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85 results for “Hornet”
Fig. 3 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 3. Vespa affinis in Singapore. A: worker capturing a flower-visiting sweat bee (Lasioglossum sp.); B: workers scavenging from a dead fish left behind at a fishing ground; C: worker with an insect caught in a spider's web; D: worker nectaring from flowers of Leea rubra. (Photos: Lim Yu Jun, Marcus Ng and Zestin Soh).
Fig. 7. The hoverfly Milesia vespoides, a in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 7. The hoverfly Milesia vespoides, a Batesian mimic of Vespa affinis and V. tropica, at Windsor Nature Park in January 2022. (Photograph by Yue Teng Lee).
Fig. 10 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 10. Vespa multimaculata feeding on inflorescences of a palm (Arecaceae) in Selangor, Malaysia in September 2022. (Photographs: Mike Hooper).
Fig. 4 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 4. Comparison of the clypeus of Vespa affinis (A) and V. tropica (B). The white arrows point to the clypeal apices, bordering a medial emargination, which are semi-circular protuberances in V. affinis but more strongly produced as triangular projections in V. tropica. (Photographs: John Lee).
Fig. 1 in Yearly and seasonal changes in species composition of hornets (Hymenoptera: Vespidae) caught with bait traps on the Sea of Japan coast
Fig. 1. Yearly changes in the species composition of hornets in Sakata Park (A) and campus of Niigata University (B).
Dataset: CYBER HORNET S&P 500 and Bitcoin 75/25 Strategy ETF (ZZZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Evolved eavesdropping: sympatric but not allopatric honey bee species can detect and use hornet alarm pheromone for defence
<p>Eavesdropping is predicted to evolve between sympatric, but not allopatric, predator and prey. The evolutionary arms race between Asian honey bees and their hornet predators has led to a remarkable defence, heat-balling, which suffocates hornets with heat and carbon dioxide. We show that the sympatric Asian species, <em>Apis cerana</em>(Ac), formed heat balls in response to Ac and hornet (<em>Vespa</em><em>velutina</em>) alarm pheromones, demonstrating eavesdropping. The allopatric species, <em>Apis</em><em>mellifera</em>(Am), only weakly responded to a live hornet and Am alarm pheromone, butnot to hornet alarm pheromone. We observed typical hornet alarm pheromone releasing behaviour, hornet sting extension, when guard bees initially attacked. Once heat balls were formed, guards released honey bee sting alarm pheromones: isopentyl acetate, octyl acetate, (<em>E</em>)-2-decen-1-yl acetate, and benzyl acetate. Only Ac heat-balled in response to realistic bee alarm pheromone component levels, <1 bee-equivalent (1 µg), of isopentyl acetate. Detailed eavesdropping experiments showed that Ac, but not Am, formed heat-balls in response to a synthetic blend of hornet alarm pheromone. Only Ac antennae showed strong, consistent responses to hornet alarm pheromone compounds and venom volatiles. These data provide the first evidence that the sympatric Ac, but not the allopatric Am, can eavesdrop upon hornet alarm pheromone and uses this information, in addition to bee alarm pheromone, to heat-ball hornets. Evolution has likely given Ac this eavesdropping ability, an adaptation that the allopatric Am does not possess.</p>
Fig. 9 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 9. Singaporean distribution of Vespa analis.
Fig. 2 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 2. Singaporean distribution of Provespa anomala.
Fig. 5 in Hornets (Vespidae: Vespinae) of Singapore: ecology, identification, and national conservation assessment
Fig. 5. Singaporean distribution of Vespa affinis.
