Improving creep strength of the fine-grained heat-affected zone of novel 9Cr martensitic heat-resistant steel via modified thermo-mechanical treatment

The infamous type Ⅳ failure within the fine-grained heat-affected zone (FGHAZ) in G115 steel weldments seriously threatens the safe operation of ultra-supercritical (USC) power plants.In this work,the traditional thermo-mechanical treatment was modified via the replacement of hot-rolling with cold rolling,i.e.,normalizing,cold rolling,and tempering (NCT),which was developed to improve the creep strength of the FGHAZ in G115 steel weldments.The NCT treatment effectively promoted the dissolution of preformed M_(23)C_(6)particles and relieved the boundary segregation of C and Cr during welding thermal cycling,which accelerated the dispersed reprecipitation of M_(23)C_(6) particles within the fresh reaustenitized grains during post-weld heat treatment.In addition,the precipitation of Cu-rich phases and MX particles was promoted evidently due to the deformation-induced dislocations.As a result,the interacting actions between precipitates,dislocations,and boundaries during creep were reinforced considerably.Following this strategy,the creep rupture life of the FGHAZ in G115 steel weldments can be prolonged by 18.6%,which can further push the application of G115 steel in USC power plants.

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

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

Causal associations between gastroesophageal reflux disease and essential hypertension: A bidirectional Mendelian randomization study

BACKGROUND Clinical studies have reported that patients with gastroesophageal reflux disease(GERD)have a higher prevalence of hypertension.AIM To performed a bidirectional Mendelian randomization(MR)analysis to investi-gate the causal link between GERD and essential hypertension.METHODS Eligible single nucleotide polymorphisms(SNPs)were selected,and weighted median,inverse variance weighted(IVW)as well as MR egger(MR-Egger)re-gression were used to examine the potential causal association between GERD and hypertension.The MR-Pleiotropy RESidual Sum and Outlier analysis was used to detect and attempt to reduce horizontal pleiotropy by removing outliers SNPs.The MR-Egger intercept test,Cochran’s Q test and“leave-one-out”sen-sitivity analysis were performed to evaluate the horizontal pleiotropy,heterogen-eities,and stability of single instrumental variable.RESULTS IVW analysis exhibited an increased risk of hypertension(OR=1.46,95%CI:1.33-1.59,P=2.14E-16)in GERD patients.And the same result was obtained in replication practice(OR=1.002,95%CI:1.0008-1.003,P=0.000498).Meanwhile,the IVW analysis showed an increased risk of systolic blood pressure(β=0.78,95%CI:0.11-1.44,P=0.021)and hypertensive heart disease(OR=1.68,95%CI:1.36-2.08,P=0.0000016)in GERD patients.Moreover,we found an decreased risk of Barrett's esophagus(OR=0.91,95%CI:0.83-0.99,P=0.043)in essential hypertension patients.CONCLUSION We found that GERD would increase the risk of essential hypertension,which provided a novel prevent and therapeutic perspectives of essential hypertension.

Exploring the role of interleukin-6 receptor blockade in epilepsy and associated neuropsychiatric conditions through a mendelian randomization study

