The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and...The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.展开更多
The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject ...The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject of source document due to models'small perspective.In order to make up these disadvantages,a multi‐domain attention pointer(MDA‐Pointer)abstractive summarisation model is proposed in this work.First,the model uses bidirectional long short‐term memory to encode,respectively,the word and sentence sequence of source document for obtaining the semantic representations at word and sentence level.Furthermore,the multi‐domain attention mechanism between the semantic representations and the summary word is established,and the proposed model can generate summary words under the proposed attention mechanism based on the words and sen-tences.Then,the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary,and the coverage mechanism is introduced,respectively,into word and sentence level to reduce the redundancy of sum-mary content.Finally,experiment validation is conducted on CNN/Daily Mail dataset.ROUGE evaluation indexes of the model without and with the coverage mechanism are improved respectively,and the results verify the validation of model proposed by this paper.展开更多
A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore...A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore,in this paper,a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words.Furthermore,it considers the importance of entity in complaint reports to ensure factual consistency of summary.Experimental results on the customer review datasets(Yelp and Amazon)and complaint report dataset(complaint reports of Shenyang in China)show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation.It unveils the effectiveness of our approach to helping in dealing with complaint reports.展开更多
Text summarization aims to generate a concise version of the original text.The longer the summary text is,themore detailed it will be fromthe original text,and this depends on the intended use.Therefore,the problem of...Text summarization aims to generate a concise version of the original text.The longer the summary text is,themore detailed it will be fromthe original text,and this depends on the intended use.Therefore,the problem of generating summary texts with desired lengths is a vital task to put the research into practice.To solve this problem,in this paper,we propose a new method to integrate the desired length of the summarized text into the encoder-decoder model for the abstractive text summarization problem.This length parameter is integrated into the encoding phase at each self-attention step and the decoding process by preserving the remaining length for calculating headattention in the generation process and using it as length embeddings added to theword embeddings.We conducted experiments for the proposed model on the two data sets,Cable News Network(CNN)Daily and NEWSROOM,with different desired output lengths.The obtained results show the proposed model’s effectiveness compared with related studies.展开更多
Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the cri...Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments.Consequently,we investigate the task of comment-aware abstractive text summarization for LPO-news,which can generate salient summary by learning pivotal case elements from the reader comments.In this paper,we present a hierarchical comment-aware encoder(HCAE),which contains four components:1)a traditional sequenceto-sequence framework as our baseline;2)a selective denoising module to filter the noisy of comments and distinguish the case elements;3)a merge module by coupling the source article and comments to yield comment-aware context representation;4)a recoding module to capture the interaction among the source article words conditioned on the comments.Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog,and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.展开更多
Text summarization is an important task in natural language processing and it has been applied in many applications.Recently,abstractive summarization has attracted many attentions.However,the traditional evaluation m...Text summarization is an important task in natural language processing and it has been applied in many applications.Recently,abstractive summarization has attracted many attentions.However,the traditional evaluation metrics that consider little semantic information,are unsuitable for evaluating the quality of deep learning based abstractive summarization models,since these models may generate new words that do not exist in the original text.Moreover,the out-of-vocabulary(OOV)problem that affects the evaluation results,has not been well solved yet.To address these issues,we propose a novel model called ENMS,to enhance existing N-gram based evaluation metrics with semantics.To be specific,we present two types of methods:N-gram based Semantic Matching(NSM for short),and N-gram based Semantic Similarity(NSS for