The identification of population bottlenecks is critical in conservation because populations that have experienced significant reductions in abundance are subject to a variety of genetic and demographic processes that can hasten extinction. Genetic bottleneck tests constitute an appealing and popular approach for determining if a population decline has occurred because they only require sampling at a single point in time, yet reflect demographic history over multiple generations. However, a review of the published literature indicates that, as typically applied, microsatellite-based bottleneck tests often do not detect bottlenecks in vertebrate populations known to have experienced declines. This observation was supported by simulations that revealed that bottleneck tests can have limited statistical power to detect bottlenecks largely as a result of limited sample sizes typically used in published studies. Moreover, commonly assumed values for mutation model parameters do not appear to encompass variation in microsatellite evolution observed in vertebrates and, on average, the proportion of multi-step mutations is underestimated by a factor of approximately two. As a result, bottleneck tests can have a higher probability of 'detecting' bottlenecks in stable populations than expected based on the nominal significance level. We provide recommendations that could add rigor to inferences drawn from future bottleneck tests and highlight new directions for the characterization of demographic history.
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Sci Rep
December 2024
Guangzhou University, School of Computer Science and Cyber Engineering, Guangzhou, 510006, China.
Underwater image enhancement (UIE) is challenging since image degradation in aquatic environments is complicated and changing over time. Existing mainstream methods rely on either physical-model or data-driven, suffering from performance bottlenecks due to changes in imaging conditions or training instability. In this article, we attempt to adapt the diffusion model to the UIE task and propose a Content-Preserving Diffusion Model (CPDM) to address the above challenges.
View Article and Find Full Text PDFParasitol Res
December 2024
Graduate School of Human Development and Environment, Kobe University, 3-11, Tsurukabuto, Nada-ku, Kobe, Hyogo, 657-8501, Japan.
Opisthorchiasis, caused by the liver fluke Opisthorchis viverrini, is endemic to Southeast Asian countries and constitutes a major health problem as it increases the risk of cholangiocarcinoma. However, owing to the complicated life cycle of O. viverrini, there is no rapid method for monitoring the risk of infection in the environment.
View Article and Find Full Text PDFAdv Mater
December 2024
Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, Institute of Molecular Aggregation Science, School of Science, Tianjin University, Tianjin, 300072, China.
2D materials are promising candidates for beyond-Si electronic devices. However, their stability is a key bottleneck in their industrial applications. The instability of 2D materials is mainly attributed to their intrinsic susceptibility to O and HO-particularly to reactive oxygen species (ROS), which have strong oxidizing properties.
View Article and Find Full Text PDFbioRxiv
December 2024
Department of Mechanical Engineering, University of Minnesota, MN, USA.
Background And Aims: High-throughput in vitro pharmacological toxicity testing is essential for drug discovery. Precision-cut liver slices (PCLS) provide a robust system for screening that is more representative of the complex 3D structure of the whole liver than isolated hepatocytes. However, PCLS are not available as off-the-shelf products, significantly limiting their translational potential.
View Article and Find Full Text PDFJ Pathol Inform
December 2024
Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia.
Cytomorphology evaluation of bone marrow cell is the initial step to diagnose different hematological diseases. This assessment is still manually performed by trained specialists, who may be a bottleneck within the clinical process. Deep learning algorithms are a promising approach to automate this bone marrow cell evaluation.
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