The comorbidity of skin and gastrointestinal tract(GIT)diseases,primarily driven by the gut-skin axis(GSA),is well established.However,the genetic contribution to the GSA remains unclear.Here,using genome-wide associa...The comorbidity of skin and gastrointestinal tract(GIT)diseases,primarily driven by the gut-skin axis(GSA),is well established.However,the genetic contribution to the GSA remains unclear.Here,using genome-wide association study(GWAS)summary statistics from European populations,we performed a genome-wide pleiotropic analysis to investigate the shared genetic basis and causal associations between skin and GIT diseases.We observed extensive genetic correlations and overlaps between skin and GIT diseases.A total of 298 pleiotropic loci were identified,75 of which were colocalized,and 61 exhibited pleiotropic effects across multiple trait pairs,including 2p16.1(PUS10),6p21.32(HLA-DRB1),10q21.2(ZNF365),and 19q13.11(SLC7A10).Additionally,five novel loci were identified based on the pleiotropic analysis;among them,the 15q22.2 locus harboring RORA was validated by the latest inflammatory bowel disease GWAS.Gene-based analysis identified 394 unique pleiotropic genes,which were enriched in GSA-associated tissues and the immune system,and protein-protein interaction analysis further revealed that the GPCR-cAMP,chromatin remodeling,JAK-STAT,and HLA-mediated immunity pathways were involved in GSA comorbidity.Notably,the JAK-STAT pathway showed strong potential for drug repurposing,with adalimumab targeting tumor necrosis factor and ustekinumab targeting interleukin-12 subunit beta already being used to treat both skin and GIT diseases.Finally,Mendelian randomization analysis identified five significant causal associations,and subsequent mediation analysis identified three potential microbiota-GIT-skin pathways.Taken together,our study demonstrated that the shared genetic factors between skin and GIT diseases were widely distributed across the genome.These findings will enhance our understanding of the genetic mechanisms underlying GSA comorbidity.展开更多
Objective:The incidence and mortality of colorectal carcinoma(CRC)continue to rise globally,highlighting the need to identify modifiable risk factors for early detection and prevention.Previous studies have demonstrat...Objective:The incidence and mortality of colorectal carcinoma(CRC)continue to rise globally,highlighting the need to identify modifiable risk factors for early detection and prevention.Previous studies have demonstrated significant associations between CRC risk and various serum metabolites as well as inflammatory cytokines;however,due to limitations in study design and potential confounding factors,the causal relationships remain unclear.This study aims to investigate the causal relationships between inflammatory cytokines,serum metabolites,and CRC risk,providing a theoretical basis for the development of novel early diagnostic biomarkers and therapeutic targets.Methods:A two-sample Mendelian randomization(MR)design was applied using summary statistics from genome-wide association studies(GWAS).Instrumental variables(IVs)were derived from:1)metabolomics GWAS data of 1400 serum metabolites(n=8299);2)cytokine GWAS data of 91 inflammatory factors(n=14824);and 3)CRC risk data from the FinnGen consortium(6847 cases and 314193 controls).The primary analysis was conducted using the inverse-variance weighted(IVW)method,with sensitivity analyses performed using MR Egger regression and the weighted median method.Effect estimates including odds ratios(OR),95%confidence intervals(CI),and false discovery rates(FDR)were calculated.Results:MR analysis indicated that higher levels of axin-1(AXIN1)(OR=0.84195%CI 0.714 to 0.991)and Fms-related tyrosine kinase 3 ligand(Flt3L)(OR=0.916,95%CI 0.844 to 0.994)were associated with a reduced risk of CRC.In contrast,higher levels of Delta/Notchlike epidermal growth factor-related receptor(DNER)(OR=1.119,95%CI 1.009 to 1.241)and vascular endothelial growth factor A(VEGF-A)(OR=1.078,95%CI 1.011 to 1.150)were associated with an increased risk of CRC(all P<0.05).Metabolomics association analysis further identified 144 serum metabolites significantly correlated with these four key inflammatory cytokines(FDR<0.05),suggesting that they may regulate CRC risk through inflammatory pathways.Conclusion:Specific inflammatory cytokines and serum metabolites have causal relationships with the risk of CRC.These findings provide insights for further exploration of potential risk factors and the development of effective prevention strategies for CRC.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent ...With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.展开更多
Members of the British Textile Machinery Association(BTMA)can look back on 2025 as a year marked by notable technological advances and continued progress in global trade,despite an uncertain and volatile market.“Our ...Members of the British Textile Machinery Association(BTMA)can look back on 2025 as a year marked by notable technological advances and continued progress in global trade,despite an uncertain and volatile market.“Our members have been very active over the past 12 months and this has resulted in new technologies for the production of technical fibres and fabrics,the introduction of AI and machine learning into process control systems and significant advances in materials testing,”says BTMA CEO Jason Kent.“There’s real excitement about what can be achieved in 2026 as we look ahead to upcoming exhibitions such as JEC Composites in Paris in March and Techtextil in Frankfurt in April.”展开更多
Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association betw...Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.展开更多
[Objectives]To investigate the potential causal relationship between psoriasis and common mental disorders,and to provide genetic epidemiological evidence for the early intervention of mental comorbidities.[Methods]Ba...[Objectives]To investigate the potential causal relationship between psoriasis and common mental disorders,and to provide genetic epidemiological evidence for the early intervention of mental comorbidities.[Methods]Based on publicly available large-scale GWAS data,a bidirectional Mendelian randomization(MR)approach was employed to assess the causal associations between psoriasis and major depressive disorder(MDD),bipolar disorder,schizophrenia,and anxiety disorders.The inverse variance weighted(IVW)method served as the primary analytical tool,supplemented by sensitivity analyses using MR-Egger and weighted median methods.Additionally,a subgroup analysis was conducted for psoriatic arthritis(PsA).[Results]Forward MR analysis revealed a significant positive causal association between the genetic predisposition to psoriasis and bipolar disorder as well as MDD,whereas no significant causal relationship was observed with schizophrenia or anxiety disorders.The reverse MR analysis found no causal effect of mental disorders on psoriasis.Subgroup analysis of PsA indicated that its genetic predisposition was significantly associated with the risk of bipolar disorder.The results of various sensitivity analyses and pleiotropy tests supported the robustness of the conclusions.[Conclusions]This study provides genetic evidence supporting a causal link between psoriasis and both MDD and bipolar disorder.In particular,patients with PsA are at a higher risk of developing bipolar disorder,highlighting the need to strengthen early screening and intervention for mental health in clinical management.展开更多
With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raisin...With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raising increasing concerns about their security.Existing fuzz testing methods include random or dictionary-based input generation,which often fail to ensure both syntactic and semantic correctness,and OpenAPIbased approaches,which offer better accuracy but typically lack detailed descriptions of endpoints,parameters,or data formats.To address these issues,this paper proposes the APIDocX fuzz testing framework.It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions to trigger APIs,capturing and extracting parameter information from communication packets.A multi-endpoint parameter adaptation method based on improved Jaccard similarity is then used to generalize these parameters to other potential API endpoints,filling in gaps in OpenAPI specifications.Experimental results demonstrate that the extracted parameters can be generalized with 79.61%accuracy.Fuzz testing using the enriched OpenAPI documents leads to improvements in test coverage,the number of valid test cases generated,and fault detection capabilities.This approach offers an effective enhancement to automated REST API security testing.展开更多
Cloud services,favored by many enterprises due to their high flexibility and easy operation,are widely used for data storage and processing.However,the high latency,together with transmission overheads of the cloud ar...Cloud services,favored by many enterprises due to their high flexibility and easy operation,are widely used for data storage and processing.However,the high latency,together with transmission overheads of the cloud architecture,makes it difficult to quickly respond to the demands of IoT applications and local computation.To make up for these deficiencies in the cloud,fog computing has emerged as a critical role in the IoT applications.It decentralizes the computing power to various lower nodes close to data sources,so as to achieve the goal of low latency and distributed processing.With the data being frequently exchanged and shared between multiple nodes,it becomes a challenge to authorize data securely and efficiently while protecting user privacy.To address this challenge,proxy re-encryption(PRE)schemes provide a feasible way allowing an intermediary proxy node to re-encrypt ciphertext designated for different authorized data requesters without compromising any plaintext information.Since the proxy is viewed as a semi-trusted party,it should be taken to prevent malicious behaviors and reduce the risk of data leakage when implementing PRE schemes.This paper proposes a new fog-assisted identity-based PRE scheme supporting anonymous key generation,equality test,and user revocation to fulfill various IoT application requirements.Specifically,in a traditional identity-based public key architecture,the key escrow problem and the necessity of a secure channel are major security concerns.We utilize an anonymous key generation technique to solve these problems.The equality test functionality further enables a cloud server to inspect whether two candidate trapdoors contain an identical keyword.In particular,the proposed scheme realizes fine-grained user-level authorization while maintaining strong key confidentiality.To revoke an invalid user identity,we add a revocation list to the system flows to restrict access privileges without increasing additional computation cost.To ensure security,it is shown that our system meets the security notion of IND-PrID-CCA and OW-ID-CCA under the Decisional Bilinear Diffie-Hellman(DBDH)assumption.展开更多
