In order to convey complete meanings,there is a phenomenon in Chinese of using multiple running sentences.Xu Jingning(2023,p.66)states,“In communication,a complete expression of meaning often requires more than one c...In order to convey complete meanings,there is a phenomenon in Chinese of using multiple running sentences.Xu Jingning(2023,p.66)states,“In communication,a complete expression of meaning often requires more than one clause,which is common in human languages.”Domestic research on running sentences includes discussions on defining the concept and structural features of running sentences,sentence properties,sentence pattern classifications and their criteria,as well as issues related to translating running sentences into English.This article primarily focuses on scholarly research into the English translation of running sentences in China,highlighting recent achievements and identifying existing issues in the study of running sentence translation.However,by reviewing literature on the translation of running sentences,it is found that current research in the academic community on non-core running sentences is limited.Therefore,this paper proposes relevant strategies to address this issue.展开更多
Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requir...Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.展开更多
【目的】探明引起安徽某鸭场雏鸭肝脏出血和大量死亡的病原及其遗传进化特征。【方法】对安徽省某鸭场的病死雏鸭中采集的出血肝脏开展鸭已知病原核酸检测、病原分离鉴定和动物回归试验,在明确其病原为鸭3型甲肝病毒(Duck hepatitis A v...【目的】探明引起安徽某鸭场雏鸭肝脏出血和大量死亡的病原及其遗传进化特征。【方法】对安徽省某鸭场的病死雏鸭中采集的出血肝脏开展鸭已知病原核酸检测、病原分离鉴定和动物回归试验,在明确其病原为鸭3型甲肝病毒(Duck hepatitis A virus type 3,DHAV-3)的基础上分析其VP1基因序列分子特征。【结果】细菌分离结果显示,未分离到细菌;经病毒核酸(RT-)PCR检测结果显示,鸭3型甲肝病毒(DHAV-3)核酸阳性,未检测出其他已知引起鸭肝出血的病毒核酸。将该阳性样品经鸭胚进行病毒分离与传代,发现接种后鸭胚发生死亡,胚体全身出血,对第5代尿囊液经RT-PCR检测为DHAV-3,将其命名为AH230225。经测定,该分离株的鸭胚半数致死量(Effective lethal dose 50,ELD_(50))为10^(−4.17)/0.1 mL。动物回归试验表明,该毒株对樱桃谷雏鸭的致死率为80%,且攻毒死亡鸭肝脏和肾脏的剖检病变与临床典型病变相近。对该分离毒的VP1基因核苷酸序列进行同源性分析,显示AH230225株的VP1基因核苷酸序列与AH07株DHAV-3(安徽分离株)的同源性最高,为98.8%,与GenBank登录的10株DHAV-3分离株VP1基因核苷酸序列同源性为90.4%~98.8%,而与DHAV-1和DHAV-2的VP1基因核苷酸序列同源性分别为62.1%~63.0%、64.6%~64.9%;基于VP1蛋白氨基酸序列的遗传进化显示,该分离株与AH07株DHAV-3处于同一小进化分支上,亲缘关系最近;而与SD01株、G株和韩国株(AP-04009、AP-03337)等亲缘关系较远,即远离DHAV-1和DHAV-2进化分支。【结论】引起安徽某鸭场雏鸭肝脏出血和大量死亡的病原为鸭3型甲肝病毒DHAV-3,同时明确了该毒株VP1基因的分子特征及遗传进化规律,为深入研究DHAV-3的致病机制和制定防控措施提供科学依据。展开更多
文摘In order to convey complete meanings,there is a phenomenon in Chinese of using multiple running sentences.Xu Jingning(2023,p.66)states,“In communication,a complete expression of meaning often requires more than one clause,which is common in human languages.”Domestic research on running sentences includes discussions on defining the concept and structural features of running sentences,sentence properties,sentence pattern classifications and their criteria,as well as issues related to translating running sentences into English.This article primarily focuses on scholarly research into the English translation of running sentences in China,highlighting recent achievements and identifying existing issues in the study of running sentence translation.However,by reviewing literature on the translation of running sentences,it is found that current research in the academic community on non-core running sentences is limited.Therefore,this paper proposes relevant strategies to address this issue.
文摘Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.
文摘【目的】探明引起安徽某鸭场雏鸭肝脏出血和大量死亡的病原及其遗传进化特征。【方法】对安徽省某鸭场的病死雏鸭中采集的出血肝脏开展鸭已知病原核酸检测、病原分离鉴定和动物回归试验,在明确其病原为鸭3型甲肝病毒(Duck hepatitis A virus type 3,DHAV-3)的基础上分析其VP1基因序列分子特征。【结果】细菌分离结果显示,未分离到细菌;经病毒核酸(RT-)PCR检测结果显示,鸭3型甲肝病毒(DHAV-3)核酸阳性,未检测出其他已知引起鸭肝出血的病毒核酸。将该阳性样品经鸭胚进行病毒分离与传代,发现接种后鸭胚发生死亡,胚体全身出血,对第5代尿囊液经RT-PCR检测为DHAV-3,将其命名为AH230225。经测定,该分离株的鸭胚半数致死量(Effective lethal dose 50,ELD_(50))为10^(−4.17)/0.1 mL。动物回归试验表明,该毒株对樱桃谷雏鸭的致死率为80%,且攻毒死亡鸭肝脏和肾脏的剖检病变与临床典型病变相近。对该分离毒的VP1基因核苷酸序列进行同源性分析,显示AH230225株的VP1基因核苷酸序列与AH07株DHAV-3(安徽分离株)的同源性最高,为98.8%,与GenBank登录的10株DHAV-3分离株VP1基因核苷酸序列同源性为90.4%~98.8%,而与DHAV-1和DHAV-2的VP1基因核苷酸序列同源性分别为62.1%~63.0%、64.6%~64.9%;基于VP1蛋白氨基酸序列的遗传进化显示,该分离株与AH07株DHAV-3处于同一小进化分支上,亲缘关系最近;而与SD01株、G株和韩国株(AP-04009、AP-03337)等亲缘关系较远,即远离DHAV-1和DHAV-2进化分支。【结论】引起安徽某鸭场雏鸭肝脏出血和大量死亡的病原为鸭3型甲肝病毒DHAV-3,同时明确了该毒株VP1基因的分子特征及遗传进化规律,为深入研究DHAV-3的致病机制和制定防控措施提供科学依据。
