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Hanwen Tong:Fudan University;Chenhao Xie:SenseDeal Intelligent Technology Co., Ltd.;Jiaqing Liang:Fudan University;Qianyu He:Fudan University;Zhiang Yue:Fudan University;Jingping Liu:East China University of Science and Technology;Yanghua Xiao:Fudan University,Fudan-Aishu Cognitive Intelligence Joint Research Center;Wenguang Wang:DataGrand Inc.

Haolun Wu:McGill University;Chen Ma:City University of Hong Kong;Yingxue Zhang:Huawei Noah"s Ark Lab;Xue Liu:McGill University;Ruiming Tang:Huawei Noah"s Ark Lab;Mark Coates:McGill University

Zihan Liu:Zhejiang University,AI Lab, Westlake Institute for Advanced Study;Yun Luo:Westlake University;Lirong Wu:AI Lab, Westlake Institute for Advanced Study;Siyuan Li:AI Lab, Westlake Institute for Advanced Study;Zicheng Liu:AI Lab, Westlake Institute for Advanced Study;Stan Li:AI Lab, Westlake Institute for Advanced Study

Fuxian Li:Tsinghua University;Huan Yan:Tsinghua University;Guangyin Jin:Tsinghua University;Yue Liu:Alibaba Group;Yong Li:Tsinghua University;Depeng Jin:Tsinghua University

Wentao Ning:The University of Hong Kong,Southern University of Science and Technology;Reynold Cheng:The University of Hong Kong;Jiajun Shen:TCL Research;Nur Al Hasan Haldar:The University of Western Australia;Ben Kao:The University of Hong Kong;Xiao Yan:Southern University of Science and Technology;Nan Huo:the University of Hong Kong;Tian Li:TCL Research;Wai Kit Lam:TCL Research;Bo Tang:Southern University of Science and Technology

Xianjie Guo:Hefei University of Technology;Yujie Wang:Hefei University of Technology;Xiaoling Huang:Hefei University of Technology;Shuai Yang:Hefei University of Technology;Kui Yu:Hefei University of Technology

Lili Zhao:University of Science and Technology of China;Linan Yue:University of Science and Technology of China;Yanqing An:University of Science and Technology of China;Yuren Zhang:University of Science and Technology of China;Jun Yu:IFLYTEK;Qi Liu:University of Science and Technology of China;Enhong Chen:University of Science and Technology of China

Quanliang Jing:Institute of Computing Technology, Chinese Academy of Sciences,University of Chinese Academy of Sciences;Shuo Liu:Institute of Computing Technology, Chinese Academy of Sciences,University of Chinese Academy of Sciences;Xinxin Fan:Institute of Computing Technology, Chinese Academy of Sciences;Jingwei Li:Department of Computer Science and Engineering, University at Buffalo, SUNY;Di Yao:Institute of Computing Technology, Chinese Academy of Sciences;Baoli Wang:Microsoft Search Technology Center Asia;Jingping Bi:Institute of Computing Technology, Chinese Academy of Sciences

Yu Hong:Fudan University;Zhixu Li:Fudan University;Jianfeng Qu:Soochow University;Jiaqing Liang:Fudan University;Yi Luo:Fudan University;Miyu Zhang:Fudan University;Yanghua Xiao:Fudan University,Fudan-Aishu Cognitive Intelligence Joint Research Center;Wei Wang:Fudan University

Yu Wang:University of Illinois at Chicago;Hengrui Zhang:University of Illinois at Chicago;Zhiwei Liu:Salesforce;Liangwei Yang:University of Illinois at Chicago;Philip S Yu:University of Illinois at Chicago

Jiangxia Cao:Institute of Information Engineering, Chinese Academy of Sciences,School of Cyber Security, University of Chinese Academy of Sciences;Xin Cong:Institute of Information Engineering, Chinese Academy of Sciences,School of Cyber Security, University of Chinese Academy of Sciences;Jiawei Sheng:Institute of Information Engineering, Chinese Academy of Sciences,School of Cyber Security, University of Chinese Academy of Sciences;Tingwen Liu:Institute of Information Engineering, Chinese Academy of Sciences,School of Cyber Security, University of Chinese Academy of Sciences;Bin Wang:Xiaomi AI Lab, Xiaomi Inc.