Although diet and gut microbial composition have been linked to chronic respiratory diseases,these associations remain difficult to interpret because of confounding and reverse causation.The gut-lung axis provides a p...Although diet and gut microbial composition have been linked to chronic respiratory diseases,these associations remain difficult to interpret because of confounding and reverse causation.The gut-lung axis provides a plausible framework for this interaction,yet direct genetic evidence is limited.Using a two-step,two-sample Mendelian randomization(MR)framework,supplemented by multivariable MR(MVMR)to adjust for pleiotropic effects and Benjamini-Hochberg false discovery rate(FDR)correction for multiple testing,we assessed the causal contributions of dietary habits and gut microbial taxa to major chronic respiratory diseases.We identified 22 dietary factors with causal effects on disease risk and 225 microbial taxa that acted as independent risk or protective contributors.Mediation analyses further showed that the effects of 12 dietary habits were transmitted through 32 specific microbial taxa.Notably,genetically predicted pork intake increased the risk of chronic obstructive pulmonary disease(COPD)(OR=10.53,95%CI[8.54,13.00]),an effect partly mediated by elevated abundance of CAG-485 sp002404675.In contrast,bread consumption conferred protection against asthma(OR=0.68,95%CI[0.64,0.72]),whereas this benefit was offset by approximately 45%through a pathway involving reduced Veillonella abundance.Collectively,these findings provide genetic support for the gut-lung axis and demonstrate that the gut microbiome functions as a causal mediator linking diet to chronic respiratory disease risk.However,since this study was based on individuals of European ancestry,caution is warranted when generalizing these causal estimates to non-European populations,such as East Asian groups.This work suggests new opportunities for microbiota-targeted prevention and therapeutic strategies.展开更多
AIM:To investigate the causal effect of obesity on cataract risk and explores the potential mediating roles of metabolites using Mendelian randomization(MR).METHODS:Summary-level data from large-scale genome-wide asso...AIM:To investigate the causal effect of obesity on cataract risk and explores the potential mediating roles of metabolites using Mendelian randomization(MR).METHODS:Summary-level data from large-scale genome-wide association studies to examine the relationship between obesity and cataract were utilized.Obesity-related traits,including body mass index(BMI),waist-to-hip ratio(WHR),and waist circumference(WC).A two-sample MR approach was employed to assess the causal effect of obesity on cataract risk,while potential mediators were identified from suitable genome-wide association studies(GWAS)datasets.Additionally,a metabolic pathway analysis was conducted.RESULTS:An increase of 1 standard deviation(SD)in BMI,WHR,and WC was associated with a significantly higher risk of cataract(BMI:odds ratio(OR)1.0017,95%confidence interval(CI):1.0001-1.0032,P=0.0320;WHR:OR 1.0029,95%CI:1.0006-1.0051,P=0.0129;WC:OR 1.0020,95%CI:1.0001-1.0038,P=0.0390].These associations remained robust after adjusting for confounding factors in multivariable MR analysis.Furthermore,a two-step MR analysis identified eight potential metabolic mediators,with one mediator showing a significant causal role in the relationship between obesity and cataract.CONCLUSION:This work highlights the importance of addressing obesity as a modifiable risk factor for cataracts,particularly through metabolic pathways.展开更多
AIM:To comprehensively assess the relationship between asthma and myopia based on the National Health and Nutrition Examination Survey(NHANES)database combined with Mendelian randomization(MR).METHODS:Initially,20497 ...AIM:To comprehensively assess the relationship between asthma and myopia based on the National Health and Nutrition Examination Survey(NHANES)database combined with Mendelian randomization(MR).METHODS:Initially,20497 subjects from the complete questionnaire cycle in the NHANES database from 2005 to 2008 were included.By exclusion criteria,8460 subjects were screened with 1676 myopia samples and 6784 control samples.Subsequently,baseline characteristics,association analyses,risk stratification analyses,and receive operating characteristic curve(ROC)were used to investigate the associations between covariates and myopia.Then,the causal relationship was explored in depth by MR analysis,and was estimated the reliability by sensitivity analyses and directionality tests.RESULTS:Baseline characteristics illustrated a significant difference between myopia and controls for both asthma and covariates(excluding gender;P<0.05).The results in all three models indicated that asthma was strongly associated with myopia and the effect on myopia was not significantly confounded by other covariates[model 3:odd ratio(OR)=1.31;95%CI=1.07-1.62;P=0.0133].The risk stratification analysis again verified that asthma remained strongly associated with myopia and was a risk factor for myopia(P<0.05,OR>1).ROC proved that the model was accurate in its prediction[area under curve(AUC)=0.7].Subsequently,the causal relationship between them was statistically significant(P<0.05)according to the inverse variance weighted(IVW)method in MR.Scatterplot showed that asthma and myopia had significant positive causality and were not affected by confounders.Forest plot displayed an increasing risk of myopia on asthma(OR>1).The funnel plot demonstrated compliance with Mendel’s second law.Sensitivity analysis and directional analysis further confirmed the confidence of the MR analysis results and a unidirectional causal relationship between them.CONCLUSION:A significant association and causality between asthma and myopia is found through the NHANES database and MR analysis,which is important implications for public health policy development and clinical practice.展开更多
AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analy...AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analysis.METHODS:The causal effects of several behavioral factors,including screen time,education time,time spent outdoors,and physical activity,on the risk of HM using univariable Mendelian randomization(MR)and MVMR analyses were first assessed.Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM.RESULTS:MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM.Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM.Additionally,MR analyses identified 13 serum metabolites significantly associated with HM,of which 12 were lipids and one was an amino acid derivative.Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM,with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC(p-16:0;30.83%).CONCLUSION:This study suggests that in addition to outdoor time,moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity.These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.展开更多
Lateral flow immunoassays(LFIAs)are low-cost,rapid,and easy to use for pointof-care testing(POCT),but the majority of the available LFIA tests are indicative,rather than quantitative,and their sensitivity in antigen t...Lateral flow immunoassays(LFIAs)are low-cost,rapid,and easy to use for pointof-care testing(POCT),but the majority of the available LFIA tests are indicative,rather than quantitative,and their sensitivity in antigen tests are usually limited at the nanogram range,which is primarily due to the passive capillary fluidics through nitrocellulose membranes,often associated with non-specific bindings and high background noise.To overcome this challenge,we report a Beads-on-a-Tip design by replacing nitrocellulose membranes with a pipette tip loaded with magnetic beads.The beads are pre-conjugated with capture antibodies that support a typical sandwich immunoassay.This design enriches the low-abundant antigen proteins and allows an active washing process to significantly reduce non-specific bindings.To further improve the detection sensitivity,we employed upconversion nanoparticles(UCNPs)as luminescent reporters and SARS-CoV-2 spike(S)antigen as a model analyte to benchmark the performance of this design against our previously reported methods.We found that the key to enhance the immunocomplex formation and signal-to-noise ratio lay in optimizing incubation time and the UCNP-to-bead ratio.We therefore successfully demonstrated that the new method can achieve a very large dynamic range from 500 fg/mL to 10μg/mL,across over 7 digits,and a limit of detection of 706 fg/mL,nearly another order of magnitude lower than the best reported LFIA using UCNPs in COVID-19 spike antigen detection.Our system offers a promising solution for ultra-sensitive and quantitative POCT diagnostics.展开更多
The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method...The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients.The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives.Based on the locally concerned U-statistic,the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero.A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic.Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.展开更多
To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a mult...To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.展开更多
Patients affected by monogenic diseases impose a substantial burden on both themselves and their families.The primary preventive measure,i.e.,invasive prenatal diagnosis,carries a risk of miscarriage and cannot be per...Patients affected by monogenic diseases impose a substantial burden on both themselves and their families.The primary preventive measure,i.e.,invasive prenatal diagnosis,carries a risk of miscarriage and cannot be performed early in pregnancy.Hence,there is a need for non-invasive prenatal testing(NIPT)for monogenic diseases.By utilizing enriched cell-free fetal DNA(cffDNA)from maternal plasma,we refine the NIPT method,which combines targeted region capture technology,haplotyping,and analysis of informative site frequency.We apply this method to 93 clinical families at genetic risk for thalassemia,encompassing various genetic variant types,to establish a workflow and evaluate its efficiency.Our approach requires only 3 ng of DNA input to generate 0.1 Gb informative target genomic data and leverages a minimum of 3%cffDNA.This method has a 98.16%success rate and 100%concordance with conventional invasive methods.Furthermore,we demonstrate the ability to analyze fetal genotypes as early as eight weeks of gestation.This study establishes an optimized NIPT method for the early detection of various thalassemia disorders during pregnancy.This technique demonstrates high accuracy and potential for clinical application in prenatal diagnosis.展开更多
