As the realm of enterprise-level conversational AI continues to evolve, it becomes evident that while generalized Large Language Models (LLMs) like GPT-3.5 bring remarkable capabilities, they also bring forth formidab...As the realm of enterprise-level conversational AI continues to evolve, it becomes evident that while generalized Large Language Models (LLMs) like GPT-3.5 bring remarkable capabilities, they also bring forth formidable challenges. These models, honed on vast and diverse datasets, have undoubtedly pushed the boundaries of natural language understanding and generation. However, they often stumble when faced with the intricate demands of nuanced enterprise applications. This research advocates for a strategic paradigm shift, urging enterprises to embrace a fine-tuning approach as a means to optimize conversational AI. While generalized LLMs are linguistic marvels, their inability to cater to the specific needs of businesses across various industries poses a critical challenge. This strategic shift involves empowering enterprises to seamlessly integrate their own datasets into LLMs, a process that extends beyond linguistic enhancement. The core concept of this approach centers on customization, enabling businesses to fine-tune the AI’s functionality to fit precisely within their unique business landscapes. By immersing the LLM in industry-specific documents, customer interaction records, internal reports, and regulatory guidelines, the AI transcends its generic capabilities to become a sophisticated conversational partner aligned with the intricacies of the enterprise’s domain. The transformative potential of this fine-tuning approach cannot be overstated. It enables a transition from a universal AI solution to a highly customizable tool. The AI evolves from being a linguistic powerhouse to a contextually aware, industry-savvy assistant. As a result, it not only responds with linguistic accuracy but also with depth, relevance, and resonance, significantly elevating user experiences and operational efficiency. In the subsequent sections, this paper delves into the intricacies of fine-tuning, exploring the multifaceted challenges and abundant opportunities it presents. It addresses the technical intricacies of data integration, ethical considerations surrounding data usage, and the broader implications for the future of enterprise AI. The journey embarked upon in this research holds the potential to redefine the role of conversational AI in enterprises, ushering in an era where AI becomes a dynamic, deeply relevant, and highly effective tool, empowering businesses to excel in an ever-evolving digital landscape.展开更多
The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules:(1)wheel profile generation,(2)multi-body dynamics simulation,and(3)an optimization algorithm.For the first module,a ...The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules:(1)wheel profile generation,(2)multi-body dynamics simulation,and(3)an optimization algorithm.For the first module,a comparably conservative rotary-scaling finetuning(RSFT)method,which introduces two design variables and an empirical formula,is proposed to fine-tune the traditional wheel profiles for improving their engineering applicability.For the second module,for the TRAXX locomotives serving on the Blankenburg–Rubeland line,an optimization function representing the relationship between the wheel profile and the wheel–rail wear number is established based on Kriging surrogate model(KSM).For the third module,a method combining the regression capability of KSM with the iterative computing power of particle swarm optimization(PSO)is proposed to quickly and reliably implement the task of optimizing wheel profiles.Finally,with the RSFT–KSM–PSO method,we propose two wear-resistant wheel profiles for the TRAXX locomotives serving on the Blankenburg–Rubeland line,namely S1002-S and S1002-M.The S1002-S profile minimizes the total wear number by 30%,while the S1002-M profile makes the wear distribution more uniform through a proper sacrifice of the tread wear number,and the total wear number is reduced by 21%.The quasi-static and hunting stability tests further demonstrate that the profile designed by the RSFT–KSM–PSO method is promising for practical engineering applications.展开更多