Qinggang Zhang:The Hong Kong Polytechnic University;Junnan Dong:The Hong Kong Polytechnic University;Keyu Duan:The Hong Kong Polytechnic University;Xiao Huang:The Hong Kong Polytechnic University;Yezi Liu:University of California Irvine;Linchuan Xu:The Hong Kong Polytechnic University

Hanwen Du:Soochow University;Hui Shi:Soochow University;Pengpeng Zhao:Soochow University;Deqing Wang:Beihang University;Victor S. Sheng:Texas Tech University;Yanchi Liu:Rutgers University;Guanfeng Liu:Macquarie University;Lei Zhao:Soochow University

Jiangui Chen:CAS Key Lab of Network Data Science and Technology, ICT, CAS,University of Chinese Academy of Sciences;Ruqing Zhang:CAS Key Lab of Network Data Science and Technology, ICT, CAS,University of Chinese Academy of Sciences;Jiafeng Guo:CAS Key Lab of Network Data Science and Technology, ICT, CAS,University of Chinese Academy of Sciences;Yiqun Liu:BNRist, DCST, Tsinghua University;Yixing Fan:CAS Key Lab of Network Data Science and Technology, ICT, CAS,University of Chinese Academy of Sciences;Xueqi Cheng:CAS Key Lab of Network Data Science and Technology, ICT, CAS,University of Chinese Academy of Sciences

Jiaqian Ren:Institute of Information Engineering, Chinese Academy of Sciences,School of Cyber Security, University of Chinese Academy of Sciences;Lei Jiang:Institute of Information Engineering, Chinese Academy of Sciences;Hao Peng:Beihang University;Lingjuan Lyu:Sony AI;Zhiwei Liu:salesforce;Chaochao Chen:Zhejiang University;Jia Wu:Macquarie University;Xu Bai:Institute of Information Engineering, Chinese Academy of Sciences;Philip S. Yu:University of Illinois Chicago

Kangzheng Liu:National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology;Feng Zhao:National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology;Hongxu Chen:Data Science and Machine Intelligence Lab, University of Technology Sydney;Yicong Li:Data Science and Machine Intelligence Lab, University of Technology Sydney;Guandong Xu:Data Science and Machine Intelligence Lab, University of Technology Sydney;Hai Jin:National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology

Junwei Zhang:Tianjin University;Ruifang He:Tianjin University;Fengyu Guo:Tianjin Normal University;Jinsong Ma:Tianjin University;Mengnan Xiao:Tianjin University

5. Liu GF, Yang J, Xu HM, Zhu J. Influence of Epistasis and QTL × Environment Interaction on Heading Date of Rice (Oryza sativa L.). J Genet Genomics. 2007; 34: 608–615. [PubMed]

30. Jiang Y, Zhang R, Liu G, Wang Z, Chen P, et al. Multifactor dimensionality reduction for detecting haplotype- haplotype interaction. Fuzzy Systems and Knowledge Discovery, 2009. FSKD"09. Sixth International Conference on. IEEE. 2009; 1: 241–245.