BACKGROUND The interplay between inflammation,immune dysregulation,and the onset of neurological disorders,including epilepsy,has become increasingly recognized.Interleukin(IL)-6,a pro-inflammatory cytokine,is suspected to not only mediate traditional inflammatory pathways but also contribute to neuroinflammatory responses that could underpin neuropsychiatric symptoms and broader psychiatric disorders in epilepsy patients.The role of IL-6 receptor(IL6R)blockade presents an intriguing target for therapeutic intervention due to its potential to attenuate these processes.neuropsychiatric conditions due to neuroinflammation.METHODS Mendelian randomization(MR)analysis employing single nucleotide poly-morphisms(SNPs)in the vicinity of the IL6R gene(total individuals=408225)was used to evaluate the putative causal relationship between IL6R blockade and epilepsy(total cases/controls=12891/312803),focal epilepsy(cases/controls=7526/399290),and generalized epilepsy(cases/controls=1413/399287).SNP weights were determined by their effect on C-reactive protein(CRP)levels and integrated using inverse variance-weighted meta-analysis as surrogates for IL6R effects.To address potential outlier and pleiotropic influences,sensitivity analyses were conducted employing a variety of MR methods under different modeling assumptions.RESULTS The genetic simulation targeting IL6R blockade revealed a modest but significant reduction in overall epilepsy risk[inverse variance weighting:Odds ratio(OR):0.827;95%confidence interval(CI):0.685-1.000;P=0.05].Subtype analysis showed variability,with no significant effect observed in generalized,focal,or specific childhood and juvenile epilepsy forms.Beyond the primary inflammatory marker CRP,the findings also suggested potential non-inflammatory pathways mediated by IL-6 signaling contributing to the neurobiological landscape of epilepsy,hinting at possible links to neuroinflammation,psychiatric symptoms,and associated mental disorders.CONCLUSION The investigation underscored a tentative causal relationship between IL6R blockade and decreased epilepsy incidence,likely mediated via complex neuroinflammatory pathways.These results encouraged further in-depth studies involving larger cohorts and multifaceted psychiatric assessments to corroborate these findings and more thoroughly delineate the neuro-psychiatric implications of IL-6 signaling in epilepsy.The exploration of IL6R blockade could herald a novel therapeutic avenue not just for seizure management but also for addressing the broader psychiatric and cognitive disturbances often associated with epilepsy.

The Genetic Association between CDKN1A and Heart Failure:Genome-Wide Exploration of m6A-SNPs and Mendelian Randomization

Objective N6-methyladenosine(m^(6)A)is a common epigenetic modification in eukaryotes.In this study,we explore the potential impact of m^(6)A-associated single nucleotide polymorphisms(m^(6)A-SNPs)on heart failure(HF).Methods Data from genome-wide association studies(GWAS)investigating HF in humans and from m^(6)A-SNPs datasets were used to identify HF-associated m^(6)A-SNPs.Their functions were explored using expression quantitative trait locus(eQTL),gene expression,and gene enrichment analyses.Mediation protein quantitative trait locus(pQTL)-Mendelian randomization(MR)was used to investigate the potential mechanism between critical protein levels and risk factors for HF.Results We screened 44 HF-associated m^(6)A-SNPs,including 10 m^(6)A-SNPs that showed eQTL signals and differential expressions in HF.The SNP rs1801270 in CDKN1A showed the strongest association with HF(P=7.75×10^(−6)).Additionally,MR verified the genetic association between the CDKN1A protein and HF,as well as the mediating effect of blood pressure(BP)in this pathway.Higher circulating level of CDKN1A was associated with a lower risk of HF(odds ratio[OR]=0.82,95%confidence interval[CI]:0.69 to 0.99).The proportions of hypertension,systolic BP,and diastolic BP were 48.10%,28.94%,and 18.02%,respectively.Associations of PDIA6(P=1.30×10^(−2))and SMAD3(P=4.80×10^(−2))with HF were also detected.Conclusion Multiple HF-related m^(6)A-SNPs were identified in this study.Genetic associations of CDKN1A and other proteins with HF and its risk factors were demonstrated,providing new ideas for further exploration of the molecular mechanisms of HF.

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

Genetically predicted fatty liver disease and risk of psychiatric disorders: A mendelian randomization study