This paper develops a wheel profile fine-tuning system(WPFTS)that comprehensively considers the influence of wheel profile on wheel damage,vehicle stability,vehicle safety,and passenger comfort.WPFTS can recommend one...This paper develops a wheel profile fine-tuning system(WPFTS)that comprehensively considers the influence of wheel profile on wheel damage,vehicle stability,vehicle safety,and passenger comfort.WPFTS can recommend one or more optimized wheel profiles according to train operators’needs,e.g.,reducing wheel wear,mitigating the development of wheel out-of-roundness(OOR),improving the shape stability of the wheel profile.Specifically,WPFTS includes four modules:(I)a wheel profile generation module based on the rotary-scaling finetuning(RSFT)method;(II)a multi-objective generation module consisting of a rigid multi-body dynamics simulation(MBS)model,an analytical model,and a rigid–flexible MBS model,for generating 11 objectives related to wheel damage,vehicle stability,vehicle safety,and passenger comfort;(III)a weight assignment module consisting of an adaptive weight assignment strategy and a manual weight assignment strategy;and(IV)an optimization module based on radial basis function(RBF)and particle swarm optimization(PSO).Finally,three cases are introduced to show how WPTFS recommends a wheel profile according to train operators’needs.Among them,a wheel profile with high shape stability,a wheel profile for mitigating the development of wheel OOR,and a wheel profile considering hunting stability and derailment safety are developed,respectively.展开更多
During cerebral cortical cortex neurogenesis two major types of progenitors generate a variety of morphologically and functionally diverse projection neurons destined for the different cortical layers in non-gyrified ...During cerebral cortical cortex neurogenesis two major types of progenitors generate a variety of morphologically and functionally diverse projection neurons destined for the different cortical layers in non-gyrified mice. Radial glia cells (RGCs) undergo mitosis in the cortical ventricular zone and exhibit an apical-basal cell polarity, whereas non-polar intermediate progenitor cells (IPCs) divide basally in the subventricular zone (Franco and Muller, 2013; Taverna et al., 2014).展开更多
Background: Sperm DNA fragmentation(sDF) has been proved to be an important parameter in order to predict in vitro the potential fertility of a semen sample. Colloid centrifugation could be a suitable technique to ...Background: Sperm DNA fragmentation(sDF) has been proved to be an important parameter in order to predict in vitro the potential fertility of a semen sample. Colloid centrifugation could be a suitable technique to select those donkey sperm more resistant to DNA fragmentation after thawing. Previous studies have shown that to elucidate the latent damage of the DNA molecule, sDF should be assessed dynamically, where the rate of fragmentation between treatments indicates how resistant the DNA is to iatrogenic damage. The rate of fragmentation is calculated using the slope of a linear regression equation. However, it has not been studied if s DF dynamics fit this model. The objectives of this study were to evaluate the effect of different after-thawing centrifugation protocols on sperm DNA fragmentation and elucidate the most accurate mathematical model(linear regression, exponential or polynomial) for DNA fragmentation over time in frozen-thawed donkey semen.Results: After submitting post-thaw semen samples to no centrifugation(UDC), sperm washing(SW) or single layer centrifugation(SLC) protocols, sD F values after 6 h of incubation were significantly lower in SLC samples than in SW or UDC.Coefficient of determination(R-2) values were significantly higher for a second order polynomial model than for linear or exponential. The highest values for acceleration of fragmentation(aSDF) were obtained for SW, fol owed by SLC and UDC.Conclusion: SLC after thawing seems to preserve longer DNA longevity in comparison to UDC and SW. Moreover,the fine-tuning of models has shown that sDF dynamics in frozen-thawed donkey semen fit a second order polynomial model, which implies that fragmentation rate is not constant and fragmentation acceleration must be taken into account to elucidate hidden damage in the DNA molecule.展开更多
Chinese Vice Premier’s visit to Africa continues to emphasize the mutual cooperation,with a focus on agriculture FOR many years,the Chinese Government has dispatched the minister of foreign affairs to Africa for the ...Chinese Vice Premier’s visit to Africa continues to emphasize the mutual cooperation,with a focus on agriculture FOR many years,the Chinese Government has dispatched the minister of foreign affairs to Africa for the first official visit of a year.This year,however,that rule was broken when Hui Liangyu,Chinese Vice Premier,made the 14-day trip. On January 6-19,Hui paid official visits to Mauritius,Zambia,the Democratic Republic of Congo(DRC),Cameroon and Senegal,focusing on economic and agri-展开更多
Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing(NLP)and yield state-of-the-art results on many downstream tasks.Recent studies on adapting BERT to new task...Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing(NLP)and yield state-of-the-art results on many downstream tasks.Recent studies on adapting BERT to new tasks mainly focus on modifying the model structure,re-designing the pre-training tasks,and leveraging external data and knowledge.The fine-tuning strategy itself has yet to be fully explored.In this paper,we improve the fine-tuning of BERT with two effective mechanisms:self-ensemble and self-distillation.The self-ensemble mechanism utilizes the checkpoints from an experience pool to integrate the teacher model.In order to transfer knowledge from the teacher model to the student model efficiently,we further use knowledge distillation,which is called self-distillation because the distillation comes from the model itself through the time dimension.Experiments on the GLUE benchmark and the Text Classification benchmark show that our proposed approach can significantly improve the adaption of BERT without any external data or knowledge.We conduct exhaustive experiments to investigate the efficiency of the self-ensemble and self-distillation mechanisms,and our proposed approach achieves a new state-of-the-art result on the SNLI dataset.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
