Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a cli...Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.展开更多
The reliability expression of dynamic driving behavior is derived from the driving shaping behavioural model; and then, in accordance with the combination of computer simulation and mathematical expression of driving ...The reliability expression of dynamic driving behavior is derived from the driving shaping behavioural model; and then, in accordance with the combination of computer simulation and mathematical expression of driving reliability, an approach for assessing the effect of driving erroneous actions on the dynamic performance of the driver vehicle system is presented. The analysis of driving erroneous actions in the driver vehicle system has been performed to show that the reliability during perception with variety widely could result in the incidents and/or accidents in traffic system.展开更多
Handling uncertainty is a key aspect of production management.This paper employs a Bayesian network(BN)approach to improve uncertainty analysis in production decisionmaking by identifying common causal factors that ma...Handling uncertainty is a key aspect of production management.This paper employs a Bayesian network(BN)approach to improve uncertainty analysis in production decisionmaking by identifying common causal factors that may affect one or more components of an integrated supply chain problem.A Bayesian network approach with learning and analytical features is proposed,allowing production managers to make risk-based decisions as well as conduct various scenarios and what-if analyses.Additionally,this approach provides managers with the possibility of updating their knowledge about uncertain factors in the light of new data.The proposed model is applied to a vaccine manufacturing and distributing chain,revealing that the company will fail to gain a profit unless it reduces costs,enhances staff quality,or acquires more customers with higher demand.展开更多
Objective: This study aims to examine the causal relationship between inflammatory factors and the probability of developing vascular dementia (VD) using Mendelian Randomization (MR) and Chinese herbal medicine predic...Objective: This study aims to examine the causal relationship between inflammatory factors and the probability of developing vascular dementia (VD) using Mendelian Randomization (MR) and Chinese herbal medicine prediction method, and to screen potential Chinese herbal medicines for the prevention and treatment of VD. Methods: Single nucleotide polymorphisms (SNPs) that exhibit a strong association with vascular dementia (VD) were identified as instrumental variables from the summary statistics of genome-wide association studies (GWAS). The primary analytical method employed was inverse variance weighting (IVW), while auxiliary analyses included the MR-Egger method, weighted median method, simple model, and weighted model. A two-way Mendelian randomization analysis was conducted to assess the causal relationship between inflammatory factors and the risk of VD, thereby identifying the key inflammatory factors involved. The MR-Egger intercept test and Cochran’s Q test were employed to assess the horizontal polymorphism and heterogeneity of instrumental variables. A sensitivity analysis was conducted by excluding one method at a time. Ultimately, based on key inflammatory factors, predictions for the prevention and treatment using traditional Chinese medicine were made, along with the screening of homologous herbal remedies. Results: Based on the results of the forward MR, the probability of developing VD was elevated when the inflammatory factors CXCL10 and CXCL5 were expressed at higher levels, whereas the probability of developing VD decreased as the expression levels of IL-13 and IL-20RA increased. These findings were supported by the assessment of pleiotropy, heterogeneity, and sensitivity. The results of the reverse MR analysis showed that there was no causal relationship between VD, as an exposure dataset, and these four inflammatory factors. According to the key inflammatory factors, 37 Chinese herbal medicines such as Siraitia grosvenorii were selected. Their characteristics including four natures, five flavors, channel tropism and treatment efficiency were cold, warm, neutral, pungent, sweet, bitter, lung meridian, spleen meridian, liver meridian, kidney meridian and clearing heat. Among them, Siraitia grosvenorii, Poria with hostwood, Perilla frutescens, and Radix Platycodi were all medicine and food homologous Chinese herbal medicines. Conclusions: The increase of CXCL10 and CXCL5 expression levels can increase the risk of VD, and the increase of IL-13 and IL-20 RA expression levels can reduce the risk of VD. Siraitia grosvenorii and other Chinese herbal medicines might be potential sources of therapeutic drugs for the treatment of VD. Medicine and food homologous Chinese herbal medicines, such as Siraitia grosvenorii, Poria with hostwood, Perilla frutescens, and Radix Platycodi, may help the elderly population with corresponding Traditional Chinese Medicine (TCM) constitutions to prevent VD.展开更多
Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significan...Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.展开更多