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)and alcohol-related liver disease(Ar-LD)constitute the primary forms of chronic liver disease,and their incidence is progressively increasing with changes in lifestyle habits.Earlier studies have do-cumented a correlation between the occurrence and development of prevalent mental disorders and fatty liver.AIM To investigate the correlation between fatty liver and mental disorders,thus ne-cessitating the implementation of a mendelian randomization(MR)study to elu-cidate this association.METHODS Data on NAFLD and ArLD were retrieved from the genome-wide association studies catalog,while information on mental disorders,including Alzheimer's disease,schizophrenia,anxiety disorder,attention deficit hyperactivity disorder(ADHD),bipolar disorder,major depressive disorder,multiple personality dis-order,obsessive-compulsive disorder(OCD),post-traumatic stress disorder(PTSD),and schizophrenia was acquired from the psychiatric genomics consor-tium.A two-sample MR method was applied to investigate mediators in signifi-cant associations.RESULTS After excluding weak instrumental variables,a causal relationship was identified between fatty liver disease and the occurrence and development of some psychia-tric disorders.Specifically,the findings indicated that ArLD was associated with a significantly elevated risk of developing ADHD(OR:5.81,95%CI:5.59-6.03,P<0.01),bipolar disorder(OR:5.73,95%CI:5.42-6.05,P=0.03),OCD(OR:6.42,95%CI:5.60-7.36,P<0.01),and PTSD(OR:5.66,95%CI:5.33-6.01,P<0.01).Meanwhile,NAFLD significantly increased the risk of developing bipolar disorder(OR:55.08,95%CI:3.59-845.51,P<0.01),OCD(OR:61.50,95%CI:6.69-565.45,P<0.01),and PTSD(OR:52.09,95%CI:4.24-639.32,P<0.01).CONCLUSION Associations were found between genetic predisposition to fatty liver disease and an increased risk of a broad range of psychiatric disorders,namely bipolar disorder,OCD,and PTSD,highlighting the significance of preven-tive measures against psychiatric disorders in patients with fatty liver disease.

Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.

Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis

Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback.The framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis.In the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding.Furthermore,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons.We evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification accuracy.The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis performance.Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.

Exploring the Impact of Alcohol Consumption and Smoking on Primary Open Angle Glaucoma: A Mendelian Randomization Study

Objective: Utilizing Mendelian Randomization, this study employs Single Nucleotide Polymorphisms (SNPs) as instrumental variables to explore the causal relationships between bibulosity, smoking, and Primary Open Angle Glaucoma (POAG). Methods: GWAS data for bibulosity, smoking, and POAG were obtained from the Social Science Genetic Association Consortium website and the IEU OpenGWAS Project website, respectively. Using a P-value threshold of −8, a linkage disequilibrium coefficient (r2) of 0.001, and a linkage disequilibrium region width of 10,000 kb, the data were aggregated, resulting in 6 SNPs for bibulosity and 253 SNPs for smoking. Three regression models, MR-Egger, Weighted Median Estimator (WME), and Random-Effects Inverse-Variance Weighted (IVW) were applied to analyze the causal impact of bibulosity and smoking on POAG. Results: The GWAS data for alcohol consumption and smoking were derived from European populations, while the GWAS data for Primary Open-Angle Glaucoma (POAG) were sourced from East Asian populations, with no gender restrictions. Analysis using three different regression models revealed that neither excessive alcohol consumption nor smoking significantly increased the risk of developing POAG. Specifically, the odds ratios with 95% confidence intervals for the alcohol consumption group were 0.854 (0.597 - 1.221) in MR-Egger regression, 0.922 (0.691 - 1.231) in WME regression, and 0.944 (0.711 - 1.252) in IVW regression. For the smoking group, the odds ratios were 1.146 (0.546 - 2.406) in MR-Egger regression, 0.850 (0.653 - 1.111) in WME regression, and 0.939 (0.780 - 1.131) in IVW regression. Given the significant heterogeneity in the SNPs associated with smoking, the focus was primarily on the results from the IVW regression model. Conclusion: Alcohol consumption and smoking are not significant risk factors for the development of POAG.

Different effects of 24 dietary intakes on gastroesophageal reflux disease: A mendelian randomization