Dramatic decrease of sugar uptake is a general phenomenon in Streptomyces at stationary phase,when antibiotics are extensively produced.Milbemycins produced by Streptomyces bingchenggensis are a group of valuable macr...Dramatic decrease of sugar uptake is a general phenomenon in Streptomyces at stationary phase,when antibiotics are extensively produced.Milbemycins produced by Streptomyces bingchenggensis are a group of valuable macrolide biopesticides,while the low yield and titer impede their broad applications in agricultural field.Considering that inadequate sugar uptake generally hinders titer improvement of desired products,we mined the underlying sugar uptake systems and fine-tuned their expression in this work.First,we screened the candidates at both genomic and transcriptomic level in S.bingchenggensis.Then,two ATP-binding cassette transporters named TP2 and TP5 were characterized to improve milbemycin titer and yield significantly.Next,the appropriate native temporal promoters were selected and used to tune the expression of TP2 and TP5,resulting in a maximal milbemycin A3/A4 titer increase by 36.9%to 3321 mg/L.Finally,TP2 and TP5 were broadly finetuned in another two macrolide biopesticide producers Streptomyces avermitilis and Streptomyces cyaneogriseus,leading to a maximal titer improvement of 34.1%and 52.6%for avermectin B1a and nemadectin,respectively.This work provides useful transporter tools and corresponding engineering strategy for Streptomyces.展开更多
A long--expected regulation to guide public fund investment is issued Chinese Premier Li Keqiang signed a State Council decree on May 5 to promulgate a regulation on government investment, which will take effect on Ju...A long--expected regulation to guide public fund investment is issued Chinese Premier Li Keqiang signed a State Council decree on May 5 to promulgate a regulation on government investment, which will take effect on July 1.展开更多
There are numerous microorganisms in nature capable of synthesizing diverse useful compounds;however,these natural microorganisms are generally inefficient in the production of target products on an industrial scale,r...There are numerous microorganisms in nature capable of synthesizing diverse useful compounds;however,these natural microorganisms are generally inefficient in the production of target products on an industrial scale,relative to either chemical synthesis or extraction methods.To achieve industrial production of useful compounds,these natural microorganisms must undergo a certain degree of mutation or effective fine-tuning strategies.This review describes how to achieve an ideal metabolic fine-tuned process,including static control strategies and dynamic control strategies.The static control strategies mainly focus on various matabolic engineering strategies,including protein engineering,upregulation/downregulation,and combinatrorial control of these metabolic engineering strategies,to enhance the flexibility of their application in fine-tuned metabolic metworks.Then,we focus on the dynamic control strategies for fine-tuned metabolic metworks.The design principles derived would guide us to construct microbial cell factories for various useful compounds.展开更多
The orderly deposition of secondary cell wall(SCW)in plants is implicated in various biological programs and is precisely controlled.Although many positive and negative regulators of SCW have been documented,the molec...The orderly deposition of secondary cell wall(SCW)in plants is implicated in various biological programs and is precisely controlled.Although many positive and negative regulators of SCW have been documented,the molecular mechanisms underlying SCW formation coordinated with distinct cellular physiological processes during plant adaptive growth remain largely unclear.Here,we report the identification of Cellulose Synthase co-expressed Kinase1(CSK1),which encodes a receptor-like cytoplasmic kinase,as a negative regulator of SCW formation and its signaling cascade in rice.Transcriptome deep sequencing of developing internodes and genome-wide co-expression assays revealed that CSK1 is co-expressed with cellulose synthase genes and is responsive to various stress stimuli.The increased SCW thickness and vigorous vessel transport in csk1 indicate that CSK1 functions as a negative regulator of SCW biosynthesis.Through