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180 results for “Gambia”
A dataset of human and Plasmodium falciparum genotypes in severe malaria cases from The Gambia and Kenya
<p>This data release contains human and <em>Plasmodium falciparum</em> malaria genotypes from the article:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The release contains genotypes from human and <em>Plasmodium falciparum</em> genetic variants, genotyped using blood samples from 4,171 children ascertained with severe symptoms of malaria at the Royal Victoria Teaching Hospital (now the Edward Francis Small Teaching Hospital), The Gambia, and from the Kilifi District Hospital (now Kilifi County Hospital), Kenya in the period 1995-2009.</p> <p>An accompanying set of association test summary statistics has also been released on Zenodo (doi: <a href="https://doi.org/10.5281/zenodo.5722497">10.5281/zenodo.5722497</a>). Please see <a href="http://www.malariagen.net/resource/32">www.malariagen.net/resource/32</a> for full details of other resources associated with the above manuscript.</p> <p> </p>
National Checklists 2017: The Gambia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from The Gambia collected using effechecka and geonames polygons
National Checklists 2019: The Gambia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from The Gambia collected using effechecka and geonames polygons
Anopheles gambiae (AgamP4) genome conservation score
<p>The conservation score storage is a result of a bioinformatics pipeline that integrates a systematic analysis of the data on genetic variation in more than 1,000 wild-caught Anopheles gambiae individuals and conserved syntenic regions of 19 Anopheles species and 3 phylogenetically more distant species of dipterans.</p> <p>The results of this analysis are gathered in the HDF5 data storage system that allows for flexible extraction and bioinformatic manipulation at each genomic position in AgamP4 reference genome.</p> <p> </p> <p> </p>
qdgc Gambia
<p>QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it.<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 20th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Gambia
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
National Checklists: The Gambia Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Summary statistics for association tests between human and Plasmodium falciparum genetic variants in 3,346 severe malaria cases from The Gambia and Kenya
<p>This dataset contains summary statistics for association tests between human and<br> <em>Plasmodium falciparum</em> malaria parasite genetic variants, using data from 3,346 severe malaria cases from The Gambia and Kenya. These results underlie the analysis described in our paper:</p> <p><strong>"Malaria protection due to sickle haemoglobin depends on parasite genotype"</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy Nguyen, Sónia M. Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor, David J. Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>:: <a href="https://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a><br> <br> The genotype data underlying these summary statistics has also been deposited on Zenodo<br> (<a href="https://zenodo.org/record/4973477">doi:10.5281/zenodo.4973477</a>). The <a href="https://www.well.ox.ac.uk/~gav/hptest)">HPTEST software</a> used to generate these results has also been deposited (<a href="https://doi.org/10.5281/zenodo.5685580">doi:10.5281/zenodo.5685580</a>). Please see the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a> for a full list of datasets which have been released with this manuscript.</p> <p><strong>Data contents.</strong></p> <p>The dataset consists of a single <a href="http://sqlite.org">sqlite database file</a> containing the results, and an accompanying README file in markdown and html format. Please see the README file for full details of the data contents.</p> <p> </p>
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
CCG Starter Data Kit: Gambia, The
<p>A starter data kit for Gambia, The</p>
Illumina RNA-Sequencing fastq data from insecticide resistant Anopheles gambiae s.l
<p>This is a dataset of Illumina RNA sequencing reads, for a project investigating resistance to Pirimiphos-methyl in the major malaria vectors, Anopheles gambiae and Anopheles coluzzii. There are four biological replicates for the following conditions:</p> <p> </p> <p>Ngousso (susceptible)</p> <p>Kisumu (susceptible)</p> <p>Bouake gambiae unexposed</p> <p>Bouake gambiae PM survivors</p> <p>Bouake coluzzii unexposed </p> <p>Bouake coluzzii PM survivors </p> <p> </p> <p>SRA submission: SUB14596876</p> <p> </p>
Social contact data for the BHDSS and FWHDSS in the Gambia (2022)
<p>Social contact data for people in the <span>Basse and Fuladu West Health and Demographic Surveillance Systems</span> (BHDSS and FWDHSS) in the Gambia. Participants reported all their direct contacts in the 24 hours preceding the survey. This survey was conducted in 2022. Data is formatted to be used in the <em>socialmixr</em> package in <em>R</em>.</p> <p>Note, for a subset of school-going participants, school contacts were observed in the classroom for a two hour period. The list of participants needs to be filtered accordingly to ensure the correct denominators are applied when calculating contact rates.</p>
Data from: Anopheles gambiae: metabolomic profiles in sugar-fed, blood-fed and Plasmodium falciparum-infected midgut
The mosquito midgut is a physiological organ essential for the nutrient acquisition as well as an interface that encounters various mosquito borne pathogens. Metabolomic characterization would reveal biochemical fingerprints that are generated by various cellular processes. The metabolite profiles of the mosquito midgut will provide an overview of the biochemical events in both physiological states and the dynamic responses to pathogen infections. In this study, the midgut metabolic profiles of Anopheles gambiae mosquitoes following feeding with sugar, human blood, mouse blood, and Plasmodium falciparum-infected human blood were examined. A mass spectrometry system coupled to liquid and gas chromatography produced a time series of metabolites in the midgut at discrete conditions (sugar feeding, 24hr and 48hr post normal blood and P. falciparum-infected blood feeding). Triplicates were included to ensure system validity. A total of 512 individual compounds were identified, 511 were assigned to 8 super-pathways and 75 sub-pathways. The dataset can be used for further inquiry into the metabolic dynamics of sugar and blood digestion and of malaria parasite infection.
Ticks, and tick-borne bacterial pathogens found on hard ticks (Acari: Ixodidae) on cattle in the Central River Region of The Gambia
Open the record for dataset details and reuse information.
Data from: Mechanisms of transcriptional regulation in Anopheles gambiae revealed by allele specific expression
Open the record for dataset details and reuse information.
Data from: Anopheles gambiae: metabolomic profiles in sugar-fed, blood-fed and Plasmodium falciparum-infected midgut
Open the record for dataset details and reuse information.
Data from: Prominent intra-specific genetic divergence within Anopheles gambiae sibling species triggered by habitat discontinuities across a riverine landscape
The Anopheles gambiae complex of mosquitoes includes malaria vectors at different stages of speciation, whose study enables a better understanding of how adaptation to divergent environmental conditions leads to evolution of reproductive isolation. We investigated the population genetic structure of closely-related sympatric taxa that have recently been proposed as separate species (An. coluzzii and An. gambiae), sampled from diverse habitats along the Gambia River in West Africa. We characterised putatively neutral microsatellite loci as well as chromosomal inversion polymorphisms known to be associated with ecological adaptation. The results revealed strong ecologically-associated population subdivisions within both species. Microsatellite loci at chromosome-3L revealed a clear differentiation between coastal and inland populations, which in An. coluzzii is reinforced by a peculiar inversion polymorphism pattern, supporting the hypothesis of genetic divergence driven by adaptation to the coastal habitat. Striking genetic differences, compatible with a strong reduction of gene-flow, were observed between An. gambiae populations west and east of an extensively rice-cultivated region exclusively occupied by An. coluzzii. Notably, this 'intra-specific' differentiation was higher than that observed between the two species and involved also the centromeric region of chromosome-X which has previously been considered a marker of speciation within this complex, suggesting that the two populations may be at an advanced stage of reproductive isolation triggered by human-made habitat fragmentation. These results confirm ongoing ecological speciation within these most important Afro-tropical malaria vectors and raise new questions on the possible effect of this process in malaria transmission.
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.