To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily tota...To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily total and cause-specific mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for long-term time trend, season and other variables. We also controlled for day of the week. Results A gently sloping V-like relationship between total mortality and temperature was found, with an optimum temperature (e.g. temperature with lowest mortality risk) value of 26.7癈 in Shanghai. For temperatures above the optimum value, total mortality increased by 0.73% for each degree Celsius increase; while for temperature below the optimum value, total mortality decreased by 1.21% for each degree Celsius increase. Conclusions Our findings indicate that temperature has an effect on daily mortality in Shanghai, and the time-series approach is a useful tool for studying the temperature-mortality association.展开更多
BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects betw...BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.展开更多
Background Few studies have investigated the effects of air pollutants on children with acute bronchitis.This study aimed to explore the acute effects of four air pollutants[fine particulate matter(PM_(2.5)),inhalable...Background Few studies have investigated the effects of air pollutants on children with acute bronchitis.This study aimed to explore the acute effects of four air pollutants[fine particulate matter(PM_(2.5)),inhalable particulate matter(PM10),sulfur dioxide(SO_(2)),and nitrogen dioxide(NO_(2))]on the daily number of children admitted to the hospital for acute bronchitis in Sichuan Province,China.Methods The 49,975 records of hospitalized children with acute bronchitis from medical institutions in nine cities/prefectures,Sichuan Province,China,as well as the simultaneous meteorological data and air pollution data from 183 monitoring sites,were collected from 1 January 2017 to 31 December 2018.A generalized additive model was adopted to analyze the exposure–response and lag effects of hospitalizations of children with acute bronchitis to air pollutants.Stratified analyses were conducted based on sex,age,and season.Results The single-pollutant model showed that a 10µg/m3 increase at lag07 of PM_(2.5),PM10,SO_(2),and NO_(2) corresponded to an increase of 1.23%[95%confidence interval(CI)0.21–2.26%],1.33%(95%CI 0.62–2.05%),23.52%(95%CI 11.52–36.81%),and 12.47%(95%CI 8.46–16.64%)in daily hospitalizations for children with acute bronchitis,respectively.Children aged 0–2 were more prone to PM_(2.5)(P<0.05).Interestingly,the effects were stronger in the warm season than in transition seasons and the cool season for PM_(2.5) and PM10(P<0.05).Conclusion The higher daily average concentrations of four pollutants in Sichuan Province can result in an increased number of children hospitalized for acute bronchitis.展开更多
Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in...Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.展开更多
The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasti...The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring meth...In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.展开更多
It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and externa...It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.展开更多
Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused signif...Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.展开更多
As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitat...As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.展开更多
Objective Observational studies have found associations between inflammatory bowel disease(IBD)and the risk of dementia,including Alzheimer’s dementia(AD)and vascular dementia(VD);however,these findings are inconsist...Objective Observational studies have found associations between inflammatory bowel disease(IBD)and the risk of dementia,including Alzheimer’s dementia(AD)and vascular dementia(VD);however,these findings are inconsistent.It remains unclear whether these associations are causal.Methods We conducted a meta-analysis by systematically searching for observational studies on the association between IBD and dementia.Mendelian randomization(MR)analysis based on summary genome-wide association studies(GWASs)was performed.Genetic correlation and Bayesian colocalization analyses were used to provide robust genetic evidence.Results Ten observational studies involving 80,565,688 participants were included in this metaanalysis.IBD was significantly associated with dementia(risk ratio[RR]=1.36,95%CI=1.04-1.78;I2=84.8%)and VD(RR=2.60,95%CI=1.18-5.70;only one study),but not with AD(RR=2.00,95%CI=0.96-4.13;I^(2)=99.8%).MR analyses did not supported significant causal associations of IBD with dementia(dementia:odds ratio[OR]=1.01,95%CI=0.98-1.03;AD:OR=0.98,95%CI=0.95-1.01;VD:OR=1.02,95%CI=0.97-1.07).In addition,genetic correlation and co-localization analyses did not reveal any genetic associations between IBD and dementia.Conclusion Our study did not provide genetic evidence for a causal association between IBD and dementia risk.The increased risk of dementia observed in observational studies may be attributed to unobserved confounding factors or detection bias.展开更多