observation of green fluorescent protein-tagged CSK1 in rice protoplasts and stable transgenic plants,we found that CSK1 is localized in the nucleus and cytoplasm adjacent to the plasma membrane.Biochemical and molecular assays demonstrated that CSK1 phosphorylates VASCULAR-RELATED NAC-DOMAIN 6(VND6),a master SCW-associated transcription factor,in the nucleus,which reduces the transcription of a suite of SCW-related genes,thereby attenuating SCW accumulation.Consistently,genetic analyses show that CSK1 functions upstream of VND6 in regulating SCW formation.Interestingly,our physiological analyses revealed that CSK1 and VND6 are involved in abscisic acid-mediated regulation of cell growth and SCW deposition.Taken together,these results indicate that the CSK1-VND6 module is an important component of the SCW biosynthesis machinery,which coordinates SCW accumulation and adaptive growth in rice.Our study not only identifies a new regulator of SCW biosynthesis but also reveals a fine-tuned mechanism for precise control of SCW deposition,offering tools for rationally tailoring agronomic traits.展开更多
Current deep learning approaches are cutting-edge methods for solving classification tasks.Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used....Current deep learning approaches are cutting-edge methods for solving classification tasks.Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used.Large models raise the major problem of their memory consumption and processor usage and lead to a prohibitive ecological footprint.In that paper,we present a novel visual analytics approach to interactively prune those networks and thus limit that issue.Our technique leverages a novel sparkline matrix visualization technique as well as a novel local metric which evaluates the discriminatory power of a filter to guide the pruning process and make it interpretable.We assess the well-founded of our approach through two realistic case studies and a user study.For both of them,the interactive refinement of the model led to a significantly smaller model having similar prediction accuracy than the original one.展开更多
文摘As the realm of enterprise-level conversational AI continues to evolve, it becomes evident that while generalized Large Language Models (LLMs) like GPT-3.5 bring remarkable capabilities, they also bring forth formidable challenges. These models, honed on vast and diverse datasets, have undoubtedly pushed the boundaries of natural language understanding and generation. However, they often stumble when faced with the intricate demands of nuanced enterprise applications. This research advocates for a strategic paradigm shift, urging enterprises to embrace a fine-tuning approach as a means to optimize conversational AI. While generalized LLMs are linguistic marvels, their inability to cater to the specific needs of businesses across various industries poses a critical challenge. This strategic shift involves empowering enterprises to seamlessly integrate their own datasets into LLMs, a process that extends beyond linguistic enhancement. The core concept of this approach centers on customization, enabling businesses to fine-tune the AI’s functionality to fit precisely within their unique business landscapes. By immersing the LLM in industry-specific documents, customer interaction records, internal reports, and regulatory guidelines, the AI transcends its generic capabilities to become a sophisticated conversational partner aligned with the intricacies of the enterprise’s domain. The transformative potential of this fine-tuning approach cannot be overstated. It enables a transition from a universal AI solution to a highly customizable tool. The AI evolves from being a linguistic powerhouse to a contextually aware, industry-savvy assistant. As a result, it not only responds with linguistic accuracy but also with depth, relevance, and resonance, significantly elevating user experiences and operational efficiency. In the subsequent sections, this paper delves into the intricacies of fine-tuning, exploring the multifaceted challenges and abundant opportunities it presents. It addresses the technical intricacies of data integration, ethical considerations surrounding data usage, and the broader implications for the future of enterprise AI. The journey embarked upon in this research holds the potential to redefine the role of conversational AI in enterprises, ushering in an era where AI becomes a dynamic, deeply relevant, and highly effective tool, empowering businesses to excel in an ever-evolving digital landscape.
基金the Assets4Rail Project which is funded by the Shift2Rail Joint Undertaking under the EU’s H2020 program(Grant No.826250)the Open Research Fund of State Key Laboratory of Traction Power of Southwest Jiaotong University(Grant No.TPL2011)+1 种基金part of the experiment data concerning the railway line is supported by the DynoTRAIN Project,funded by European Commission(Grant No.234079)The first author is also supported by the China Scholarship Council(Grant No.201707000113).
