2篇
您的检索式：作者名="Manyuan Guo"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Structural and molecular basis for foot-andmouth disease virus neutralization by two potent protective antibodies显示文摘Dear Editor,Foot-and-mouth disease(FMD)is an economically devastating and highly contagious viral disease of cloven-hoofed animals with a global distribution.The causative agent,FMD virus(FMDV)is a small non-enveloped RNA virus,belonging to the Aphthoviruses genus within Picornaviridae family(Tuthill et al.,2010).Control of FMD has been largely reliant on vaccinations with inactivated virus vaccines.However,significant antigenic diversity within FMDV serotypes and inability of the vaccines to induce immune protection for a long duration of time impinge on the efficacy of available vaccines.The roles of neutralizing antibodies(NAbs)as the principal protective components of the immune responses to FMDV vaccination or infection have been well established(Pay and Hingley,1987;Juleff et al.,2009).Passive immunization of NAbs has also been demonstrated to be effective in curing FMD and many viral diseases(Harmsen et al.,2007;Qiu et al.,2018).A deep understanding of the molecular basis for viral neutralization by antibodies and the identification of key viral epitopes would aid in the development of potent rationally designed broad-spectrum vaccine. | Hu Dong Pan Liu Manyuan Bai Kang Wang Rui Feng Dandan Zhu Yao Sun Suyu Mu Haozhou Li Michiel Harmsen Shiqi Sun Xiangxi Wang Huichen Guo | 2022 | Protein & Cell2022，13，6： | 3 |
| 2 | GPDCCL: Cross-Domain Named Entity Recognition with Span-Based Domain Confusion Contrastive Learning显示文摘The goal of cross-domain named entity recognition is to transfer mod-els learned from labelled source domain data to unlabelled or lightly labelled target domain datasets.This paper discusses how to adapt a cross-domain sen-timent analysis model to thefield of named entity recognition,as the sentiment analysis model is more relevant to the tasks and data characteristics of named entity recognition.Most previous classification methods were based on a token-wise approach,and this paper introduces entity boundary information to prevent the model from being affected by a large number of nonentity labels.Specifically,adversarial training is used to enable the model to learn domain-confusing knowl-edge,and contrastive learning is used to reduce domain shift problems.The entity boundary information is transformed into a global boundary matrix representing sentence-level target labels,enabling the model to learn explicit span boundary information.Experimental results demonstrate that this method achieves good per-formance compared to multiple cross-domain named entity recognition models on the SciTech dataset.Ablation experiments reveal that the method of introducing entity boundary information significantly improves KL divergence and contrastive learning. | Ye Wang Chenxiao Shi Lijie Li Manyuan Guo | 2023 | 国际计算机前沿大会会议论文集2023，，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员