Multiple sensory cues emanating from humans are thought to guide blood-feeding female mosquitoes to a host. To determine the relative contribution of carbon dioxide (CO2) detection to mosquito host-seeking behavior, we mutated the AaegGr3 gene, a subunit of the heteromeric CO2 receptor in Aedes aegypti mosquitoes. Gr3 mutants lack electrophysiological and behavioral responses to CO2. These mutants also fail to show CO2-evoked responses to heat and lactic acid, a human-derived attractant, suggesting that CO2 can gate responses to other sensory stimuli. Whereas attraction of Gr3 mutants to live humans in a large semi-field environment was only slightly impaired, responses to an animal host were greatly reduced in a spatial-scale-dependent manner. Synergistic integration of heat and odor cues likely drive host-seeking behavior in the absence of CO2 detection. We reveal a networked series of interactions by which multimodal integration of CO2, human odor, and heat orchestrates mosquito attraction to humans.
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http://dx.doi.org/10.1016/j.cell.2013.12.044 | DOI Listing |
Adv Mater
January 2025
Division of Intelligent and Biomechanical Systems, State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Haidian, Beijing, 100084, China.
Quantitative assessment for post-stroke spasticity remains a significant challenge due to the encountered variable resistance during passive stretching, which can lead to the widely used modified Ashworth scale (MAS) for spasticity assessment depending heavily on rehabilitation physicians. To address these challenges, a high-force-output triboelectric soft pneumatic actuator (TENG-SPA) inspired by a lobster tail is developed. The bioinspired TENG-SPA can generate approximately 20 N at 0.
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January 2025
Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan.
Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities-RGB, thermal, and depth-using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 × 12 to 640 × 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation.
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January 2025
State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 401133, China.
With the advancement of federated learning (FL), there is a growing demand for schemes that support multi-task learning on multi-modal data while ensuring robust privacy protection, especially in applications like intelligent connected vehicles. Traditional FL schemes often struggle with the complexities introduced by multi-modal data and diverse task requirements, such as increased communication overhead and computational burdens. In this paper, we propose a novel privacy-preserving scheme for multi-task federated split learning across multi-modal data (MTFSLaMM).
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January 2025
Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference.
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December 2024
Faculty of Information Science and Technology, Beijing University of Technology, Beijing 100124, China.
With the increasing complexity of urban roads and rising traffic flow, traffic safety has become a critical societal concern. Current research primarily addresses drivers' attention, reaction speed, and perceptual abilities, but comprehensive assessments of cognitive abilities in complex traffic environments are lacking. This study, grounded in cognitive science and neuropsychology, identifies and quantitatively evaluates ten cognitive components related to driving decision-making, execution, and psychological states by analyzing video footage of drivers' actions.
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