short),to improve several widely-used evaluation metrics including ROUGE(Recall-Oriented Understudy for Gisting Evaluation),BLEU(Bilingual Evaluation Understudy),etc.NSM and NSS work in different ways.The former calculates the matching degree directly,while the latter mainly improves the similarity measurement.Moreover we propose an N-gram representation mechanism to explore the vector representation of N-grams(including skip-grams).It serves as the basis of our ENMS model,in which we exploit some simple but effective integration methods to solve the OOV problem efficiently.Experimental results over the TAC AESOP dataset show that the metrics improved by our methods are well correlated with human judgements and can be used to better evaluate abstractive summarization methods.展开更多
Manganese-oxidizing microbes are capable of oxidizing Mn(Ⅱ) to manganese oxides through direct enzymatic and indirect mechanisms.However,bacterial Mn(Ⅱ) oxidation in alkaline environment remains unclear.This study i...Manganese-oxidizing microbes are capable of oxidizing Mn(Ⅱ) to manganese oxides through direct enzymatic and indirect mechanisms.However,bacterial Mn(Ⅱ) oxidation in alkaline environment remains unclear.This study isolated an alkali-tolerant bacterium Pseudorhizobium flavum MXJ-1 from marine surface sediments that can oxidize Mn(Ⅱ) to biogenic manganese oxides(BioMnO_(x)).Characterization of BioMnO_(x) reveals that rod-shaped bacterial cells produce amorphous BioMnO_(x) containing mixed valences of Mn.The effects of different pH,carbon and nitrogen sources,metal ions on growth and Mn(Ⅱ) oxidation activity of strain MXJ-1 were investigated.Results elucidate that strain MXJ-1 adapts to a broad range of pH from 5.0 to 10.0,achieves a maximum Mn(Ⅱ) oxidation percentage of 69.41 %,but there is no significant correlation between biomass and Mn(Ⅱ) oxidation.Cu(Ⅱ) inhibited Mn(Ⅱ) oxidation at concentration as low as 20 μmol/L,and Mn(Ⅱ) oxidation decreased with Mn(Ⅱ) levels increasing to 500 μmol/L.Genomic analysis identified two genes encoding animal heme peroxidases(AHPs) and three encoding dihydrolipoyl dehydrogenases(DLDHs),highlighting the potential role of superoxide in Mn(Ⅱ) oxidation.Several alkali-tolerance genes,including those encoding Na+/H+ antiporter,were also identified,which probably associated with alkali-tolerance of MXJ-1.Superoxide detection by nitro blue tetrazolium(NBT) assay indicates it involved in Mn(Ⅱ) oxidation.This study isolated a Mn(Ⅱ)-oxidizing,alkali-tolerant bacterium,expanding insights into microbial Mn(Ⅱ) oxidation in extreme environments.展开更多
Exogenous organic input impacts soil phosphorus transformation.Meanwhile,dissolved organic matter(DOM)is crucial for biogeochemical functions.Nevertheless,the interaction between the structural composition of DOM and ...Exogenous organic input impacts soil phosphorus transformation.Meanwhile,dissolved organic matter(DOM)is crucial for biogeochemical functions.Nevertheless,the interaction between the structural composition of DOM and phosphorus during the soil formation process of phosphogypsum(PG)remains unknown.This study explores the interaction between the structural composition of DOM and phosphorus in enhanced PG under the participation of fungal microorganisms through different application amounts of exogenous organic matter and culture time.Results show that application of exogenous organic matter led to varying degrees of increase in dissolved organic carbon(DOC)concentration and humification extent in the soil-like substrate.Additionally,the relative abundance of protein-like component C3 exhibited a trend of initial increase followed by decline over time.The contents of available phosphorus(AP),microbial biomass phosphorus(MBP),and active phosphorus pools(Active-P)in the soil-like substrate are all enhanced overall.Furthermore,a significant correlation exists between DOC and AP as well as MBP.This suggests that DOM is a crucial factor in enhancing the phosphorus availability of the soil-like substrate.The enrichment of known phosphate-solubilizing fungi in culturing favors the decomposition,activation and utilization of hard-to-mineralize phosphorus components in the soil-like substrate.These findings help understand DOM’s biogeochemical behavior and offer insights into PG utilization and the sustainable development of China’s phosphorus industry.展开更多
A new type of asymmetric hydrogen atom abstraction catalysts,originated from the cinchona alkaloid family of natural products,has been successfully developed to access enantioselective epimerizations of meso-diols.Aft...A new type of asymmetric hydrogen atom abstraction catalysts,originated from the cinchona alkaloid family of natural products,has been successfully developed to access enantioselective epimerizations of meso-diols.After undergoing single-electron oxidation,the catalyst fulfills desymmetrization of meso-diols by selectively traping a hydrogen atom from a carbon center,which subsequently recaptures a hydrogen atom via abstraction from a thiol.The publication of this work will have a significant influence in the field of asymmetric radical chemistry.展开更多
