Respiratory syncytial virus(RSV)is a ubiquitous respiratory virus that affects individuals of all ages;however,there is a notable lack of targeted treatments.RSV infection is associated with a range of respiratory sym...Respiratory syncytial virus(RSV)is a ubiquitous respiratory virus that affects individuals of all ages;however,there is a notable lack of targeted treatments.RSV infection is associated with a range of respiratory symptoms,including bronchiolitis and pneumonia.Baicalin(BA)exhibits significant therapeutic effects against RSV infection through mechanisms of viral inhibition and anti-inflammatory action.Nonetheless,the clinical application of BA is constrained by its low solubility and bioavailability.In this study,we prepared BA nanodrugs(BA NDs)with enhanced water solubility utilizing the supramolecular self-assembled strategy,and we further conducted a comparative analysis of this pharmacological activity between free drugs and NDs of BA.Both in vitro and in vivo results demonstrated that BA NDs significantly enhanced the dual effects of viral inhibition and inflammation relief compared to free BA,attributed to prolonged lung retention,improved cellular uptake,and increased targeting affinity.Our study confirms that the nanosizing strategy,a straightforward approach to enhance drug solubility,can also increase biological activity compared to free drugs with the same content,thereby providing a potential ND for RSV treatment.This correlation analysis between the existing forms of drugs and their biological activity offers a novel perspective for research on the active ingredients of traditional Chinese medicine.展开更多
Lysine succinylation is a novel,naturally occurring posttranslational modification(PTM)in living organisms.Global lysine succinylation identification has been performed at the proteomic level in various species;howeve...Lysine succinylation is a novel,naturally occurring posttranslational modification(PTM)in living organisms.Global lysine succinylation identification has been performed at the proteomic level in various species;however,the study of lysine succinylation in plant species is relatively limited.Patchouli plant(P.cablin(Blanco)Benth.,Lamiaceae)is a globally important industrial plant and medicinal herb.In the present study,lysine succinylome analysis was carried out in patchouli plants to determine the potential regulatory role of lysine succinylation in patchouli growth,development,and physiology.The global succinylation sites and proteins in patchouli plants were screened with an immunoprecipitation affinity enrichment technique and advanced mass spectrometry-based proteomics.Several bioinformatic analyses,such as function classification and enrichment,subcellular location predication,metabolic pathway enrichment and protein−protein interaction networking,were conducted to characterize the functions of the identified sites and proteins.In total,1097 succinylation sites in 493 proteins were detected in patchouli plants,among which 466 succinylation sites in 241 proteins were repeatedly identified within three independent experiments.The functional characterization of these proteins indicated that the tricarboxylic acid(TCA)cycle,oxidative phosphorylation,photosynthesis processes,and amino acid biosynthesis may be regulated by lysine succinylation.In addition,these succinylated proteins showed a wide subcellular location distribution,although the chloroplast and cytoplasm were the top two preferred cellular components.Our study suggested the important role of lysine succinylation in patchouli plant physiology and biology and could serve as a useful reference for succinylation studies in other medicinal plants.展开更多
the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of di...the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the ‘new' and ‘comment' mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content.A series of experiments tested on public datasets demonstrate that: 1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; 2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.展开更多
Static analysis presents significant challenges in alarm handling, where probabilistic models and alarm prioritization are essential methods for addressing these issues. These models prioritize alarms based on user fe...Static analysis presents significant challenges in alarm handling, where probabilistic models and alarm prioritization are essential methods for addressing these issues. These models prioritize alarms based on user feedback, thereby alleviating the burden on users to manually inspect alarms. However, they often encounter limitations related to efficiency and issues such as false generalization. While learning-based approaches have demonstrated promise, they typically incur high training costs and are constrained by the predefined structures of existing models. Moreover, the integration of large language models (LLMs) in static analysis has yet to reach its full potential, often resulting in lower accuracy rates in vulnerability identification. To tackle these challenges, we introduce BinLLM, a novel framework that harnesses the generalization capabilities of LLMs to enhance alarm probability models through rule learning. Our approach integrates LLM-derived abstract rules into the probabilistic model, using alarm paths and critical statements from static analysis. This integration enhances the model’s reasoning capabilities, improving its effectiveness in prioritizing genuine bugs while mitigating false generalizations. We evaluated BinLLM on a suite of C programs and observed 40.1% and 9.4% reduction in the number of checks required for alarm verification compared to two state-of-the-art baselines, Bingo and BayeSmith, respectively, underscoring the potential of combining LLMs with static analysis to improve alarm management.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.:82474195)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.:021093002882)+2 种基金the Youth Medical Innovation Research Project of China(Grant No.:P24021887623)Taizhou Science and Technology Support Project,China(Grant No.:TS202420)grants from Nanjing Medical University,China(Grant Nos.:TZKY20230104 and 2024KF0292).
