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55 results for “vector species”
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
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>
Path-finding algorithm as a dispersal assessment method for invasive species with human-vectored long-distance dispersal event
<p><strong>Aim</strong>: An assessment method that can precisely represent human-vectored long-distance dispersals (HVLDD) is currently in need for effective management of invasive species. Here, we focused on HVLDD happening along roads and proposed a path-finding algorithm as a more precise dispersal assessment tool than the most widely used Euclidean distance method by using pine wilt disease (PWD) as a case study.</p> <p><strong>Location</strong>: Busan Metropolitan City, Republic of Korea</p> <p><strong>Methods</strong>: A path-finding algorithm, which calculates distances by considering spatial distribution of road networks, was tested for its effectiveness in estimating dispersal distances of HVLDD events. To this end, annual HVLDD cases were classified from entire PWD occurrence data from 2016 to 2019 and their dispersal distances were calculated using the path-finding algorithm and the Euclidean distance method. We constructed potential dispersal ranges based on the occurrence points in 2016, 2017, and 2018 using the respective year's mean dispersal distance for both methods, and their performances in accounting for each subsequent year's HVLDD cases were compared to determine which method calculated more precise distances. The information on which road class contributed more to dispersal occurrences and distances was analysed as well using the proposed algorithm.</p> <p><strong>Results</strong>: The potential dispersal ranges of the path-finding algorithm accounted for more future anthropogenic infection cases than the ones that used the Euclidean distance method, validating its higher functionality. It also revealed that most HVLDDs started and ended on small roads, and large roads constituted the majority of the total dispersal length.</p> <p><strong>Main Conclusions</strong>: The path-finding algorithm has proven to be a more effective dispersal assessment method for HVLDD events. It can help design effective control strategies. Thus, we encourage using the path-finding algorithm for dispersal assessment of invasive species that move along road networks, as well as for the development of more powerful HVLDD prediction models.a</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>
A plant virus differentially alters DNA methylation in two cryptic species of a hemipteran vector
<p>This study investigated DNA methylation patterns in two cryptic species (B and Q) of the sweet potato whitefly, <em>Bemisia tabaci</em> (Gennadius), following the acquisition of the tomato yellow curl virus, a single-stranded DNA virus. The methylation levels in genomic features such as promoters, gene bodies, and transposable elements in both cryptic species were described in this study. While overall trends were found to be similar, specific differences in methylation levels were observed. Virus-induced differentially methylated regions (DMRs) were associated with different genes in each cryptic species and were negatively correlated with differential gene expression. These DMRs were analyzed for changes in gene expression and alternative splicing, revealing clusters of hyper- and hypomethylated genes related to virus-vector interactions, immune functions, and detoxification processes. These methylation differences may help explain the distinct biological and physiological traits observed between the B and Q cryptic species.</p>
Fig. 1 in Thrips species (Thysanoptera: Thripidae) in Brazilian papaya (Brassicales: Caricaceae) orchards as potential virus vectors
Fig. 1. Location of the 20 papaya orchards sampled in the main Brazilian papaya-producing and -exporting region, Espírito Santo State, Brazil.
Рис. 1. КоΛичество макрокониΑий грибов роΑа Fusarium (% от общего чисΛа эΛементов морфоΛогии) на органах и в физиоΛогических жиΑкостях картофеΛьной коровки Fig. 1. Number of macroconidia of fungus species from the genus Fusarium (% of the total number of morphological elements) on organs and in physiological fluids of the potato ladybird beetle in On the vector characteristics of the potato ladybird beetle Henosepilachna Vigintioctomaculata (Motsch.) (Coleoptera, Coccinellidae) in the system "phytophagous insect - plant pathogen - plant"
Рис. 1. КоΛичество макрокониΑий грибов роΑа Fusarium (% от общего чисΛа эΛементов морфоΛогии) на органах и в физиоΛогических жиΑкостях картофеΛьной коровки Fig. 1. Number of macroconidia of fungus species from the genus Fusarium (% of the total number of morphological elements) on organs and in physiological fluids of the potato ladybird beetle
Fig. 6 in Differences in infection patterns of vector-borne blood-stage parasites of sympatric Malagasy primate species (Microcebus murinus, M. ravelobensis)
Fig. 6. Phylogenetic tree of 33 filarial nematode species constructed on the basis of partial COI sequences using the Maximum Likelihood method. The percentage of replicate trees in which the associated species clustered together in the bootstrap test (1000 replicates) is shown next to the branches. Branch lengths is measured in the number of substitutions per site. Thelazia callipaeda was included as an outgroup. The sequence of the present study is framed in red.
Fig. 5 in Differences in infection patterns of vector-borne blood-stage parasites of sympatric Malagasy primate species (Microcebus murinus, M. ravelobensis)
Fig. 5. Phylogenetic tree of Onchocercidae species constructed on the basis of partial ITS1 sequences using the Maximum Likelihood method. The percentage of replicate trees in which the associated species clustered together in the bootstrap test (1000 replicates) is shown next to the branches. Branch lengths is measured in the number of substitutions per site. The sequences of the present study are framed in red. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Differences in infection patterns of vector-borne blood-stage parasites of sympatric Malagasy primate species (Microcebus murinus, M. ravelobensis)
Fig. 3. Number of samples (blood smears) per month. Microfilaria positive samples are shown in dark blue for M. murinus and dark brown for M. ravelobensis, microfilaria negative samples in light blue for M. murinus and light brown for M. ravelobensis. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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.
A plant virus differentially alters DNA methylation in two cryptic species of a hemipteran vector
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Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling
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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.