The exponential growth of video content has driven significant advancements in video summarization techniques in recent years. Breakthroughs in deep learning have been particularly transformative, enabling more effect...The exponential growth of video content has driven significant advancements in video summarization techniques in recent years. Breakthroughs in deep learning have been particularly transformative, enabling more effective detection of key information and creating new possibilities for video synopsis. To summarize recent progress and accelerate research in this field,this paper provides a comprehensive review of deep learningbased video summarization methods developed over the past decade. We begin by examining the research landscape of video abstraction technologies and identifying core challenges in video summarization. Subsequently, we systematically analyze prevailing deep learning frameworks and methodologies employed in current video summarization systems, offering researchers a clear roadmap of the field's evolution. Unlike previous review works,we first classify research papers based on the structural hierarchy of the video(from frame-level to shot-level to video-level),then further categorize them according to the summary backbone model(feature extraction and spatiotemporal modeling).This approach provides a more systematic and hierarchical organization of the documents. Following this comprehensive review,we summarize the benchmark datasets and evaluation metrics commonly employed in the field. Finally, we analyze persistent challenges and propose insightful directions for future research,providing a forward-looking perspective on video summarization technologies. This systematic literature review is of great reference value to new researchers exploring the fields of deep learning and video summarization.展开更多
International Journal of Minerals,Metallurgy and Materials is dedicated to the publication and the dissemination of original research articles(and occasional invited reviews)in the fields of Minerals,Metallurgy and Ma...International Journal of Minerals,Metallurgy and Materials is dedicated to the publication and the dissemination of original research articles(and occasional invited reviews)in the fields of Minerals,Metallurgy and Materials.It is covered by EI Compendex,SCI Expanded,Chemical Abstract,etc.展开更多
The Chinese Journal of Natural Medicines(CJNM,ISSN 2095-6975),founded in 2003 and sponsored by China Pharmaceutical University,is a peer-reviewed journal published monthly in print by Science Press and online by Elsev...The Chinese Journal of Natural Medicines(CJNM,ISSN 2095-6975),founded in 2003 and sponsored by China Pharmaceutical University,is a peer-reviewed journal published monthly in print by Science Press and online by Elsevier.CJNM is currently indexed in Science Citation Index Expanded(SCIE),MEDLINE,BIOSIS Previews,BIOSIS Toxicology,CAB Abstracts.展开更多
In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false ...In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.展开更多
General Information Journal of Chinese Pharmaceutical Sciences(JCPS)is a peer-reviewed bimonthly journal founded in 1992.JCPS has been indexed in the Core Journals of China's science and technology field.At presen...General Information Journal of Chinese Pharmaceutical Sciences(JCPS)is a peer-reviewed bimonthly journal founded in 1992.JCPS has been indexed in the Core Journals of China's science and technology field.At present it has been included by Chemical Abstracts(CA),UIPD,Index of Copurnicus(IC),CABI,SCOPUS,JSTChina,DOAJ,EBSCO,CSTPCD,Chinese Science Citation Database(CSCD),the Chinese National Knowledge Infrastructure(CNKI)and World Journal Clout Index(WJCI)Report.展开更多
Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medic...Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medical practice,a rigorous and systematic evaluation of their medical competence is imperative.This study presents a comprehensive review of the established methodologies and benchmarks for evaluating the medical competence of LLMs,encompassing a thorough analysis of current assessment practices across medical knowledge,clinical practice competence,and ethical-safety considerations.By integrating clinician competency assessment frameworks into LLMs evaluation,we propose a structured tri-dimensional framework that systematically organizes existing evaluation approaches according to medical theoretical knowledge,clinical practice ability,and ethical-safety considerations.Furthermore,this research provides critical insights into future developmental trajectories while establishing foundational frameworks and standardization protocols for the integration of LLMs into medical practice.展开更多
文摘The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.
基金supported by the National Social Science Foundation of China(2017CG29)the Science and Technology Research Project of Chongqing Municipal Education Commission(2019CJ50)the Natural Science Foundation of Chongqing(2017CC29).
