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Figure 4 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 4. Melanization of Heterorhabditis bacteriophora within Aedes aegypti larvae (3rd instar). A melanized H. bacteriophora within dead Ae. aegypti larvae (a), close up picture of melanized nematode upon larval dissection (b), nematodes representing different stages of melanization recovered from one dead Ae. aegypti larvae (c). Arrows indicate melanized nematode within Ae. aegypti larvae.
Figure 7 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 7. Aedes aegypti larval mortality when exposed to supernatants and cell suspensions of Xenorhabdus nematophila (X. n.) and Photorhabdus laumondii (P. l.) in 24 well plates. Different uppercase or lower letters above error bars indicate statistical significance (Tukey's test p ≤ 0.05).
Figure 3 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 3. Different stages of Heterorhabditis bacteriophora colonization of Aedes aegypti larvae (3rd instar). H. bacteriophora within larvae at 2-day post inoculation (a), H. bacteriophora emerging out of larvae upon larval dissection at 7-day post inoculation) (b), adult H. bacteriophora within larvae along with large number of infective juveniles (IJs) released from another adult H. bacteriophora (c). Black arrows indicate adult H. bacteriophora, whereas green arrows indicate newly emerged IJs.
Figure 1 in Evaluation of the feeding patterns of important mosquito vector species using molecular techniques
Figure 1. Sampling localities of Anopheles sacharovi, Culex pipiens, Culex tritaeniorhynchus populations (1. Kadirli, 2. Düziçi, 3. Türkoğlu, 4. Dörtyol, 5. Kırıkhan, 6. Kozan, 7. Yumurtalık, 8. Karataş, 9. Ceyhan, 10. Tuzla, 11. Tarsus, 12.Huzurkent, 13. Manavgat, 14. Isparta, 15. Burdur, 16. Sandıklı, 17. Akköy, 18. Dalaman, 19. Söke, 20. Manisa).
Figure 2 in Evaluation of the feeding patterns of important mosquito vector species using molecular techniques
Figure 2. Agarose-gel image of cytb gene region of possible hosts (M: marker (100- 1000 bp) 1. Goat; 2. Bird; 3. Human; 4. Cow; 5. Dog; 6. Horse)
Fig. 4 in Utilising a novel surveillance system to investigate species of Forcipomyia (Lasiohelea) (Diptera: Ceratopogonidae) as the suspected vectors of Leishmania macropodum (Kinetoplastida: Trypanosomatidae) in the Darwin region of Australia
Fig. 4. Assessment of L. macropodum DNA using FTAṜ card technology. FTAṜ cards were exposed to field-collected F. (Lasiohelea). Cards with and without insects adhered were processed and parasite load was determined with qPCR. 4.83% (7/145) FTAṜ cards were positive for L. macropodum DNA. Black columns show parasite load detected on each positive FTAṜ card (numbered FTA.1 – FTA.7). Dashed columns show the number of insects adhered to each card. When dashed columns are absent, this signifies the absence of insects on the positive cards.
Fig. 1 in Utilising a novel surveillance system to investigate species of Forcipomyia (Lasiohelea) (Diptera: Ceratopogonidae) as the suspected vectors of Leishmania macropodum (Kinetoplastida: Trypanosomatidae) in the Darwin region of Australia
Fig. 1. Wax-paper cups were used to contain and maintain field-collected biting midges. (A) Honey-coated FTAṜ cards were left at room temperature for 48 h allowing even absorption of honey into the cards. (B) A 2.5 cm slit was carved into the bottom of disposable cup and sealed with adhesive tape. (C) Insects were aspirated directly into the bottom of the containers through a small perforation created before field collection. Once biting midges were collected from the macropods, the small perforation was sealed with a rubber plug. Gauze was used as a lid to seal the top of the containers and fastened securely with a rubber band. The honey-coated FTAṜ card was inserted through the bottom slit after insect collection and once again sealed with adhesive tape.