This paper proposes an interdisciplinary talent training model that combines foreign language education with area studies.The model aims to cultivate international ocean affairs professionals with cross-cultural commu...This paper proposes an interdisciplinary talent training model that combines foreign language education with area studies.The model aims to cultivate international ocean affairs professionals with cross-cultural communication skills,in-depth regional and country knowledge,and practical expertise in ocean affairs.Additionally,the paper presents specific training pathways and policy recommendations for implementing this model.展开更多
In modern society,the globalization of literary works is evident,with exceptional literary pieces from various countries spreading worldwide.Among these,children’s literature,due to the specificity of its target audi...In modern society,the globalization of literary works is evident,with exceptional literary pieces from various countries spreading worldwide.Among these,children’s literature,due to the specificity of its target audience,imposes distinct requirements on children’s books,compelling translators to approach the text from a child’s perspective.“The Little Prince”has renowned both within and outside of China,and a careful reading of this work can provide us with much inspiration.To this end,the present study adopts the perspective of Gideon Toury’s Descriptive Translation Studies to conduct an in-depth analysis of the different English and Chinese translations in conjunction with the original French novel.This approach aims to better guide literary research and explores translation methods for children’s literature through the analysis of translation norms and rules.展开更多
Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribu...Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.展开更多
Mindfulness would enhance university students’emotional well-being and study engagement.However,the role of affect(positive and negative emotions)and psychological resources(psychological capital)linking mindfulness ...Mindfulness would enhance university students’emotional well-being and study engagement.However,the role of affect(positive and negative emotions)and psychological resources(psychological capital)linking mindfulness to study engagement remain underexplored.This cross-sectional study surveyed 688 Chinese university students(females=413,mean age=20.3,SD=0.83),using validated self-report measures of mindfulness,positive and negative emotions,psychological capital,and study engagement.Structural equation modeling and bias-corrected bootstrap analyses(5000 resamples)revealed that mindfulness directly enhanced positive emotions,psychological capital,and study engagement,while reducing negative emotions.Positive emotions partially mediated the positive effect of mindfulness on psychological capital and study engagement.Negative emotions partially and negatively mediated only the mindfulness-psychological capital link.Psychological capital independently mediated the mindfulness-engagement relationship,and two sequential mediation pathways emerged:(a)mindfulness→positive emotions→psychological capital→higher study engagement and(b)mindfulness→reduced negative emotions→psychological capital→higher study engagement.Consistent with broaden-and-build(B&B)theory and Conservation of Resources(COR)theory,these findings suggest that mindfulness fosters study engagement primarily by promoting positive emotional experiences and strengthening psychological capital.By implication,university student support programs should employ mindfulness-based interventions to cultivate emotional and psychological resources for higher students’engagement and overall well-being.展开更多
BACKGROUND Gastrointestinal endoscopy technology has significantly improved the diagnostic accuracy and the successful treatment of gastrointestinal diseases.However,a series of ethical issues have emerged,such as exp...BACKGROUND Gastrointestinal endoscopy technology has significantly improved the diagnostic accuracy and the successful treatment of gastrointestinal diseases.However,a series of ethical issues have emerged,such as expanding treatment indications,which affect the fair distribution of medical resources.There is limited research on ethical issues in the field of digestive endoscopy.AIM To investigate the level of ethical awareness among gastrointestinal endoscopy practitioners and analyze the ethical issues involved in gastrointestinal endoscopy technology.METHODS A questionnaire survey was performed to collect relevant data(gender,age,degree of education,professional title,personnel category,the level of understanding medical ethical principles,ethics training and its learning pathways)from gastrointestinal endoscopy practitioners at the Second Hospital of Dalian Medical University and Dalian Friendship Hospital,including licensed physicians and nurses(including trainees and graduate students).RESULTS The majority of gastrointestinal endoscopy practitioners have received training on ethics,but there is still considerable room for improvement in their ethical awareness.Different learning pathways may affect the mastery of ethical principles, and understanding of ethical principles is more easily achieved through hospital ethics institutions.CONCLUSIONTo address the ethical issues in gastrointestinal endoscopy technology, it is necessary to enhance the humanisticeducation of gastrointestinal endoscopy practitioners, incorporate ethical standards into the technology assessmentprocess, and establish a patient-centered diagnostic and treatment model to improve the ethical awareness of practitionersand achieve a balance between technology and ethics.展开更多
