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Ying Yu on Muck Rack

Ying Yu

As seen in: Color Research & Application, Nature, Wiley, Wiley Online Library, arXiv, PLOS ONE, BMJ Open, Frontiers, Frontiers in Immunology, Frontiers in Nutrition and
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Articles

Exploring a Facilitator-Enabled Flexible iSupport for Dementia Program: An Internal Pilot Mixed-Methods Study

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By Ying Yu, Lily Dongxia Xiao, Shahid Ullah
| Wiley Online Library Verified
1 Introduction ISupport for Dementia is an internet- and hardcopy-based psychoeducation program, developed by the World Health Organisation (WHO) to support the international community in achieving the Global Action Plan for supporting caregivers of people living with dementia (PLWD) (WHO 2017). This program was adapted for the Australian context and comprises six modules and 30 units to support carers of PLWD in developing the knowledge and skills necessary to care for them at home (Xiao et al.
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Thermal error prediction for gantry guideway grinder spindle based on CNN-BiLSTM-Attention

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By Pengtao Nan, Jin Deng, Yu Ye, Ying Yu, Yongdong Qu
| Nature Verified
Abstract Aiming at the strong nonlinearity, time-series lag, and multi-physics coupling characteristics of spindle thermal error in gantry guideway grinders, this paper constructs a CNN-BiLSTM-Attention hybrid thermal error prediction model. This work adopts the combined strategy of mature Fuzzy C-Means (FCM) clustering and Grey Relational Analysis (GRA) to realize the preprocessing simplification of temperature measuring points.
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Benchmarking RNA-seq with the Quartet and MAQC reference materials to establish best practices for accurate alternative splicing analysis

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By Duo Wang, Jiaxin Zhao, Qingwang Chen, Yanxi Han, Yaqing Liu, Yuanfeng Zhang, Cong Liu, Wanwan Hou, Ying Yu, Leming Shi, Yuanting Zheng, Jinming Li, Rui Zhang
| Nature Verified
Abstract Previous limited characterization of short-read RNA-seq (srRNA-seq) accuracy in alternative splicing (AS) analysis due to methodological diversity and lack of reference standards, has left unclear how to achieve optimal performance—an issue increasingly critical with the rise of long-read sequencing. To address this, we conduct a large-scale reference-based benchmarking study across 42 laboratories and 207 analysis pipelines leveraging the Quartet and MAQC reference materials.
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