Theory predicts that, in organisms with complex life cycles, if the earlier-stage limiting factor induces weak later-stage phenotypes, the development of the later-stage trait should evolve to reduce carry-over effects. Local adaptations could thus favour decoupling of later stages. However, decoupling is not always possible. In this study, we used a widespread amphibian, the European fire salamander (Salamandra salamandra), to assess the role of local adaptations to environmental stressful conditions experienced at the larval stage. We exposed 150 larvae from different altitudes to two conditions: rich food and poor food condition. Conditions in early life stages can affect an individual's traits, either as a direct effect or mediated through outcomes in successive life stages. To distinguish between effects of rearing conditions and local adaptation, we searched for a causal model. The causal model detected effects of both food treatment and population origin (altitude) on all life stages. Larvae reared under rich food condition metamorphosed earlier, had higher growth rates and reached smaller size at metamorphosis. Significant differences occurred between larvae of different origin: low-altitude individuals performed poorly under the poor food treatment. Moreover, larvae from higher altitudes were slower with rich food and faster with poor food compared to those from lower altitudes. Our results underline that environmental conditions and local adaptation can interplay in determining the plasticity of larval stages, still adaptations can maximize the growth efficiency of early stages in oligotrophic environments, leading to divergent pathways across populations and environmental conditions.
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http://dx.doi.org/10.1111/jeb.14155 | DOI Listing |
Sensors (Basel)
January 2025
Huawei Technologies Co., Ltd., Chengdu 610000, China.
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January 2025
School of Automation, Southeast University, Nanjing 210096, China.
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January 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.
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January 2025
School of Mechanical Engineering, Guizhou University, Guiyang 550028, China.
Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information.
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January 2025
School of Computer Science, Northeast Electric Power University, Jilin 132012, China.
Satellites frequently encounter atmospheric haze during imaging, leading to the loss of detailed information in remote sensing images and significantly compromising image quality. This detailed information is crucial for applications such as Earth observation and environmental monitoring. In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net).
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