Measurement of ultrasonic vocalizations (USVs) produced by adult rats represents a highly useful index of emotional arousal. The associations found between 50 kHz USV production and a variety of behavioural and pharmacological protocols increasingly suggests they serve as a marker of positive motivational states. This study used a powerful within-subjects design to investigate the relationships among individual differences in approach to a sweet-food reward, predisposition to emit 50 kHz USVs spontaneously, and 50 kHz USVs emission following acute systemic administration of amphetamine. Both approach motivation and predisposition to call were found to not correlate with each other but did predict 50 kHz USV response to acute amphetamine. These two behavioural phenotypes appear to represent dissociable predictors of acute amphetamine-induced emission of 50 kHz USVs in a non-sensitization paradigm. In contrast to that, a measure of sucrose preference was not found to predict 50 kHz USV emission following amphetamine. Acute amphetamine was also found to increase average sound frequency of emitted USVs and selectively increase the proportion of Trill subtype 50 kHz USVs. Together, these data demonstrate that acute amphetamine-induced 50 kHz USVs in the adult rat represent more than just a univariate motivational state and may represent the product of dissociable subsystems of emotional behavior.
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http://dx.doi.org/10.1016/j.bbr.2018.05.009 | DOI Listing |
Sensors (Basel)
December 2024
Computer Engineering, Faculty of Electrical & Electronics, Yildiz Technical University, 34220 Istanbul, Türkiye.
Developing autonomous navigation techniques for surface vehicles remains an important research area, and accurate global path planning is essential. For mobile robots-particularly for Unmanned Surface Vehicles (USVs)-a key challenge is ensuring that sharp turns and sharp breaks are avoided. Therefore, global path planning must not only calculate the shortest path but also provide smoothness.
View Article and Find Full Text PDFJ Imaging
November 2024
Engineering Sciences Laboratory, National School of Applied Sciences of Kenitra, Ibn Tofail University, Kenitra 14000, Morocco.
The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned for maritime surveillance needs. Deployed on the NVIDIA Jetson TX2 platform, the system features an innovative architecture and perception module optimized for real-time operations and energy efficiency.
View Article and Find Full Text PDFISA Trans
November 2024
National Key Laboratory of Autonomous Marine Vehicle Technology, Harbin Engineering University, Harbin 150001, China. Electronic address:
Favorable neighboring interactions and economical transmission costs are the foundations of formation-containment control (FCC), while the complex marine environments hamper its expansion on networked unmanned surface vehicles (USVs). In this context, this paper investigates an intermittent dynamic event-triggered control scheme for USVs experiencing communication interruptions to achieve FCC. Specifically, the control architecture consists of two synchronously working sub-layers.
View Article and Find Full Text PDFeNeuro
December 2024
Department of Cell Biology, Duke University Medical School, Durham, North Carolina, USA.
Epilepsy Aphasia Syndrome (EAS) is a spectrum of childhood disorders that exhibit complex co-morbidities that include epilepsy and the emergence of cognitive and language disorders. CNKSR2 is an X-linked gene in which mutations are linked to EAS. We previously demonstrated Cnksr2 knockout (KO) mice model key phenotypes of EAS analogous to those present in clinical patients with mutations in the gene.
View Article and Find Full Text PDFSensors (Basel)
November 2024
School of Automation, Beijing Information Science and Technology University, Beijing 100192, China.
To address the design and application requirements for USVs (Unmanned Surface Vehicles) to autonomously escape from constrained environments using a minimal number of sensors, we propose a path planning algorithm based on the RRT* (Rapidly Exploring Random Tree*) method, referred to as BN-RRT* (Blind Navigation Rapidly Exploring Random Tree*). This algorithm utilizes the positioning information provided by the GPS onboard the USV and combines collision detection data from collision sensors to navigate out of the trapped space. To mitigate the inherent randomness of the RRT* algorithm, we integrate the Artificial Potential Field (APF) method to enhance directional guidance during the sampling process.
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