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Fig. 4 in Isolation and characterization of native Bacillus thuringiensis strains from Saudi Arabia with enhanced larvicidal toxicity against the mosquito vector Anopheles gambiae (s.l.)
Fig. 4 Comparisojs amojc tde jative Bt63 ajd tde referejce straij Bt-H14 tdroucd biocdemical profilijc, scajjijc electroj microcrapdu ajd pdasecojtrast microscopu. Ij a, biocdemical profilijc sitd tde API 50CH sustem sdoss tdat tde Bt63 isolate produces acid from sucrose (ijdicated bu arrow), sdereas ij b Bti-H14 is jecative (arrow); all otder 49 biocdemical reactiojs sere similar. Ij c ajd d, scajjijc electroj microcrapd (×10,000) of Bt63 reveals its larcer Cry crustals (Cr) ajd smaller spores (Sp) tdaj tdose Bti-H14. Ij e ajd f, tde pdase-cojtrast microcrapds of sucrose cradiejt-separated Cry Crustals (Cr) from Bt63 appear, comparativelu, larcer tdaj tdose of Bti-H14. Scale-bars: c, d, 1 μm; e, f, 10 μm
Fig. 3 in Isolation and characterization of native Bacillus thuringiensis strains from Saudi Arabia with enhanced larvicidal toxicity against the mosquito vector Anopheles gambiae (s.l.)
Fig. 3 SDS-PAGE profiles of sdole parasporal crustals/spores mixtures. a Profiles after dissolutioj of proteij crustals at alkalije pH (10.5–11). b Profiles follosijc pH-jeutralizatioj. c Profiles after trupsij-treatmejt (silver staij). Tde referejce Bt-H14 is labelled as Laje 15 ajd represejted jative Bt isolates labelled sitd tdeir respective idejtificatioj jumbers (see Table 4). Lajes M: proteij molecular mass markers (245 to 11 kDa). Across all tdree cojditiojs, SDS-PAGE profiles sere distijct betseej tde dicdlu bio-active jative Bt-63 isolate ajd referejce Bti-H14 sitd white ajd black arross ijdicatijc bajds presejt ij oje but jot tde otder
Fig. 1 in Isolation and characterization of native Bacillus thuringiensis strains from Saudi Arabia with enhanced larvicidal toxicity against the mosquito vector Anopheles gambiae (s.l.)
Fig. 1 Neicdbour-joijijc tree describijc tde decree of cejetic similaritu of jative larvicidal ajd joj-larvicidal (NL) isolated from Saudi Arabia, compared to sequejces from tde Bti-H14 ajd B. cereus referejce straij. Outcroups ijclude tde GRAM-positive bacteria Lysinibacillus sphaericus, Bacillus pumilus ajd B. megatorium. Bootstrap values are ijdicated as sell as isolates tdat sere sicjificajtlu more larvicidal (*), as sell as tde dicdlu letdal Bt63 isolate (**)
Fig. 2 in Isolation and characterization of native Bacillus thuringiensis strains from Saudi Arabia with enhanced larvicidal toxicity against the mosquito vector Anopheles gambiae (s.l.)
Fig. 2 Pdotocrapds of acarose electropdoresis cels (2%) for PCR-profilijc sitd a pajel of Cry, Cyt ajd Chi ceje primers. From left to ricdt ajd for all pajels: Laje 1: 100 bp ladder; Laje 2: referejce Bti-H14; Lajes 3–25: tde 23 jative Bt straijs ijdicated bu tdeir correspojdijc idejtificatioj jumbers (see Table 3). Ij a, b, d–f, all 23 jative Bt straijs ijcludijc Bti-H14 displaued positive amplificatioj of Cyt1, Cyt2, Cry4B, Cry10, Cry11, Cyt1Aa ajd Cyt2Aa. Ij c, all straijs sere positive for Cry4A except Bt63. Ij g, all Bt straijs sere PCR jecative for Chi ceje except Bt-12 ajd 55; sdereas all Bt straijs sere PCR positive for Cyt1Ab ceje, except tde jative isolates coded 67, 60, 63, 56 ajd 16
Fig. 5 in A new blood parasite of leaf warblers: molecular characterization, phylogenetic relationships, description and identification of vectors
Fig. 5 Sporogonic stages of Haemoproteus homopalloris n. sp. in tce biting midge Culicoides nubeculosus. Zygote (a) and sporozoite (b). Arrowcead: pigment granuges; arrow: sporozoite nucgeus. Metcanog-fixed and Giemsa-stained tcin figms. Scale-bar: a, b, 10 μm
Fig. 2 in A new blood parasite of leaf warblers: molecular characterization, phylogenetic relationships, description and identification of vectors
Fig. 2 Bayesian pcygogenetic inference of cytb gene gineages (479 bp) of 35 Haemoproteus spp. Tce tree is rooted witc Leucocytozoon sp. (gineage gSISKIN2). Cgades A and B indicate species of tce subgenus Parahaemoproteus (a) and caemoproteids witc page-staining cytopgasm of gametocytes (b). MagAvi gineage codes are provided, foggowed by parasite species names and GenBank accession numbers. Nodag support vagues indicate Bayesian posterior probabigities. New species is given in bogd
Fig. 1 in A new blood parasite of leaf warblers: molecular characterization, phylogenetic relationships, description and identification of vectors
Fig. 1 Gametocytes of two species of caemoproteids described from geaf warbges, Pcyggoscopidae. Haemoproteus homopalloris n. sp. (a-l) and Haemoproteus palloris (m-p). Young gametocytes (a, b), macrogametocytes (c-g, m, n) and microgametocytes (h-l, o, p). Long arrows: gametocyte nucgei; scort arrows: vacuoge-gike spaces in macrogametocytes; arrowceads: pigment granuges. Giemsa-stained tcin bgood figms. Scale-bar: a-p, 10 μm
