AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 201...AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 2018 to 2023.A total of 2591 patients were enrolled,including 197 patients with TAO(case group)and 2394 patients with hyperthyroidism without TAO(control group).Serum lipid parameters,including triglycerides,total cholesterol,high-density lipoprotein(HDL),low-density lipoprotein(LDL),and the HDL/total cholesterol ratio,as well as thyroid function markers,were compared between the two groups.Correlation analyses were performed to evaluate the associations between serum lipid levels and key ocular manifestations of TAO,including exophthalmos degree,clinical activity score,and disease severity.Furthermore,Mendelian randomization(MR)analysis was conducted using genome-wide association study(GWAS)datasets,with hyperthyroidism as the exposure variable and serum lipid parameters as the outcome variables,to infer the causal relationship between hyperthyroidism,lipid metabolism,and TAO progression.RESULTS:The TAO group consisted of 101 males and 96 females,while the hyperthyroidism group included 706 males and 1688 females.Compared with the control group,patients with TAO had significantly higher levels of triglycerides(1.83±1.21 vs 1.40±1.08 mmol/L,P<0.01),total cholesterol,LDL,and HDL.Correlation analysis showed that triglyceride levels were positively correlated with exophthalmos degree,whereas HDL levels were inversely correlated with exophthalmos degree.No significant associations were found between serum lipid levels and clinical activity score(P>0.1).MR analysis confirmed that hyperthyroidism exerted a causal effect in reducing serum triglycerides[inverse-variance weighting odds ratio(OR)=0.035,95%confidence interval(CI):0.01-0.12]and total cholesterol(OR=0.085,95%CI:0.02-0.34),with no evidence of horizontal pleiotropy(MR-PRESSO P>0.05).CONCLUSION:Elevated serum triglyceride levels are an independent risk factor for TAO severity,especially exophthalmos,and triglyceride metabolism is inversely regulated by thyroid function.展开更多
Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-h...Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.展开更多
Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association betw...Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.展开更多
AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)da...AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)datasets were utilized for this two-sample MR analysis.Inflammatory cytokine-related GWAS data were extracted from The University of Bristol’s Research Data Repository,and myopia-related GWAS data were obtained from the FinnGen project.Single nucleotide polymorphisms(SNPs)associated with inflammatory cytokines were systematically selected as instrumental variables(IVs)based on three rigorous criteria:relevance,independence,and exclusion of pleiotropy.Five MR methods were employed for causal inference:the inverse-variance weighted(IVW)method as the primary analysis,supplemented by MREgger regression,weighted median estimator,simple mode,and weighted mode approaches.Sensitivity analyses were performed to evaluate the robustness of the causal estimates.RESULTS:A total of 773 myopia-associated SNPs were identified.MR analysis revealed that higher levels of macrophage inflammatory protein 1-α(MIP-1α)were associated with a 17%reduced risk of myopia[odds ratio(OR)=0.83;95%confidence interval(CI):0.69-0.99;P<0.05].In contrast,elevated levels of eotaxin(OR=1.26;95%CI:1.07-1.47;P<0.01),stromal cell-derived factor-1α(SDF-1α;OR=1.68;95%CI:1.08-2.62;P<0.05),and interleukin-2 receptor subunit alpha(IL-2Rα;OR=1.25;95%CI:1.01-1.53;P<0.05)were significantly associated with an increased risk of myopia.Sensitivity analyses confirmed the reliability of these results.CONCLUSION:This study provides evidence supporting a causal relationship between specific inflammatory cytokines and myopia.MIP-1αmay act as a protective factor against myopia,while eotaxin,SDF-1α,and IL-2Rαare potential risk factors for myopia.These findings emphasize the critical role of inflammatory pathways in the pathogenesis of myopia,offering novel insights for the development of preventive and therapeutic strategies for myopia.展开更多
Objective Previous Mendelian randomization(MR)studies have suggested an association between the gut microbiome and metabolic-associated fatty liver disease(MAFLD).However,the reliance on 16S rRNA sequencing data has l...Objective Previous Mendelian randomization(MR)studies have suggested an association between the gut microbiome and metabolic-associated fatty liver disease(MAFLD).However,the reliance on 16S rRNA sequencing data has led to inconsistent findings and limited species-level insights.To address this,we conducted a de novo MR analysis using species-level shotgun metagenomic data,combined it with a meta-analysis to consolidate the existing evidence,and explored metabolite-mediated pathways.Methods Bidirectional MR analyses were performed between 883 gut microbiota taxa(derived from shotgun metagenomic genome-wide association study)and MAFLD.Published MR studies(up to December 1,2024)were identified using PubMed,Embase,Web of Science,and the Cochrane Library for meta-analysis.Multivariable MR(MVMR)and mediation analyses were applied to assess the mediating effects of 1,400 blood metabolites.Results The de novo MR identified 25 MAFLD-associated microbial taxa.Integration with 7 published studies revealed 34 causal taxa,including 10 at the species level.Among the 1,400 metabolites,53 showed causal links with MAFLD.MVMR and mediation analyses identified deoxycholate as a mediator of the effect of Bifidobacterium on MAFLD risk(22.06%mediation proportion).Conclusion This study elucidated the connections between species-level gut microbiota and MAFLD,highlighting the interplay between microbiota,metabolites,and disease pathogenesis.These findings provide novel insights into the potential therapeutic targets for MAFLD.展开更多
