Mice use ultrasonic vocalizations (USVs) to communicate each other and to convey their emotional state. USVs have been greatly characterized in specific life phases and contexts, such as mother isolation-induced USVs for pups or female-induced USVs for male mice during courtship. USVs can be acquired by means of specific tools and later analyzed on the base of both quantitative and qualitative parameters. Indeed, different ultrasonic call categories exist and have already been defined. The understanding of different calls meaning is still missing, and it will represent an essential step forward in the field of USVs. They have long been studied in the ethological context, but recently they emerged as a precious instrument to study pathologies characterized by deficits in communication, in particular neurodevelopmental disorders (NDDs), such as autism spectrum disorders. This review covers the topics of USVs characteristics in mice, contexts for USVs emission and factors that modulate their expression. A particular focus will be devoted to mouse USVs in the context of NDDs. Indeed, several NDDs murine models exist and an intense study of USVs is currently in progress, with the aim of both performing an early diagnosis and to find a pharmacological/behavioral intervention to improve patients' quality of life.
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http://dx.doi.org/10.4103/1673-5374.300340 | 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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