Nesting insects perform learning flights to establish a visual representation of the nest environment that allows them to subsequently return to the nest. It has remained unclear when insects learn what during these flights, what determines their overall structure, and, in particular, how what is learned is used to guide an insect's return. We analyzed learning flights in ground-nesting wasps (Sphecidae: Cerceris australis) using synchronized high-speed cameras to determine 3D head position and orientation. Wasps move along arcs centered on the nest entrance, whereby rapid changes in gaze assure that the nest is seen at lateral positions in the left or the right visual field. Between saccades, the wasps translate along arc segments around the nest while keeping gaze fixed. We reconstructed panoramic views along the paths of learning and homing wasps to test specific predictions about what wasps learn during their learning flights and how they use this information to guide their return. Our evidence suggests that wasps monitor changing views during learning flights and use the differences they experience relative to previously encountered views to decide when to begin a new arc. Upon encountering learned views, homing wasps move left or right, depending on the nest direction associated with that view, and in addition appear to be guided by features on the ground close to the nest. We test our predictions on how wasps use views for homing by simulating homing flights of a virtual wasp guided by views rendered in a 3D model of a natural wasp environment.
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http://dx.doi.org/10.1016/j.cub.2015.12.052 | DOI Listing |
Viruses
December 2024
JES Tech, Human Health and Performance Directorate, Houston, TX 77058, USA.
Many biological markers of normal and disease states can be detected in saliva. The benefits of saliva collection for research include being non-invasive, ease of frequent sample collection, saving time, and being cost-effective. A small volume (≈1 mL) of saliva is enough for these analyses that can be collected in just a few minutes.
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December 2024
Laboratory of Physiology, Department of Medicine, University of Patras, Patras, Greece.
β-adrenergic receptors (β-ARs) play a critical role in modulating learning, memory, emotionality, and long-term synaptic plasticity. Recent studies indicate that β-ARs are necessary for long-term potentiation (LTP) induction in the ventral hippocampus under moderate synaptic activation conditions that do not typically induce LTP. To explore potential dorsoventral differences in β-AR-mediated effects, we applied the β-AR agonist isoproterenol (10 μM, 30 min) to dorsal and ventral hippocampal slices, recording field excitatory postsynaptic potentials (fEPSPs) and population spikes (PSs) from the CA1 region.
View Article and Find Full Text PDFPLoS One
January 2025
School of Physical Education, Jinjiang College, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
In athletes' competitions and daily training, in order to further strengthen the athletes' sports level, it is usually necessary to analyze the athletes' sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and positions of the athletes, sports equipment, field boundaries and other targets in the sports scene. However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. Based on this, we propose for the first time a supervised object detection method for scenarios in the field of motion management.
View Article and Find Full Text PDFPLoS One
January 2025
Vocational Training Center, FoShan Open University, FoShan, Guangdong Province, China.
Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. Notably, Kernel Extreme Learning Machine (KELM), as a fast and effective classification method, has received widespread attention.
View Article and Find Full Text PDFPLoS One
January 2025
Logistics service company, Civil Aviation Flight University of China, Guanghan, Sichuan, China.
The risk assessment and prevention in traditional airport safety assurance usually rely on human experience for analysis, and there are problems such as heavy manual workload, excessive subjectivity, and significant limitations. This article proposed a risk assessment and prevention mechanism for airport security assurance that integrated LSTM algorithm. It analyzed the causes of malfunctioning flights by collecting airport flight safety log datasets.
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