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genoty...Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genotype data and 681 participants with gene expression data from the Molecular Epidemiology of ARDS(MEARDS),the Molecular Epidemiology of Sepsis in the ICU(MESSI),and the Molecular Diagnosis and Risk Stratification of Sepsis(MARS)cohorts in a three-step study focusing on sepsis-associated ARDS and sepsis-only controls.First,we identified and validated interferon-related genes associated with sepsis-associated ARDS risk using genetically regulated gene expression(GReX).Second,we examined the association of the confirmed gene(interferon regulatory factor 1,IRF1)with ARDS risk and survival and conducted a mediation analysis.Through discovery and validation,we found that the GReX of IRF1 was associated with ARDS risk(odds ratio[OR_(MEARDS)]=0.84,P=0.008;OR_(MESSI)=0.83,P=0.034).Furthermore,individual-level measured IRF1 expression was associated with reduced ARDS risk(OR=0.58,P=8.67×10^(-4)),and improved overall survival in ARDS patients(hazard ratio[HR_(28-day)]=0.49,P=0.009)and sepsis patients(HR_(28-day)=0.76,P=0.008).Mediation analysis revealed that IRF1 may enhance immune function by regulating the major histocompatibility complex,including HLA-F,which mediated more than 70%of protective effects of IRF1 on ARDS.The findings were validated by in vitro biological experiments including time-series infection dynamics,overexpression,knockout,and chromatin immunoprecipitation sequencing.Early prophylactic interventions to activate IRF1 in sepsis patients,thereby regulating HLA-F,may reduce the risk of ARDS and mortality,especially in severely ill patients.展开更多
Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying...Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying etiology remains complex.Recent studies suggest a close link between the gut microbiota and the synthesis,absorption,and metabolism of these vitamins.However,the specific causal relationships between the gut microbiota composition and vitamin deficiencies remain poorly understood.Identifying key bacterial species and understanding their role in vitamin metabolism could provide critical insights for targeted interventions.Methods We conducted a two-sample Mendelian randomization(MR)study to assess the causal relationship between the gut microbiota and vitamin deficiencies(A,B12,D).The genome-wide association study data for vitamin deficiencies were sourced from the FinnGen biobank,and the gut microbiota data were from the MiBioGen consortium.MR analyses included inverse variance-weighted(IVW),MR‒Egger,weighted median,and weighted mode approaches.Sensitivity analyses and reverse causality assessments were performed to ensure robustness and validate the findings.Results After FDR adjustment,vitamin B12 deficiency was associated with the class Verrucomicrobiae,order Verrucomicrobiales,family Verrucomicrobiaceae,and genus Akkermansia.Vitamin A deficiency was associated with the phylum Firmicutes and the genera Fusicatenibacter and Ruminiclostridium 6.Additional associations for vitamin B12 deficiency included the Enterobacteriaceae and Rhodospirillaceae and the genera Coprococcus 2,Lactococcus,and Ruminococcaceae UCG002.Vitamin D deficiency was associated with the genera Allisonella,Eubacterium,and Tyzzerella 3.Lachnospiraceae and Lactococcus were common risk factors for both B12 and D deficiency.Sensitivity analyses confirmed the robustness of the findings against heterogeneity and horizontal pleiotropy,and reverse MR tests indicated no evidence of reverse causality.Conclusions Our findings reveal a possible causal relationship between specific gut microbiota characteristics and vitamin A,B12 and D deficiencies,providing a theoretical basis for addressing these nutritional deficiencies through the modulation of the gut microbiota in the future and laying the groundwork for related interventions.展开更多
Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subju...Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.展开更多
The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitatio...The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.展开更多
Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(M...Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.展开更多