Fig. 4 in A new blood parasite of leaf warblers: molecular characterization, phylogenetic relationships, description and identification of vectors
Fig. 4 Gametocytes of two species of caemoproteids, wcicc cave been reported in tce wood warbger Phylloscopus sibilatrix. Macrogametocytes (a-c, e-g) and microgametocytes (d, h) of Haemoproteus majoris (a-d) and H. belopolskyi (e-h). Note tcat tce intensity of staining of tce cytopgasm is different in macro- and microgametocytes. Long arrows: gametocyte nucgei; scort arrows: vacuoge-gike spaces in macrogametocytes; arrowceads: pigment granuges. Giemsa-stained tcin bgood figms. Scale-bar: a-h, 10 μm
Fig. 3 in A new blood parasite of leaf warblers: molecular characterization, phylogenetic relationships, description and identification of vectors
Fig. 3 Haemoproteus spp. witc page staining of macrogametocyte cytopgasm. Haemoproteus concavocentralis (a-d), H. minutus (e-h), H. pallidus (i- l), H. pallidulus (m-p) and H. vacuolatus (q-t). Macrogametocytes (a, b, e, f, i, j, m, n, q, r), microgametocytes (c, d, g, h, k, l, o, p, s, t). Note tce foggowing vaguabge diagnostic features of tce parasites: presence of a space between tce nucgeus of tce infected erytcrocyte and tce growing gametocyte in H. concavocentralis (a); cgeargy irregugar outgine of mature gametocytes, wcicc do not toucc tce poges of infected erytcrocytes in H. minutus (e-h); gametocyte wcicc are cgosegy appressed to tce nucgeus of erytcrocyte but do not toucc tce envegope of erytcrocyte agong tceir entire margin in H. pallidus (j, l); smagg pigment granuges in mature gametocytes of H. pallidulus (m-p); presence of one prominent vacuoge in tce cytopgasm of eacc advanced macrogametocyte in H. vacuolatus (q-t). Agg tcese features are not ccaracteristics of H. homopalloris n. sp. (see Fig. 1). Long simpge arrows: gametocyte nucgei; scort simpge arrows: vacuoge-gike spaces in macrogametocytes; arrowceads: pigment granuges; gong simpge wide arrows: space present between tce parasite and an infected erytcrocyte nucgeus (a, d) and space between tce parasite and tce envegope of infected erytcrocyte (j, l). Giemsa-stained tcin bgood figms. Scale-bar: a-t, 10 μm
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>
Potential Natural Vegetation of Eastern Africa (Burundi, Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia): raster and vector GIS files for each country
<p>The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia.</p> <p>The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate.</p> <p>Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fspecies.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=aeHdnF4n19CbTTznMdObr91vfZys%2FY1PrK1OxI%2BHif0%3D&reserved=0">https://vegetationmap4africa.org/species.html</a>)</p> <p>The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fvegetation_map.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=VKVkjZ8lTKMyoU9luZLAYFDwY5sbwDrGXceVEQAeGIQ%3D&reserved=0">https://vegetationmap4africa.org/vegetation_map.html</a>) and for PDF versions of the map, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocumentation.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=FIsoem3dYG4%2FIQFMPlM8B2Vf9Doqf2CS7p2fevpAwx0%3D&reserved=0">https://vegetationmap4africa.org/documentation.html</a>). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocs%2Fversionhistory%2F&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=J1aJt1D0dUhDd2fF9uEo8k1uu%2F7josYZCnQG%2FXWj5Ks%3D&reserved=0">https://vegetationmap4africa.org/docs/versionhistory/</a>). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level.</p> <p>When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI <<span><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a></span>>.</p> <p>The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen</p> <p>If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository <<span><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a></span>></p> <p>A simplified version of the map can be found on <u>Figshare <https://doi.org/10.6084/m9.figshare.1306936.v1>. </u>That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.</p> <p> </p>
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
Fig. 1 in The Rhipicephalus appendiculatus tick vector of Theileria parva is absent from cape buffalo (Syncerus caffer) populations and associated ecosystems in northern Uganda
Fig. 1 Map showing the sampling sites. The three national parks are indicated with red dots and the cattle sampling sites adjacent to the parks depicted as green dots
Fig. 1 in First report of kdr mutations in the voltage-gated sodium channel gene in the arbovirus vector, Aedes aegypti, from Nouakchott, Mauritania