孟德尔随机化(Mendelian randomization,MR)技术,依托于全基因组关联研究(genome-wide association study,GWAS)发现的遗传变异,开辟了一种深入解析疾病因果机制的新路径。在胆囊癌(gallbladder cancer,GBC)研究中,MR技术特别显示出其...孟德尔随机化(Mendelian randomization,MR)技术,依托于全基因组关联研究(genome-wide association study,GWAS)发现的遗传变异,开辟了一种深入解析疾病因果机制的新路径。在胆囊癌(gallbladder cancer,GBC)研究中,MR技术特别显示出其独特的优势,成功克服了传统研究中的混杂偏倚问题,为GBC的研究注入了新的活力。本文概述了传统流行病学研究发现的GBC风险因素及MR技术在GBC研究中的应用与进展,突出MR在阐明病因、提出新建议以及开启新视角方面的潜力。通过详细分析GBC的病理机制、风险因素及MR方法的实际应用案例,提出了针对GBC预防、诊断与治疗的新思路,为未来该领域的研究奠定了基础。展开更多
提升Q学习(Q-learning)算法在复杂环境中的数据效率与决策准确度,无疑是算法性能优化所面临的关键挑战。将因果模型引入Q学习算法,通过揭示变量间的因果关系,从而提高Q学习算法的性能是新兴且热门的研究方向。该文提出一种基于因果模型...提升Q学习(Q-learning)算法在复杂环境中的数据效率与决策准确度,无疑是算法性能优化所面临的关键挑战。将因果模型引入Q学习算法,通过揭示变量间的因果关系,从而提高Q学习算法的性能是新兴且热门的研究方向。该文提出一种基于因果模型的Q学习算法,C-Q学习(Causal-model based Q-learning)算法。该算法包括基于智能体利用Q学习算法与环境交互过程中关键变量之间的因果关系,构建结构因果模型;采用因果推断理论中的后门调整的方法去除模型中影响奖励的混淆因子所引起的混淆效应,评估了更为准确的Q值,并且精准识别出每个状态下可能获得最高奖励的动作,优化Q学习算法的动作选择过程。最后,将Q学习算法、Eva-Q学习算法、C-Q学习算法在栅格环境中进行仿真实验。仿真实验结果表明,C-Q学习算法在路径长度、规划时间、数据效率和决策准确度等多个指标上均优于其余两种算法。展开更多
基金supported by an internal fund from Macao Polytechnic University(RP/FCSD-02/2022).
文摘Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.
文摘The reliability expression of dynamic driving behavior is derived from the driving shaping behavioural model; and then, in accordance with the combination of computer simulation and mathematical expression of driving reliability, an approach for assessing the effect of driving erroneous actions on the dynamic performance of the driver vehicle system is presented. The analysis of driving erroneous actions in the driver vehicle system has been performed to show that the reliability during perception with variety widely could result in the incidents and/or accidents in traffic system.
文摘Handling uncertainty is a key aspect of production management.This paper employs a Bayesian network(BN)approach to improve uncertainty analysis in production decisionmaking by identifying common causal factors that may affect one or more components of an integrated supply chain problem.A Bayesian network approach with learning and analytical features is proposed,allowing production managers to make risk-based decisions as well as conduct various scenarios and what-if analyses.Additionally,this approach provides managers with the possibility of updating their knowledge about uncertain factors in the light of new data.The proposed model is applied to a vaccine manufacturing and distributing chain,revealing that the company will fail to gain a profit unless it reduces costs,enhances staff quality,or acquires more customers with higher demand.
文摘Objective: This study aims to examine the causal relationship between inflammatory factors and the probability of developing vascular dementia (VD) using Mendelian Randomization (MR) and Chinese herbal medicine prediction method, and to screen potential Chinese herbal medicines for the prevention and treatment of VD. Methods: Single nucleotide polymorphisms (SNPs) that exhibit a strong association with vascular dementia (VD) were identified as instrumental variables from the summary statistics of genome-wide association studies (GWAS). The primary analytical method employed was inverse variance weighting (IVW), while auxiliary analyses included the MR-Egger method, weighted median method, simple model, and weighted model. A two-way Mendelian randomization analysis was conducted to assess the causal relationship between inflammatory factors and the risk of VD, thereby identifying the key inflammatory factors involved. The MR-Egger intercept test and Cochran’s Q test were employed to assess the horizontal polymorphism and heterogeneity of instrumental variables. A sensitivity analysis was conducted by excluding one method at a time. Ultimately, based on key inflammatory factors, predictions for the prevention and treatment using traditional Chinese medicine were made, along with the screening of homologous herbal remedies. Results: Based on the results of the forward MR, the probability of developing VD was elevated when the inflammatory factors CXCL10 and CXCL5 were expressed at higher levels, whereas the probability of developing VD decreased as the expression levels of IL-13 and IL-20RA increased. These findings were supported by the assessment of pleiotropy, heterogeneity, and sensitivity. The results of the reverse MR analysis showed that there was no causal relationship between VD, as an exposure dataset, and these four inflammatory factors. According to the key inflammatory factors, 37 Chinese herbal medicines such as Siraitia grosvenorii were selected. Their characteristics including four natures, five flavors, channel tropism and treatment efficiency were cold, warm, neutral, pungent, sweet, bitter, lung meridian, spleen meridian, liver meridian, kidney meridian and clearing heat. Among them, Siraitia grosvenorii, Poria with hostwood, Perilla frutescens, and Radix Platycodi were all medicine and food homologous Chinese herbal medicines. Conclusions: The increase of CXCL10 and CXCL5 expression levels can increase the risk of VD, and the increase of IL-13 and IL-20 RA expression levels can reduce the risk of VD. Siraitia grosvenorii and other Chinese herbal medicines might be potential sources of therapeutic drugs for the treatment of VD. Medicine and food homologous Chinese herbal medicines, such as Siraitia grosvenorii, Poria with hostwood, Perilla frutescens, and Radix Platycodi, may help the elderly population with corresponding Traditional Chinese Medicine (TCM) constitutions to prevent VD.