Emerging evidence highlights the role of thyroid hormones in cancer,although findings are controversial.Research on thyroid-related traits in lung carcinogenesis is limited.Using UK Biobank data,we performed bidirecti...Emerging evidence highlights the role of thyroid hormones in cancer,although findings are controversial.Research on thyroid-related traits in lung carcinogenesis is limited.Using UK Biobank data,we performed bidirectional Mendelian randomization(MR)to assess causal associations between lung cancer risk and thyroid dysfunction(hypothyroidism and hyperthyroidism)or functional traits(free thyroxine[FT4]and normal-range thyroid-stimulating hormone[TSH]).Furthermore,in the smoking-behavior-stratified MR analysis,we evaluated the mediating effect of thyroid-related phenotypes on the association between smoking behaviors and lung cancer.We demonstrated significant associations between lung cancer risk and hypothyroidism(hazard ratio[HR]=1.14,95%confidence interval[CI]=1.03–1.26,P=0.009)and hyperthyroidism(HR=1.55,95%CI=1.29–1.87,P=1.90×10^(-6))in the UKB.Moreover,the MR analysis indicated a causal effect of thyroid dysfunction on lung cancer risk(ORinverse variance weighted[IVW]=1.09,95%CI=1.05–1.13,P=3.12×10^(-6)for hypothyroidism;ORIVW=1.08,95%CI=1.04–1.12,P=8.14×10^(-5)for hyperthyroidism).We found that FT4 levels were protective against lung cancer risk(ORIVW=0.93,95%CI=0.87–0.99,P=0.030).Additionally,the stratified MR analysis demonstrated distinct causal effects of thyroid dysfunction on lung cancer risk among smokers.Hyperthyroidism mediated the effect of smoking behaviors,especially the age of smoking initiation(17.66%mediated),on lung cancer risk.Thus,thyroid dysfunction phenotypes play causal roles in lung cancer development exclusively among smokers and act as mediators in the causal pathway from smoking to lung cancer.展开更多
Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additi...Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additional information for the decision maker or the financial analyst.The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts.In this study,we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series.Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk,as well as machine learning approaches without the proposed methodology.This improvement is statistically significant,as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.展开更多
This letter critiques the article by Xu et al in World Journal of Psychiatry,which developed a nomogram to predict cognitive impairment in elderly hypertensive patients using nutritional and biochemical parameters.Whi...This letter critiques the article by Xu et al in World Journal of Psychiatry,which developed a nomogram to predict cognitive impairment in elderly hypertensive patients using nutritional and biochemical parameters.While the model's use of variables like body mass index,albumin,hemoglobin,alkaline phosphatase,and mini-nutritional assessment scores is promising,we raise concerns about the small validation cohort size,potential reverse causality in cross-sectional data,insufficiently discussed mechanisms for alkaline phosphatase as a risk factor,omission of key cognitive predictors,and possible overfitting given high area under the curve values.We suggest external validation,longitudinal studies,and calibration metrics to enhance the model's robustness and clinical utility.展开更多
Dear Editor,Observational studies in epidemiology have identified a correlation between hypothyroidism and cholelithiasis[1–2].However,the causal relationship between the two diseases remains unclear.To investigate t...Dear Editor,Observational studies in epidemiology have identified a correlation between hypothyroidism and cholelithiasis[1–2].However,the causal relationship between the two diseases remains unclear.To investigate the potential causal relationship,we employed a two-sample bidirectional Mendelian randomization(MR)analysis.展开更多
Countries worldwide are leveraging the 2030 Agenda and its 17 Sustainable Development Goals(SDGs)for build-ing a more resilient and sustainable future.One solution in this endeavour is transitioning towards a bioecono...Countries worldwide are leveraging the 2030 Agenda and its 17 Sustainable Development Goals(SDGs)for build-ing a more resilient and sustainable future.One solution in this endeavour is transitioning towards a bioeconomy(BE),utilizing renewable resources and low-carbon value chains to meet food,energy,and materials demands.However,BE is neither inherently circular nor sustainable,compromising SDGs’progress.Therefore,we con-ducted a detailed ex-post analysis using correlation,transfer entropy,and network analysis to understand the complex causal interactions between BE and SDGs.Moving beyond correlation,we explored the directional in-fluence of interactions within the BE-SDG nexus.Our findings reveal a robust bidirectional influence between 19 BE criteria and 109 SDG targets across all goals among 48 European countries.While BE can drive progress to-ward SDGs,a balanced distribution of synergies and trade-offs constrains its impact.Collaborative efforts among European countries would effectively drive towards achieving both BE and SDGs.SDG 13 has positive influences from lowered fossil fuel emissions and negative ones due to land use changes and intensified agriculture,which releases stored carbon.Similarly,SDG 15 emerges as a positive influence,as healthy ecosystem services foster a resilient BE.Despite efforts towards SDG 12,Europe’s unsustainable consumption impedes BE supply chains.While BE practices are intended to accelerate sustainability,they fall short of playing a transformational role in achieving the SDGs.A shift towards a cohesive,collaborative strategy that leverages synergies and mitigates trade-offs can enhance the BE’s impact,advancing Europe closer to achieving the 2030 Agenda.展开更多
Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,th...Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,the complexity of LRE systems and the“black-box”nature of current deep learning-based diagnostic methods hinder interpretable fault diagnosis.This paper establishes Granger causality(GC)extraction-based component-wise multi-layer perceptron(GCMLP),achieving high fault diagnosis accuracy while leveraging GC to enhance diagnostic interpretability.First,component-wise MLP networks are constructed for distinct LRE variables to extract inter-variable GC relationships.Second,dedicated predictors are designed for each variable,leveraging historical data and GC relationships to forecast future states,thereby ensuring GC reliability.Finally,the extracted GC features are utilized for fault classification,guaranteeing feature discriminability and diagnosis accuracy.This study simulates six critical fault modes in LRE using Simulink.Based on the generated simulation data,GCMLP demonstrates superior fault localization accuracy compared to benchmark methods,validating its efficacy and robustness.展开更多
The Sustainable Development Goals(SDGs)represent a solemn commitment by United Nations member states,but achieving them faces numerous challenges,particularly armed conflicts.Here,we analyzed the impact of armed confl...The Sustainable Development Goals(SDGs)represent a solemn commitment by United Nations member states,but achieving them faces numerous challenges,particularly armed conflicts.Here,we analyzed the impact of armed conflict on SDG progress and its driving mechanism through causal inference methods and machine learning technique.The results show that between 2000 and 2021,armed conflicts slowed overall SDG progress by 3.43%,equivalent to a setback of 18 years.The Middle East was the most affected region,with a 6.10%slowdown in progress.The impact of different types of conflict varies across specific goals:interstate conflicts primarily affect SDG 5(Gender Equality)and SDG 7(Affordable and Clean Energy),while intrastate conflicts have a larger impact on SDG 4(Quality Education)and SDG 9(Industry,Innovation and Infrastructure).Additionally,SDG 15(Life on Land)is severely affected by both types of conflict,with long-term consequences.As armed conflicts increase,the development progress would regress rapidly in a non-linear manner.To achieve the SDGs by 2030,it is crucial not only to prevent conflicts but also to proactively address and mitigate their impacts on development.展开更多
Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed ...Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.展开更多
基金Supported by the National Natural Science Foundation of China(No.82371104)the Natural Science Foundation of Hunan Province(No.2023JJ30851).
文摘AIM:To clarify the clinical correlations and causal relationships between lipid metabolism and the progression of thyroid-associated ophthalmopathy(TAO).METHODS:This case-control study retrieved clinical data from 2018 to 2023.A total of 2591 patients were enrolled,including 197 patients with TAO(case group)and 2394 patients with hyperthyroidism without TAO(control group).Serum lipid parameters,including triglycerides,total cholesterol,high-density lipoprotein(HDL),low-density lipoprotein(LDL),and the HDL/total cholesterol ratio,as well as thyroid function markers,were compared between the two groups.Correlation analyses were performed to evaluate the associations between serum lipid levels and key ocular manifestations of TAO,including exophthalmos degree,clinical activity score,and disease severity.Furthermore,Mendelian randomization(MR)analysis was conducted using genome-wide association study(GWAS)datasets,with hyperthyroidism as the exposure variable and serum lipid parameters as the outcome variables,to infer the causal relationship between hyperthyroidism,lipid metabolism,and TAO progression.RESULTS:The TAO group consisted of 101 males and 96 females,while the hyperthyroidism group included 706 males and 1688 females.Compared with the control group,patients with TAO had significantly higher levels of triglycerides(1.83±1.21 vs 1.40±1.08 mmol/L,P<0.01),total cholesterol,LDL,and HDL.Correlation analysis showed that triglyceride levels were positively correlated with exophthalmos degree,whereas HDL levels were inversely correlated with exophthalmos degree.No significant associations were found between serum lipid levels and clinical activity score(P>0.1).MR analysis confirmed that hyperthyroidism exerted a causal effect in reducing serum triglycerides[inverse-variance weighting odds ratio(OR)=0.035,95%confidence interval(CI):0.01-0.12]and total cholesterol(OR=0.085,95%CI:0.02-0.34),with no evidence of horizontal pleiotropy(MR-PRESSO P>0.05).CONCLUSION:Elevated serum triglyceride levels are an independent risk factor for TAO severity,especially exophthalmos,and triglyceride metabolism is inversely regulated by thyroid function.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00518960)in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00563192).