Escalating global energy demands and climate urgency necessitate advanced electrochemical energy conversion and storage technologies(EECSTs)like electrocatalysis and rechargeable batteries.Improving their performance ...Escalating global energy demands and climate urgency necessitate advanced electrochemical energy conversion and storage technologies(EECSTs)like electrocatalysis and rechargeable batteries.Improving their performance relies on elucidating reaction mechanisms and structure-performance relationships via in situ studies.This review summarizes recent in situ studies of EECSTs through a variety of advanced characterization techniques aiming at mapping reaction pathways for the rational design of overall high-performance reaction systems.We outline the principles,capabilities,advantages,and limitations of various in situ techniques.Their applications in in situ studies of fuel cells,water/CO_(2)electrolysis,and lithium batteries are highlighted with representative examples.These studies enable dynamic tracking of chemical and structural evolution of overall reaction systems,including materials,intermediates,products,and surroundings during operation,providing insights critical to rational system design.Future advancements will involve integrating multimodal in situ/operando approaches with artificial intelligence to enable real-time monitoring at practical scales.Such integration promises precise mechanistic insights and robust structure-performance correlations,ultimately accelerating the development of high-performance EECSTs aligned with sustainability and market requirements.展开更多
Objectives:In recent years,mental health has emerged as a pressing public health concern in China,driven by mounting societal pressures and fast-paced urban lifestyles.Physical activity,a well-established means of enh...Objectives:In recent years,mental health has emerged as a pressing public health concern in China,driven by mounting societal pressures and fast-paced urban lifestyles.Physical activity,a well-established means of enhancing psychological well-being,has received growing scholarly and policy attention.This study uses panel data from the 2020 and 2022 waves of the China Family Panel Studies(CFPS)to examine the impact of exercise frequency on mental health(with indicators such as CESD-8 depression scores)among college students and young employees,thereby providing empirical support for targeted mental health interventions.Methods:This study examines the relationship between individual exercise frequency and mental health among college students and young employees,using panel data from the 2020 and 2022 waves of the China Family Panel Studies(CFPS),with the Chinese version of the 8-item Center for Epidemiologic Studies Depression Scale(CESD-8)depression scores,self-rated health,and life satisfaction as outcome variables.Specifically,this study tests three hypotheses:(H1)increased exercise frequency significantly reduces depression symptoms and enhances well-being;(H2)the effects of exercise vary by social roles,with stronger mental health benefits among employed individuals and those with lower education;and(H3)lifestyle factors such as smoking amount,sleep duration,and Body Mass Index(BMI)partially mediate the relationship between exercise and mental health.Employing a two-way fixed effects model,baseline results indicate that a one-unit increase in exercise frequency significantly reduces the CESD-8 score by 0.183 points.To address potential endogeneity and spurious regression concerns,an instrumental variable(IV)approach is further applied.The heterogeneity analysis differentiates between students and employed individuals.Results:Among students,the effects of exercise on mental health are not statistically significant,regardless of education level.In contrast,for the employed,exercise demonstrates a significant positive impact on mental health,with particularly pronounced effects among those with lower educational attainment.These findings underscore the importance of promoting exercise as part of comprehensive mental health strategies.Mediation analysis indicates that the beneficial effect of exercise on mental health is partially transmitted through reductions in adverse health behaviors,especially smoking.Conclusions:Policymakers should integrate physical activity promotion into health interventions,prioritizing vulnerable groups to enhance psychological resilience and foster inclusive,health-oriented development.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
文摘To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily total and cause-specific mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for long-term time trend, season and other variables. We also controlled for day of the week. Results A gently sloping V-like relationship between total mortality and temperature was found, with an optimum temperature (e.g. temperature with lowest mortality risk) value of 26.7癈 in Shanghai. For temperatures above the optimum value, total mortality increased by 0.73% for each degree Celsius increase; while for temperature below the optimum value, total mortality decreased by 1.21% for each degree Celsius increase. Conclusions Our findings indicate that temperature has an effect on daily mortality in Shanghai, and the time-series approach is a useful tool for studying the temperature-mortality association.
