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58 results for “fly control”
Data from: A novel protein-based fruit fly trap in melon flies Bactrocera cucurbitae for effective pest control management
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A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning
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Optimized composite microspheres containing spinosad and an attractant for control of a fruit fly pest
<p>These original data and analysis graph are for the manuscript titled "<strong>Optimized composite microspheres containing spinosad and an attractant for control of a fruit fly pest</strong>". If readers are interested in this paper, you can email to me and discuss the result. Also, we wecolme the coopertion on the fruit fly control program and techniques. </p>
Data from: The function and organization of the motor system controlling flight maneuvers in flies
Animals face the daunting task of controlling their limbs using a small set of highly constrained actuators. This problem is particularly demanding for insects such as Drosophila, which must adjust wing motion for both quick voluntary maneuvers and slow compensatory reflexes using only a dozen pairs of muscles. To identify strategies by which animals execute precise actions using sparse motor networks, we imaged the activity of a complete ensemble of wing control muscles in intact, flying flies. Our experiments uncovered a remarkably efficient logic in which each of the four skeletal elements at the base of the wing are equipped with both large phasically active muscles capable of executing large changes and smaller tonically active muscles specialized for continuous fine-scaled adjustments. Based on the responses to a broad panel of visual motion stimuli, we have developed a model by which the motor array regulates aerodynamically functional features of wing motion.
[Dataset] Tuneable reflexes control antennal positioning in flying hawkmoths
<p>The datasets used to study airflow-dependent antennal positioning in flying hawkmoths.</p>
Data from: Genetic diversity and population structure of the tsetse fly Glossina fuscipes fuscipes (Diptera: Glossinidae) in Northern Uganda: implications for vector control
Uganda is the only country where the chronic and acute forms of human African Trypanosomiasis (HAT) or sleeping sickness both occur and are separated by < 100 km in areas north of Lake Kyoga. In Uganda, Glossina fuscipes fuscipes is the main vector of the Trypanosoma parasites responsible for these diseases as well for the animal African Trypanosomiasis (AAT), or Nagana. We used highly polymorphic microsatellite loci and a mitochondrial DNA (mtDNA) marker to provide fine scale spatial resolution of genetic structure of G. f. fuscipes from 42 sampling sites from the northern region of Uganda where a merger of the two disease belts is feared. Based on microsatellite analyses, we found that G. f. fuscipes in northern Uganda are structured into three distinct genetic clusters with varying degrees of interconnectivity among them. Based on genetic assignment and spatial location, we grouped the sampling sites into four genetic units corresponding to northwestern Uganda in the Albert Nile drainage, northeastern Uganda in the Lake Kyoga drainage, western Uganda in the Victoria Nile drainage, and a transition zone between the two northern genetic clusters characterized by high level of genetic admixture. An analysis using HYBRIDLAB supported a hybrid swarm model as most consistent with tsetse genotypes in these admixed samples. Results of mtDNA analyses revealed the presence of 30 haplotypes representing three main haplogroups, whose location broadly overlaps with the microsatellite defined clusters. Migration analyses based on microsatellites point to moderate migration among the northern units located in the Albert Nile, Achwa River, Okole River, and Lake Kyoga drainages, but not between the northern units and the Victoria Nile drainage in the west. Effective population size estimates were variable with low to moderate sizes in most populations and with evidence of recent population bottlenecks, especially in the northeast unit of the Lake Kyoga drainage. Our microsatellite and mtDNA based analyses indicate that G. f. fuscipes movement along the Achwa and Okole rivers may facilitate northwest expansion of the Rhodesiense disease belt in Uganda. We identified tsetse migration corridors and recommend a rolling carpet approach from south of Lake Kyoga northward to minimize disease dispersal and prevent vector re-colonization. Additionally, our findings highlight the need for continuing tsetse monitoring efforts during and after control.
Effect of Intensive Fly Control on Trachoma and Ocular Chlamydia Infection in Tanzania
ClinicalTrials.gov study NCT00347763. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Clinical Study to Develop a Controlled Human Infection Model Using Leishmania Major-infected Sand Flies
ClinicalTrials.gov study NCT04512742. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Genetic diversity and population structure of the tsetse fly Glossina fuscipes fuscipes (Diptera: Glossinidae) in Northern Uganda: implications for vector control
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Data from: The function and organization of the motor system controlling flight maneuvers in flies
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Data from: A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda
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Data from: Multisensory control of orientation in tethered flying Drosophila
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Fig. 3 in Efficacy Of Protein Bait Sprays In Controlling Fruit Flies (Diptera: Tephritidae) Infesting Angled Luffa And Bitter Gourd In Thailand
Fig. 3. Percentage of infested fruits of angled luffa in plots treated with Pinnacle bait and untreated. (Only Bactrocera cucurbitae and B. tau were found.)
Data from: Interception by two predatory fly species is explained by a proportional navigation feedback controller
When aiming to capture a fast-moving target, animals can follow it until they catch up, or try to intercept it. In principle, interception is the more complicated strategy, but also more energy efficient. To study whether simple feedback controllers can explain interception behaviours by animals with miniature brains, we have reconstructed and studied the predatory flights of the robber fly Holcocephala fusca and killer fly Coenosia attenuata. Although both species catch other aerial arthropods out of the air, Holcocephala contrasts prey against the open sky, while Coenosia hunts against clutter and at much closer range. Thus, their solutions to this target catching task may differ significantly. We reconstructed in three dimensions the flight trajectories of these two species and those of the presented targets they were attempting to intercept. We then tested their recorded performances against simulations. We found that both species intercept targets on near time-optimal courses. To investigate the guidance laws that could underlie this behaviour, we tested three alternative control systems (pure pursuit, deviated pursuit and proportional navigation). Only proportional navigation explains the timing and magnitude of fly steering responses, but with differing gain constants and delays for each fly species. Holcocephala uses a dimensionless navigational constant of N ≈ 3 with a time delay of ≈28 ms to intercept targets over a comparatively long range. This constant is optimal, as it minimizes the control effort required to hit the target. In contrast, Coenosia uses a constant of N ≈ 1.5 with a time delay of ≈18 ms, this setting may allow Coenosia to cope with the extremely high line-of-sight rotation rates, which are due to close target proximity, and thus prevent overcompensation of steering. This is the first clear evidence of interception supported by proportional navigation in insects. This work also demonstrates how by setting different gains and delays, the same simple feedback controller can yield the necessary performance in two different environments.
Data from: Interception by two predatory fly species is explained by a proportional navigation feedback controller
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Data from: Sensory processing by motoneurons: a numerical model for low-level flight control in flies
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Expression of Sodalis glossinidius genes derived from self-cleared and control Glossina palpalis gambiensis flies
GEO Series GSE48360. Sodalis glossinidius. 8 samples. Type: Expression profiling by array.
Difference of gene expression in Malpighian tubules (MTs) betweeen control, "yki3SA" tumor, and "yki3SA+ITP-i" tumor-bearing flies
GEO Series GSE240166. Drosophila melanogaster. 9 samples. Type: Expression profiling by high throughput sequencing.
Malpighian tubules (MT) gene expression between control and yki3SA gut-tumor-bearing flies.
GEO Series GSE204866. Drosophila melanogaster. 6 samples. Type: Expression profiling by high throughput sequencing.
RNA-sequencing of the female adult fly guts with enterocyte-specific overexpression and silencing of lark in control and Pseudomonas entomophila infected states.
GEO Series GSE128488. Drosophila melanogaster. 18 samples. Type: Expression profiling by high throughput sequencing.
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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.