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Sökning: WFRF:(Yang Zhikai)

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1.
  • Zhang, Guojie, et al. (författare)
  • Comparative genomics reveals insights into avian genome evolution and adaptation
  • 2014
  • Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 346:6215, s. 1311-1320
  • Tidskriftsartikel (refereegranskat)abstract
    • Birds are the most species-rich class of tetrapod vertebrates and have wide relevance across many research fields. We explored bird macroevolution using full genomes from 48 avian species representing all major extant clades. The avian genome is principally characterized by its constrained size, which predominantly arose because of lineage-specific erosion of repetitive elements, large segmental deletions, and gene loss. Avian genomes furthermore show a remarkably high degree of evolutionary stasis at the levels of nucleotide sequence, gene synteny, and chromosomal structure. Despite this pattern of conservation, we detected many non-neutral evolutionary changes in protein-coding genes and noncoding regions. These analyses reveal that pan-avian genomic diversity covaries with adaptations to different lifestyles and convergent evolution of traits.
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2.
  • Yang, Zhikai, et al. (författare)
  • Lesion Localization in Digital Breast Tomosynthesis with Deformable Transformers by Using 2.5D Information
  • 2024
  • Ingår i: Medical Imaging 2024: Computer-Aided Diagnosis. - : SPIE-Intl Soc Optical Eng.
  • Konferensbidrag (refereegranskat)abstract
    • In this study, we adapted a transformer-based method to localize lesions in digital breast tomosynthesis (DBT) images. Compared with convolutional neural network-based object detection methods, the transformer-based method does not require non-maximum suppression postprocessing. Integrated deformable convolution detection transformers can better capture small-size lesions. We added transfer learning to tackle the issue of the lack of annotated data from DBT. To validate the superiority of the transformer-based detection method, we compared the results with deep-learning object detection methods. The experimental results demonstrated that the proposed method performs better than all comparison methods.
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