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153. Cross-neutralizing antibodies bind a SARS-CoV-2 cryptic site and resist circulating variants.

Author

Li, Tingting, Xue, Wenhui, Zheng, Qingbing, Song, Shuo, Yang, Chuanlai, Xiong, Hualong, Zhang, Sibo, Hong, Minqing, Zhang, Yali, Yu, Hai, Zhang, Yuyun, Sun, Hui, Huang, Yang, Deng, Tingting, Chi, Xin, Li, Jinjin, Wang, Shaojuan, Zhou, Lizhi, Chen, Tingting, Wang, Yingbin, Cheng, Tong, Zhang, Tianying, Yuan, Quan, Zhao, Qinjian, Zhang, Jun, McLellan, Jason S, Zhou, Z Hong, Zhang, Zheng, Li, Shaowei, Gu, Ying, and Xia, Ningshao

Subjects
CHO Cells, Vero Cells, Animals, Humans, Cricetulus, Mice, Receptors, Virus, Antibodies, Monoclonal, Antibodies, Viral, Epitopes, Neutralization Tests, Immunization, Passive, Binding Sites, Protein Multimerization, HEK293 Cells, Pandemics, Sf9 Cells, Spike Glycoprotein, Coronavirus, Middle East Respiratory Syndrome Coronavirus, Broadly Neutralizing Antibodies, Chlorocebus aethiops, COVID-19, SARS-CoV-2, Prevention, Vaccine Related, Emerging Infectious Diseases, Pneumonia & Influenza, Biotechnology, Pneumonia, Infectious Diseases, Lung, Immunization, Biodefense, Infection
Abstract

The emergence of numerous variants of SARS-CoV-2, the causative agent of COVID-19, has presented new challenges to the global efforts to control the COVID-19 pandemic. Here, we obtain two cross-neutralizing antibodies (7D6 and 6D6) that target Sarbecoviruses' receptor-binding domain (RBD) with sub-picomolar affinities and potently neutralize authentic SARS-CoV-2. Crystal structures show that both antibodies bind a cryptic site different from that recognized by existing antibodies and highly conserved across Sarbecovirus isolates. Binding of these two antibodies to the RBD clashes with the adjacent N-terminal domain and disrupts the viral spike. Both antibodies confer good resistance to mutations in the currently circulating SARS-CoV-2 variants. Thus, our results have direct relevance to public health as options for passive antibody therapeutics and even active prophylactics. They can also inform the design of pan-sarbecovirus vaccines.

Published
2021

154. SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

Author

Wang, Xiangjun, Song, Junxiao, Qi, Penghui, Peng, Peng, Tang, Zhenkun, Zhang, Wei, Li, Weimin, Pi, Xiongjun, He, Jujie, Gao, Chao, Long, Haitao, and Yuan, Quan

Subjects
Computer Science - Machine Learning
Abstract

AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of computation needed are big obstacles for the community to conduct further research in this direction. We propose a deep reinforcement learning agent, StarCraft Commander (SCC). With order of magnitude less computation, it demonstrates top human performance defeating GrandMaster players in test matches and top professional players in a live event. Moreover, it shows strong robustness to various human strategies and discovers novel strategies unseen from human plays. In this paper, we will share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game., Comment: ICML 2021 camera ready

Published
2020

157. Trends and Factors Influencing the Evolution of Spatial Patterns of Cropland toward Large-Scale Agricultural Production in China

Author

Xinyan Wang, Qingyu Feng, Boyong Li, Yinlin Fan, Huihui Fan, Nengliang Yang, Yuan Quan, Huanru Ding, and Yunlu Zhang

Subjects
scaled agriculture, social and economic factors, landscape patterns, agricultural production, arable land, China, Agriculture
Abstract

