Taining Sha

ORCID: 0009-0003-6065-8744
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About
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Research Areas
  • Advanced Chemical Sensor Technologies
  • Air Quality Monitoring and Forecasting
  • Gaussian Processes and Bayesian Inference
  • Gas Sensing Nanomaterials and Sensors
  • Cancer Diagnosis and Treatment
  • Data Stream Mining Techniques
  • Lung Cancer Diagnosis and Treatment
  • Natural Fiber Reinforced Composites
  • Osteoarthritis Treatment and Mechanisms
  • Salivary Gland Tumors Diagnosis and Treatment
  • Innovations in Concrete and Construction Materials
  • Tumors and Oncological Cases
  • Machine Learning and Data Classification

Yangzhou University
2024-2025

Central South University
2023

Xiangya Hospital Central South University
2023

Jilin University
2018-2022

ABSTRACT Background Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile organic compounds (VOCs) shows promise, yet its effectiveness assessing nodules remains underexplored. Methods Employing a prospective study design from June 2023 to January 2024 at Affiliated Hospital...

10.1002/cam4.70545 article EN cc-by Cancer Medicine 2025-01-01

<h3>Background</h3> Hand osteoarthritis is a common heterogeneous joint disorder with differences in aetiology and pathophysiology from the large weight-bearing knee or hip osteoarthritis.<sup>[1]-[3]</sup> Its pathophysiological mechanism remains largely unexplored, partially because of limited access to clinical sample tissues lack animal models.<sup>[2]</sup> To date, there no known cure for hand osteoarthritis, indicating an urgent need better understanding underlying mechanisms so that...

10.1136/annrheumdis-2023-eular.4327 article EN Annals of the Rheumatic Diseases 2023-05-30

Time-dependent data often exhibit characteristics, such as non-stationarity and heavy-tailed errors, that would be inappropriate to model with the typical assumptions used in popular models. Thus, more flexible approaches are required able accommodate issues. To this end, we propose a Bayesian mixture of student-$t$ processes an overall-local scale structure for covariance. Moreover, use sequential Monte Carlo (SMC) sampler order perform online inference arrive real-time. We demonstrate...

10.48550/arxiv.2311.00564 preprint EN cc-by arXiv (Cornell University) 2023-01-01
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