Neonicotinoids are pesticides used to protect crops but with known secondary influences at sublethal doses on bees. Honeybees use their sense of smell to identify the queen and nestmates, to signal danger and to distinguish flowers during foraging. Few behavioural studies to date have examined how neonicotinoid pesticides affect the ability of bees to distinguish odours. Here, we used a differential learning task to test how neonicotinoid exposure affects learning, memory and olfactory perception in foraging-age honeybees. Bees fed with thiamethoxam could not perform differential learning and could not distinguish odours during short- and long-term memory tests. Our data indicate that thiamethoxam directly impacts the cognitive processes involved in working memory required during differential olfactory learning. Using a combination of behavioural assays, we also identified that thiamethoxam has a direct impact on the olfactory perception of similar odours. Honeybees fed with other neonicotinoids (clothianidin, imidacloprid, dinotefuran) performed the differential learning task, but at a slower rate than the control. These bees could also distinguish the odours. Our data are the first to show that neonicotinoids have compound specific effects on the ability of bees to perform a complex olfactory learning task. Deficits in decision making caused by thiamethoxam exposure could mean that this is more harmful than other neonicotinoids, leading to inefficient foraging and a reduced ability to identify nestmates.
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http://dx.doi.org/10.1242/jeb.217174 | DOI Listing |
J Integr Neurosci
January 2025
Sports, Exercise and Brain Sciences Laboratory, Sports Coaching College, Beijing Sport University, 100084 Beijing, China.
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College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Domain-generalizable re-identification (DG Re-ID) aims to train a model on one or more source domains and evaluate its performance on unseen target domains, a task that has attracted growing attention due to its practical relevance. While numerous methods have been proposed, most rely on discriminative or contrastive learning frameworks to learn generalizable feature representations. However, these approaches often fail to mitigate shortcut learning, leading to suboptimal performance.
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January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
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January 2025
Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge.
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January 2025
Department of Informatics-Science and Engineering (DISI), University of Bologna, 40126 Bologna, Italy.
Person re-identification (re-id) is a critical computer vision task aimed at identifying individuals across multiple non-overlapping cameras, with wide-ranging applications in intelligent surveillance systems. Despite recent advances, the domain gap-performance degradation when models encounter unseen datasets-remains a critical challenge. CLIP-based models, leveraging multimodal pre-training, offer potential for mitigating this issue by aligning visual and textual representations.
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