Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to characterize the behavior of mice freely interacting with a novel object. Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of the striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability are explained by a reinforcement-learning (RL) model of threat prediction in which behavior arises from a novelty-induced initial threat prediction (akin to "shaping bonus") and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems.
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http://dx.doi.org/10.1016/j.neuron.2022.08.022 | DOI Listing |
Sensors (Basel)
December 2024
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
IoT (Internet of Things) networks are vulnerable to network viruses and botnets, while facing serious network security issues. The prediction of payload states in IoT networks can detect network attacks and achieve early warning and rapid response to prevent potential threats. Due to the instability and packet loss of communications between victim network nodes, the constructed protocol state machines of existing state prediction schemes are inaccurate.
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December 2024
School of AI Convergence, Sungshin Women's University, 34 da-gil 2, Bomun-ro, Seongbuk-gu, Seoul 02844, Republic of Korea.
This paper proposes a machine learning approach to detect threats using short-term PPG (photoplethysmogram) signals from a commercial smartwatch. In supervised learning, having accurately annotated training data is essential. However, a key challenge in the threat detection problem is the uncertainty regarding how accurately data labeled as 'threat' reflect actual threat responses since participants may react differently to the same experiments.
View Article and Find Full Text PDFMaterials (Basel)
December 2024
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China.
Temperature-induced cracks during the construction of large concrete structures, such as water gates and bridges, caused by hydration heat, pose a serious threat to structural safety and reliability. To address this, various countries have developed temperature control standards and guidelines for mass concrete structures, providing design direction and evaluation criteria. China and the United States (U.
View Article and Find Full Text PDFPlants (Basel)
December 2024
Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea.
Invasive weed species exhibit both advantages, such as the potential for allelochemicals in bioherbicide development, and risks, including their threat to crop production. Therefore, this study aims to identify an allelochemical from , an invasive weed species. The dose-dependent effects of shoot and root extracts (SSE, SRE) on the signaling in the forage crop and germination in various weed species (, , , , and ) were evaluated.
View Article and Find Full Text PDFPlants (Basel)
December 2024
School of Agriculture and Food Sustainability, The University of Queensland, Gatton, QLD 4343, Australia.
Parthenium weed ( L.) is one of the most noxious and fast-spreading invasive alien species, posing a major threat to ecosystems, agriculture, and public health worldwide. Mechanistic and correlative species distribution models are commonly employed to determine the potential habitat suitability of parthenium weed.
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