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3. Design of cell-type-specific hyperstable IL-4 mimetics via modular de novo scaffolds.

Author

Yang, Huilin, Ulge, Umut, Quijano-Rubio, Alfredo, Bernstein, Zachary, Maestas, David, Chun, Jung-Ho, Wang, Wentao, Lin, Jian-Xin, Jude, Kevin, Singh, Srujan, Orcutt-Jahns, Brian, Li, Peng, Mou, Jody, Chung, Liam, Kuo, Yun-Huai, Ali, Yasmin, Meyer, Aaron, Grayson, Warren, Heller, Nicola, Garcia, K, Leonard, Warren, Silva, Daniel-Adriano, Elisseeff, Jennifer, Baker, David, and Spangler, Jamie

Subjects
Animals, Interleukin-4, Cytokines, Signal Transduction
Abstract

The interleukin-4 (IL-4) cytokine plays a critical role in modulating immune homeostasis. Although there is great interest in harnessing this cytokine as a therapeutic in natural or engineered formats, the clinical potential of native IL-4 is limited by its instability and pleiotropic actions. Here, we design IL-4 cytokine mimetics (denoted Neo-4) based on a de novo engineered IL-2 mimetic scaffold and demonstrate that these cytokines can recapitulate physiological functions of IL-4 in cellular and animal models. In contrast with natural IL-4, Neo-4 is hyperstable and signals exclusively through the type I IL-4 receptor complex, providing previously inaccessible insights into differential IL-4 signaling through type I versus type II receptors. Because of their hyperstability, our computationally designed mimetics can directly incorporate into sophisticated biomaterials that require heat processing, such as three-dimensional-printed scaffolds. Neo-4 should be broadly useful for interrogating IL-4 biology, and the design workflow will inform targeted cytokine therapeutic development.

Published
2023

4. Exploration of Innovative Modular Design for High-Speed Train Seats

Author

Wu, Chenhui, Li, Jian, Ma, Siqun, Yang, Huilin, Hu, Chenxiao, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Chen, Zhong, editor, Yacob, Shakila, editor, Guan, Guiyun, editor, and Mohd Nor, Nor Fariza, editor

Published
2024
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5. Discussion on the interior design of straddle monorail from the perspective of user experience

Author

Yang, Huilin, Li, Jian, Ma, Siqun, Hu, Chenxiao, Wu, Chenhui, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Chen, Zhong, editor, Yacob, Shakila, editor, Guan, Guiyun, editor, and Mohd Nor, Nor Fariza, editor

Published
2024
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15. Preclinical proof of principle for orally delivered Th17 antagonist miniproteins

Author

Berger, Stephanie, Seeger, Franziska, Yu, Ta-Yi, Aydin, Merve, Yang, Huilin, Rosenblum, Daniel, Guenin-Macé, Laure, Glassman, Caleb, Arguinchona, Lauren, Sniezek, Catherine, Blackstone, Alyssa, Carter, Lauren, Ravichandran, Rashmi, Ahlrichs, Maggie, Murphy, Michael, Pultz, Ingrid Swanson, Kang, Alex, Bera, Asim K., Stewart, Lance, Garcia, K. Christopher, Naik, Shruti, Spangler, Jamie B., Beigel, Florian, Siebeck, Matthias, Gropp, Roswitha, and Baker, David

Published
2024
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20. LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Flexible and Consistent Face Alignment

Author

Yang, Huilin, Lyu, Junyan, Cheng, Pujin, and Tang, Xiaoying

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We innovatively propose a flexible and consistent face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way. Instead of predicting facial landmarks via heatmap or coordinate regression, we formulate this task in a diffeomorphic registration manner and predict momenta that uniquely parameterize the deformation between initial boundary and true boundary, and then perform large deformation diffeomorphic metric mapping (LDDMM) simultaneously for curve and landmark to localize the facial landmarks. Due to the embedding of LDDMM into a deep network, LDDMM-Face can consistently annotate facial landmarks without ambiguity and flexibly handle various annotation schemes, and can even predict dense annotations from sparse ones. Our method can be easily integrated into various face alignment networks. We extensively evaluate LDDMM-Face on four benchmark datasets: 300W, WFLW, HELEN and COFW-68. LDDMM-Face is comparable or superior to state-of-the-art methods for traditional within-dataset and same-annotation settings, but truly distinguishes itself with outstanding performance when dealing with weakly-supervised learning (partial-to-full), challenging cases (e.g., occluded faces), and different training and prediction datasets. In addition, LDDMM-Face shows promising results on the most challenging task of predicting across datasets with different annotation schemes., Comment: 11 pages, 4 figures

Published
2021

39. Whole-Heart Reconstruction with Explicit Topology Integrated Learning

Author

Yang, Huilin, Tam, Roger, Tang, Xiaoying, 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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