文摘The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject of source document due to models'small perspective.In order to make up these disadvantages,a multi‐domain attention pointer(MDA‐Pointer)abstractive summarisation model is proposed in this work.First,the model uses bidirectional long short‐term memory to encode,respectively,the word and sentence sequence of source document for obtaining the semantic representations at word and sentence level.Furthermore,the multi‐domain attention mechanism between the semantic representations and the summary word is established,and the proposed model can generate summary words under the proposed attention mechanism based on the words and sen-tences.Then,the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary,and the coverage mechanism is introduced,respectively,into word and sentence level to reduce the redundancy of sum-mary content.Finally,experiment validation is conducted on CNN/Daily Mail dataset.ROUGE evaluation indexes of the model without and with the coverage mechanism are improved respectively,and the results verify the validation of model proposed by this paper.
基金supported by National Natural Science Foundation of China(62276058,61902057,41774063)Fundamental Research Funds for the Central Universities(N2217003)Joint Fund of Science&Technology Department of Liaoning Province and State Key Laboratory of Robotics,China(2020-KF-12-11).
文摘A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore,in this paper,a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words.Furthermore,it considers the importance of entity in complaint reports to ensure factual consistency of summary.Experimental results on the customer review datasets(Yelp and Amazon)and complaint report dataset(complaint reports of Shenyang in China)show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation.It unveils the effectiveness of our approach to helping in dealing with complaint reports.
基金funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant Number 102.05-2020.26。
文摘Text summarization aims to generate a concise version of the original text.The longer the summary text is,themore detailed it will be fromthe original text,and this depends on the intended use.Therefore,the problem of generating summary texts with desired lengths is a vital task to put the research into practice.To solve this problem,in this paper,we propose a new method to integrate the desired length of the summarized text into the encoder-decoder model for the abstractive text summarization problem.This length parameter is integrated into the encoding phase at each self-attention step and the decoding process by preserving the remaining length for calculating headattention in the generation process and using it as length embeddings added to theword embeddings.We conducted experiments for the proposed model on the two data sets,Cable News Network(CNN)Daily and NEWSROOM,with different desired output lengths.The obtained results show the proposed model’s effectiveness compared with related studies.
基金supported by the National Key Research and Development Program of China (2018YFC0830105,2018YFC 0830101,2018YFC0830100)the National Natural Science Foundation of China (Grant Nos.61972186,61762056,61472168)+1 种基金the Yunnan Provincial Major Science and Technology Special Plan Projects (202002AD080001)the General Projects of Basic Research in Yunnan Province (202001AT070046,202001AT070047).
文摘Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments.Consequently,we investigate the task of comment-aware abstractive text summarization for LPO-news,which can generate salient summary by learning pivotal case elements from the reader comments.In this paper,we present a hierarchical comment-aware encoder(HCAE),which contains four components:1)a traditional sequenceto-sequence framework as our baseline;2)a selective denoising module to filter the noisy of comments and distinguish the case elements;3)a merge module by coupling the source article and comments to yield comment-aware context representation;4)a recoding module to capture the interaction among the source article words conditioned on the comments.Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog,and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.62172149,61632009,62172159,and 62172372the Natural Science Foundation of Hunan Province of China under Grant No.2021JJ30137the Open Project of ZHEJIANG LAB under Grant No.2019KE0AB02.
文摘Text summarization is an important task in natural language processing and it has been applied in many applications.Recently,abstractive summarization has attracted many attentions.However,the traditional evaluation metrics that consider little semantic information,are unsuitable for evaluating the quality of deep learning based abstractive summarization models,since these models may generate new words that do not exist in the original text.Moreover,the out-of-vocabulary(OOV)problem that affects the evaluation results,has not been well solved yet.To address these issues,we propose a novel model called ENMS,to enhance existing N-gram based evaluation metrics with semantics.To be specific,we present two types of methods:N-gram based Semantic Matching(NSM for short),and N-gram based Semantic Similarity(NSS for short),to improve several widely-used evaluation metrics including ROUGE(Recall-Oriented Understudy for Gisting Evaluation),BLEU(Bilingual Evaluation Understudy),etc.NSM and NSS work in different ways.The former calculates the matching degree directly,while the latter mainly improves the similarity measurement.Moreover we propose an N-gram representation mechanism to explore the vector representation of N-grams(including skip-grams).It serves as the basis of our ENMS model,in which we exploit some simple but effective integration methods to solve the OOV problem efficiently.Experimental results over the TAC AESOP dataset show that the metrics improved by our methods are well correlated with human judgements and can be used to better evaluate abstractive summarization methods.