BACKGROUND In observational studies,dietary intakes are associated with gastroesophageal re-flux disease(GERD).AIM To conduct a two-sample mendelian randomization(MR)analysis to determine whether those associations are causal.METHODS To explore the relationship between dietary intake and the risk of GERD,we extracted appropriate single nucleotide polymorphisms from genome-wide asso-ciation study data on 24 dietary intakes.Three methods were adopted for data analysis:Inverse variance weighting,weighted median methods,and MR-Egger's method.The odds ratio(OR)and 95%confidence interval(CI)were used to eva-luate the causal association between dietary intake and GERD.RESULTS Our univariate Mendelian randomization(UVMR)results showed significant evidence that pork intake(OR,2.83;95%CI:1.76-4.55;P=1.84×10–5),beer intake(OR,2.70,95%CI:2.00-3.64;P=6.54×10–11),non-oily fish intake(OR,2.41;95%CI:1.49-3.91;P=3.59×10–4)have a protective effect on GERD.In addition,dried fruit intake(OR,0.37;95%CI:0.27-0.50;6.27×10–11),red wine intake(OR,0.34;95%CI:0.25-0.47;P=1.90×10-11),cheese intake(OR,0.46;95%CI:0.39-0.55;P=3.73×10-19),bread intake(OR,0.72;95%CI:0.56-0.92;P=0.0009)and cereal intake(OR,0.45;95%CI:0.36-0.57;P=2.07×10-11)were negatively associated with the risk of GERD.There was a suggestive asso-ciation for genetically predicted coffee intake(OR per one SD increase,1.22,95%CI:1.03-1.44;P=0.019).Multi-variate Mendelian randomization further confirmed that dried fruit intake,red wine intake,cheese intake,and cereal intake directly affected GERD.In contrast,the impact of pork intake,beer intake,non-oily fish intake,and bread intake on GERD was partly driven by the common risk factors for GERD.However,after adjusting for all four elements,there was no longer a suggestive association between coffee intake and GERD.CONCLUSION This study provides MR evidence to support the causal relationship between a broad range of dietary intake and GERD,providing new insights for the treatment and prevention of GERD.

Socioeconomic traits and the risk of Barrett’s esophagus and gastroesophageal reflux disease: A Mendelian randomization study

BACKGROUND Previous observational studies have shown that the prevalence of gastroesophageal reflux disease(GERD)and Barrett’s esophagus(BE)is associated with socioeconomic status.However,due to the methodological limitations of traditional observational studies,it is challenging to definitively establish causality.AIM To explore the causal relationship between the prevalence of these conditions and socioeconomic status using Mendelian randomization(MR).METHODS We initially screened single nucleotide polymorphisms(SNPs)to serve as proxies for eight socioeconomic status phenotypes for univariate MR analysis.The inverse variance weighted(IVW)method was used as the primary analytical method to estimate the causal relationship between the eight socioeconomic status phenotypes and the risk of GERD and BE.We then collected combinations of SNPs as composite proxies for the eight socioeconomic phenotypes to perform multivariate MR(MVMR)analyses based on the IVW MVMR model.Furthermore,a two-step MR mediation analysis was used to examine the potential mediation of the associations by body mass index,major depressive disorder(MDD),smoking,alcohol consumption,and sleep duration.RESULTS The study identified three socioeconomic statuses that had a significant impact on GERD.These included household income[odds ratio(OR):0.46;95% confidence interval(95%CI):0.31-0.70],education attainment(OR:0.23;95%CI:0.18-0.29),and the Townsend Deprivation Index at recruitment(OR:1.57;95%CI:1.04-2.37).These factors were found to independently and predominantly influence the genetic causal effect of GERD.Furthermore,the mediating effect of educational attainment on GERD was found to be mediated by MDD(proportion mediated:10.83%).Similarly,the effect of educational attainment on BE was mediated by MDD(proportion mediated:10.58%)and the number of cigarettes smoked per day(proportion mediated:3.50%).Additionally,the mediating effect of household income on GERD was observed to be mediated by sleep duration(proportion mediated:9.75%)CONCLUSION This MR study shed light on the link between socioeconomic status and GERD or BE,providing insights for the prevention of esophageal cancer and precancerous lesions.

Organic matter enrichment model of fine-grained rocks in volcanic rift lacustrine basin:A case study of lower submember of second member of Lower Cretaceous Shahezi Formation in Lishu rift depression of Songliao Basin,NE China