基金This study was reviewed and approved by the Ethics Committee of The First Psychiatric Hospital of Harbin.
文摘BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.
基金The authors received financial support from the National Natural Science Foundation of China(No.72174032)the research projects of“Xinglin Scholars”Nursery Talent in 2021 Research Plan of Chengdu University of Traditional Chinese Medicine through grants MPRC2021013.
文摘Background Few studies have investigated the effects of air pollutants on children with acute bronchitis.This study aimed to explore the acute effects of four air pollutants[fine particulate matter(PM_(2.5)),inhalable particulate matter(PM10),sulfur dioxide(SO_(2)),and nitrogen dioxide(NO_(2))]on the daily number of children admitted to the hospital for acute bronchitis in Sichuan Province,China.Methods The 49,975 records of hospitalized children with acute bronchitis from medical institutions in nine cities/prefectures,Sichuan Province,China,as well as the simultaneous meteorological data and air pollution data from 183 monitoring sites,were collected from 1 January 2017 to 31 December 2018.A generalized additive model was adopted to analyze the exposure–response and lag effects of hospitalizations of children with acute bronchitis to air pollutants.Stratified analyses were conducted based on sex,age,and season.Results The single-pollutant model showed that a 10µg/m3 increase at lag07 of PM_(2.5),PM10,SO_(2),and NO_(2) corresponded to an increase of 1.23%[95%confidence interval(CI)0.21–2.26%],1.33%(95%CI 0.62–2.05%),23.52%(95%CI 11.52–36.81%),and 12.47%(95%CI 8.46–16.64%)in daily hospitalizations for children with acute bronchitis,respectively.Children aged 0–2 were more prone to PM_(2.5)(P<0.05).Interestingly,the effects were stronger in the warm season than in transition seasons and the cool season for PM_(2.5) and PM10(P<0.05).Conclusion The higher daily average concentrations of four pollutants in Sichuan Province can result in an increased number of children hospitalized for acute bronchitis.
基金supported by the National Natural Science Foundation of China (41471335, 41271407)the National Remote Sensing Survey and Assessment of Eco-Environment Change between 2000 and 2010, China (STSN-1500)+2 种基金the National Key Technologies R&D Program of China during the 12th Five-Year Plan period (2013BAD05B03)the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05050601)the International Science and Technology (S&T) Cooperation Program of China (2012DFG22050)
文摘Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.
文摘The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
基金provided by the Program for New Century Excellent Talents in University (No. NCET-06-0477)the Independent Research Project of the State Key Laboratory of Coal Resources and Mine Safety of China University of Mining and Technology (No. SKLCRSM09X01)the Fundamental Research Funds for the Central Universities
文摘In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.
基金National Key Research and Development Program of China,Grant/Award Number:2018YFB2101003National Natural Science Foundation of China,Grant/Award Numbers:51991395,U1806226,51778033,51822802,71901011,U1811463,51991391Science and Technology Major Project of Beijing,Grant/Award Number:Z191100002519012。
文摘It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.
基金The National Natural Science Foundation of China under contract No.42406188the Natural Science Foundation of Liaoning Province under contract No.2024-BS-022+1 种基金the Dalian High-Level Talent Innovation Program under contract No.2022RG02the Fundamental Research Funds for the Central Universities under contract No.3132025107.
文摘Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.
文摘As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.
基金supported by the China Postdoctoral Science Foundation(Grant No.2021M703366)Shenzhen Science and Technology Program(Grant No.KQTD20190929172835662).
文摘Objective Observational studies have found associations between inflammatory bowel disease(IBD)and the risk of dementia,including Alzheimer’s dementia(AD)and vascular dementia(VD);however,these findings are inconsistent.It remains unclear whether these associations are causal.Methods We conducted a meta-analysis by systematically searching for observational studies on the association between IBD and dementia.Mendelian randomization(MR)analysis based on summary genome-wide association studies(GWASs)was performed.Genetic correlation and Bayesian colocalization analyses were used to provide robust genetic evidence.Results Ten observational studies involving 80,565,688 participants were included in this metaanalysis.IBD was significantly associated with dementia(risk ratio[RR]=1.36,95%CI=1.04-1.78;I2=84.8%)and VD(RR=2.60,95%CI=1.18-5.70;only one study),but not with AD(RR=2.00,95%CI=0.96-4.13;I^(2)=99.8%).MR analyses did not supported significant causal associations of IBD with dementia(dementia:odds ratio[OR]=1.01,95%CI=0.98-1.03;AD:OR=0.98,95%CI=0.95-1.01;VD:OR=1.02,95%CI=0.97-1.07).In addition,genetic correlation and co-localization analyses did not reveal any genetic associations between IBD and dementia.Conclusion Our study did not provide genetic evidence for a causal association between IBD and dementia risk.The increased risk of dementia observed in observational studies may be attributed to unobserved confounding factors or detection bias.