Dataset for Identification of giant hornet, Vespa mandarinia, queen sex pheromone components
<p>The Vespidae are a diverse family of wasps and hornets that contain invasive species and are formidable predators of insects, including social bees. Recently, the world’s largest hornet, <em>Vespa mandarinia</em> Smith(Hymenoptera: Vespidae), which occurs naturally in the Indomalayan region, has been found in Canada and United States. Some simulations indicate it could rapidly spread throughout Washington, Oregon, and parts of the eastern USA, threaten native bees and honey bees, and harm bee-pollinated crop production worth nearly $12 million annually. There is consequently an urgent need to learn more about <em>V. mandarinia</em>’s reproductive biology and to develop trapping methods to locate its nests and control its reproduction. We identified <em>V. mandarinia</em> queen-produced sex pheromone from the 5<sup>th</sup> and 6<sup>th</sup> intersegmental sternal glands of virgin queens. The major active compounds were hexanoic acid (HA), octanoic acid (OA) and decanoic acid (DA). When placed in field traps, the synthetic compounds and a queen-equivalent mixture rapidly attracted hundreds of males. This dataset provides the data used in this paper.</p>
Fig. 3 in Yearly and seasonal changes in species composition of hornets (Hymenoptera: Vespidae) caught with bait traps on the Sea of Japan coast
Fig. 3. Seasonal abundance of Vespa ducalis collected by bait traps at Sakata Park and
Fig. 2 in Yearly and seasonal changes in species composition of hornets (Hymenoptera: Vespidae) caught with bait traps on the Sea of Japan coast
Fig. 2. Seasonal abundance of Vespa analis collected by bait traps at Sakata Park and
Fig. 4 in Yearly and seasonal changes in species composition of hornets (Hymenoptera: Vespidae) caught with bait traps on the Sea of Japan coast
Fig. 4. Nonmetric multidimensional scaling (NMDS) plot comparing hornet species
CTU Hornet 65 Niner: A Network Dataset of Geographically Distributed Low-Interaction Honeypots
<p>CTU Hornet 65 Niner is a dataset of 65 days of network traffic attacks captured in cloud servers used as honeypots to help understand how geography may impact the inflow of network attacks. The honeypots were placed in nine different geographical locations: Amsterdam, London, Frankfurt, San Francisco, New York, Singapore, Toronto, Bangalore, and Sydney. The data was captured from April 28th to July 1st, 2024.</p> <p>The nine cloud servers were created and configured following identical instructions using Ansible [1] in DigitalOcean [2] cloud provider. The network capture was performed using the Zeek [3] network monitoring tool, which was installed on each cloud server. The cloud servers had only one service running (SSH on a non-standard port) and were fully dedicated to being used as a honeypot. No honeypot software was used in this dataset.</p> <p>The dataset is composed of nine scenarios:</p> <ul> <li>Honeypot-Cloud-DigitalOcean-Geo-1: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-2: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-3: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-4: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-5: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-6: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-7: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-8: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-9: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> </ul> <p><strong>References:</strong></p> <p>[1] Ansible IT Automation Engine, https://www.ansible.com/. Accessed on 08/28/2024.</p> <p>[2] DigitalOcean, https://www.digitalocean.com/. Accessed on 08/28/2024.</p> <p>[3] Zeek Documentation, https://docs.zeek.org/en/master/index.html. Accessed on 08/28/2024.</p> <p><strong>Funding:</strong></p> <p>The authors acknowledge support by the Strategic Support for the Development of Security Research in the Czech Republic 2019--2025 (IMPAKT 1) program, by the Ministry of the Interior of the Czech Republic under No. VJ02010020 -- AI-Dojo: Multi-agent testbed for the research and testing of AI-driven cyber security technologies.</p>
FIGURE 10 in A large hornet mimic clearwing moth of the genus Lamellisphecia Kallies & Arita 2004 (Lepidoptera, Sesiidae) from Nanling, Guangdong, southern China
FIGURE 10. Lamellisphecia champaensis, male, Laos. Fig. 11. Lamellisphecia wiangensis, male, Thailand.
Data from: Evolution of wing shape in hornets: why is the wing venation efficient for species identification?