Based on sedimentary characteristics of the fine-grained rocks of the lower submember of second member of the Lower Cretaceous Shahezi Formation(K_(1)sh_(2)^(L))in the Lishu rift depression,combined with methods of organic petrology,analysis of major and trace elements as well as biological marker compound,the enrichment conditions and enrichment model of organic matter in the fine-grained sedimentary rocks in volcanic rift lacustrine basin are investigated.The change of sedimentary paleoenvironment controls the vertical distribution of different lithofacies types in the K_(1)sh_(2)^(L)and divides it into the upper and lower parts.The lower part contains massive siliceous mudstone with bioclast-bearing siliceous mudstone,whereas the upper part is mostly composed of laminated siliceous shale and laminated fine-grained mixed shale.The kerogen types of organic matter in the lower and upper parts are typesⅡ_(2)–Ⅲand typesⅠ–Ⅱ_(1),respectively.The organic carbon content in the upper part is higher than that in the lower part generally.The enrichment of organic matter in volcanic rift lacustrine basin is subjected to three favorable conditions.First,continuous enhancement of rifting is the direct factor increasing the paleo-water depth,and the rise of base level leads to the expansion of deep-water mudstone/shale deposition range.Second,relatively strong underwater volcanic eruption and rifting are simultaneous,and such event can provide a lot of nutrients for the lake basin,which is conducive to the bloom of algae,resulting in higher productivity of typesⅠ–Ⅱ_(1)kerogen.Third,the relatively dry paleoclimate leads to a decrease in input of fresh water and terrestrial materials,including TypeⅢkerogen from terrestrial higher plants,resulting in a water body with higher salinity and anoxic stratification,which is more favorable for preservation of organic matter.The organic matter enrichment model of fine-grained sedimentary rocks of volcanic rift lacustrine basin is established,which is of reference significance to the understanding of the organic matter enrichment mechanism of fine-grained sedimentary rocks of Shahezi Formation in Songliao Basin and even in the northeast China.

Dissecting Causal Relationships Between Plasma Metabolites and Osteoporosis:A Bidirectional Mendelian Randomization Study

Objective To investigate the causal relationships between plasma metabolites and osteoporosis via Mendelian randomization(MR) analysis.Methods Bidirectional MR was used to analyze pooled data from different genome-wide association studies(GWAS). The causal effect of plasma metabolites on osteoporosis was estimated using the inverse variance weighted method, intersections of statistically significant metabolites obtained from different sources of osteoporosis-related GWAS aggregated data was determined, and then sensitivity analysis was performed on these metabolites. Heterogeneity between single nucleotide polymorphisms was evaluated by Cochran's Q test. Horizontal pleiotropy was assessed through the application of the MR-Egger intercept method and the MRPRESSO method. The causal effect of osteoporosis on plasma metabolites was also evaluated using the inverse variance weighted method. Additionally, pathway analysis was conducted to identify potential metabolic pathways involved in the regulation of osteoporosis.Results Primary analysis and sensitivity analysis showed that 77 and 61 plasma metabolites had a causal relationship with osteoporosis from the GWAS data in the GCST90038656 and GCST90044600 datasets, respectively. Five common metabolites were identified via intersection. X-13684 levels and the glucose-to-maltose ratio were negatively associated with osteoporosis, whereas glycoursodeoxycholate levels and arachidoylcarnitine(C20) levels were positively associated with osteoporosis(all P < 0.05). The relationship between X-11299 levels and osteoporosis showed contradictory results(all P < 0.05). Pathway analysis indicated that glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, galactose metabolism, arginine biosynthesis, and starch and sucrose metabolism pathways were participated in the development of osteoporosis.Conclusion We found a causal relationship between plasma metabolites and osteoporosis. These results offer novel perspectives with important implications for targeted metabolite-focused interventions in the management of osteoporosis.

Causal Relationship between Chronic Obstructive Pulmonary Disease and Abdominal Aortic Aneurysm: A Mendelian Randomization Study