文摘Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.
基金supported by the Yanzhao Gold Talent Project of Hebei Province(NO.HJZD202506)。
文摘Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.
文摘AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)datasets were utilized for this two-sample MR analysis.Inflammatory cytokine-related GWAS data were extracted from The University of Bristol’s Research Data Repository,and myopia-related GWAS data were obtained from the FinnGen project.Single nucleotide polymorphisms(SNPs)associated with inflammatory cytokines were systematically selected as instrumental variables(IVs)based on three rigorous criteria:relevance,independence,and exclusion of pleiotropy.Five MR methods were employed for causal inference:the inverse-variance weighted(IVW)method as the primary analysis,supplemented by MREgger regression,weighted median estimator,simple mode,and weighted mode approaches.Sensitivity analyses were performed to evaluate the robustness of the causal estimates.RESULTS:A total of 773 myopia-associated SNPs were identified.MR analysis revealed that higher levels of macrophage inflammatory protein 1-α(MIP-1α)were associated with a 17%reduced risk of myopia[odds ratio(OR)=0.83;95%confidence interval(CI):0.69-0.99;P<0.05].In contrast,elevated levels of eotaxin(OR=1.26;95%CI:1.07-1.47;P<0.01),stromal cell-derived factor-1α(SDF-1α;OR=1.68;95%CI:1.08-2.62;P<0.05),and interleukin-2 receptor subunit alpha(IL-2Rα;OR=1.25;95%CI:1.01-1.53;P<0.05)were significantly associated with an increased risk of myopia.Sensitivity analyses confirmed the reliability of these results.CONCLUSION:This study provides evidence supporting a causal relationship between specific inflammatory cytokines and myopia.MIP-1αmay act as a protective factor against myopia,while eotaxin,SDF-1α,and IL-2Rαare potential risk factors for myopia.These findings emphasize the critical role of inflammatory pathways in the pathogenesis of myopia,offering novel insights for the development of preventive and therapeutic strategies for myopia.
基金supported by grants from the National Natural Science Foundation of China(82270924)the CAMS Innovation Fund for Medical Sciences(CIFMS 2021-I2M-1-016)the National High Level Hospital Clinical Research Funding(2022-PUMCH-C-014,2025-PUMCH-C-041).
文摘Objective Previous Mendelian randomization(MR)studies have suggested an association between the gut microbiome and metabolic-associated fatty liver disease(MAFLD).However,the reliance on 16S rRNA sequencing data has led to inconsistent findings and limited species-level insights.To address this,we conducted a de novo MR analysis using species-level shotgun metagenomic data,combined it with a meta-analysis to consolidate the existing evidence,and explored metabolite-mediated pathways.Methods Bidirectional MR analyses were performed between 883 gut microbiota taxa(derived from shotgun metagenomic genome-wide association study)and MAFLD.Published MR studies(up to December 1,2024)were identified using PubMed,Embase,Web of Science,and the Cochrane Library for meta-analysis.Multivariable MR(MVMR)and mediation analyses were applied to assess the mediating effects of 1,400 blood metabolites.Results The de novo MR identified 25 MAFLD-associated microbial taxa.Integration with 7 published studies revealed 34 causal taxa,including 10 at the species level.Among the 1,400 metabolites,53 showed causal links with MAFLD.MVMR and mediation analyses identified deoxycholate as a mediator of the effect of Bifidobacterium on MAFLD risk(22.06%mediation proportion).Conclusion This study elucidated the connections between species-level gut microbiota and MAFLD,highlighting the interplay between microbiota,metabolites,and disease pathogenesis.These findings provide novel insights into the potential therapeutic targets for MAFLD.
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
基金supported by the National Natural Science Foundation of China(Grant No.82220108002 to F.C.and Grant No.82273737 to R.Z.)the U.S.National Institutes of Health(Grant Nos.CA209414,HL060710,and ES000002 to D.C.C.,Grant Nos.CA209414 and CA249096 to Y.L.)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)supported by the Qing Lan Project of the Higher Education Institutions of Jiangsu Province and the Outstanding Young Level Academic Leadership Training Program of Nanjing Medical University.