基金supported by“Dalian Maritime University Teaching Reform Research Fund 2022 Annual Project”(Fund No.XJG2022-96).
文摘This paper proposes an interdisciplinary talent training model that combines foreign language education with area studies.The model aims to cultivate international ocean affairs professionals with cross-cultural communication skills,in-depth regional and country knowledge,and practical expertise in ocean affairs.Additionally,the paper presents specific training pathways and policy recommendations for implementing this model.
文摘In modern society,the globalization of literary works is evident,with exceptional literary pieces from various countries spreading worldwide.Among these,children’s literature,due to the specificity of its target audience,imposes distinct requirements on children’s books,compelling translators to approach the text from a child’s perspective.“The Little Prince”has renowned both within and outside of China,and a careful reading of this work can provide us with much inspiration.To this end,the present study adopts the perspective of Gideon Toury’s Descriptive Translation Studies to conduct an in-depth analysis of the different English and Chinese translations in conjunction with the original French novel.This approach aims to better guide literary research and explores translation methods for children’s literature through the analysis of translation norms and rules.
基金Under the auspices of National Natural Science Foundation of China(No.42171407,42077242)Key Program of National Natural Science Foundation of China(No.42330607)。
文摘Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.
文摘Mindfulness would enhance university students’emotional well-being and study engagement.However,the role of affect(positive and negative emotions)and psychological resources(psychological capital)linking mindfulness to study engagement remain underexplored.This cross-sectional study surveyed 688 Chinese university students(females=413,mean age=20.3,SD=0.83),using validated self-report measures of mindfulness,positive and negative emotions,psychological capital,and study engagement.Structural equation modeling and bias-corrected bootstrap analyses(5000 resamples)revealed that mindfulness directly enhanced positive emotions,psychological capital,and study engagement,while reducing negative emotions.Positive emotions partially mediated the positive effect of mindfulness on psychological capital and study engagement.Negative emotions partially and negatively mediated only the mindfulness-psychological capital link.Psychological capital independently mediated the mindfulness-engagement relationship,and two sequential mediation pathways emerged:(a)mindfulness→positive emotions→psychological capital→higher study engagement and(b)mindfulness→reduced negative emotions→psychological capital→higher study engagement.Consistent with broaden-and-build(B&B)theory and Conservation of Resources(COR)theory,these findings suggest that mindfulness fosters study engagement primarily by promoting positive emotional experiences and strengthening psychological capital.By implication,university student support programs should employ mindfulness-based interventions to cultivate emotional and psychological resources for higher students’engagement and overall well-being.
文摘BACKGROUND Gastrointestinal endoscopy technology has significantly improved the diagnostic accuracy and the successful treatment of gastrointestinal diseases.However,a series of ethical issues have emerged,such as expanding treatment indications,which affect the fair distribution of medical resources.There is limited research on ethical issues in the field of digestive endoscopy.AIM To investigate the level of ethical awareness among gastrointestinal endoscopy practitioners and analyze the ethical issues involved in gastrointestinal endoscopy technology.METHODS A questionnaire survey was performed to collect relevant data(gender,age,degree of education,professional title,personnel category,the level of understanding medical ethical principles,ethics training and its learning pathways)from gastrointestinal endoscopy practitioners at the Second Hospital of Dalian Medical University and Dalian Friendship Hospital,including licensed physicians and nurses(including trainees and graduate students).RESULTS The majority of gastrointestinal endoscopy practitioners have received training on ethics,but there is still considerable room for improvement in their ethical awareness.Different learning pathways may affect the mastery of ethical principles, and understanding of ethical principles is more easily achieved through hospital ethics institutions.CONCLUSIONTo address the ethical issues in gastrointestinal endoscopy technology, it is necessary to enhance the humanisticeducation of gastrointestinal endoscopy practitioners, incorporate ethical standards into the technology assessmentprocess, and establish a patient-centered diagnostic and treatment model to improve the ethical awareness of practitionersand achieve a balance between technology and ethics.
