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28,833 results on '"Guo, Jun"'

301. Benefit, recurrence pattern, and toxicity to adjuvant anti-PD-1 monotherapy varies by ethnicity and melanoma subtype: An international multicenter cohort study

Author

Bai, Xue, Lawless, Aleigha R., Czapla, Juliane A., Gerstberger, Stefanie C., Park, Benjamin C., Jung, Seungyeon, Johnson, Rebecca, Yamazaki, Naoya, Ogata, Dai, Umeda, Yoshiyasu, Li, Caili, Guo, Jun, Flaherty, Keith T., Nakamura, Yasuhiro, Namikawa, Kenjiro, Long, Georgina V., Menzies, Alexander M., Johnson, Douglas B., Sullivan, Ryan J., Boland, Genevieve M., and Si, Lu

Published
2024
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324. Unexpected worker mating and colony-founding in a superorganism

Author

Zhuang, Mingsheng, Colgan, Thomas J., Guo, Yulong, Zhang, Zhengyi, Liu, Fugang, Xia, Zhongyan, Dai, Xueyan, Zhang, Zhihao, Li, Yuanjian, Wang, Liuhao, Xu, Jin, Guo, Yueqin, Qu, Yingping, Yao, Jun, Yang, Huipeng, Yang, Fan, Li, Xiaoying, Guo, Jun, Brown, Mark J. F., and Li, Jilian

Published
2023
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337. Safety and efficacy of Pucotenlimab (HX008) - a humanized immunoglobulin G4 monoclonal antibody in patients with locally advanced or metastatic melanoma: a single-arm, multicenter, phase II study

Author

Cui, Chuanliang, Chen, Yu, Luo, Zhiguo, Zou, Zhengyun, Jiang, Yu, Pan, Hongming, Fan, Qingxia, Zhao, Jianfu, Xu, Qing, Jiang, Renbing, Wang, Xuan, Ma, Taiyang, Guo, Zhen, Si, Lu, Chi, Zhihong, Sheng, Xinan, Dou, Yiwei, Tan, Qian, Wu, Di, and Guo, Jun

Published
2023
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344. Corydalis sunhangii (Papaveraceae): A new species from Xizang, China, based on plastome and morphological data

Author

Jun‐Tong Chen, Magnus Lidén, Xian‐Han Huang, Shun‐Quan Yang, Xin‐Jian Zhang, Qun Liu, Qi‐Lun Su, Guo‐Jun Hua, Jian Luo, and Tao Deng

Subjects
China, Corydalis, new species, phylogeny, plastome, Ecology, QH540-549.5
Abstract

Abstract A new species of Papaveraceae, Corydalis sunhangii, in the section Trachycarpae, is described and illustrated from Nyingchi City, Xizang, China. The new species has some resemblance to Corydalis kingdonis, but differs by radical leaves prominent, usually several, blade tripinnate (vs. insignificant, few, blade bi‐ to triternate); cauline leaf usually one, much smaller than radical leaves, usually situated in lower half of stem (vs. usually two, larger than radical leaves, concentrated in upper third of stem); racemes densely 13–35‐flowered (vs. rather lax, 4–11‐flowered); claw of lower petal shallowly saccate (vs. very prominently and deeply saccate); capsule oblong, with raised lines of dense papillae (vs. broadly obovoid, smooth). Phylogenetic analysis, based on 68 protein‐coding plastid genes of 49 samples, shows that C. sunhangii is not closely related to any hitherto described species, which is consistent with our morphological analysis.

Published
2024
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345. SHMT2 Mediates Small‐Molecule‐Induced Alleviation of Alzheimer Pathology Via the 5′UTR‐dependent ADAM10 Translation Initiation

Author

Li Song, Qiu‐Ling Pan, Gui‐Feng Zhou, Sheng‐Wei Liu, Bing‐Lin Zhu, Pei‐Jia Lin, Xiao‐Tong Hu, Jing‐Si Zha, Yan Long, Biao Luo, Jian Chen, Ying Tang, Jing Tang, Xiao‐Jiao Xiang, Xiao‐Yong Xie, Xiao‐Juan Deng, and Guo‐Jun Chen

Subjects
5′UTR, ADAM10, SHMT2, Alzheimer's disease, Kenpaullone, RNA binding protein, Science
Abstract

Abstract It is long been suggested that one‐carbon metabolism (OCM) is associated with Alzheimer's disease (AD), whereas the potential mechanisms remain poorly understood. Taking advantage of chemical biology, that mitochondrial serine hydroxymethyltransferase (SHMT2) directly regulated the translation of ADAM metallopeptidase domain 10 (ADAM10), a therapeutic target for AD is reported. That the small‐molecule kenpaullone (KEN) promoted ADAM10 translation via the 5′ untranslated region (5′UTR) and improved cognitive functions in APP/PS1 mice is found. SHMT2, which is identified as a target gene of KEN and the 5′UTR‐interacting RNA binding protein (RBP), mediated KEN‐induced ADAM10 translation in vitro and in vivo. SHMT2 controls AD signaling pathways through binding to a large number of RNAs and enhances the 5′UTR activity of ADAM10 by direct interaction with GAGGG motif, whereas this motif affected ribosomal scanning of eukaryotic initiation factor 2 (eIF2) in the 5′UTR. Together, KEN exhibits therapeutic potential for AD by linking OCM with RNA processing, in which the metabolic enzyme SHMT2 “moonlighted” as RBP by binding to GAGGG motif and promoting the 5′UTR‐dependent ADAM10 translation initiation.

