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3,011 results on '"Yang, Xiaoli"'

8. LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype

Author

Shankar, Vivek, Yang, Xiaoli, Krishna, Vrishab, Tan, Brent, Silva, Oscar, Rojansky, Rebecca, Ng, Andrew, Valvert, Fabiola, Briercheck, Edward, Weinstock, David, Natkunam, Yasodha, Fernandez-Pol, Sebastian, and Rajpurkar, Pranav

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, I.5.1, I.5.2, I.5.4, J.3
Abstract

The accurate classification of lymphoma subtypes using hematoxylin and eosin (H&E)-stained tissue is complicated by the wide range of morphological features these cancers can exhibit. We present LymphoML - an interpretable machine learning method that identifies morphologic features that correlate with lymphoma subtypes. Our method applies steps to process H&E-stained tissue microarray cores, segment nuclei and cells, compute features encompassing morphology, texture, and architecture, and train gradient-boosted models to make diagnostic predictions. LymphoML's interpretable models, developed on a limited volume of H&E-stained tissue, achieve non-inferior diagnostic accuracy to pathologists using whole-slide images and outperform black box deep-learning on a dataset of 670 cases from Guatemala spanning 8 lymphoma subtypes. Using SHapley Additive exPlanation (SHAP) analysis, we assess the impact of each feature on model prediction and find that nuclear shape features are most discriminative for DLBCL (F1-score: 78.7%) and classical Hodgkin lymphoma (F1-score: 74.5%). Finally, we provide the first demonstration that a model combining features from H&E-stained tissue with features from a standardized panel of 6 immunostains results in a similar diagnostic accuracy (85.3%) to a 46-stain panel (86.1%)., Comment: To be published in Proceedings of the 3rd Machine Learning for Health symposium, Proceedings of Machine Learning Research (PMLR)

Published
2023

12. Nonlinear Analysis of Pseudo-Dynamic Active Earth Pressure Considering a Multi-segment Rotational Failure Mechanism

Author

Chen, Hui, Zhang, Dongming, Yang, Xiaoli, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Wu, Wei, editor, Leung, Chun Fai, editor, Zhou, Yingxin, editor, and Li, Xiaozhao, editor

Published
2024
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13. GSA-UBS: A Novel Medical Hyperspectral Band Selection Based on Gravitational Search Algorithm

Author

Zhang, Chenglong, Yang, Xiaoli, Zhang, Aizhu, Yu, Dexin, Liu, Nian, Ma, Xiaopeng, 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, Ren, Jinchang, editor, Hussain, Amir, editor, Liao, Iman Yi, editor, Chen, Rongjun, editor, Huang, Kaizhu, editor, Zhao, Huimin, editor, Liu, Xiaoyong, editor, Ma, Ping, editor, and Maul, Thomas, editor

Published
2024
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14. An Improved Physically-Based Constitutive Model for the Hot Deformation Behavior of GH4698 Superalloy by Considering Dynamic Softening Mechanism

Author

Yan, PeiZhi, Wen, DongXu, Huang, Liang, Yang, XiaoLi, Zhang, ZhiCheng, Li, JianJun, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Mocellin, Katia, editor, Bouchard, Pierre-Olivier, editor, Bigot, Régis, editor, and Balan, Tudor, editor

Published
2024
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17. Effect of Component Migration during the Extraction Process on Functional Properties of Seed Storage Proteins from Camellia oleifera Abel.

Author

YANG Xiaoli, ZHOU Jisong, SHANG Wei, PENG Dengfeng, DENG Qianchun, JIN Weiping

Subjects
camellia oleifera abel. seed storage proteins, alkaline extraction and acid precipitation, saponin, component migration, functional properties, Food processing and manufacture, TP368-456
Abstract

In this study, the effect of component migration and interaction with concomitant saponins on the composition and functional properties of the seed storage proteins from Camellia oleifera Abel., a typical woody oil crop in China, was investigated using proteomics and molecular docking. The results showed that the optimal parameters for alkaline extraction were solid/liquid ratio 1:10 (g/mL) and pH 8.0, which resulted in an extraction rate of 75.8%, and a saponin-to-protein ratio of 2.60. The 11S globulins were precipitated at pH 4.5 with a saponin-to-protein ratio of 0.36. Albumin accounted for 69% of the total storage proteins, and coexisted with saponin during the extraction process, thus resulting in a high saponin-to-protein ratio of 3.73. The foaming properties of albumin and globulin from C. oleifera Abel. seeds were larger than 200%, and the emulsifying activity index was 16.7 m2/g. The interaction between saponins and proteins significantly improved the emulsion stability. This study provides theoretical support for the high-value utilization of proteins from C. oleifera Abel. and the development of functional food ingredients.