基金supported by the National Key Research and Development Program of China(2023YFA1508004)the National Natural Science Foundation of China(T2293692,22502164,92472203,22222903,52271229,22472074,22272069,22361132532,and 22021001)+6 种基金the Industry-University-Research Joint Innovation Project of Fujian Province(2023H6029)the Beijing National Laboratory for Molecular Sciences(BNLMS202305)the Scientific and Technological Project of Yunnan Precious Metals Laboratory(YPML-20240502063)the Liaoning Binhai Laboratory(Grant No.2024-05)the State Key Laboratory of Fine Chemicals,Dalian University of Technology(KF 2401)the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20240897the China Postdoctoral Science Foundation(No.2025M770016).
文摘Escalating global energy demands and climate urgency necessitate advanced electrochemical energy conversion and storage technologies(EECSTs)like electrocatalysis and rechargeable batteries.Improving their performance relies on elucidating reaction mechanisms and structure-performance relationships via in situ studies.This review summarizes recent in situ studies of EECSTs through a variety of advanced characterization techniques aiming at mapping reaction pathways for the rational design of overall high-performance reaction systems.We outline the principles,capabilities,advantages,and limitations of various in situ techniques.Their applications in in situ studies of fuel cells,water/CO_(2)electrolysis,and lithium batteries are highlighted with representative examples.These studies enable dynamic tracking of chemical and structural evolution of overall reaction systems,including materials,intermediates,products,and surroundings during operation,providing insights critical to rational system design.Future advancements will involve integrating multimodal in situ/operando approaches with artificial intelligence to enable real-time monitoring at practical scales.Such integration promises precise mechanistic insights and robust structure-performance correlations,ultimately accelerating the development of high-performance EECSTs aligned with sustainability and market requirements.
文摘Objectives:In recent years,mental health has emerged as a pressing public health concern in China,driven by mounting societal pressures and fast-paced urban lifestyles.Physical activity,a well-established means of enhancing psychological well-being,has received growing scholarly and policy attention.This study uses panel data from the 2020 and 2022 waves of the China Family Panel Studies(CFPS)to examine the impact of exercise frequency on mental health(with indicators such as CESD-8 depression scores)among college students and young employees,thereby providing empirical support for targeted mental health interventions.Methods:This study examines the relationship between individual exercise frequency and mental health among college students and young employees,using panel data from the 2020 and 2022 waves of the China Family Panel Studies(CFPS),with the Chinese version of the 8-item Center for Epidemiologic Studies Depression Scale(CESD-8)depression scores,self-rated health,and life satisfaction as outcome variables.Specifically,this study tests three hypotheses:(H1)increased exercise frequency significantly reduces depression symptoms and enhances well-being;(H2)the effects of exercise vary by social roles,with stronger mental health benefits among employed individuals and those with lower education;and(H3)lifestyle factors such as smoking amount,sleep duration,and Body Mass Index(BMI)partially mediate the relationship between exercise and mental health.Employing a two-way fixed effects model,baseline results indicate that a one-unit increase in exercise frequency significantly reduces the CESD-8 score by 0.183 points.To address potential endogeneity and spurious regression concerns,an instrumental variable(IV)approach is further applied.The heterogeneity analysis differentiates between students and employed individuals.Results:Among students,the effects of exercise on mental health are not statistically significant,regardless of education level.In contrast,for the employed,exercise demonstrates a significant positive impact on mental health,with particularly pronounced effects among those with lower educational attainment.These findings underscore the importance of promoting exercise as part of comprehensive mental health strategies.Mediation analysis indicates that the beneficial effect of exercise on mental health is partially transmitted through reductions in adverse health behaviors,especially smoking.Conclusions:Policymakers should integrate physical activity promotion into health interventions,prioritizing vulnerable groups to enhance psychological resilience and foster inclusive,health-oriented development.
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.