Published
2024
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346. Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification

Author

Li, Mingkun, Li, Chun-Guang, and Guo, Jun

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in unsupervised setting. Existing methods for unsupervised person Re-ID are usually built upon the pseudo labels from clustering. However, the quality of clustering depends heavily on the quality of the learned features, which are overwhelmingly dominated by the colors in images especially in the unsupervised setting. In this paper, we propose a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which cluster structure is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework. To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively. Extensive experiments conducted on three benchmark datasets demonstrate superior performance of our proposal.

Published
2021
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347. Cross-domain Contrastive Learning for Unsupervised Domain Adaptation

Author

Wang, Rui, Wu, Zuxuan, Weng, Zejia, Chen, Jingjing, Qi, Guo-Jun, and Jiang, Yu-Gang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. Most existing UDA methods learn domain-invariant feature representations by minimizing feature distances across domains. In this work, we build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets. Exploring the same set of categories shared by both domains, we introduce a simple yet effective framework CDCL, for domain alignment. In particular, given an anchor image from one domain, we minimize its distances to cross-domain samples from the same class relative to those from different categories. Since target labels are unavailable, we use a clustering-based approach with carefully initialized centers to produce pseudo labels. In addition, we demonstrate that CDCL is a general framework and can be adapted to the data-free setting, where the source data are unavailable during training, with minimal modification. We conduct experiments on two widely used domain adaptation benchmarks, i.e., Office-31 and VisDA-2017, for image classification tasks, and demonstrate that CDCL achieves state-of-the-art performance on both datasets., Comment: IEEE Transactions on Multimedia

Published
2021

348. A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech

Author

Yang, Jingxuan, Xu, Kerui, Xu, Jun, Li, Si, Gao, Sheng, Guo, Jun, Xue, Nianwen, and Wen, Ji-Rong

Subjects
Computer Science - Computation and Language
Abstract

In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and CDP are closely related, and a joint model benefits both tasks. We refer to our model as DiscProReco, and it first encodes the tokens in each utterance in a conversation with a directed Graph Convolutional Network (GCN). The token states for an utterance are then aggregated to produce a single state for each utterance. The utterance states are then fed into a biaffine classifier to construct a conversational discourse graph. A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer. The joint model is trained and evaluated on a new Structure Parsing-enhanced Dropped Pronoun Recovery (SPDPR) dataset that we annotated with both two types of information. Experimental results on the SPDPR dataset and other benchmarks show that DiscProReco significantly outperforms the state-of-the-art baselines of both tasks., Comment: Accepted by ACL2021

Published
2021

349. Self-Supervised Graph Representation Learning via Topology Transformations

Author

Gao, Xiang, Hu, Wei, and Qi, Guo-Jun

Subjects
Computer Science - Machine Learning, Computer Science - Social and Information Networks, Electrical Engineering and Systems Science - Signal Processing
Abstract

We present the Topology Transformation Equivariant Representation learning, a general paradigm of self-supervised learning for node representations of graph data to enable the wide applicability of Graph Convolutional Neural Networks (GCNNs). We formalize the proposed model from an information-theoretic perspective, by maximizing the mutual information between topology transformations and node representations before and after the transformations. We derive that maximizing such mutual information can be relaxed to minimizing the cross entropy between the applied topology transformation and its estimation from node representations. In particular, we seek to sample a subset of node pairs from the original graph and flip the edge connectivity between each pair to transform the graph topology. Then, we self-train a representation encoder to learn node representations by reconstructing the topology transformations from the feature representations of the original and transformed graphs. In experiments, we apply the proposed model to the downstream node classification, graph classification and link prediction tasks, and results show that the proposed method outperforms the state-of-the-art unsupervised approaches., Comment: Accepted to IEEE Transactions on Knowledge and Data Engineering (TKDE)

Published
2021

350. A dimension expanded preconditioning technique for block two-by-two linear equations

Author

Luo Wei-Hua, Carpentieri Bruno, and Guo Jun

Subjects
block two-by-two, linear equations, preconditioner, navier-stokes equations, 65f08, 65f10, Mathematics, QA1-939
Abstract

In this article, we introduce a novel block preconditioner for block two-by-two linear equations by expanding the dimension of the coefficient matrix. Theoretical results on the eigenvalues distribution of the preconditioned matrix are obtained, and a feasible implementation is discussed. Some numerical examples, including the solution of the Navier-Stokes equations, are presented to support the theoretical findings and demonstrate the preconditioner’s efficiency.

Published
2023
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