文摘The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules:(1)wheel profile generation,(2)multi-body dynamics simulation,and(3)an optimization algorithm.For the first module,a comparably conservative rotary-scaling finetuning(RSFT)method,which introduces two design variables and an empirical formula,is proposed to fine-tune the traditional wheel profiles for improving their engineering applicability.For the second module,for the TRAXX locomotives serving on the Blankenburg–Rubeland line,an optimization function representing the relationship between the wheel profile and the wheel–rail wear number is established based on Kriging surrogate model(KSM).For the third module,a method combining the regression capability of KSM with the iterative computing power of particle swarm optimization(PSO)is proposed to quickly and reliably implement the task of optimizing wheel profiles.Finally,with the RSFT–KSM–PSO method,we propose two wear-resistant wheel profiles for the TRAXX locomotives serving on the Blankenburg–Rubeland line,namely S1002-S and S1002-M.The S1002-S profile minimizes the total wear number by 30%,while the S1002-M profile makes the wear distribution more uniform through a proper sacrifice of the tread wear number,and the total wear number is reduced by 21%.The quasi-static and hunting stability tests further demonstrate that the profile designed by the RSFT–KSM–PSO method is promising for practical engineering applications.
基金This work was supported by China Scholarship Council(Grant No.201707000113).
文摘This paper develops a wheel profile fine-tuning system(WPFTS)that comprehensively considers the influence of wheel profile on wheel damage,vehicle stability,vehicle safety,and passenger comfort.WPFTS can recommend one or more optimized wheel profiles according to train operators’needs,e.g.,reducing wheel wear,mitigating the development of wheel out-of-roundness(OOR),improving the shape stability of the wheel profile.Specifically,WPFTS includes four modules:(I)a wheel profile generation module based on the rotary-scaling finetuning(RSFT)method;(II)a multi-objective generation module consisting of a rigid multi-body dynamics simulation(MBS)model,an analytical model,and a rigid–flexible MBS model,for generating 11 objectives related to wheel damage,vehicle stability,vehicle safety,and passenger comfort;(III)a weight assignment module consisting of an adaptive weight assignment strategy and a manual weight assignment strategy;and(IV)an optimization module based on radial basis function(RBF)and particle swarm optimization(PSO).Finally,three cases are introduced to show how WPTFS recommends a wheel profile according to train operators’needs.Among them,a wheel profile with high shape stability,a wheel profile for mitigating the development of wheel OOR,and a wheel profile considering hunting stability and derailment safety are developed,respectively.
文摘During cerebral cortical cortex neurogenesis two major types of progenitors generate a variety of morphologically and functionally diverse projection neurons destined for the different cortical layers in non-gyrified mice. Radial glia cells (RGCs) undergo mitosis in the cortical ventricular zone and exhibit an apical-basal cell polarity, whereas non-polar intermediate progenitor cells (IPCs) divide basally in the subventricular zone (Franco and Muller, 2013; Taverna et al., 2014).
