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476 results on '"Xu, Zhengyang"'

5. StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides

Author

Wang, Zhe, Wu, Jianping, Zheng, Mengjun, Geng, Chenchen, Zhen, Borui, Zhang, Wei, Wu, Hui, Xu, Zhengyang, Xu, Gang, Chen, Si, and Li, Xiang

Subjects
Quantitative Biology - Biomolecules
Abstract

Many tools exist for extracting structural and physiochemical descriptors from linear peptides to predict their properties, but similar tools for hydrocarbon-stapled peptides are lacking.Here, we present StaPep, a Python-based toolkit designed for generating 2D/3D structures and calculating 21 distinct features for hydrocarbon-stapled peptides.The current version supports hydrocarbon-stapled peptides containing 2 non-standard amino acids (norleucine and 2-aminoisobutyric acid) and 6 nonnatural anchoring residues (S3, S5, S8, R3, R5 and R8).Then we established a hand-curated dataset of 201 hydrocarbon-stapled peptides and 384 linear peptides with sequence information and experimental membrane permeability, to showcase StaPep's application in artificial intelligence projects.A machine learning-based predictor utilizing above calculated features was developed with AUC of 0.85, for identifying cell-penetrating hydrocarbon-stapled peptides.StaPep's pipeline spans data retrieval, cleaning, structure generation, molecular feature calculation, and machine learning model construction for hydrocarbon-stapled peptides.The source codes and dataset are freely available on Github: https://github.com/dahuilangda/stapep_package., Comment: 26 pages, 6 figures

Published
2024

21. Segment Membranes and Nuclei from Histopathological Images via Nuclei Point-Level Supervision

Author

Li, Hansheng, Xu, Zhengyang, Zhou, Mo, Shi, Xiaoshuang, Kang, Yuxin, Bu, Qirong, Lv, Hong, Li, Ming, Lin, Mingzhen, Cui, Lei, Feng, Jun, Yang, Wentao, Yang, Lin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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49. Associations Between Urban Green Space Quality and Mental Wellbeing: Systematic Review.

Author

Xu, Zhengyang, Marini, Sofia, Mauro, Mario, Maietta Latessa, Pasqualino, Grigoletto, Alessia, and Toselli, Stefania

Abstract

With the rapidity of urbanisation, concerns about citizens' mental wellbeing issues are on the rise, and simultaneously, the issue of land use conflicts is becoming increasingly prominent. As a nature-based solution, the role of urban green space has been continually emphasised in the past decade. In urban areas facing scarce land resources, improving the quality of green spaces appears to be an important approach. This review aimed to systematically elaborate the studies regarding the associations between urban green space (UGS) qualities and mental wellbeing, following the Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Twenty-two articles were included, and most of them had a cross-sectional design. From the analysis of the data, it emerged that the definition of the quality of green space is heterogeneous. Natural elements, particularly vegetation diversity and water features, consistently showed positive associations with mental wellbeing, while the effects of spatial features like accessibility showed mixed results. The impact of facilities and amenities appeared more complex, with their benefits heavily dependent on the design and maintenance. More evidence is needed to determine the mental wellbeing benefits of maintenance and the development of facilities and amenities for UGSs. In addition, the assessment of mental wellbeing relied on various self-reported scales, with different scales targeting different aspects. Instrumental measurements were rarely employed. Future research should employ more rigorous experimental methods and standardised quality assessment tools. [ABSTRACT FROM AUTHOR]

Published
2025
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50. Holistic Hosting Capacity Enhancement Through Sensitivity-Driven Flexibility Deployment and Uncertainty-Aware Optimization in Modern Distribution Networks.

Author

Pan, Wenjie, Han, Jun, Cai, Chao, Chen, Haofei, Liu, Hong, and Xu, Zhengyang

Subjects
POWER distribution networks, ROBUST optimization, SOCIAL integration, MATHEMATICAL optimization, RESOURCE allocation
Abstract

This study presents a novel sensitivity-driven distributionally robust optimization framework designed to enhance hosting capacity in renewable-powered distribution networks through targeted flexibility resource deployment. The proposed approach integrates temporal sensitivity mapping with robust optimization techniques to prioritize resource allocation across high-sensitivity nodes, addressing uncertainties in renewable energy generation and load demand. By leveraging a dynamic interaction between sensitivity scores and temporal system conditions, the framework achieves efficient and resilient operation under extreme variability scenarios. Key methodological innovations include the incorporation of a social force model-based sensitivity mapping technique, a layered optimization approach balancing system-wide and localized decisions, and a robust uncertainty set to safeguard performance against distributional shifts. The framework is validated using a synthesized test system, incorporating realistic renewable generation profiles, load patterns, and energy storage dynamics. Results demonstrate a significant improvement in hosting capacity, with system-wide enhancements of up to 35% and a 50% reduction in renewable curtailment. Moreover, sensitivity-driven resource deployment ensures efficient utilization of flexibility resources, achieving a peak allocation efficiency of 90% during critical periods. This research provides a comprehensive tool for addressing the challenges of renewable integration and grid stability in modern power systems, offering actionable insights for resource allocation strategies under uncertainty. The proposed methodology not only advances the state-of-the-art in sensitivity-based optimization but also paves the way for scalable, resilient energy management solutions in high-renewable penetration scenarios. [ABSTRACT FROM AUTHOR]

Published
2025
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