Yao Wang

ORCID: 0009-0003-3670-3429
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About
Contact & Profiles
Research Areas
  • Advanced Image Fusion Techniques
  • RNA modifications and cancer
  • Interconnection Networks and Systems
  • Remote-Sensing Image Classification
  • Distributed and Parallel Computing Systems
  • Face recognition and analysis
  • Image Enhancement Techniques
  • Parallel Computing and Optimization Techniques
  • Advanced Image Processing Techniques
  • Image and Signal Denoising Methods
  • RNA and protein synthesis mechanisms
  • Cancer-related molecular mechanisms research

Jiangsu University
2024

National University of Defense Technology
2024

Ningbo University
2023-2024

Gastric cancer (GC) is a serious disease with high mortality and poor prognosis. It known that tRNA halves play key roles in the progression of cancer. This study explored function half tRF-41-YDLBRY73W0K5KKOVD GC. Quantitative real-time reverse transcription-polymerase chain reaction was used to measure RNA levels. The level GC cells regulated by its mimics or inhibitor. Cell proliferation evaluated using Counting Kit-8 EdU cell assay. A Transwell assay detect migration. Flow cytometry...

10.1089/dna.2022.0495 article EN DNA and Cell Biology 2023-03-01

The pursuit of super-resolution (SR) with large upscaling factors such as 8×, for enhancing the spatial resolution low-resolution (LR) remote sensing images is a persistent and challenging problem. To address this issue, we propose Progressive Feature Enhancement SR (PFESR) network an 8× factor. Given limited high-frequency information provided by single LR image, improved style transfer technology to generate auxiliary details that aid in recovery high-resolution (HR) images. Additionally,...

10.1109/tgrs.2023.3310518 article EN IEEE Transactions on Geoscience and Remote Sensing 2023-01-01

Abstract Cloth-Changing person Re-identification(CC-ReID) aims at retrieving the target despite changes in clothing. However, this task faces some challenges: (1) CC-ReID involves considerable within-class variability due to clothing changes, while between-class variations can be subtle. (2) There's a need explore more discriminative fine-grained features. Existing research has primarily tackled these issues by integrating cross-modality information (\emph{e.g.}, silhouettes, gaits, and 3D...

10.21203/rs.3.rs-3909529/v1 preprint EN cc-by Research Square (Research Square) 2024-02-01
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