文摘Respiratory syncytial virus(RSV)is a ubiquitous respiratory virus that affects individuals of all ages;however,there is a notable lack of targeted treatments.RSV infection is associated with a range of respiratory symptoms,including bronchiolitis and pneumonia.Baicalin(BA)exhibits significant therapeutic effects against RSV infection through mechanisms of viral inhibition and anti-inflammatory action.Nonetheless,the clinical application of BA is constrained by its low solubility and bioavailability.In this study,we prepared BA nanodrugs(BA NDs)with enhanced water solubility utilizing the supramolecular self-assembled strategy,and we further conducted a comparative analysis of this pharmacological activity between free drugs and NDs of BA.Both in vitro and in vivo results demonstrated that BA NDs significantly enhanced the dual effects of viral inhibition and inflammation relief compared to free BA,attributed to prolonged lung retention,improved cellular uptake,and increased targeting affinity.Our study confirms that the nanosizing strategy,a straightforward approach to enhance drug solubility,can also increase biological activity compared to free drugs with the same content,thereby providing a potential ND for RSV treatment.This correlation analysis between the existing forms of drugs and their biological activity offers a novel perspective for research on the active ingredients of traditional Chinese medicine.
基金support for this research was provided in part by a grant from Project 81803657 supported by the National Natural Science Foundation of Chinathe Guangdong Education department Key Promotion Platform Construction Project,Lingnan Key Laboratory of Chinese Medicine Resources Ministry of Education(2014KTSPT016)+1 种基金special funds for the Construction of Traditional Chinese Medicine in Guangdong Province(No.20181075)the earmarked fund for Guangdong Education Department Innovation Strong School Project(No.E1-KFD015181K28/2017KQNCX039).
文摘Lysine succinylation is a novel,naturally occurring posttranslational modification(PTM)in living organisms.Global lysine succinylation identification has been performed at the proteomic level in various species;however,the study of lysine succinylation in plant species is relatively limited.Patchouli plant(P.cablin(Blanco)Benth.,Lamiaceae)is a globally important industrial plant and medicinal herb.In the present study,lysine succinylome analysis was carried out in patchouli plants to determine the potential regulatory role of lysine succinylation in patchouli growth,development,and physiology.The global succinylation sites and proteins in patchouli plants were screened with an immunoprecipitation affinity enrichment technique and advanced mass spectrometry-based proteomics.Several bioinformatic analyses,such as function classification and enrichment,subcellular location predication,metabolic pathway enrichment and protein−protein interaction networking,were conducted to characterize the functions of the identified sites and proteins.In total,1097 succinylation sites in 493 proteins were detected in patchouli plants,among which 466 succinylation sites in 241 proteins were repeatedly identified within three independent experiments.The functional characterization of these proteins indicated that the tricarboxylic acid(TCA)cycle,oxidative phosphorylation,photosynthesis processes,and amino acid biosynthesis may be regulated by lysine succinylation.In addition,these succinylated proteins showed a wide subcellular location distribution,although the chloroplast and cytoplasm were the top two preferred cellular components.Our study suggested the important role of lysine succinylation in patchouli plant physiology and biology and could serve as a useful reference for succinylation studies in other medicinal plants.
基金supported by the National Key Basic Research Program of China (No. 2013CB329603)National Natural Science Foundation (No.61562004,No.61431008)Basic Research Foundation of Shanghai Committee of Science and Technology (No. 13JC1403501) of China
文摘the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the ‘new' and ‘comment' mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content.A series of experiments tested on public datasets demonstrate that: 1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; 2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.
基金supported by the National Natural Science Foundation of China(Nos.U20B2048 and 62471301)。
文摘Static analysis presents significant challenges in alarm handling, where probabilistic models and alarm prioritization are essential methods for addressing these issues. These models prioritize alarms based on user feedback, thereby alleviating the burden on users to manually inspect alarms. However, they often encounter limitations related to efficiency and issues such as false generalization. While learning-based approaches have demonstrated promise, they typically incur high training costs and are constrained by the predefined structures of existing models. Moreover, the integration of large language models (LLMs) in static analysis has yet to reach its full potential, often resulting in lower accuracy rates in vulnerability identification. To tackle these challenges, we introduce BinLLM, a novel framework that harnesses the generalization capabilities of LLMs to enhance alarm probability models through rule learning. Our approach integrates LLM-derived abstract rules into the probabilistic model, using alarm paths and critical statements from static analysis. This integration enhances the model’s reasoning capabilities, improving its effectiveness in prioritizing genuine bugs while mitigating false generalizations. We evaluated BinLLM on a suite of C programs and observed 40.1% and 9.4% reduction in the number of checks required for alarm verification compared to two state-of-the-art baselines, Bingo and BayeSmith, respectively, underscoring the potential of combining LLMs with static analysis to improve alarm management.