基金supported by the Hubei Provincial Engineering Research Center of Slope Habitat Construction Technique Using Cement-based Materials Open Research Program (Grant No. 2022SNJ112022SNJ12)+4 种基金National Natural Science Foundation of China (Grant No. 42371014)Hubei Key Laboratory of Disaster Prevention and Mitigation (China Three Gorges University) Open Research Program (Grant No. 2022KJZ122023KJZ19)CRSRI Open Research Program (Grant No. CKWV2021888/KY)the Key Laboratory of Mountain Hazards and Earth Surface Processes, Chinese Academy of Sciences (Grant No. KLMHESP20-0)。
文摘Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.
文摘孟德尔随机化(Mendelian randomization,MR)技术,依托于全基因组关联研究(genome-wide association study,GWAS)发现的遗传变异,开辟了一种深入解析疾病因果机制的新路径。在胆囊癌(gallbladder cancer,GBC)研究中,MR技术特别显示出其独特的优势,成功克服了传统研究中的混杂偏倚问题,为GBC的研究注入了新的活力。本文概述了传统流行病学研究发现的GBC风险因素及MR技术在GBC研究中的应用与进展,突出MR在阐明病因、提出新建议以及开启新视角方面的潜力。通过详细分析GBC的病理机制、风险因素及MR方法的实际应用案例,提出了针对GBC预防、诊断与治疗的新思路,为未来该领域的研究奠定了基础。
文摘目的:颅内动脉瘤(intracranial aneurysm,IA)起病隐匿,一旦破裂,致死率、致残率均较高。心血管代谢因素可能与其形成和破裂相关。本研究旨在总结孟德尔随机化(Mendelian randomization,MR)方法在IA心血管代谢因素研究中的应用,以期为阐明IA的病因和发病机制提供线索。方法:检索PubMed、Embase、Web of Science、中国知网和万方数据知识服务平台建库至2024年2月21日收录的基于MR方法的IA研究相关文献,由2名研究者独立进行文献筛选、数据提取和质量评价,采用叙述整合方法对纳入文献进行定性系统综述。结果:共纳入2017至2024年发表的11篇文献(11项研究),其中4项被判定为高质量研究。研究探讨了血压、血脂、血糖、肥胖相关指标和炎症因子与IA及其亚型的关系,但存在重复发表的问题。4项基于同一欧洲人群不同筛选标准的MR研究及1项基于不同欧洲人群的MR研究均发现血压是IA及其亚型的危险因素;其他MR研究中血脂、血糖、肥胖相关指标和炎症因子与IA及其亚型关联的结果不一致。结论:血压升高可增加IA及其亚型的发生风险,对于其他心血管代谢因素与IA及其亚型的关联需要进一步研究。MR研究仍存在局限性,需综合其他证据,谨慎进行因果推断。
文摘提升Q学习(Q-learning)算法在复杂环境中的数据效率与决策准确度,无疑是算法性能优化所面临的关键挑战。将因果模型引入Q学习算法,通过揭示变量间的因果关系,从而提高Q学习算法的性能是新兴且热门的研究方向。该文提出一种基于因果模型的Q学习算法,C-Q学习(Causal-model based Q-learning)算法。该算法包括基于智能体利用Q学习算法与环境交互过程中关键变量之间的因果关系,构建结构因果模型;采用因果推断理论中的后门调整的方法去除模型中影响奖励的混淆因子所引起的混淆效应,评估了更为准确的Q值,并且精准识别出每个状态下可能获得最高奖励的动作,优化Q学习算法的动作选择过程。最后,将Q学习算法、Eva-Q学习算法、C-Q学习算法在栅格环境中进行仿真实验。仿真实验结果表明,C-Q学习算法在路径长度、规划时间、数据效率和决策准确度等多个指标上均优于其余两种算法。
