基于Partial New Causality的因果脑网络情绪识别

为了研究情绪产生过程中脑区以及通道之间的因果作用,在部分格兰杰与新型因果关系的基础上,提出一种用于研究时间序列之间因果关系的部分新型因果关系(PNC)方法。在不同情绪下选取脑区内的8个通道,用PNC计算脑区内通道之间的因果连接关系,根据连接关系构建因果网络;对因果网络中节点的信息流向和介数属性进行分析,将PNC因果网络和Granger因果网络节点之间的因果连接视为一种特征送入SVM中训练分类。实验结果表明,基于PNC因果网络和Granger因果网络的平均识别精度分别为76.4%和68.5%,PNC可用于计算时间序列之间的因果关系。

Causal role of immune cells in obstructive sleep apnea hypopnea syndrome:Mendelian randomization study

BACKGROUND Despite being one of the most prevalent sleep disorders,obstructive sleep apnea hypoventilation syndrome(OSAHS)has limited information on its immunologic foundation.The immunological underpinnings of certain major psychiatric diseases have been uncovered in recent years thanks to the extensive use of genome-wide association studies(GWAS)and genotyping techniques using highdensity genetic markers(e.g.,SNP or CNVs).But this tactic hasn't yet been applied to OSAHS.Using a Mendelian randomization analysis,we analyzed the causal link between immune cells and the illness in order to comprehend the immunological bases of OSAHS.AIM To investigate the immune cells'association with OSAHS via genetic methods,guiding future clinical research.METHODS A comprehensive two-sample mendelian randomization study was conducted to investigate the causal relationship between immune cell characteristics and OSAHS.Summary statistics for each immune cell feature were obtained from the GWAS catalog.Information on 731 immune cell properties,such as morphologic parameters,median fluorescence intensity,absolute cellular,and relative cellular,was compiled using publicly available genetic databases.The results'robustness,heterogeneity,and horizontal pleiotropy were confirmed using extensive sensitivity examination.RESULTS Following false discovery rate(FDR)correction,no statistically significant effect of OSAHS on immunophenotypes was observed.However,two lymphocyte subsets were found to have a significant association with the risk of OSAHS:Basophil%CD33dim HLA DR-CD66b-(OR=1.03,95%CI=1.01-1.03,P<0.001);CD38 on IgD+CD24-B cell(OR=1.04,95%CI=1.02-1.04,P=0.019).CONCLUSION This study shows a strong link between immune cells and OSAHS through a gene approach,thus offering direction for potential future medical research.

Causal temporal graph attention network for fault diagnosis of chemical processes

Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.

Immune cell signatures and causal association with irritable bowel syndrome:A mendelian randomization study

BACKGROUND The mucosal barrier's immune-brain interactions,pivotal for neural development and function,are increasingly recognized for their potential causal and therapeutic relevance to irritable bowel syndrome(IBS).Prior studies linking immune inflammation with IBS have been inconsistent.To further elucidate this relationship,we conducted a Mendelian randomization(MR)analysis of 731 immune cell markers to dissect the influence of various immune phenotypes on IBS.Our goal was to deepen our understanding of the disrupted brain-gut axis in IBS and to identify novel therapeutic targets.AIM To leverage publicly available data to perform MR analysis on 731 immune cell markers and explore their impact on IBS.We aimed to uncover immunophenotypic associations with IBS that could inform future drug development and therapeutic strategies.METHODS We performed a comprehensive two-sample MR analysis to evaluate the causal relationship between immune cell markers and IBS.By utilizing genetic data from public databases,we examined the causal associations between 731 immune cell markers,encompassing median fluorescence intensity,relative cell abundance,absolute cell count,and morphological parameters,with IBS susceptibility.Sensitivity analyses were conducted to validate our findings and address potential heterogeneity and pleiotropy.RESULTS Bidirectional false discovery rate correction indicated no significant influence of IBS on immunophenotypes.However,our analysis revealed a causal impact of IBS on 30 out of 731 immune phenotypes(P<0.05).Nine immune phenotypes demonstrated a protective effect against IBS[inverse variance weighting(IVW)<0.05,odd ratio(OR)<1],while 21 others were associated with an increased risk of IBS onset(IVW≥0.05,OR≥1).CONCLUSION Our findings underscore a substantial genetic correlation between immune cell phenotypes and IBS,providing valuable insights into the pathophysiology of the condition.These results pave the way for the development of more precise biomarkers and targeted therapies for IBS.Furthermore,this research enriches our comprehension of immune cell roles in IBS pathogenesis,offering a foundation for more effective,personalized treatment approaches.These advancements hold promise for improving IBS patient quality of life and reducing the disease burden on individuals and their families.