Background: Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory lung condition associated with significant morbidity and mortality. Observational studies indicate a positive correlation between COPD and the risk of abdominal aortic aneurysm (AAA), suggesting individuals with COPD are more likely to develop AAA. However, the causal relationship between COPD and AAA remains unclear. Method: This study employed a bidirectional Mendelian Randomization (MR) approach to assess the causal relationship between COPD and AAA. A two-step MR analysis was conducted to evaluate the mediating effect of 1400 circulating metabolites between COPD and AAA. Expression quantitative trait loci (eQTL) were sourced from the MRC Integrative Epidemiology Unit (MRC-IEU) database, and MR analysis was performed using the TwoSampleMR R package. The results were filtered using the Inverse Variance Weighted (IVW) method to identify genes strongly associated with both COPD and AAA. Furthermore, the Super Exact Test R package was utilized to determine the overlapping genes between COPD and AAA. Enrichment analysis for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted using the clusterProfiler R package. Protein-protein interaction (PPI) analysis was carried out using STRING v12.0. Results: The IVW method indicated a causal relationship between the risk increase of COPD and AAA (OR: 1.47, 95% CI: 1.16 - 1.86, p = 0.001). Among 1400 circulating metabolites, plasma-free proline was identified as mediating the relationship between COPD and AAA, with a mediation effect proportion of −4.6% (95% CI: −9.032%, −0.164%, p = 0.042). Additionally, PPI analysis revealed 20 functionally interrelated genes mediating the linkage between COPD and AAA. KEGG enrichment analysis showed functional enrichment of these genes in the pathway of aldosterone synthesis and secretion. Conclusion: Our study supports a causal relationship between COPD and an increased risk of AAA. Specifically, plasma-free proline and pathways related to aldosterone synthesis and secretion may play key roles in the connection between COPD and AAA.

Causal association of obesity-related anthropometric traits with myopia and the mediating role of educational attainment:a Mendelian randomization study

AIM:To study the causal relationship between obesityrelated anthropometric traits and myopia and the mediating role of educational attainment(EA).METHODS:Univariable Mendelian randomization(UVMR)was performed to evaluate the causal association between body mass index(BMI),height,waist-hip ratio(WHR,adjusted for BMI),and mean spherical equivalent(MSE).BMI was divided into fat and fat-free mass and included in multivariable Mendelian randomization(MVMR)to explore the roles of different BMI components in the causal relationship between BMI and MSE.A mediation analysis based on two-step Mendelian randomization(MR)was carried out.Specifically,UVMR was conducted to estimate the causal effect of BMI on EA.The direct effect of EA on MSE was estimated from MVMR.The mediation effect of EA in the BMI-EA-MSE model was calculated by the product of coefficients method.Expression quantitative trait loci(eQTL)-MR,reverse MR,and Linkage Disequilibrium Score Regression(LDSC)were performed to assess the robustness.RESULTS:Genetically predicted higher BMI had a positive total effect on MSE(βIVW=0.26 D,95%CI=0.14 to 0.37 D,P<0.001),whereas there was no significant association between height,WHR,and MSE.Fat mass was found to play a significant role in the effect of body mass on MSE(βIVW=0.50 D,95%CI=0.21 to 0.78 D,P=0.001),but there was no significant association between fat-free mass and MSE.The causal effect of BMI on EA was-0.14(95%CI=-0.16 to-0.11,P<0.001),and the direct effect of EA on MSE was-0.63 D(95%CI=-0.81 to-0.44 D,P<0.001).The mediating effect of EA in the BMI-EA-MSE model was 0.09 D(95%CI=0.06 to 0.12 D),with a mediation proportion of 33%(95%CI=22.1%to 44.6%).No reverse causal associations were detected except for BMI on EA.The results of eQTL-MR and LDSC were consistent with each MR analysis.CONCLUSION:Genetically predicted higher BMI decreases the degree of myopia with a 33%mediation proportion by EA,and fat mass provides a dominant protective role in body mass-myopia.As a supplement to previous observational studies,it provides strong evidence for the relationship between anthropometric traits and refractive errors and offers a theoretical basis for future measures to prevent and control myopia.

Mendelian randomization analysis of causal relationship between cheese intake and diabetic retinopathy