54. Rossi ME, Orf JH, Liu LJ, Dong Z, Rajcan I. Genetic basis of soybean adaptation to North American vs. Asian mega-environments in two independent populations from Canadian × Chinese crosses. Theor Appl Genet. 2013; 126: 1809–23. 10.1007/s00122-013-2094-9

The intestine is the main organ for nutrient digestion and absorption and colonizes trillions of microorganisms (Yang et al., 2018). According to statistics, the human intestine inhabits over 1014 microorganisms, about 10 times the total amount of human cells (Koboziev et al., 2014). Mounting evidence indicated that gut microbiota played essential roles in immunity, intestinal homeostasis, and epithelium differentiation (Liu et al., 2020; Xiang et al., 2020). Moreover, numerous investigations also revealed the positive regulation roles of gut microbiota in intestinal barrier function, metabolism, and host health (Cani and Delzenne, 2009; Dong et al., 2020). Some bacteria have the ability to restrict the proliferation of pathogenic and opportunistic pathogens in the intestine by producing beneficial metabolites, which was considered a vital barrier against pathogen infection (Wang et al., 2018a). Although intestinal microorganisms reside in the intestine, they may cause systemic effects. Numerous studies provided supporting evidence that gut microbiota was a central or driving factor of many diseases, injuring both near and far organ systems (Acharya and Bajaj, 2021). Gut microbial alterations may extend their detrimental influences beyond the intestine and impair liver and brain (Albhaisi et al., 2020). However, gut microbial homeostasis is easily affected by many factors, such as stress, antibiotics, heavy metal, and pesticide (Kakade et al., 2020). Early studies revealed that gut microbial alternations were associated with many diseases, including diarrhea, diabetes, obesity, and even colorectal cancer (Frazier et al., 2011; Wang et al., 2018b).

Given feces cannot fully display the gut microbial abundance and diversity, we collected intestinal content for 16S rDNA amplicon sequencing. Our results indicated an observably reduced alpha diversity in the gut microbial community of chickens exposed to thiram, indicating its gut microbial dysbiosis. Typically, the gut microbial community changes dynamically within limits under the influence of age, diet, and environment and these physiological fluctuations cannot affect normal intestinal functions (Wang et al., 2018b; Li et al., 2021). However, the ecological balance of the gut microbial community may be broken and changed significantly, when the external environment shifts dramatically, including long-term exposure to antibiotics, heavy metals, and pesticides (Li et al., 2019; Zhong et al., 2021). Early investigations demonstrated that the higher gut microbial diversity and abundance were beneficial to the intestine to perform complex physiological functions and energy utilization, whereas the decreased microbial diversity may threaten the host"s health (Wang et al., 2018b, 2021). Several previous studies revealed that the declined gut microbial diversity can significantly affect the metabolism of fat and carbohydrates, thereby further accelerating fat accumulation and inducing obesity and diabetes (DiBaise et al., 2008; Cani et al., 2012). Furthermore, the reduced gut microbial diversity has also been demonstrated to be closely related to the occurrence of cardiovascular diseases, diarrhea, allergies, and asthma (Tang and Hazen, 2014; Han et al., 2017). The intestine is closely associated with host immunity, metabolism, and nutrient absorption, which in turn depends on the stabilized gut microbial community (Tremaroli and Backhed, 2012; Rooks and Garrett, 2016). Therefore, imbalanced gut microbiota can also affect the immunological function and intestinal permeability of the host, which may increase morbidity (Liu et al., 2019). Moreover, gut microbial dysbiosis can impair intestinal functions and selectively promote the growth of pathogens, which may induce the occurrence of many diseases in neighbor or local organs, such as diarrhea, hepatic injury, and inflammatory bowel diseases (Frazier et al., 2011; Sheehan and Shanahan, 2017). Notably, some opportunistic pathogens that do not initially exhibit pathogenicity may also induce the occurrence of diseases, in the case of hypoimmunity and gut microbial dysbiosis (Wang et al., 2019). During gut microbial alternations, some toxic metabolites produced from pathogens can enter the intestinal hepatic circulation via the intestinal barrier, thereby further exacerbating the hepatic injury (Hussain et al., 2020; Zhong et al., 2021). Currently, thiram has been demonstrated to induce hepatic injury, but the potential relationship between gut microbial dysbiosis and thiram-induced liver damage remained to be investigated (Zhang et al., 2018). The results of PCoA analysis revealed that the experimental group and control group were separated from each other, suggesting an obvious difference in the gut microbial principal component between CI and TI groups. Consequently, we suspected that thiram exposure may the important driving force for shifts in the principal components of gut microbiota.

