Non-alcoholic fatty liver disease(NAFLD)has become the world’s largest chronic liver disease in the 21st century,affecting 20%-30%of the world’s population.As the epidemiology,etiology,and pathogenesis of NAFLD have...Non-alcoholic fatty liver disease(NAFLD)has become the world’s largest chronic liver disease in the 21st century,affecting 20%-30%of the world’s population.As the epidemiology,etiology,and pathogenesis of NAFLD have been studied in-depth,it has been gradually recognized that most patients with NAFLD have one or more combined metabolic abnormalities known as metabolic syndrome.In 2020,the international expert group changed the name of NAFLD to metabolic-associated fatty liver disease(MAFLD)and proposed new diagnostic criteria for MAFLD and MAFLD-related liver cirrhosis,as well as the conceptual framework of other cause-related fatty liver diseases to avoid diagnosis based on the exclusion of other causes and better reflect its pathogenesis.However,there are still many ambiguities in the term,and changing the name does not address the unmet key needs in the field.The change from NAFLD to MAFLD was not just a change of definition.The problems and challenges are summarized as follows:epidemiology,children,rationality of"metabolism,"diagnostic criteria,double/multiple causes,drug discovery,clinical trials,and awareness raising.Metabolic-associated fatty liver disease has complex disease characteristics,and there are still some problems that need to be solved.展开更多
Methods in programs must be accurately named to facilitate source code analysis and comprehension.With the evolution of software,method names may be inconsistent with their implemented method bodies,leading to inaccur...Methods in programs must be accurately named to facilitate source code analysis and comprehension.With the evolution of software,method names may be inconsistent with their implemented method bodies,leading to inaccurate or buggy method names.Debugging method names remains an important topic in the literature.Although researchers have proposed several approaches to suggest accurate method names once the method bodies have been modified,two main drawbacks remain to be solved:there is no analysis of method name structure,and the programming context information is not captured efficiently.To resolve these drawbacks and suggest more accurate method names,we propose a novel automated approach based on the analysis of the method name structure and lexical analysis with the programming context information.Our approach first leverages deep feature representation to embed method names and method bodies in vectors.Then,it obtains useful verb-tokens from a large method corpus through structural analysis and noun-tokens from method bodies through lexical analysis.Finally,our approach dynamically combines these tokens to form and recommend high-quality and project-specific method names.Experimental results over 2111 Java testing methods show that the proposed approach can achieve a Hit Ratio,or Hit@5,of 33.62%and outperform the state-of-the-art approach by 14.12%in suggesting accurate method names.We also demonstrate the effectiveness of structural and lexical analyses in our approach.展开更多
基金Tianjin Key Medical Discipline(Specialty)Construction Project(TJYXZDXK-059B)Tianjin Health Science and Technology Project key discipline special(TJWJ2022XK034)Research Project of Chinese Traditional Medicine and Chinese Traditional Medicine Combined with Western Medicine of Tianjin Municipal Health and Family Planning Commission(2021022)。
文摘Non-alcoholic fatty liver disease(NAFLD)has become the world’s largest chronic liver disease in the 21st century,affecting 20%-30%of the world’s population.As the epidemiology,etiology,and pathogenesis of NAFLD have been studied in-depth,it has been gradually recognized that most patients with NAFLD have one or more combined metabolic abnormalities known as metabolic syndrome.In 2020,the international expert group changed the name of NAFLD to metabolic-associated fatty liver disease(MAFLD)and proposed new diagnostic criteria for MAFLD and MAFLD-related liver cirrhosis,as well as the conceptual framework of other cause-related fatty liver diseases to avoid diagnosis based on the exclusion of other causes and better reflect its pathogenesis.However,there are still many ambiguities in the term,and changing the name does not address the unmet key needs in the field.The change from NAFLD to MAFLD was not just a change of definition.The problems and challenges are summarized as follows:epidemiology,children,rationality of"metabolism,"diagnostic criteria,double/multiple causes,drug discovery,clinical trials,and awareness raising.Metabolic-associated fatty liver disease has complex disease characteristics,and there are still some problems that need to be solved.
基金Project supported by the National Natural Science Foundation of China(Nos.61902181 and 62002161)the China Postdoctoral Science Foundation(No.2020M671489)+1 种基金the CCF-Tencent Open Research Fund(No.RAGR20200106)and the Nanjing University of Aeronautics and Astronautics Postgraduate Research and Practice Innovation Program(No.xcxjh20211612)。
文摘Methods in programs must be accurately named to facilitate source code analysis and comprehension.With the evolution of software,method names may be inconsistent with their implemented method bodies,leading to inaccurate or buggy method names.Debugging method names remains an important topic in the literature.Although researchers have proposed several approaches to suggest accurate method names once the method bodies have been modified,two main drawbacks remain to be solved:there is no analysis of method name structure,and the programming context information is not captured efficiently.To resolve these drawbacks and suggest more accurate method names,we propose a novel automated approach based on the analysis of the method name structure and lexical analysis with the programming context information.Our approach first leverages deep feature representation to embed method names and method bodies in vectors.Then,it obtains useful verb-tokens from a large method corpus through structural analysis and noun-tokens from method bodies through lexical analysis.Finally,our approach dynamically combines these tokens to form and recommend high-quality and project-specific method names.Experimental results over 2111 Java testing methods show that the proposed approach can achieve a Hit Ratio,or Hit@5,of 33.62%and outperform the state-of-the-art approach by 14.12%in suggesting accurate method names.We also demonstrate the effectiveness of structural and lexical analyses in our approach.
