Premise: Identifying the environmental factors responsible for natural selection across different habitats is crucial for understanding the process of local adaptation in plants. Despite its importance, few studies have successfully isolated the environmental factors driving local adaptation in nature. In this study, we evaluated the agents of selection responsible for local adaptation of the monkeyflower Mimulus guttatus to California's coastal and inland habitats.
Methods: We implemented a manipulative reciprocal transplant experiment at coastal and inland sites, where we excluded aboveground stressors in an effort to elucidate their role in the evolution of local adaptation.
Results: Excluding aboveground stressors, most likely a combination of salt spray and herbivory, completely rescued inland annual plant fitness when transplanted to coastal habitat. The exclosures in inland habitat provided a benefit to the performance of coastal perennial plants. However, the exclosures are unlikely to provide much fitness benefit to the coastal plants at the inland site because of their general inability to flower in time to escape from the summer drought.
Conclusions: Our study demonstrates that a distinct set of selective agents (aboveground vs. belowground) are responsible for local adaptation at opposite ends of an environmental gradient.
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http://dx.doi.org/10.1002/ajb2.1419 | DOI Listing |
Sensors (Basel)
January 2025
Huawei Technologies Co., Ltd., Chengdu 610000, China.
Metasurface-based imaging is attractive due to its low hardware costs and system complexity. However, most of the current metasurface-based imaging systems require stochastic wavefront modulation, complex computational post-processing, and are restricted to 2D imaging. To overcome these limitations, we propose a scanning virtual aperture imaging system.
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January 2025
School of Automation, Southeast University, Nanjing 210096, China.
Transferring knowledge learned from standard GelSight sensors to other visuotactile sensors is appealing for reducing data collection and annotation. However, such cross-sensor transfer is challenging due to the differences between sensors in internal light sources, imaging effects, and elastomer properties. By understanding the data collected from each type of visuotactile sensors as domains, we propose a few-sample-driven style-to-content unsupervised domain adaptation method to reduce cross-sensor domain gaps.
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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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