Considering the essential expansion of agricultural production, current research primarily focuses on static factors, such as the distribution of fine-grained arable land, omitting an in-depth analysis of its developmental dynamics and key drivers. Addressing this knowledge gap is crucial for enhancing the scalability of agricultural production. This research utilizes landscape ecology techniques, correlation analysis, random forest algorithms, and structural equation modeling to explore spatial pattern trends of arable land in the Beijing–Tianjin–Hebei region. Its objective is to clarify how the expansion of agricultural production scale affects food production through changes in arable land patterns and to determine the impact of socio-economic factors on these configurations. The results show that: (1) the landscape pattern of arable land is transitioning to a more fragmented arrangement with complex contours, (2) grain yield per unit area correlates positively with the landscape pattern index in Beijing, negatively in Hebei, and exhibits no significant correlation in Tianjin, and (3) land ownership plays a crucial role in land fragmentation, alterations in land morphology, and influences other socio-economic variables. Analyzing the spatial pattern of arable land in conjunction with socio-economic factors is essential for developing holistic land management approaches, improving resource efficiency, minimizing external inputs, and mitigating food security challenges.

Published
2024
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166. Large Scale Product Graph Construction for Recommendation in E-commerce

Author

Yang, Xiaoyong, Zhu, Yadong, Zhang, Yi, Wang, Xiaobo, and Yuan, Quan

Subjects
Computer Science - Information Retrieval
Abstract

Building a recommendation system that serves billions of users on daily basis is a challenging problem, as the system needs to make astronomical number of predictions per second based on real-time user behaviors with O(1) time complexity. Such kind of large scale recommendation systems usually rely heavily on pre-built index of products to speedup the recommendation service so that online user waiting time is un-noticeable. One important indexing structure is the product-product index, where one can retrieval a list of ranked products given a seed product. The index can be viewed as a weighted product-product graph. In this paper, we present our novel technologies to efficiently build such kind of indexed product graphs. In particular, we propose the Swing algorithm to capture the substitute relationships between products, which can utilize the substructures of user-item click bi-partitive graph. Then we propose the Surprise algorithm for the modeling of complementary product relationships, which utilizes product category information and solves the sparsity problem of user co-purchasing graph via clustering technique. Base on these two approaches, we can build the basis product graph for recommendation in Taobao. The approaches are evaluated comprehensively with both offline and online experiments, and the results demonstrate the effectiveness and efficiency of the work.

Published
2020

167. Recent advanced applications of metasurfaces in multi-dimensions

Author

Yuan Quan, Ge Qin, Chen Linsen, Zhang Yi, Yang Yuhang, Cao Xun, Wang Shuming, Zhu Shining, and Wang Zhenlin

Subjects
application, dimension, metasurface, wavelength scale, Physics, QC1-999
Abstract

Unlike traditional optical components, which rely on the gradual accumulation of light along the optical path over a distance much larger than the wavelength to form a wavefront, metasurfaces manipulate light field properties on the wavelength thickness by specially arranging various meta-atoms. Due to the ease of integration and compact planar structure, metasurfaces play a key role in the light field manipulations. Here, we review the recent advances of metasurfaces in multi-dimensions, including light wavelength, polarization, orbital angular momentum(OAM), and angular response. Progress in these fields has brought new applications in areas such as imaging, display, communication, and information encryption, etc. Finally, we also discuss the challenges and prospects of metasurfaces applications.

Published
2023
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169. Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts

Author

An, Ya-Hui, Dong, Qiang, Yuan, Quan, and Wang, Chao

Subjects
Computer Science - Social and Information Networks, Computer Science - Information Retrieval
Abstract

Nowadays, recommender systems (RSes) are becoming increasingly important to individual users and business marketing, especially in the online e-commerce scenarios. However, while the majority of recommendation algorithms proposed in the literature have focused their efforts on improving prediction accuracy, other important aspects of recommendation quality, such as diversity of recommendations, have been more or less overlooked. In the latest decade, recommendation diversity has drawn more research attention, especially in the models based on user-item bipartite networks. In this paper, we introduce a family of approaches to extract fabricated experts from users in RSes, named as the Expert Tracking Approaches (ExTrA for short), and explore the capability of these fabricated experts in improving the recommendation diversity, by highlighting them in a well-known bipartite network-based method, called the Mass Diffusion (MD for short) model. These ExTrA-based models are compared with two state-of-the-art MD-improved models HHP and BHC, with respect to recommendation accuracy and diversity. Comprehensive empirical results on three real-world datasets MovieLens, Netflix and RYM show that, our proposed ExTrA-based models can achieve significant diversity gain while maintain comparable level of recommendation accuracy.

