Artificial Intelligence,in general,and particularly Natural language Processing(NLP)has made unprecedented progress recently in many areas of life,automating and enabling a lot of activities such as speech recognition...Artificial Intelligence,in general,and particularly Natural language Processing(NLP)has made unprecedented progress recently in many areas of life,automating and enabling a lot of activities such as speech recognition,language translations,search engines,and text-generations,among others.Software engineering and Software Development Life Cycle(SDLC)is also not left out.Indeed,one of the most critical starting points of SDLC is the requirement engineering stage which,traditionally,has been dominated by business analysts.Unfortunately,these analysts have always done the job not just in a monotonous way,but also in an error-prone,tedious,and inefficient manner,thus leading to poorly crafted works with lots of requirement creep and sometimes technical debts.This work,which is the first iteration in a series,looks at how this crucial initial stage could not just be automated but also improved using the latest techniques in Artificial Intelligence and NLP.Using the popular and available PROMISE dataset,the emphasis,for this first part,is on improving requirement engineering,particularly the classification of Functional and Non-functional Requirements.Transformer-powered BERT(Bidirectional Encoder Representations from Transformers)Large Language Model(LLM)was adopted with validation performances of 0.93,0.88,and 0.88.The experimental results showed that Base-BERT LLM,its distilled counterpart,Distil-BERT,and its domain-specific version,Code-BERT,can be reliable in these tasks.We believe that our findings could encourage the adoption of LLM,such as BERT,in Requirement Engineering(RE)-related tasks like the FR/NFR classification.This kind of insight can help RE researchers as well as industry practitioners in their future work.展开更多
The specific functions of the genes encoding arginine biosynthesis enzymes in plants are not well characterized. We report the isolation and characterization of Arabidopsis thaliana N-acetylglutamate kinase （NAGK）, ...The specific functions of the genes encoding arginine biosynthesis enzymes in plants are not well characterized. We report the isolation and characterization of Arabidopsis thaliana N-acetylglutamate kinase （NAGK）, which catalyzes the second step of arginine biosynthesis. NAGK is a plastid-localized protein and is expressed during most developmental processes in Arabidopsis. Heterologous expression of the Arabidopsis NAGK gene in a NAGK-deficient Escherichia coli strain fully restores bacterial growth on arginine-deficient medium, nagk mutant pollen tubes grow more slowly than wild type pollen tubes and the phenotype is restored by either specifically through complementation by NAGK in pollen, or exogenous supplementation of arginine, nagk female gametophytes are defective in micropylar pollen tube guidance due to the fact that female gametophyte cell fate specification was specifically affected. Expression of NAGK in synergid cells rescues the defect of nagk female gametophytes. Loss- of-function of NAGK results in Arabidopsis embryos not developing beyond the four-celled embryo stage. The embryo-defective phenotype in nagk/NAGK plants cannot be rescued by watering nagk/NAGK plants with arginine or ornithine supplementation. In conclusion, our results reveal a novel role of NAGK and arginine in regulating gametophyte function and embryo development, and provide valuable insights into arginine transport during embryo development.展开更多
文摘Artificial Intelligence,in general,and particularly Natural language Processing(NLP)has made unprecedented progress recently in many areas of life,automating and enabling a lot of activities such as speech recognition,language translations,search engines,and text-generations,among others.Software engineering and Software Development Life Cycle(SDLC)is also not left out.Indeed,one of the most critical starting points of SDLC is the requirement engineering stage which,traditionally,has been dominated by business analysts.Unfortunately,these analysts have always done the job not just in a monotonous way,but also in an error-prone,tedious,and inefficient manner,thus leading to poorly crafted works with lots of requirement creep and sometimes technical debts.This work,which is the first iteration in a series,looks at how this crucial initial stage could not just be automated but also improved using the latest techniques in Artificial Intelligence and NLP.Using the popular and available PROMISE dataset,the emphasis,for this first part,is on improving requirement engineering,particularly the classification of Functional and Non-functional Requirements.Transformer-powered BERT(Bidirectional Encoder Representations from Transformers)Large Language Model(LLM)was adopted with validation performances of 0.93,0.88,and 0.88.The experimental results showed that Base-BERT LLM,its distilled counterpart,Distil-BERT,and its domain-specific version,Code-BERT,can be reliable in these tasks.We believe that our findings could encourage the adoption of LLM,such as BERT,in Requirement Engineering(RE)-related tasks like the FR/NFR classification.This kind of insight can help RE researchers as well as industry practitioners in their future work.
基金supported by the Fund of Key Basic Theory Research of Ministry of Science and Technology of China(2013CB945100)the National Natural Science Foundation of China(31570317,31270362)
文摘The specific functions of the genes encoding arginine biosynthesis enzymes in plants are not well characterized. We report the isolation and characterization of Arabidopsis thaliana N-acetylglutamate kinase （NAGK）, which catalyzes the second step of arginine biosynthesis. NAGK is a plastid-localized protein and is expressed during most developmental processes in Arabidopsis. Heterologous expression of the Arabidopsis NAGK gene in a NAGK-deficient Escherichia coli strain fully restores bacterial growth on arginine-deficient medium, nagk mutant pollen tubes grow more slowly than wild type pollen tubes and the phenotype is restored by either specifically through complementation by NAGK in pollen, or exogenous supplementation of arginine, nagk female gametophytes are defective in micropylar pollen tube guidance due to the fact that female gametophyte cell fate specification was specifically affected. Expression of NAGK in synergid cells rescues the defect of nagk female gametophytes. Loss- of-function of NAGK results in Arabidopsis embryos not developing beyond the four-celled embryo stage. The embryo-defective phenotype in nagk/NAGK plants cannot be rescued by watering nagk/NAGK plants with arginine or ornithine supplementation. In conclusion, our results reveal a novel role of NAGK and arginine in regulating gametophyte function and embryo development, and provide valuable insights into arginine transport during embryo development.
