Lian-Wang Guo | Innovation Impact | Best Researcher Award

Dr. Lian-Wang Guo | Innovation Impact | Best Researcher Award

Dr. Lian-Wang Guo | University of Virginia | United States

The Guo Lab at the University of Virginia investigates the fundamental and translational biology of vascular wall remodeling and retinal degeneration, with a central focus on how epigenetic mechanisms drive pathological cell-state transitions. Specifically, the group studies the roles of histone-code “readers” and “writers” in orchestrating chromatin dynamics that contribute to disease progression. By dissecting how these epigenetic regulators influence cellular phenotypes, the lab seeks to identify novel therapeutic targets capable of preventing or reversing harmful remodeling processes. A major emphasis of the lab’s work is bridging mechanistic discoveries with translational innovation. Their research pipeline spans from uncovering basic molecular dysfunctions to engineering practical therapeutic interventions. For instance, the Guo Lab investigates the epigenetic underpinnings of vascular wall thickening and stenosis following surgical procedures such as vein grafting and angioplasty. These studies illuminate how chromatin dysregulation contributes to post-surgical complications and guides the development of targeted therapeutic strategies. In parallel, the lab collaborates with surgeons and bioengineers to design precision delivery systems for chromatin-modulating “epi-drugs.” One pioneering approach involves the creation of bio-adhesive nanoparticles engineered to be “painted” directly onto vein grafts, aiming to preserve long-term graft patency. Another strategy focuses on combating restenosis after angioplasty by developing injectable biomembrane-camouflaged carriers capable of homing in on vascular lesions. These cutting-edge delivery systems enhance therapeutic specificity and minimize off-target effects, accelerating the translation of epigenetic therapies into clinically viable solutions. The lab’s innovative research direction has resulted in multiple approved and pending patents, demonstrating its impact at both scientific and translational fronts. Ultimately, the Guo Lab strives to solve critical medical challenges by targeting dysregulated epigenetic mechanisms and ensuring a seamless continuum from mechanistic discovery to therapeutic application.

Profile: Google Scholar

Featured Publications

Klionsky, D. J., Abdel-Aziz, A. K., Abdelfatah, S., Abdellatif, M., Abdoli, A., Abel, S., … (2021). Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition). Autophagy, 17(1), 1–382.

Kumar, A., D’Souza, S. S., Moskvin, O. V., Toh, H., Wang, B., Zhang, J., Swanson, S., … (2017). Specification and diversification of pericytes and smooth muscle cells from mesenchymoangioblasts. Cell Reports, 19(9), 1902–1916.

Yu, Q., Wang, B., Chen, Z., Urabe, G., Glover, M. S., Shi, X., Guo, L.-W., Kent, K. C., & Li, L. (2017). Electron-transfer/higher-energy collision dissociation (EThcD)-enabled intact glycopeptide/glycoproteome characterization. Journal of the American Society for Mass Spectrometry, 28(9), 1751–1764.

Borck, P. C., Guo, L.-W., & Plutzky, J. (2020). BET epigenetic reader proteins in cardiovascular transcriptional programs. Circulation Research, 126(9), 1190–1208.

Goel, S. A., Guo, L.-W., Liu, B., & Kent, K. C. (2012). Mechanisms of post-intervention arterial remodelling. Cardiovascular Research, 96(3), 363–371.

Zent, J., & Guo, L.-W. (2018). Signaling mechanisms of myofibroblastic activation: Outside-in and inside-out. Cellular Physiology and Biochemistry, 49(3), 848–868.

DiRenzo, D. M., Chaudhary, M. A., Shi, X., Franco, S. R., Zent, J., Wang, K., Guo, L.-W., … (2016). A crosstalk between TGF-β/Smad3 and Wnt/β-catenin pathways promotes vascular smooth muscle cell proliferation. Cellular Signalling, 28(5), 498–505.