Published
2020
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170. Alleviating the recommendation bias via rank aggregation

Author

Dong, Qiang, Yuan, Quan, and Shi, Yang-Bo

Subjects
Computer Science - Social and Information Networks, Computer Science - Information Retrieval
Abstract

The primary goal of a recommender system is often known as "helping users find relevant items", and a lot of recommendation algorithms are proposed accordingly. However, these accuracy-oriented methods usually suffer the problem of recommendation bias on popular items, which is not welcome to not only users but also item providers. To alleviate the recommendation bias problem, we propose a generic rank aggregation framework for the recommendation results of an existing algorithm, in which the user- and item-oriented ranking results are linearly aggregated together, with a parameter controlling the weight of the latter ranking process. Experiment results of a typical algorithm on two real-world data sets show that, this framework is effective to improve the recommendation fairness of any existing accuracy-oriented algorithms, while avoiding significant accuracy loss., Comment: published on Physica A: Statistical Mechanics and its Applications, 2019, volume 534, article ID: 122073

Published
2020
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173. A New Type of Endometrial Cancer Models in Mice Revealing the Functional Roles of Genetic Drivers and Exploring their Susceptibilities

Author

Jingyao Chen, Siqi Dai, Lei Zhao, Yiman Peng, Chongen Sun, Hongling Peng, Qian Zhong, Yuan Quan, Yue Li, Xuelan Chen, Xiangyu Pan, Ailing Zhong, Manli Wang, Mengsha Zhang, Shengyong Yang, You Lu, Zhong Lian, Yu Liu, Shengtao Zhou, Zhengyu Li, Feifei Na, and Chong Chen

Subjects
animal model, drug screening, endometrial cancer, organoid, organoid‐initiated precision cancer models, Science
Abstract

Abstract Endometrial cancer (EC) is the most common female reproductive tract cancer and its incidence has been continuously increasing in recent years. The underlying mechanisms of EC tumorigenesis remain unclear, and efficient target therapies are lacking, for both of which feasible endometrial cancer animal models are essential but currently limited. Here, an organoid and genome editing‐based strategy to generate primary, orthotopic, and driver‐defined ECs in mice is reported. These models faithfully recapitulate the molecular and pathohistological characteristics of human diseases. The authors names these models and similar models for other cancers as organoid‐initiated precision cancer models (OPCMs). Importantly, this approach can conveniently introduce any driver mutation or a combination of driver mutations. Using these models,it is shown that the mutations in Pik3ca and Pik3r1 cooperate with Pten loss to promote endometrial adenocarcinoma in mice. In contrast, the Kras G12D mutati led to endometrial squamous cell carcinoma. Then, tumor organoids are derived from these mouse EC models and performed high‐throughput drug screening and validation. The results reveal distinct vulnerabilities of ECs with different mutations. Taken together, this study develops a multiplexing approach to model EC in mice and demonstrates its value for understanding the pathology of and exploring the potential treatments for this malignancy.

Published
2023
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177. Combination therapy with oncolytic virus and T cells or mRNA vaccine amplifies antitumor effects

Author

Fu, Rao, Qi, Ruoyao, Xiong, Hualong, Lei, Xing, Jiang, Yao, He, Jinhang, Chen, Feng, Zhang, Liang, Qiu, Dekui, Chen, Yiyi, Nie, Meifeng, Guo, Xueran, Zhu, Yuhe, Zhang, Jinlei, Yue, Mingxi, Cao, Jiali, Wang, Guosong, Que, Yuqiong, Fang, Mujing, Wang, Yingbin, Chen, Yixin, Cheng, Tong, Ge, Shengxiang, Zhang, Jun, Yuan, Quan, Zhang, Tianying, and Xia, Ningshao

Published
2024
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