AbstractOrganisms experience environments that vary across both space and time. Such environmental heterogeneity shapes standing genetic variation and may influence species' capacity to adapt to rapid environmental change. However, we know little about the kind of genetic variation that is involved in local adaptation to environmental variability. To address this gap, we sequenced the whole genomes of 140 purple sea urchins () from seven populations that vary in their degree of pH variability. Despite no evidence of global population structure, we found a suite of single-nucleotide polymorphisms (SNPs) tightly correlated with local pH variability (outlier SNPs), which were overrepresented in regions putatively involved in gene regulation (long noncoding RNA and enhancers), supporting the idea that variation in regulatory regions is important for local adaptation to variability. In addition, outliers in genes were found to be (i) enriched for biomineralization and ion homeostasis functions related to low pH response, (ii) less central to the protein-protein interaction network, and (iii) underrepresented among genes highly expressed during early development. Taken together, these results suggest that loci that underlie local adaptation to pH variability in purple sea urchins fall in regions with potentially low pleiotropic effects (based on analyses involving regulatory regions, network centrality, and expression time) involved in low pH response (based on functional enrichment).
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http://dx.doi.org/10.1086/726013 | DOI Listing |
J Insect Physiol
March 2025
Research Institute of Applied Biology, Shanxi University, Taiyuan 030006, PR China. Electronic address:
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Key Laboratory of Applied Marine Biotechnology by the Ministry of Education, School of Marine Sciences, Ningbo University, Ningbo, Zhejiang Province 315211, People's Republic of China. Electronic address:
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Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, Garmisch-Partenkirchen, 82467, Germany.
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Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan, ROC.
When typhoons strike, heavy rainfall frequently triggers inundation disasters, causing significant impacts on agricultural crops. Efficiently understanding the effects of typhoon-induced flooding and implementing effective disaster mitigation measures are critical challenges for decision-makers. This study proposes a real-time dynamic inundation risk assessment framework to address these challenges, focusing on adaptation strategies and risk evaluation for paddy fields during typhoons.
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Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China. Electronic address:
Automated pulmonary anomaly detection using computed tomography (CT) examinations is important for the early warning of pulmonary diseases and can support clinical diagnosis and decision-making. Most training of existing pulmonary disease detection and lesion segmentation models requires expert annotations, which is time-consuming and labour-intensive, and struggles to generalize across atypical diseases. In contrast, unsupervised anomaly detection alleviates the demand for dataset annotation and is more generalizable than supervised methods in detecting rare pathologies.
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