>Latitudinal extent: </b>4.6314 to 4.7436</p><p><b>Longitudinal extent: </b>117.4556 to 117.6249</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Diptera<br> -  -  -  - Culicidae<br> -  -  -  -  - <i>Aedes</i><br> -  -  -  -  -  - <i>Aedes albopictus</i><br> -  -  -  -  - <i>Anopheles</i><br> -  -  -  -  -  - [Anoph]<br> -  -  -  -  - <i>Armigeres</i><br> -  -  -  -  -  - [Armigeres]<br> -  -  -  -  - <i>Culex</i><br> -  -  -  -  -  - [Culex1]<br> -  -  -  -  -  - [Culex2]<br> -  -  -  -  -  - [Culex3]<br> -  -  -  -  -  - [CulexOP]<br> -  -  -  -  - [Species7]<br> -  -  -  -  - [Species8]<br> -  -  -  -  - [Species9]<br> -  -  -  -  - <i>Uranotaenia</i><br> -  -  -  -  -  - [Urano1]<br> -  -  -  -  -  - [Urano2]<br> -  -  -  -  - <i>Zeugnomyia</i><br> -  -  -  -  -  - <i>Zeugnomyia gracilis</i><br></div><p></p>
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.