文摘Interferon-related genes are involved in antiviral responses,inflammation,and immunity,which are closely related to sepsis-associated acute respiratory distress syndrome(ARDS).We analyzed 1972 participants with genotype data and 681 participants with gene expression data from the Molecular Epidemiology of ARDS(MEARDS),the Molecular Epidemiology of Sepsis in the ICU(MESSI),and the Molecular Diagnosis and Risk Stratification of Sepsis(MARS)cohorts in a three-step study focusing on sepsis-associated ARDS and sepsis-only controls.First,we identified and validated interferon-related genes associated with sepsis-associated ARDS risk using genetically regulated gene expression(GReX).Second,we examined the association of the confirmed gene(interferon regulatory factor 1,IRF1)with ARDS risk and survival and conducted a mediation analysis.Through discovery and validation,we found that the GReX of IRF1 was associated with ARDS risk(odds ratio[OR_(MEARDS)]=0.84,P=0.008;OR_(MESSI)=0.83,P=0.034).Furthermore,individual-level measured IRF1 expression was associated with reduced ARDS risk(OR=0.58,P=8.67×10^(-4)),and improved overall survival in ARDS patients(hazard ratio[HR_(28-day)]=0.49,P=0.009)and sepsis patients(HR_(28-day)=0.76,P=0.008).Mediation analysis revealed that IRF1 may enhance immune function by regulating the major histocompatibility complex,including HLA-F,which mediated more than 70%of protective effects of IRF1 on ARDS.The findings were validated by in vitro biological experiments including time-series infection dynamics,overexpression,knockout,and chromatin immunoprecipitation sequencing.Early prophylactic interventions to activate IRF1 in sepsis patients,thereby regulating HLA-F,may reduce the risk of ARDS and mortality,especially in severely ill patients.
文摘Objective Vitamin deficiencies,particularly in vitamins A,B12,and D,are prevalent across populations and contribute significantly to a range of health issues.While these deficiencies are well documented,the underlying etiology remains complex.Recent studies suggest a close link between the gut microbiota and the synthesis,absorption,and metabolism of these vitamins.However,the specific causal relationships between the gut microbiota composition and vitamin deficiencies remain poorly understood.Identifying key bacterial species and understanding their role in vitamin metabolism could provide critical insights for targeted interventions.Methods We conducted a two-sample Mendelian randomization(MR)study to assess the causal relationship between the gut microbiota and vitamin deficiencies(A,B12,D).The genome-wide association study data for vitamin deficiencies were sourced from the FinnGen biobank,and the gut microbiota data were from the MiBioGen consortium.MR analyses included inverse variance-weighted(IVW),MR‒Egger,weighted median,and weighted mode approaches.Sensitivity analyses and reverse causality assessments were performed to ensure robustness and validate the findings.Results After FDR adjustment,vitamin B12 deficiency was associated with the class Verrucomicrobiae,order Verrucomicrobiales,family Verrucomicrobiaceae,and genus Akkermansia.Vitamin A deficiency was associated with the phylum Firmicutes and the genera Fusicatenibacter and Ruminiclostridium 6.Additional associations for vitamin B12 deficiency included the Enterobacteriaceae and Rhodospirillaceae and the genera Coprococcus 2,Lactococcus,and Ruminococcaceae UCG002.Vitamin D deficiency was associated with the genera Allisonella,Eubacterium,and Tyzzerella 3.Lachnospiraceae and Lactococcus were common risk factors for both B12 and D deficiency.Sensitivity analyses confirmed the robustness of the findings against heterogeneity and horizontal pleiotropy,and reverse MR tests indicated no evidence of reverse causality.Conclusions Our findings reveal a possible causal relationship between specific gut microbiota characteristics and vitamin A,B12 and D deficiencies,providing a theoretical basis for addressing these nutritional deficiencies through the modulation of the gut microbiota in the future and laying the groundwork for related interventions.
基金supported by China’s MOE project of Key Research Institute of Humanities and Social Sciences at Universities(22JJD720021)the project of Shandong University(11090087395308).
文摘Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach.
基金supported by the Natural Science Foundation of China(Nos.U22A2099,62273113,62203461,62203365)the Innovation Project of Guangxi Graduate Education under Grant YCBZ2023130by the Guangxi Higher Education Undergraduate Teaching Reform Project Key Project,grant number 2022JGZ130.
文摘The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.