Dong H., Liu B., Li A., Iqbal M., Mehmood K., Jamil T., et al.. (2020). Microbiome Analysis reveals the attenuation effect of Lactobacillus from Yaks on Diarrhea via modulation of gut microbiota. Front. Cell Infect. Microbiol. 10, 610781. 10.3389/fcimb.2020.610781 PubMed] [CrossRef]

Kakade A., Salama E. S., Pengya F., Liu P., Li X. (2020). Long-term exposure of high concentration heavy metals induced toxicity, fatality, and gut microbial dysbiosis in common carp, Cyprinus carpio. Environ. Pollut.

Kong A., Zhang C., Cao Y., Cao Q., Liu F., Yang Y., et al.. (2020). The fungicide thiram perturbs gut microbiota community and causes lipid metabolism disorder in chickens. Ecotoxicol Environ Saf. 206, 111400. 10.1016/j.ecoenv.2020.111400 [PubMed] [CrossRef]

Liu J., Wang H. W., Lin L., Miao C. Y., Zhang Y., Zhou B. H. (2019). Intestinal barrier damage involved in intestinal microflora changes in fluoride-induced mice. Chemosphere

Liu Z., Li A., Wang Y., Iqbal M., Zheng A., Zhao M., et al.. (2020). Comparative analysis of microbial community structure between healthy and Aeromonas veronii-infected Yangtze finless porpoise. Microb. Cell Fact.

Melbye P., Olsson A., Hansen T. H., Sondergaard H. B., Bang O. A. (2019). Short-chain fatty acids and gut microbiota in multiple sclerosis. Acta Neurol. Scand.

Wang Y., Li A., Liu J., Mehmood K., Wangdui B., Shi H., et al.. (2019). L. Pseudomesenteroides and L. Johnsonii isolated from yaks in Tibet modulate gut microbiota in mice to ameliorate enteroinvasive Escherichia coli-induced diarrhea. Microb. Pathog. 132, 1–9. 10.1016/j.micpath.2019.04.020 [PubMed] [CrossRef]

Wang Y., Zhang H., Zhu L., Xu Y., Liu N., Sun X., et al.. (2018b). Dynamic distribution of gut microbiota in goats at different ages and health states. Front. Microbiol.

Yang H., Xiao Y., Gui G., Li J., Wang J., Li D. (2018). Microbial community and short-chain fatty acid profile in gastrointestinal tract of goose. Poult. Sci. 97, 1420–1428. 10.3382/ps/pex438 [PubMed] [CrossRef]

Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an exact token-by-token match between a target sequence with a generated sequence. Such training objective is sub-optimal when the target sequence is not perfect, e.g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available. To address the challenge, we propose a novel Edit-Invariant Sequence Loss (EISL), which computes the matching loss of a target n-gram with all n-grams in the generated sequence. EISL is designed to be robust to various noises and edits in the target sequences. Moreover, the EISL computation is essentially an approximate convolution operation with target n-grams as kernels, which is easy to implement and efficient to compute with existing libraries. To demonstrate the effectiveness of EISL, we conduct experiments on a wide range of tasks, including machine translation with noisy target sequences, unsupervised text style transfer with only weak training signals, and non-autoregressive generation with non-predefined generation order. Experimental results show our method significantly outperforms the common CE loss and other strong baselines on all the tasks. EISL has a simple API that can be used as a drop-in replacement of the CE loss: https://github.com/guangyliu/EISL.

Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou and Xipeng Qiu (zyfei20@fudan.edu.cn)

Pengshan Cai, Fei Liu, Adarsha Bajracharya, Weisong Liu, Dan Berlowitz, Joe Sills, Alok Kapoor, Richeek Pradhan, David Levy and hong yu (pengshancai@cs.umass.edu)