Sabina Ştefan Source Confirmed
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
John Brown University
faculty
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Biography and Research Information
OverviewAI-generated summary
Sabina Ştefan is a faculty member at John Brown University whose research encompasses a range of atmospheric and climate-related phenomena. Her work involves the study of atmospheric aerosols and clouds, meteorological simulations, and climate variability, with a focus on air quality and its impacts on health. Ştefan's research also extends to atmospheric chemistry, as evidenced by ground-based measurements of cloud properties, wind, and turbulence. She has modeled volcanic ash hazards and analyzed winter temperature patterns in Romania in relation to atmospheric circulation. Her broader interests extend to the study of viral genome sequences, using nucleotide substitution models to understand strand-specific substitution biases.
Metrics
- h-index: 17
- Publications: 143
- Citations: 1,118
Selected Publications
- Author response: Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models (2025) DOI
- Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models (2025) DOI
- Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models (2025) DOI
- Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models (2025) DOI
- Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models (2023) DOI
- Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models (2023) DOI
- Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models (2022) DOI
- Longitudinal tracking of vascular properties in Alzheimer's disease using optical coherence tomography (2022) DOI
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