基金supported by the National Natural Science Foundation of China(No.42277107)Liaoning Province Natural Science Foundation Joint Funds(Ph.D.Scientific Research Program)(No.2023-BSBA-024/DUT24BS004)the Open Project of State Key Laboratory of Urban Water Resource and Environment,China,Harbin Institute of Technology(No.HC202330)。
文摘Manganese-oxidizing microbes are capable of oxidizing Mn(Ⅱ) to manganese oxides through direct enzymatic and indirect mechanisms.However,bacterial Mn(Ⅱ) oxidation in alkaline environment remains unclear.This study isolated an alkali-tolerant bacterium Pseudorhizobium flavum MXJ-1 from marine surface sediments that can oxidize Mn(Ⅱ) to biogenic manganese oxides(BioMnO_(x)).Characterization of BioMnO_(x) reveals that rod-shaped bacterial cells produce amorphous BioMnO_(x) containing mixed valences of Mn.The effects of different pH,carbon and nitrogen sources,metal ions on growth and Mn(Ⅱ) oxidation activity of strain MXJ-1 were investigated.Results elucidate that strain MXJ-1 adapts to a broad range of pH from 5.0 to 10.0,achieves a maximum Mn(Ⅱ) oxidation percentage of 69.41 %,but there is no significant correlation between biomass and Mn(Ⅱ) oxidation.Cu(Ⅱ) inhibited Mn(Ⅱ) oxidation at concentration as low as 20 μmol/L,and Mn(Ⅱ) oxidation decreased with Mn(Ⅱ) levels increasing to 500 μmol/L.Genomic analysis identified two genes encoding animal heme peroxidases(AHPs) and three encoding dihydrolipoyl dehydrogenases(DLDHs),highlighting the potential role of superoxide in Mn(Ⅱ) oxidation.Several alkali-tolerance genes,including those encoding Na+/H+ antiporter,were also identified,which probably associated with alkali-tolerance of MXJ-1.Superoxide detection by nitro blue tetrazolium(NBT) assay indicates it involved in Mn(Ⅱ) oxidation.This study isolated a Mn(Ⅱ)-oxidizing,alkali-tolerant bacterium,expanding insights into microbial Mn(Ⅱ) oxidation in extreme environments.
基金supported by the Science and Technology Major Program of Yunnan(No.202402AG0500103)the Industrial Innovation Talent Project of Yunnan(No.XDYC-CYCX-2023007)the National Key Research and Development Program of China(No.2023YFC3709100).
文摘Exogenous organic input impacts soil phosphorus transformation.Meanwhile,dissolved organic matter(DOM)is crucial for biogeochemical functions.Nevertheless,the interaction between the structural composition of DOM and phosphorus during the soil formation process of phosphogypsum(PG)remains unknown.This study explores the interaction between the structural composition of DOM and phosphorus in enhanced PG under the participation of fungal microorganisms through different application amounts of exogenous organic matter and culture time.Results show that application of exogenous organic matter led to varying degrees of increase in dissolved organic carbon(DOC)concentration and humification extent in the soil-like substrate.Additionally,the relative abundance of protein-like component C3 exhibited a trend of initial increase followed by decline over time.The contents of available phosphorus(AP),microbial biomass phosphorus(MBP),and active phosphorus pools(Active-P)in the soil-like substrate are all enhanced overall.Furthermore,a significant correlation exists between DOC and AP as well as MBP.This suggests that DOM is a crucial factor in enhancing the phosphorus availability of the soil-like substrate.The enrichment of known phosphate-solubilizing fungi in culturing favors the decomposition,activation and utilization of hard-to-mineralize phosphorus components in the soil-like substrate.These findings help understand DOM’s biogeochemical behavior and offer insights into PG utilization and the sustainable development of China’s phosphorus industry.