Wing venation has long been used for insect identification. Lately, the characterization of venation shape using geometric morphometrics has further improved the potential of using the wing for insect identification. However, external factors inducing variation in wing shape could obscure specific differences, preventing accurate discrimination of species in heterogeneous samples. Here, we show that interspecific difference is the main source of wing shape variation within social wasps. We found that a naive clustering of wing shape data from taxonomically and geographically heterogeneous samples of workers returned groups congruent with species. We also confirmed that individuals can be reliably attributed to their genus, species and populations on the basis of their wing shape. Our results suggested that the shape variation reflects the evolutionary history with a potential influence of other factors such as body shape, climate and mimicry selective pressures. However, the high dimensionality of wing shape variation may have prevented absolute convergences between the different species. Wing venation shape is thus a taxonomically relevant marker combining the accuracy of quantitative characters with the specificity required for identification criteria. This marker may also highlight adaptive processes that could help understand the wing's influence on insect flight.
Data from: Phylogenomic analysis of yellowjackets and hornets (Hymenoptera: Vespidae, Vespinae)
The phylogenetic relationships among genera of the subfamily Vespinae (yellowjackets and hornets) remain unclear. Yellowjackets and hornets constitute one of the only two lineages of highly eusocial wasps, and the distribution of key behavioral traits correlates closely with the current classification of the group. The potential of the Vespinae to elucidate the evolution of social life, however, remains limited due to ambiguous genus-level relationships. Here, we address the relationships among genera within the Vespinae using transcriptomic (RNA-seq) data. We sequenced the transcriptomes of six vespid wasps, including three of the four genera recognized in the Vespinae, combined our data with publicly available transcriptomes, and assembled two matrices comprising 1,507 and 3,356 putative single-copy genes. The results of our phylogenomic analyses recover Dolichovespula as more closely related to Vespa than to Vespula, therefore challenging the prevailing hypothesis of yellowjacket (Vespula + Dolichovespula) monophyly. This suggests that traits such as large colony size and high paternity arose in the genus Vespula following its early divergence from the remaining vespine genera.
Figure 7 in The pre-overwintering nests and the immature stages of the hornet Vespa fumida van der Vecht (Hymenoptera: Vespidae)
Figure 7. Ultrastructures of Vespa fumida (scanning electron micrographs). (A) Mandible, frontal view. (B) Mandible, caudal view. (C) Abdominal spicules and sparse minute setae marked by s1, s2, s3, s4. (D) Magnification of the parts of s1–4, showing the setae. (E) Collar processes at perimeter of primary tracheal opening, outer surface view, showing with numerous microbranches. (F) Collar processes inner surface view, showing many branches all over. (G) Distal portion of collar process, showing branches bearing similar to the bamboo shoots. a, posterior mandibular condyle; c, anterior mandibular acetabulum.
Figure 3 in The pre-overwintering nests and the immature stages of the hornet Vespa fumida van der Vecht (Hymenoptera: Vespidae)
Figure 3. Larvae of Vespa fumida. (A) Head, frontal view. (B) Head, lateral view. (C) Head, caudal view. (D–G) Mandibles of the second to final instar larvae. (H) Labrum. (I) Palate. (J) Spicules on spiracular atrium. H–J: drawings according to the scanning electron micrographs. Abbreviations: ant, antenna; atp, anterior tentorial pit; ata, anterior tentorial arm; bs, basiconic sensillum; cd, cardo; clp, clypeus; cp, conical papilla; cpc, conical papillae congregated; cas, campaniform sensillum; dfm, dorsal frontal muscle; dta, dorsal tentorial arm; dtp, dorsal tentorial pit; for, occipital foramen; fr, frons; g, gena; ga, galea; lm, labrum; lbp, labial palp; ma, mandible; mx, maxillary; mxp, maxillary palp; mp, minute pit; pb, parietal band; ps, punctures bearing minute setae; ptb, posterior tentorial bridge; st, stipes; scp, sclerotized patch; spi, spicules; spn, spinnert; v, vertex.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.