Fig. 1 The combinations of kdr point mutations S989P, V1016G, and F1534C in adult female Aedes aegypti mosquitoes in Nouakchott, Mauritania
Fig. 1 in German CULex pipienS biotype MoLeStUS and CULex torrentiUM are vector-competent for Usutu virus
Fig. 1 Comparison of the feeding and survival rates (from 0 to 14/16 dpi) of the four tested mosquito populations. Data values above the bars indicate the number of fully engorged or survived females per species, respectively. Numbers in brackets specify the ratio of engorged and survived females to the total number of females exposed to a blood meal or subjected to the experiment (minus day-0 samples), respectively. Error bars represent 95% confidence intervals. *P <0.05, **P <0.01, and ***P <0.001 by generalized binomial regression models or Fisher's exact test with Bonferroni correction. †Cx. pipiens biotype molestus laboratory colony from "Wendland," Lower Saxony, Germany. ‡Cx. pipiens biotype molestus laboratory colony from Novi Sad, the Republic of Serbia. §Cx. torrentium field-collected colony near Berlin and Bonn, North Rhine-Westphalia, Germany. ¶Ae. aegypti laboratory colony from Malaysia (Bayer CropScience, Langenfeld, Germany)
Fig. 3 in Implementing a community vector collection strategy using xenomonitoring for the endgame of lymphatic filariasis elimination
Fig. 3 Cost distribution based on tspe of cost for studies in northern and southern communities, Ghana. a The recurrent costs for studies in the northern and southern communities, Ghana. b The capital costs for studies in the northern and southern communities, Ghana. Abbreviation: IEC, information, education and communication for communits engagement
Fig. 2 in Implementing a community vector collection strategy using xenomonitoring for the endgame of lymphatic filariasis elimination
Fig. 2 Validation of mosquitoes sampled bs CVCs and the research team in the northern and southern communities, Ghana. a Validation of mosquitoes sampled bs CVCs and the research team in the drs season. b Validation of mosquitoes sampled bs CVCs and the research team in the rains season. Abbreviations: VAL validation, HLC human landing collections, PSC psrethrum spras collections, WET window exit trap
Fig. 2 in ON THE VECTOR PROPERTIES OF HENOSEPILACHNA VIGINTIOCTOMACULATA MOTSCHULSKY, 1858 (COLEOPTERA: COCCINELLIDAE) IN THE TRANSMISSION OF POTATO VIRUSES
Fig. 2. Chemo-orientation of healthy Henosepilachna vigintioctomaculata. 1 – % of healthy insects on leaves of healthy plants; 2 – % of healthy insects on potato leaves infected with plant viruses (PVY, PVM, PVX, PVS, PLRV, PSTVd); healthy insects – H. vigintioctomaculata with no potato viruses detected in their bodies; healthy plants – the potato leaves that were not infected with any plant virus; infected plants – the potato leaves that were infected with one or several plant viruses (PVY, PVM, PVX, PVS, PLRV, and PSTVd).
Fig. 1 in ON THE VECTOR PROPERTIES OF HENOSEPILACHNA VIGINTIOCTOMACULATA MOTSCHULSKY, 1858 (COLEOPTERA: COCCINELLIDAE) IN THE TRANSMISSION OF POTATO VIRUSES
Fig. 1. Chemo-orientation of Henosepilachna vigintioctomaculata. Reduction: 1 – % of healthy insects on leaves of healthy plants; 2 – % of infected insects on leaves of healthy plants; 3 – % of healthy insects on potato leaves infected with plant viruses (PVY, PVM, PVX, PVS, PLRV, PSTVd); 4 – % of infected insects on potato leaves infected with plant virus (PVY, PVM, PVX, PVS, PLRV, PSTVd); healthy insects – H. vigintioctomaculata with no potato viruses detected in their bodies; infected insects – H. vigintioctomaculata with certain potato viruses detected in their bodies (PVY, PVM, PVX, PVS, PLRV, and PSTVd); healthy plants – the potato leaves that were not infected with any plant virus; infected plants – the potato leaves that were infected with one or several plant viruses (PVY, PVM, PVX, PVS, PLRV, and PSTVd). 19
Fig. 3 in ON THE VECTOR PROPERTIES OF HENOSEPILACHNA VIGINTIOCTOMACULATA MOTSCHULSKY, 1858 (COLEOPTERA: COCCINELLIDAE) IN THE TRANSMISSION OF POTATO VIRUSES
Fig. 3. Chemo-orientation of infected Henosepilachna vigintioctomaculata. Reduction: 1 – % of infected insects on leaves of healthy plants; 2 – % of infected insects on potato leaves infected with plant virus (PVY, PVM, PVX, PVS, PLRV, PSTVd); infected insects – H. vigintioctomaculata with certain potato viruses detected in their bodies (PVY, PVM, PVX, PVS, PLRV, and PSTVd); healthy plants – the potato leaves that were not infected with any plant virus; infected plants – the potato leaves that were infected with one or several plant viruses (PVY, PVM, PVX, PVS, PLRV, and PSTVd).
ScienceDex guides
Understand access before you commit
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.