基金supported by the National Natural Science Foundation of China(No.22208302)the Natural Science Foundation of Zhejiang Province of China(LQ21B020006).
文摘A new type of asymmetric hydrogen atom abstraction catalysts,originated from the cinchona alkaloid family of natural products,has been successfully developed to access enantioselective epimerizations of meso-diols.After undergoing single-electron oxidation,the catalyst fulfills desymmetrization of meso-diols by selectively traping a hydrogen atom from a carbon center,which subsequently recaptures a hydrogen atom via abstraction from a thiol.The publication of this work will have a significant influence in the field of asymmetric radical chemistry.
基金supported by UKRI(EP/Z000025/1)Horizon Europe Programme under the MSCA grant for the ACMod project(101130271)。
文摘The exponential growth of video content has driven significant advancements in video summarization techniques in recent years. Breakthroughs in deep learning have been particularly transformative, enabling more effective detection of key information and creating new possibilities for video synopsis. To summarize recent progress and accelerate research in this field,this paper provides a comprehensive review of deep learningbased video summarization methods developed over the past decade. We begin by examining the research landscape of video abstraction technologies and identifying core challenges in video summarization. Subsequently, we systematically analyze prevailing deep learning frameworks and methodologies employed in current video summarization systems, offering researchers a clear roadmap of the field's evolution. Unlike previous review works,we first classify research papers based on the structural hierarchy of the video(from frame-level to shot-level to video-level),then further categorize them according to the summary backbone model(feature extraction and spatiotemporal modeling).This approach provides a more systematic and hierarchical organization of the documents. Following this comprehensive review,we summarize the benchmark datasets and evaluation metrics commonly employed in the field. Finally, we analyze persistent challenges and propose insightful directions for future research,providing a forward-looking perspective on video summarization technologies. This systematic literature review is of great reference value to new researchers exploring the fields of deep learning and video summarization.
文摘International Journal of Minerals,Metallurgy and Materials is dedicated to the publication and the dissemination of original research articles(and occasional invited reviews)in the fields of Minerals,Metallurgy and Materials.It is covered by EI Compendex,SCI Expanded,Chemical Abstract,etc.
文摘The Chinese Journal of Natural Medicines(CJNM,ISSN 2095-6975),founded in 2003 and sponsored by China Pharmaceutical University,is a peer-reviewed journal published monthly in print by Science Press and online by Elsevier.CJNM is currently indexed in Science Citation Index Expanded(SCIE),MEDLINE,BIOSIS Previews,BIOSIS Toxicology,CAB Abstracts.
基金supported by the research start-up funds for invited doctor of Lanzhou University of Technology under Grant 14/062402。
文摘In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.
文摘General Information Journal of Chinese Pharmaceutical Sciences(JCPS)is a peer-reviewed bimonthly journal founded in 1992.JCPS has been indexed in the Core Journals of China's science and technology field.At present it has been included by Chemical Abstracts(CA),UIPD,Index of Copurnicus(IC),CABI,SCOPUS,JSTChina,DOAJ,EBSCO,CSTPCD,Chinese Science Citation Database(CSCD),the Chinese National Knowledge Infrastructure(CNKI)and World Journal Clout Index(WJCI)Report.
基金Guangzhou Science and Technology Program,Grant/Award Numbers:2025B03J0110,2024A03J1074,2024A03J0927。
文摘Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medical practice,a rigorous and systematic evaluation of their medical competence is imperative.This study presents a comprehensive review of the established methodologies and benchmarks for evaluating the medical competence of LLMs,encompassing a thorough analysis of current assessment practices across medical knowledge,clinical practice competence,and ethical-safety considerations.By integrating clinician competency assessment frameworks into LLMs evaluation,we propose a structured tri-dimensional framework that systematically organizes existing evaluation approaches according to medical theoretical knowledge,clinical practice ability,and ethical-safety considerations.Furthermore,this research provides critical insights into future developmental trajectories while establishing foundational frameworks and standardization protocols for the integration of LLMs into medical practice.