Fig. 2 in Utilising a novel surveillance system to investigate species of Forcipomyia (Lasiohelea) (Diptera: Ceratopogonidae) as the suspected vectors of Leishmania macropodum (Kinetoplastida: Trypanosomatidae) in the Darwin region of Australia
Fig. 2. Leishmania macropodum DNA detection by qPCR. Individual or pools of F. (Lasiohelea) species were assessed for the presence of L. macropodum DNA. Only positive samples are shown, with each pair of columns representing results from one sample. Black columns depict the parasitic load detected and the dashed columns show the number of insects processed in that sample. Asterisks represent groups that contained ≥ 5 × 106 F. (Lasiohelea) parasites.
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. 3 in Carrier and vector of Pectobacterium carotovorum subsp. carotovorum and its handling through a base of entomopathogenic fungi in Agave sp.
Fig. 3. Massive growth of the Metarhizium anisopliae fungus on the Pectobacterium carotovora bacterium.
Fig. 2 in Carrier and vector of Pectobacterium carotovorum subsp. carotovorum and its handling through a base of entomopathogenic fungi in Agave sp.
Fig. 2. Countable growth of the Pectobacterium carotovora bacterium in a BD Bioxion cultivation medium (MacConkey agar).
Fig. 1 in Carrier and vector of Pectobacterium carotovorum subsp. carotovorum and its handling through a base of entomopathogenic fungi in Agave sp.
Fig. 1. (A) Growth of the Beauveria bassiana bacterium; (B) Growth of the Pectobacterium carotovorum bacterium.
Fig. 1 in Molecular screening of ticks of the genus Amblyomma (Acari: Ixodidae) infesting South African reptiles with comments on their potential to act as vectors for Hepatozoon fitzsimonsi (Dias, 1953) (Adeleorina: Hepatozoidae)
Fig. 1. Maximum likelihood analysis of Amblyomma tick species based on the 16S rRNA sequences. Bootstrap values at the major nodes are of percentage agreement among 1000 replicates. The branch scale represents substitutions per site.
Fig. 2 in Molecular screening of ticks of the genus Amblyomma (Acari: Ixodidae) infesting South African reptiles with comments on their potential to act as vectors for Hepatozoon fitzsimonsi (Dias, 1953) (Adeleorina: Hepatozoidae)
Fig. 2. Maximum likelihood analysis of species of Hepatozoon based on the 18S rRNA sequences. Bootstrap values at the major nodes are of percentage agreement among 1000 replicates. The branch scale represents substitutions per site.
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.
Fig. 5 in Ectoparasites of hedgehogs: From flea mite phoresy to their role as vectors of pathogens
Fig. 5. Phylogenetic analysis of the 16S rRNA gene (281 bp) of Ehrlichia and Anaplasma spp. detected in this study (Bold) and relationship with other Ehrlichia/ Anaplasma spp. The evolutionary history was inferred by using the Maximum Likelihood method based on the Kimura 2-parameter model (Kimura, 1980). Initial tree (s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 37.72% sites). GenBank accession number and country of origin are presented for each sequence.
Fig. 3 in Ectoparasites of hedgehogs: From flea mite phoresy to their role as vectors of pathogens
Fig. 3. Phylogenetic analysis of the gltA gene (345 bp) of Rickettsia asembonensis detected in this study (Bold) and relationship with other Rickettsia spp. The evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura 3-parameter model (Tamura, 1992). A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories [+G, parameter = 0.2157]). GenBank accession number and country of origin are presented for each sequence.
Fig. 4 in Ectoparasites of hedgehogs: From flea mite phoresy to their role as vectors of pathogens
Fig. 4. Phylogenetic analysis of the ompA gene (579 bp) of Rickettsia slovaca and Rickettsia massiliae detected in this study (Bold) and relationship with other Rickettsia spp. The evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura 3-parameter model (Tamura, 1992). Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 20.90% sites). GenBank accession number and country of origin are presented for each sequence.
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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.