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93 results on '"Tingsong Jiang"'

1. Proximity Labeling and Genetic Screening Reveal that DSG2 is a Counter Receptor of Siglec‐9 and Suppresses Macrophage Phagocytosis

Author

Ying Wu, Yuyu You, Tingsong Jiang, Yuqi He, Qingchi Fan, Xinlei Zeng, Ting Li, Yuxing Lu, Liang Qi, Fengxia Zhou, Lingyu Sun, Danyang Wang, Yong Zou, Guigen Zhang, Yanqiu Yuan, and Yang Mao

Subjects
DSG2, genetic screening, phagocytosis, proximity labeling, siglec‐9, Science
Abstract

Abstract Cancer cells present sialylated glycoconjugates that modulate the activity of various immune cells within the tumor microenvironment through trans interaction with immunosuppressive Siglec receptors. Identifying counter receptors for Siglecs can provide valuable targets for cancer immunotherapy, but it presents significant challenges. Here, the identification of DSG2 (Desmoglein 2) as a dominant counter receptor of Siglec‐9 in melanoma cells is reported, using a workflow that combines the strength of proximity labeling and the advantage of CRISPR knockout screening. It is further demonstrated that the interaction between DSG2 and Siglec‐9 is mainly dependent on sialic acid‐bearing N‐glycans on DSG2. Importantly, blocking trans interaction between DSG2 and Siglec‐9 significantly enhances macrophage phagocytosis of melanoma cells and, to a lesser extent, other cancer cells. The work thus suggests sialylated DSG2 as a potential “don't eat me” signal molecule with therapeutic potentials in cancer immunotherapy.

Published
2025
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2. RBUE: a ReLU-based uncertainty estimation method for convolutional neural networks

Author

Yufeng Xia, Jun Zhang, Zhiqiang Gong, Tingsong Jiang, and Wen Yao

Subjects
ReLU based, Uncertainty estimation, Diverse predictions, Convolutional neural networks, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64
Abstract

Abstract Convolutional neural networks (CNNs) have successfully demonstrated their powerful predictive performance in a variety of tasks. However, it remains a challenge to estimate the uncertainty of these predictions simply and accurately. Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty accurately, but it is expensive to train and test. MC-Dropout is another popular method that is less costly but lacks the diversity of predictions resulting in less accurate uncertainty estimates. To combine the benefits of both, we introduce a ReLU-Based Uncertainty Estimation (RBUE) method. Instead of using the randomness of the Dropout module during the test phase (MC-Dropout) or using the randomness of the initial weights of CNNs (Deep Ensemble), RBUE uses the randomness of activation function to obtain diverse outputs in the testing phase to estimate uncertainty. Under the method, we propose strategy MC-DropReLU and develop strategy MC-RReLU. The uniform distribution of the activation function’s position in CNNs allows the randomness to be well transferred to the output results and gives a more diverse output, thus improving the accuracy of the uncertainty estimation. Moreover, our method is simple to implement and does not need to modify the existing model. We experimentally validate the RBUE on three widely used datasets, CIFAR10, CIFAR100, and TinyImageNet. The experiments demonstrate that our method has competitive performance but is more favorable in training time.

Published
2023
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