文摘Causality,the science of cause and effect,has made it possible to create a new family of models.Such models are often referred to as causal models.Unlike those of mathematical,numerical,empirical,or machine learning(ML)nature,causal models hope to tie the cause(s)to the effect(s)pertaining to a phenomenon(i.e.,data generating process)through causal principles.This paper presents one of the first works at creating causal models in the area of structural and construction engineering.To this end,this paper starts with a brief review of the principles of causality and then adopts four causal discovery algorithms,namely,PC(Peter-Clark),FCI(fast causal inference),GES(greedy equivalence search),and GRa SP(greedy relaxation of the sparsest permutation),have been used to examine four phenomena,including predicting the load-bearing capacity of axially loaded members,fire resistance of structural members,shear strength of beams,and resistance of walls against impulsive(blast)loading.Findings from this study reveal the possibility and merit of discovering complete and partial causal models.Finally,this study also proposes two simple metrics that can help assess the performance of causal discovery algorithms.
基金funded by the National Natural Science Foundation of China(Grant Nos.82220108002 to F.C.,82273737 to R.Z.,82473728 to Y.W.)the US National Institutes of Health(Grant Nos.CA209414,HL060710,ES000002 to D.C.C.,CA209414,CA249096 to Y.L.)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).R.Z.was partially supported by the Qing Lan Project of the Higher Education Institutions of Jiangsu Province and the Outstanding Young-Level Academic Leadership Training Program of Nanjing Medical University.
文摘Emerging evidence highlights the role of thyroid hormones in cancer,although findings are controversial.Research on thyroid-related traits in lung carcinogenesis is limited.Using UK Biobank data,we performed bidirectional Mendelian randomization(MR)to assess causal associations between lung cancer risk and thyroid dysfunction(hypothyroidism and hyperthyroidism)or functional traits(free thyroxine[FT4]and normal-range thyroid-stimulating hormone[TSH]).Furthermore,in the smoking-behavior-stratified MR analysis,we evaluated the mediating effect of thyroid-related phenotypes on the association between smoking behaviors and lung cancer.We demonstrated significant associations between lung cancer risk and hypothyroidism(hazard ratio[HR]=1.14,95%confidence interval[CI]=1.03–1.26,P=0.009)and hyperthyroidism(HR=1.55,95%CI=1.29–1.87,P=1.90×10^(-6))in the UKB.Moreover,the MR analysis indicated a causal effect of thyroid dysfunction on lung cancer risk(ORinverse variance weighted[IVW]=1.09,95%CI=1.05–1.13,P=3.12×10^(-6)for hypothyroidism;ORIVW=1.08,95%CI=1.04–1.12,P=8.14×10^(-5)for hyperthyroidism).We found that FT4 levels were protective against lung cancer risk(ORIVW=0.93,95%CI=0.87–0.99,P=0.030).Additionally,the stratified MR analysis demonstrated distinct causal effects of thyroid dysfunction on lung cancer risk among smokers.Hyperthyroidism mediated the effect of smoking behaviors,especially the age of smoking initiation(17.66%mediated),on lung cancer risk.Thus,thyroid dysfunction phenotypes play causal roles in lung cancer development exclusively among smokers and act as mediators in the causal pathway from smoking to lung cancer.
基金ANID for supporting the Fondecyt project 1200555.
文摘Financial time series have been analyzed with a wide variety of models and approaches,some of which can forecast with great accuracy.However,most of these models,especially the machine learning ones,cannot show additional information for the decision maker or the financial analyst.The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts.In this study,we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series.Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk,as well as machine learning approaches without the proposed methodology.This improvement is statistically significant,as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.
文摘This letter critiques the article by Xu et al in World Journal of Psychiatry,which developed a nomogram to predict cognitive impairment in elderly hypertensive patients using nutritional and biochemical parameters.While the model's use of variables like body mass index,albumin,hemoglobin,alkaline phosphatase,and mini-nutritional assessment scores is promising,we raise concerns about the small validation cohort size,potential reverse causality in cross-sectional data,insufficiently discussed mechanisms for alkaline phosphatase as a risk factor,omission of key cognitive predictors,and possible overfitting given high area under the curve values.We suggest external validation,longitudinal studies,and calibration metrics to enhance the model's robustness and clinical utility.
基金by grants from the Jiangsu Province 333 High-level Talent Training Project(Grant No.LGY2016010)the Nanjing Science and Technology Development Plan(Grant No.201715003)the Jiangsu Province Six Talent Peaks(Grant No.WSN-030).