Guangqin Li | Innovation Impact | Best Researcher Award

Prof. Guangqin Li | Innovation Impact | Best Researcher Award

Prof. Guangqin Li | Anhui University of Finance & Economics | China

Dr. Guangqin Li began his academic journey with a doctorate in Urban Economics and Management from Shanghai University of Finance and Economics and now serves as a professor and master’s supervisor at Anhui University of Finance & Economics. With deep research interests in the digital economy, industrial economy, environmental economy, and regional economy, he has established himself as a versatile scholar bridging theoretical insights and empirical applications. His bibliometric profile reflects 37 published documents, 1,847 citations from 1,765 citing documents, and an h-index of 18, underscoring his growing impact within the academic community. His publications span studies on the green transformation of industry, the interface of urban innovation and infrastructure investment, and the spatial dynamics of economic development. He has contributed extensively through consultancy projects and collaborations with industry and regional authorities and serves on editorial boards of key journals in his fields. His research output has helped inform policy discussions on how new infrastructure investment can catalyze innovation in cities, particularly by influencing talent agglomeration and economic clustering. Poised at the cutting edge of his disciplines, he continues to mentor rising scholars and engage in interdisciplinary work, positioning his future contributions to shape the intersection of digitalization, regional growth, and sustainability.

Profiles: Scopus | Orcid 

Featured Publications 

Zhang, B., & Li, G. (2025). New infrastructure special debt, agglomeration and urban innovation: Evidence from China. Economic Modelling.

Liu, W., & Li, G. (2025). Sci-tech finance and urban entrepreneurial activity: Evidence from China. Economic Change and Restructuring.

Li, G., & Niu, W. (2025). How does fintech promote urban innovation? Empirical evidence from China. Economic Change and Restructuring.

Fang, X., Liu, M., & Li, G. (2024). Can the green credit policy promote green innovation in enterprises? Empirical evidence from China. Technological and Economic Development of Economy.

Li, G., Jin, Y., & Gao, X. (2023). Digital transformation and pollution emission of enterprises: Evidence from China’s micro-enterprises. Energy Reports.

Bao Liu | Emerging Technologies | Best Researcher Award

Dr. Bao Liu | Emerging Technologies | Best Researcher Award

Dr. Bao Liu | Xi’an University of Science and Technology | China

Dr. Liu Bao is an Associate Professor and Academic Leader in the field of Pattern Recognition and Intelligent Systems at the School of Electrical and Control Engineering, Xi’an University of Science and Technology, where he also serves as a Graduate Supervisor and Project-based Ph.D. Supervisor. He earned his doctorate in engineering from Xi’an Jiaotong University, completed postdoctoral research at Xi’an University of Science and Technology, and broadened his academic experience as a visiting fellow at Macquarie University in Australia. Recognized as a Senior Data Analyst by the Ministry of Industry and Information Technology of China, Dr. Liu is an active member of several national academic societies and professional committees. His research focuses on multi-source information fusion and intelligent technologies for coal fire disaster prevention and control, integrating advanced computational and automation techniques to address complex industrial challenges. Throughout his career, he has led diverse national, provincial, and industry-based research projects and contributed extensively to scientific publications and technological innovation through patents and software developments. As a committed educator and mentor, Dr. Liu has inspired students to excel in academic and professional pursuits and has been honored with multiple awards recognizing his dedication to teaching, research, and academic service.

Profile: Orcid

Featured Publications

Liu, B., Liu, Q., & Wu, Z. (2026, February). A novel robust Student’s t scale mixture distribution based Kalman filter. Signal Processing. https://doi.org/10.1016/j.sigpro.2025.110296

Liu, B., Wu, Z., & Liu, Q. (2025). Gaussian mixture model-based variational Bayesian approach for extended target tracking. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2025.3565347

Liu, B., & Jiang, W. (2024). DFKD: Dynamic focused knowledge distillation approach for insulator defect detection. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2024.3485446

Liu, B., & Jiang, W. (2024, December). LA-YOLO: Bidirectional adaptive feature fusion approach for small object detection of insulator self-explosion defects. IEEE Transactions on Power Delivery. https://doi.org/10.1109/TPWRD.2024.3467915

Liu, B., Zhou, N., & Wang, Z. (2024, December 27). DFI-YOLOv8 based defect detection method for fan blades. In Proceedings of the 2024 Conference on [Insert Conference Name]. https://doi.org/10.1145/3722405.3722437