基金partially supported by grants RZ2009-00006-00-00(Instituto Nacional de Investigacion y Tecnología Agraria y Alimentaria,Ministerio de Ciencia e Innovación,Spain)AGL-2013-42726-R(Secretaria de Estado de Investigacion,Desarrollo e Innovacion,Ministerio de Economia y Competitividad,Spain)+1 种基金supported by a Ph.D.fellowship from the ceiA3(Andalucia,Spain)with funding provided by Banco Santander through its Global Division,Santander Universidadesfunded by the Swedish Foundation for Equine Research,Stockholm,Sweden(H14-47-008)
文摘Background: Sperm DNA fragmentation(sDF) has been proved to be an important parameter in order to predict in vitro the potential fertility of a semen sample. Colloid centrifugation could be a suitable technique to select those donkey sperm more resistant to DNA fragmentation after thawing. Previous studies have shown that to elucidate the latent damage of the DNA molecule, sDF should be assessed dynamically, where the rate of fragmentation between treatments indicates how resistant the DNA is to iatrogenic damage. The rate of fragmentation is calculated using the slope of a linear regression equation. However, it has not been studied if s DF dynamics fit this model. The objectives of this study were to evaluate the effect of different after-thawing centrifugation protocols on sperm DNA fragmentation and elucidate the most accurate mathematical model(linear regression, exponential or polynomial) for DNA fragmentation over time in frozen-thawed donkey semen.Results: After submitting post-thaw semen samples to no centrifugation(UDC), sperm washing(SW) or single layer centrifugation(SLC) protocols, sD F values after 6 h of incubation were significantly lower in SLC samples than in SW or UDC.Coefficient of determination(R-2) values were significantly higher for a second order polynomial model than for linear or exponential. The highest values for acceleration of fragmentation(aSDF) were obtained for SW, fol owed by SLC and UDC.Conclusion: SLC after thawing seems to preserve longer DNA longevity in comparison to UDC and SW. Moreover,the fine-tuning of models has shown that sDF dynamics in frozen-thawed donkey semen fit a second order polynomial model, which implies that fragmentation rate is not constant and fragmentation acceleration must be taken into account to elucidate hidden damage in the DNA molecule.
文摘Chinese Vice Premier’s visit to Africa continues to emphasize the mutual cooperation,with a focus on agriculture FOR many years,the Chinese Government has dispatched the minister of foreign affairs to Africa for the first official visit of a year.This year,however,that rule was broken when Hui Liangyu,Chinese Vice Premier,made the 14-day trip. On January 6-19,Hui paid official visits to Mauritius,Zambia,the Democratic Republic of Congo(DRC),Cameroon and Senegal,focusing on economic and agri-
基金supported by the National Key Research and Development Program of China under Grant No.2020AAA0106700the National Natural Science Foundation of China under Grant No.62022027.
文摘Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing(NLP)and yield state-of-the-art results on many downstream tasks.Recent studies on adapting BERT to new tasks mainly focus on modifying the model structure,re-designing the pre-training tasks,and leveraging external data and knowledge.The fine-tuning strategy itself has yet to be fully explored.In this paper,we improve the fine-tuning of BERT with two effective mechanisms:self-ensemble and self-distillation.The self-ensemble mechanism utilizes the checkpoints from an experience pool to integrate the teacher model.In order to transfer knowledge from the teacher model to the student model efficiently,we further use knowledge distillation,which is called self-distillation because the distillation comes from the model itself through the time dimension.Experiments on the GLUE benchmark and the Text Classification benchmark show that our proposed approach can significantly improve the adaption of BERT without any external data or knowledge.We conduct exhaustive experiments to investigate the efficiency of the self-ensemble and self-distillation mechanisms,and our proposed approach achieves a new state-of-the-art result on the SNLI dataset.
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.
基金This work was financially supported by National Natural Science Foundation of China(Grant Nos:31772242,31972348,and 31672092).
文摘Dramatic decrease of sugar uptake is a general phenomenon in Streptomyces at stationary phase,when antibiotics are extensively produced.Milbemycins produced by Streptomyces bingchenggensis are a group of valuable macrolide biopesticides,while the low yield and titer impede their broad applications in agricultural field.Considering that inadequate sugar uptake generally hinders titer improvement of desired products,we mined the underlying sugar uptake systems and fine-tuned their expression in this work.First,we screened the candidates at both genomic and transcriptomic level in S.bingchenggensis.Then,two ATP-binding cassette transporters named TP2 and TP5 were characterized to improve milbemycin titer and yield significantly.Next,the appropriate native temporal promoters were selected and used to tune the expression of TP2 and TP5,resulting in a maximal milbemycin A3/A4 titer increase by 36.9%to 3321 mg/L.Finally,TP2 and TP5 were broadly finetuned in another two macrolide biopesticide producers Streptomyces avermitilis and Streptomyces cyaneogriseus,leading to a maximal titer improvement of 34.1%and 52.6%for avermectin B1a and nemadectin,respectively.This work provides useful transporter tools and corresponding engineering strategy for Streptomyces.
文摘A long--expected regulation to guide public fund investment is issued Chinese Premier Li Keqiang signed a State Council decree on May 5 to promulgate a regulation on government investment, which will take effect on July 1.