文摘Dear Editor,Observational studies in epidemiology have identified a correlation between hypothyroidism and cholelithiasis[1–2].However,the causal relationship between the two diseases remains unclear.To investigate the potential causal relationship,we employed a two-sample bidirectional Mendelian randomization(MR)analysis.
基金funding from the German Federal Ministry of Ed-ucation and Research(BMBF)for the BIOCLIMAPATHS project(Grant No.01LS1906A)under the Axis-ERANET callfunding from the European Research Council(ERC)Starting Grant 2022 for the BEYONDSDG project(Grant No.101077492)。
文摘Countries worldwide are leveraging the 2030 Agenda and its 17 Sustainable Development Goals(SDGs)for build-ing a more resilient and sustainable future.One solution in this endeavour is transitioning towards a bioeconomy(BE),utilizing renewable resources and low-carbon value chains to meet food,energy,and materials demands.However,BE is neither inherently circular nor sustainable,compromising SDGs’progress.Therefore,we con-ducted a detailed ex-post analysis using correlation,transfer entropy,and network analysis to understand the complex causal interactions between BE and SDGs.Moving beyond correlation,we explored the directional in-fluence of interactions within the BE-SDG nexus.Our findings reveal a robust bidirectional influence between 19 BE criteria and 109 SDG targets across all goals among 48 European countries.While BE can drive progress to-ward SDGs,a balanced distribution of synergies and trade-offs constrains its impact.Collaborative efforts among European countries would effectively drive towards achieving both BE and SDGs.SDG 13 has positive influences from lowered fossil fuel emissions and negative ones due to land use changes and intensified agriculture,which releases stored carbon.Similarly,SDG 15 emerges as a positive influence,as healthy ecosystem services foster a resilient BE.Despite efforts towards SDG 12,Europe’s unsustainable consumption impedes BE supply chains.While BE practices are intended to accelerate sustainability,they fall short of playing a transformational role in achieving the SDGs.A shift towards a cohesive,collaborative strategy that leverages synergies and mitigates trade-offs can enhance the BE’s impact,advancing Europe closer to achieving the 2030 Agenda.
文摘Liquid rocket engine(LRE)fault diagnosis is critical for successful space launch missions,enabling timely avoidance of safety hazards,while accurate post-failure analysis prevents subsequent economic losses.However,the complexity of LRE systems and the“black-box”nature of current deep learning-based diagnostic methods hinder interpretable fault diagnosis.This paper establishes Granger causality(GC)extraction-based component-wise multi-layer perceptron(GCMLP),achieving high fault diagnosis accuracy while leveraging GC to enhance diagnostic interpretability.First,component-wise MLP networks are constructed for distinct LRE variables to extract inter-variable GC relationships.Second,dedicated predictors are designed for each variable,leveraging historical data and GC relationships to forecast future states,thereby ensuring GC reliability.Finally,the extracted GC features are utilized for fault classification,guaranteeing feature discriminability and diagnosis accuracy.This study simulates six critical fault modes in LRE using Simulink.Based on the generated simulation data,GCMLP demonstrates superior fault localization accuracy compared to benchmark methods,validating its efficacy and robustness.
基金supported and funded by the Special Research Assistant Program of Chinese Academy of Sciences(Grant No.E3S30015Y5)Maria de Maeztu excellence accreditation 2023–2026(Ref.CEX2021-001201-M),funded by MCIN/AEI/10.13039/501100011033。
文摘The Sustainable Development Goals(SDGs)represent a solemn commitment by United Nations member states,but achieving them faces numerous challenges,particularly armed conflicts.Here,we analyzed the impact of armed conflict on SDG progress and its driving mechanism through causal inference methods and machine learning technique.The results show that between 2000 and 2021,armed conflicts slowed overall SDG progress by 3.43%,equivalent to a setback of 18 years.The Middle East was the most affected region,with a 6.10%slowdown in progress.The impact of different types of conflict varies across specific goals:interstate conflicts primarily affect SDG 5(Gender Equality)and SDG 7(Affordable and Clean Energy),while intrastate conflicts have a larger impact on SDG 4(Quality Education)and SDG 9(Industry,Innovation and Infrastructure).Additionally,SDG 15(Life on Land)is severely affected by both types of conflict,with long-term consequences.As armed conflicts increase,the development progress would regress rapidly in a non-linear manner.To achieve the SDGs by 2030,it is crucial not only to prevent conflicts but also to proactively address and mitigate their impacts on development.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82204396,82304491,and 82400511).
文摘Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.