AIM:To assess whether there is a possible causal link between the intake of cheese and the risk of diabetic retinopathy(DR)utilizing a two-sample Mendelian randomization(MR)analysis.METHODS:The research data were obtained from summary statistics of genome-wide association studies(GWAS).Genetic loci closely related to cheese intake were extracted as instrumental variables(IVs),and DR was the outcome variable.The data were extracted from individuals of European ethnicity.The data of cheese intake consisted of 451486 samples with 9851867 single nucleotide polymorphisms(SNPs),while the DR data consisted of 206234 samples with 16380446 SNPs.Sixty-one genetic loci closely related to cheese intake were selected as IVs.MR analysis was performed by inverse-variance weighted(IVW)method and MR-Egger regression respectively.The causal relationship between cheese intake and DR was evaluated using odds ratios(ORs)and 95%confidence intervals(CIs).Egger-intercept test was used to test horizontal pleiotropy and sensitivity analysis was performed by leave-one-out test.RESULTS:The P value of the IVW method was less than 0.05,indicating a significant negative correlation between cheese intake and DR.MR-Egger regression showed that the intercept was 0.01 with a standard error of 0.022,and a P-value of 0.634,indicating no evidence of horizontal pleiotropy affecting the IVs related to the exposure factors.Besides,heterogeneity tests confirmed the absence of heterogeneity,and the“leave-one-out”sensitivity analysis demonstrated that the results were stable.CONCLUSION:Cheese intake is causally negatively correlated with the occurrence of DR,and cheese intake could reduce the risk of DR.

A survey of fine-grained visual categorization based on deep learning

Deep learning has achieved excellent results in various tasks in the field of computer vision,especially in fine-grained visual categorization.It aims to distinguish the subordinate categories of the label-level categories.Due to high intra-class variances and high inter-class similarity,the fine-grained visual categorization is extremely challenging.This paper first briefly introduces and analyzes the related public datasets.After that,some of the latest methods are reviewed.Based on the feature types,the feature processing methods,and the overall structure used in the model,we divide them into three types of methods:methods based on general convolutional neural network(CNN)and strong supervision of parts,methods based on single feature processing,and meth-ods based on multiple feature processing.Most methods of the first type have a relatively simple structure,which is the result of the initial research.The methods of the other two types include models that have special structures and training processes,which are helpful to obtain discriminative features.We conduct a specific analysis on several methods with high accuracy on pub-lic datasets.In addition,we support that the focus of the future research is to solve the demand of existing methods for the large amount of the data and the computing power.In terms of tech-nology,the extraction of the subtle feature information with the burgeoning vision transformer(ViT)network is also an important research direction.

Risk Factors of Depression Screened by Two-Sample Mendelian Randomization Analysis:A Systematic Review

Objective This study explored the potentially modifiable factors for depression and major depressive disorder(MDD)from the MR-Base database and further evaluated the associations between drug targets with MDD.Methods We analyzed two-sample of Mendelian randomization(2SMR)using genetic variant depression(n=113,154)and MDD(n=208,811)from Genome-Wide Association Studies(GWAS).Separate calculations were performed with modifiable risk factors from MR-Base for 1,001 genomes.The MR analysis was performed by screening drug targets with MDD in the DrugBank database to explore the therapeutic targets for MDD.Inverse variance weighted(IVW),fixed-effect inverse variance weighted(FE-IVW),MR-Egger,weighted median,and weighted mode were used for complementary calculation.Results The potential causal relationship between modifiable risk factors and depression contained 459 results for depression and 424 for MDD.Also,the associations between drug targets and MDD showed that SLC6A4,GRIN2A,GRIN2C,SCN10A,and IL1B expression are associated with an increased risk of depression.In contrast,ADRB1,CHRNA3,HTR3A,GSTP1,and GABRG2 genes are candidate protective factors against depression.Conclusion This study identified the risk factors causally associated with depression and MDD,and estimated 10 drug targets with significant impact on MDD,providing essential information for formulating strategies to prevent and treat depression.

Randomization Strategies in Image Steganography Techniques:A Review

Image steganography is one of the prominent technologies in data hiding standards.Steganographic system performance mostly depends on the embedding strategy.Its goal is to embed strictly confidential information into images without causing perceptible changes in the original image.The randomization strategies in data embedding techniques may utilize random domains,pixels,or region-of-interest for concealing secrets into a cover image,preventing information from being discovered by an attacker.The implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility,but it is challenging.A systematic approach is used with a standard methodology to carry out this study.This review concentrates on the critical examination of several embedding strategies,incorporating experimental results with state-of-the-art methods emphasizing the robustness,security,payload capacity,and visual quality metrics of the stego images.The fundamental ideas of steganography are presented in this work,along with a unique viewpoint that sets it apart from previous works by highlighting research gaps,important problems,and difficulties.Additionally,it offers a discussion of suggested directions for future study to advance and investigate uncharted territory in image steganography.