Uncovering Causal Relationships for Debiased Repost Prediction Using Deep Generative Models

Microblogging platforms like X(formerly Twitter)and Sina Weibo have become key channels for spreading information online.Accurately predicting information spread,such as users’reposting activities,is essential for applications including content recommendation and analyzing public sentiment.Current advanced models rely on deep representation learning to extract features from various inputs,such as users’social connections and repost history,to forecast reposting behavior.Nonetheless,these models frequently ignore intrinsic confounding factors,which may cause the models to capture spurious relationships,ultimately impacting prediction performance.To address this limitation,we propose a novel Debiased Reposting Prediction model(DRP).Our model mitigates the influence of confounding variables by incorporating intervention operations from causal inference,enabling it to learn the causal associations between features and user reposting behavior.Specifically,we introduce a memory network within DRP to enhance the model’s perception of confounder distributions.This network aggregates and learns confounding information dispersed across different training data batches by optimizing the reconstruction loss.Furthermore,recognizing the challenge of acquiring prior knowledge of causal graphs,which is crucial for causal inference,we develop a causal discovery module within DRP(CD-DRP).This module allows the model to autonomously uncover the causal graph of feature variables by analyzing microblogging data.Experimental results on multiple real-world datasets demonstrate that our proposed method effectively uncovers causal relationships between variables,exhibits strong time efficiency,and outperforms state-of-the-art models in prediction performance(improved by 2.54%)and overfitting reduction(by 7.44%).

Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome

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.

Causal genetic regulation of DNA replication on immune microenvironment in colorectal tumorigenesis: Evidenced by an integrated approach of trans-omics and GWAS

The interplay between DNA replication stress and immune microenvironment alterations is known to play a crucial role in colorectal tumorigenesis,but a comprehensive understanding of their association with and relevant biomarkers involved in colorectal tumorigenesis is lacking.To address this gap,we conducted a study aiming to investigate this association and identify relevant biomarkers.We analyzed transcriptomic and proteomic profiles of 904 colorectal tumor tissues and 342 normal tissues to examine pathway enrichment,biological activity,and the immune microenvironment.Additionally,we evaluated genetic effects of single variants and genes on colorectal cancer susceptibility using data from genome-wide association studies(GWASs)involving both East Asian(7062 cases and 195745 controls)and European(24476 cases and 23073 controls)populations.We employed mediation analysis to infer the causal pathway,and applied multiplex immunofluorescence to visualize colocalized biomarkers in colorectal tumors and immune cells.Our findings revealed that both DNA replication activity and the flap structure-specific endonuclease 1(FEN1)gene were significantly enriched in colorectal tumor tissues,compared with normal tissues.Moreover,a genetic variant rs4246215 G>T in FEN1 was associated with a decreased risk of colorectal cancer(odds ratio=0.94,95%confidence interval:0.90–0.97,P_(meta)=4.70×10^(-9)).Importantly,we identified basophils and eosinophils that both exhibited a significantly decreased infiltration in colorectal tumors,and were regulated by rs4246215 through causal pathways involving both FEN1 and DNA replication.In conclusion,this trans-omics incorporating GWAS data provides insights into a plausible pathway connecting DNA replication and immunity,expanding biological knowledge of colorectal tumorigenesis and therapeutic targets.

TCAS-PINN:Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.