基金This work was supported by the National Key Research and Development Program of China(2017YFC1600403)the National Science Fund for Excellent Young Scholars(21822806)+2 种基金the National Natural Science Foundation of China(31670095,31770097)the Fundamental Research Funds for the Central Universities(JUSRP51701A)the National First-class Discipline Program of Light Industry Technology and Engineering(LITE2018-08).
文摘There are numerous microorganisms in nature capable of synthesizing diverse useful compounds;however,these natural microorganisms are generally inefficient in the production of target products on an industrial scale,relative to either chemical synthesis or extraction methods.To achieve industrial production of useful compounds,these natural microorganisms must undergo a certain degree of mutation or effective fine-tuning strategies.This review describes how to achieve an ideal metabolic fine-tuned process,including static control strategies and dynamic control strategies.The static control strategies mainly focus on various matabolic engineering strategies,including protein engineering,upregulation/downregulation,and combinatrorial control of these metabolic engineering strategies,to enhance the flexibility of their application in fine-tuned metabolic metworks.Then,we focus on the dynamic control strategies for fine-tuned metabolic metworks.The design principles derived would guide us to construct microbial cell factories for various useful compounds.
基金supported by the National Nature Science Foundation of China(NSFC,32030077)to Y.Z.CAS project for young scientists in basic research(YSBR-078)to B.Z.+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant no.XDA24010102)to Y.Z.Youth Innovation Promotion Association CAS(Y202030)to B.Z.the State Key Laboratory of Plant Genomics to Y.Z.
文摘The orderly deposition of secondary cell wall(SCW)in plants is implicated in various biological programs and is precisely controlled.Although many positive and negative regulators of SCW have been documented,the molecular mechanisms underlying SCW formation coordinated with distinct cellular physiological processes during plant adaptive growth remain largely unclear.Here,we report the identification of Cellulose Synthase co-expressed Kinase1(CSK1),which encodes a receptor-like cytoplasmic kinase,as a negative regulator of SCW formation and its signaling cascade in rice.Transcriptome deep sequencing of developing internodes and genome-wide co-expression assays revealed that CSK1 is co-expressed with cellulose synthase genes and is responsive to various stress stimuli.The increased SCW thickness and vigorous vessel transport in csk1 indicate that CSK1 functions as a negative regulator of SCW biosynthesis.Through observation of green fluorescent protein-tagged CSK1 in rice protoplasts and stable transgenic plants,we found that CSK1 is localized in the nucleus and cytoplasm adjacent to the plasma membrane.Biochemical and molecular assays demonstrated that CSK1 phosphorylates VASCULAR-RELATED NAC-DOMAIN 6(VND6),a master SCW-associated transcription factor,in the nucleus,which reduces the transcription of a suite of SCW-related genes,thereby attenuating SCW accumulation.Consistently,genetic analyses show that CSK1 functions upstream of VND6 in regulating SCW formation.Interestingly,our physiological analyses revealed that CSK1 and VND6 are involved in abscisic acid-mediated regulation of cell growth and SCW deposition.Taken together,these results indicate that the CSK1-VND6 module is an important component of the SCW biosynthesis machinery,which coordinates SCW accumulation and adaptive growth in rice.Our study not only identifies a new regulator of SCW biosynthesis but also reveals a fine-tuned mechanism for precise control of SCW deposition,offering tools for rationally tailoring agronomic traits.
基金We acknowledge the Nouvelle-Aquitaine Region,Bordeaux Métropole and SUEZ,le LyRE for mainly funding and supporting this work through the Convention N°AAPR2020-2019-8171810。
文摘Current deep learning approaches are cutting-edge methods for solving classification tasks.Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used.Large models raise the major problem of their memory consumption and processor usage and lead to a prohibitive ecological footprint.In that paper,we present a novel visual analytics approach to interactively prune those networks and thus limit that issue.Our technique leverages a novel sparkline matrix visualization technique as well as a novel local metric which evaluates the discriminatory power of a filter to guide the pruning process and make it interpretable.We assess the well-founded of our approach through two realistic case studies and a user study.For both of them,the interactive refinement of the model led to a significantly smaller model having similar prediction accuracy than the original one.