Published
2024
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24. 'We Felt Transformed': A Mentoring Program for University English Teachers in China

Author

Smith, Melissa K., Wen, Li, Wang, Yan, Wang, Jing, Yang, Xiaoli, and Wang, Zhao

Abstract

This article explores a mentoring program for English language teachers at a university in China. At the end of the program, the teachers and mentor reported feeling transformed and set out to investigate what was transformative about their experiences by writing final reflections. Then, the teachers and mentor became the researchers, and using a case study approach, examined the reflections for mentions of change, noting themes that emerged. They also drew on other data sources in order to understand and describe these transformative elements. Four elements identified in the literature, reflection, ownership, collaboration, and personalization, emerged in a way that was customized to the group. Two additional elements also emerged, empowering encouragement and bridging the gap between theory and practice. Both the tweaking and adding of elements led to some lessons for teachers and mentors and the observation that the form of professional development is not as important as its transformative style.

Published
2023
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25. Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light

Author

Xu, Yuhua, Yang, Xiaoli, Yu, Yushan, Jia, Wei, Chu, Zhaobi, and Guo, Yulan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

It is well known that the passive stereo system cannot adapt well to weak texture objects, e.g., white walls. However, these weak texture targets are very common in indoor environments. In this paper, we present a novel stereo system, which consists of two cameras (an RGB camera and an IR camera) and an IR speckle projector. The RGB camera is used both for depth estimation and texture acquisition. The IR camera and the speckle projector can form a monocular structured-light (MSL) subsystem, while the two cameras can form a binocular stereo subsystem. The depth map generated by the MSL subsystem can provide external guidance for the stereo matching networks, which can improve the matching accuracy significantly. In order to verify the effectiveness of the proposed system, we build a prototype and collect a test dataset in indoor scenes. The evaluation results show that the Bad 2.0 error of the proposed system is 28.2% of the passive stereo system when the network RAFT is used. The dataset and trained models are available at https://github.com/YuhuaXu/MonoStereoFusion., Comment: CVPR 2022

Published
2022

31. Defect Classification of Weld Metallographic Structure Based on Data Augmentation of Poisson Fusion

Author

BAI Xiongfei, GONG Shuicheng, LI Xuesong, XU Bo, YANG Xiaoli, WANG Mingyan

Subjects
weld metallographic structure, data augmentation, poisson fusion, defect classification, Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Naval architecture. Shipbuilding. Marine engineering, VM1-989
Abstract

The classification of the defects in welding applications based on the metallographic structure images plays an important part in industrial welding quality inspections. In order to improve the classification performance of defects in the weld metallographic structure images with a small sample dataset available (the amount of samples being less than 30), a Poisson fusion method is used for data augmentation of the defect images and the ResNet18_PRO network is proposed. Both of the methods notably improve the defects classification performance. During data augmentation, the defect area is extracted from original defect samples via digital image processing, and the defect area is fused with normal samples by the Poisson fusion method to generate new defect samples, thus increasing the number of defect samples. The model in this paper is improved based on the ResNet18 network. The downsampling structure is improved to reduce the information loss in the original downsampling structure, and an improved space pyramid pooling structure is added at the end of the network to integrate multi-scale feature information. The classification performance before and after data augmentation is compared by different classification models, which verifies the significant effect of the data augmentation on the classification performance. Meanwhile, the ablation experiment of the ResNet18_PRO is conducted to verify the effectiveness of the improved network structure and the training strategy. It is found that the average classification accuracy of ResNet18_PRO reaches 98.83% and the average F1-score reaches 98.76%, which greatly improves the classification accuracy of metallographic structure defects. Finally, the network is trained and tested with another industrial defect dataset and obtains good classification results. These results show that the proposed network has a good robustness and practical application value.

Published
2023
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44. Gastrointestinal symptoms of long COVID-19 related to the ectopic colonization of specific bacteria that move between the upper and lower alimentary tract and alterations in serum metabolites

Author

Zhang, Deyu, Weng, Siyuan, Xia, Chuanchao, Ren, Yuqing, Liu, Zaoqu, Xu, Yudi, Yang, Xiaoli, Wu, Ruhao, Peng, Lisi, Sun, Liqi, Zhu, Jiaqi, Liang, Xuesong, Jia, Yin, Wang, Huaizhou, Chen, Qian, Liu, Dongtian, Chen, Yi, Guo, Honglei, Han, Xinwei, Jin, Zhendong, Chen, Cui, Yang, Xia, Li, Zhaoshen, and Huang, Haojie

Published
2023
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48. Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations

Author

Köchl, Katharina, Schopper, Tobias, Durmaz, Vedat, Parigger, Lena, Singh, Amit, Krassnigg, Andreas, Cespugli, Marco, Wu, Wei, Yang, Xiaoli, Zhang, Yanchong, Wang, Welson Wen-Shang, Selluski, Crystal, Zhao, Tiehan, Zhang, Xin, Bai, Caihong, Lin, Leon, Hu, Yuxiang, Xie, Zhiwei, Zhang, Zaihui, Yan, Jun, Zatloukal, Kurt, Gruber, Karl, Steinkellner, Georg, and Gruber, Christian C.

Published
2023
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