Causal association between 731 immunocyte phenotypes and liver cirrhosis: A bidirectional two-sample mendelian randomization analysis

BACKGROUND Liver cirrhosis is a progressive hepatic disease whose immunological basis has attracted increasing attention.However,it remains unclear whether a concrete causal association exists between immunocyte phenotypes and liver cirrhosis.AIM To explore the concrete causal relationships between immunocyte phenotypes and liver cirrhosis through a mendelian randomization(MR)study.METHODS Data on 731 immunocyte phenotypes were obtained from genome-wide assoc-iation studies.Liver cirrhosis data were derived from the Finn Gen dataset,which included 214403 individuals of European ancestry.We used inverse variable weighting as the primary analysis method to assess the causal relationship.Sensitivity analyses were conducted to evaluate heterogeneity and horizontal pleiotropy.RESULTS The MR analysis demonstrated that 11 immune cell phenotypes have a positive association with liver cirrhosis[P<0.05,odds ratio(OR)>1]and that 9 immu-nocyte phenotypes were negatively correlated with liver cirrhosis(P<0.05,OR<1).Liver cirrhosis was positively linked to 9 immune cell phenotypes(P<0.05,OR>1)and negatively linked to 10 immune cell phenotypes(P<0.05;OR<1).None of these associations showed heterogeneity or horizontally pleiotropy(P>0.05).CONCLUSION This bidirectional two-sample MR study demonstrated a concrete causal association between immunocyte phenotypes and liver cirrhosis.These findings offer new directions for the treatment of liver cirrhosis.

Mendelian Randomization Study of Causal Relationship between Inflammatory Factors and Vascular Dementia and Chinese Herbal Medicines Screening for Prevention and Treatment

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.

Causal association between 25-hydroxyvitamin D status and cataract development:A two-sample Mendelian randomization study

BACKGROUND Vitamin deficiencies are linked to various eye diseases,and the influence of vitamin D on cataract formation has been noted in prior research.However,detailed investigations into the causal relationship between 25-(OH)D status and cataract development remain scarce.AIM To explore a possible causal link between cataracts and vitamin D.METHODS In this study,we explored the causal link between 25-(OH)D levels and cataract development using Mendelian randomization.Our analytical approach included inverse-variance weighting(IVW),MR-Egger,weighted median,simple mode,and weighted mode methods.The primary analyses utilized IVW with random effects,supplemented by sensitivity and heterogeneity tests using both IVW and MR-Egger.MR-Egger was also applied for pleiotropy testing.Additionally,a leave-one-out analysis helped identify potentially impactful single-nucleotide polymorphisms.RESULTS The analysis revealed a positive association between 25-(OH)D levels and the risk of developing cataracts(OR=1.11,95%CI:1.00-1.22;P=0.032).The heterogeneity test revealed that our IVW analysis exhibited minimal heterogeneity(P>0.05),and the pleiotropy test findings confirmed the absence of pleiotropy within our IVW analysis(P>0.05).Furthermore,a search of the human genotype-phenotype association database failed to identify any potentially relevant risk-factor single nucleotide polymorphisms.CONCLUSION There is a potential causal link between 25-(OH)D levels and the development of cataracts,suggesting that greater 25-(OH)D levels may be a contributing risk factor for cataract formation.Further experimental research is required to confirm these findings.

Noncausal relationship between body weight and breast cancer based on bidirectional Mendelian randomization evidence

Background:Some observational associations between body weight and breast cancer have attracted attention.However,the causal relationship between these 2 factors remains unclear,and more clinical outcomes are needed for its validation.Methods:Based on statistical data from a Genome Wide Association Study,we performed a bidirectional Mendelian randomization analysis to assess the bidirectional causal relationship between body weight and breast cancer using 4 methods,with inverse variance weighting as the primarymethod.To verify the robustness and reliability of the causal relationship,we performed a sensitivity analysis using horizontal pleiotropy,outlier,and one-by-one elimination tests.Results:The inverse variance weighting results revealed no significant positive causal relationship between body weight and breast cancer.Similarly,the reverse analysis revealed no causal effect of breast cancer on body weight.Conclusions:The relationship between body weight and breast cancer may be attributed to confounding factors.

What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding

People learn causal relations since childhood using counterfactual reasoning. Counterfactual reasoning uses counterfactual examples which take the form of “what if this has happened differently”. Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and present counterfactual samples cannot help users gain a deeper understanding of the system. Without a way to verify their understanding, the users can even be misled by such explanations. Such limitations can be overcome through an interactive and iterative framework that allows the users to explore their desired “what-if” scenarios. The purpose of our research is to develop such a framework. In this paper, we present our “what-if” XAI framework (WiXAI), which visualizes the artificial intelligence (AI) classification model from the perspective of the user’s sample and guides their “what-if” exploration. We also formulated how to use the WiXAI framework to generate counterfactuals and understand the feature-feature and feature-output relations in-depth for a local sample. These relations help move the users toward causal understanding.

Analysis of causal relationship between blood metabolites and prostate cancer by two-sample Mendelian randomization

Objective:To investigate the causal relationship between blood metabolite levels and the occurrence of prostate cancer by using two-sample Mendelian randomization method.Methods:Pooled data from public databases for genome-wide association analyses of blood metabolites and prostate cancer were selected,and inverse variance weighting(IVW)was used as the primary method for estimating the causal effects,while heterogeneity tests,gene multiplicity tests and sensitivity analyses were performed to assess the stability and reliability of the results.Results:A total of six known metabolites were found to potentially increase the risk of prostate cancer development(P<0.05),namely fructose,allantoin,5-hydroxytryptophan,potassium ketoisocaproate,glycyltryptophan,and 1-heptadecanoyl-glycerol-3-phosphorylcholine,with no heterogeneity or genetic pleiotropy found.Conclusion:Six known blood metabolites may be potential risk factors for prostate cancer development in European populations.

梅赛德斯-奔驰CLA级概念车北美首秀

全新的MMA平台:2024年1月10日,全球最大的消费电子盛会--2024美国拉斯维加斯消费电子展(CES 2024)如期而至。梅赛德斯-奔驰(以下简称奔驰)携多项前瞻科技参展,以“超个性化”的数字创新全面革新座舱体验,定义数字时代的科技风潮。基于奔驰模块化架构(MMA)平台打造的CLA级概念车在展会现场上演北美首秀,诠释了全新梅奔驰新生代电动家族的崭新面貌,如图1。去年9月4日,CLA级概念车在2023德国国际汽车及智慧出行博览会(IAAMOBILITY2023)全球首发,它代表的电动未来令人心向往之。

计量经济学中的因果推断:过去、现在与未来

自从可信度革命以来,因果推断在计量经济学中的地位日益重要,因果推断方法也层出不穷,甚至令实证研究者眼花缭乱。本文系统梳理了因果推断在计量经济学中的历史渊源与发展脉络,涵盖随机实验、自然实验、断点回归、匹配估计、双稳健估计、工具变量法、双重差分法、合成控制法、回归控制法、分位数控制法等主流因果推断方法。与已有的文献综述相比,本文更注重因果推断方法的历史起源,以帮助实证研究者更好地把握其精神实质,同时将所涉文献更新至2024年的最新前沿(尤其是工具变量法与双重差分法的最新进展),并展望未来的发展方向。

端粒长度与10种常见肌肉骨骼疾病的关系孟德尔随机化分析

背景:多项观察性研究表明,端粒长度与肌肉骨骼疾病之间存在潜在的关联,然而它们之间的潜在机制仍不清楚。目的:利用两样本孟德尔随机化分析来探索端粒长度与肌肉骨骼疾病之间的遗传因果关系。方法:从英国生物银行中获得端粒长度的全基因组关联研究汇总数据。从FinnGen财团中获得了关于10种常见肌肉骨骼疾病(骨坏死、骨髓炎、骨质疏松、类风湿关节炎、腰痛、椎管狭窄、痛风、肩周炎、强直性脊柱炎和下肢深静脉血栓)的全基因组关联研究汇总数据。使用逆方差加权、孟德尔随机化-Egger和加权中位数方法评估端粒长度与10种肌肉骨骼疾病的因果关系,逆方差加权作为主要的孟德尔随机化分析方法,并进行敏感性分析探讨结果稳健性。结果与结论:①逆方差加权法结果表明,遗传预测的端粒长度与类风湿关节炎(OR=0.78,95%CI:0.64-0.95,P=0.015)和骨坏死(OR=0.56,95%CI:0.36-0.90,P=0.016)风险之间存在负向因果关系,但未发现端粒长度与其他8种肌肉骨骼疾病之间存在因果关系(P均>0.05)。②敏感性分析结果表明因果关系稳健,孟德尔随机化-Egger截距分析未检测到潜在的水平多效性(P均>0.05)。③此项孟德尔随机化研究支持端粒长度对类风湿关节炎和骨坏死的保护作用的结论,然而,未来将需要更多的基础和临床研究来验证。

免疫细胞介导不同脂质与膝骨关节炎的相关性:欧洲个体全基因组关联分析

背景:观察性研究发现脂质与免疫细胞对于骨关节炎进展具有双重影响,但其具体作用机制尚不清楚。目的:探究脂质与膝骨关节炎的因果关系是否受免疫细胞调控。方法:179种脂质来源于Linda Ottensman数据库,从Open GWAS上获取到了731种免疫细胞相关的单核苷酸多态性作为工具变量,膝骨关节炎的全基因组关联研究数据来自于骨关节炎遗传学联盟。首先采用双样本孟德尔随机化方法从遗传学角度分别探讨脂质与膝骨关节炎、免疫细胞与膝骨关节炎、脂质与免疫细胞的因果关系。另外采用贝叶斯加权孟德尔随机化验证脂质与膝骨关节炎的因果关系,反向孟德尔随机化和Steiger方向性检验评估正向关系。随后进行敏感性分析,包括Cochran’s Q检验消除异质性,MR-PRESSO全局检验和MR-Egger截距排除水平多效性,留一法评估单个单核苷酸多态性驱动因素对随机估计的影响。最后,通过两步孟德尔随机化方法确定特定免疫细胞性状在脂质与膝骨关节炎因果关系中的中介效应。结果与结论:①逆方差加权法和贝叶斯加权算法共同确定8种与膝骨关节炎存在因果关系的脂质种类;②Cochran’s Q、MR-PRESSO和MR-Egger截距结果均无统计学意义(P<0.05);③同时共计算得出了3组中介关系,介导比例最低为11.85%,最高为45.48%;④提示免疫细胞水平在脂质与膝骨关节炎的调节进程中发挥潜在作用,这一发现能为深入探索膝骨关节炎的作用机制提供新的视角。

基于TRIZ理论高压电塔防坠保护装置的设计与分析

为解决现阶段电力行业检修人员攀爬电塔维护线路过程中存在坠落风险和作业流程繁琐的问题,运用TRIZ创新理论中因果链分析工具,寻找系统技术矛盾和作业流程中存在的问题,通过寻求问题根源得到解决方案,并绘制功能模型图。利用功能模型图配合物-场模型及标准解改进完善方案,将方案具体化后得到高压电塔防坠保护装置的最终设计方案。利用ADAMS软件搭建最终方案的虚拟样机,并对其整个工作过程进行模拟仿真。仿真结果表明,所设计的高压电塔防坠保护装置方案合理,运行稳定可靠。该装置的研制对电力行业的发展具有积极意义。

离子注入诱变植物乳杆菌选育CLA高产突变株的研究

以A6-1为出发菌株,采用N+离子注入的方法进行诱变处理,注入能量为50keV,注入剂量为1×1013、3×1013、5×1013、8×1013、10×1013、30×1013、50×1013、80×1013、100×1013ion/cm2,真空度为10-3Pa。结果显示,菌体的存活率随离子注入剂量的增加呈"马鞍型"曲线,"鞍脊"出现在30×1013ions/cm2～50×1013ions/cm2之间,此时菌体的存活率在20%～35%之间。综合考虑存活率、总突变率、正突变率和突变幅度等因素,推荐30×1013ions/cm2作为离子注入植物乳杆菌的适宜诱变剂量。从所有的注入处理中挑选CLA转化能力提高了50%以上的突变株进行传代稳定性试验,发现F菌株经8次传代后产CLA的稳定性最好。该突变株各代的产量平均为162.5μg/ml,比出发菌株提高了69.87%,将此突变菌株命名为A6-1F。