As deep learning(DL)models are increasingly deployed in sensitive domains(e.g.,healthcare),concerns over privacy and security have intensified.Conventional penetration testing frameworks,such asOWASP and NIST,are effe...As deep learning(DL)models are increasingly deployed in sensitive domains(e.g.,healthcare),concerns over privacy and security have intensified.Conventional penetration testing frameworks,such asOWASP and NIST,are effective for traditional networks and applications but lack the capabilities to address DL-specific threats,such asmodel inversion,membership inference,and adversarial attacks.This review provides a comprehensive analysis of penetration testing for the privacy of DL models,examining the shortfalls of existing frameworks,tools,and testing methodologies.Through systematic evaluation of existing literature and empirical analysis,we identify three major contributions:(i)a critical assessment of traditional penetration testing frameworks’inadequacies when applied to DL-specific privacy vulnerabilities,(ii)a comprehensive evaluation of state-of-the-art privacy-preserving methods and their integration with penetration testing workflows,and(iii)the development of a structured framework that combines reconnaissance,threat modeling,exploitation,and post-exploitation phases specifically tailored for DL privacy assessment.Moreover,this review evaluates popular solutions such as IBMAdversarial Robustness Toolbox and TensorFlowPrivacy,alongside privacy-preserving techniques(e.g.,Differential Privacy,Homomorphic Encryption,and Federated Learning),which we systematically analyze through comparative studies of their effectiveness,computational overhead,and practical deployment constraints.While these techniques offer promising safeguards,their adoption is hindered by accuracy loss,performance overheads,and the rapid evolution of attack strategies.Our findings reveal that no single existing solution provides comprehensive protection,which leads us to propose a hybrid approach that strategically combines multiple privacy-preserving mechanisms.The findings of this survey underscore an urgent need for automated,regulationcompliant penetration testing frameworks specifically tailored to DL systems.We argue for hybrid privacy solutions that combinemultiple protectivemechanisms to ensure bothmodel accuracy and privacy.Building on our analysis,we present actionable recommendations for developing adaptive penetration testing strategies that incorporate automated vulnerability assessment,continuous monitoring,and regulatory compliance verification.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
Smartphone-based electrocardiograms(ECGs)are increasingly utilized for monitoring atrial fibrillation(AF)recurrence after catheter ablation(CA),referred to as smartphone AF burden(SMURDEN).The SMURDEN data often exhib...Smartphone-based electrocardiograms(ECGs)are increasingly utilized for monitoring atrial fibrillation(AF)recurrence after catheter ablation(CA),referred to as smartphone AF burden(SMURDEN).The SMURDEN data often exhibit complex patterns of zero AF episodes,which may arise from either true AF-free status(structural zeros)or missed AF episodes due to intermittent monitoring(random zeros).Such a mixture of AF-free and at-risk patients can lead to zero-inflation in the data.The authors propose a novel zero-inflation test for binomial regression models to identify recurrence-free AF populations.Unlike traditional approaches requiring fully specified zero-inflated models,the proposed test utilizes a weighted average of the discrepancies between observed and expected zero proportions,with weights determined by binomial sizes.A closed-form test statistic is developed,and its asymptotic distribution is derived using estimating equations.Simulations demonstrate superior performance over existing methods,and real-world AF monitoring data validate the practical utility of our proposed test.展开更多
基金supported by grants from the National Natural Science Foundation of China(Grant No.32470658)the National Key Research and Development Program of China(Grant Nos.2022YFC2502400 and 2022YFC2502402).
文摘The comorbidity of skin and gastrointestinal tract(GIT)diseases,primarily driven by the gut-skin axis(GSA),is well established.However,the genetic contribution to the GSA remains unclear.Here,using genome-wide association study(GWAS)summary statistics from European populations,we performed a genome-wide pleiotropic analysis to investigate the shared genetic basis and causal associations between skin and GIT diseases.We observed extensive genetic correlations and overlaps between skin and GIT diseases.A total of 298 pleiotropic loci were identified,75 of which were colocalized,and 61 exhibited pleiotropic effects across multiple trait pairs,including 2p16.1(PUS10),6p21.32(HLA-DRB1),10q21.2(ZNF365),and 19q13.11(SLC7A10).Additionally,five novel loci were identified based on the pleiotropic analysis;among them,the 15q22.2 locus harboring RORA was validated by the latest inflammatory bowel disease GWAS.Gene-based analysis identified 394 unique pleiotropic genes,which were enriched in GSA-associated tissues and the immune system,and protein-protein interaction analysis further revealed that the GPCR-cAMP,chromatin remodeling,JAK-STAT,and HLA-mediated immunity pathways were involved in GSA comorbidity.Notably,the JAK-STAT pathway showed strong potential for drug repurposing,with adalimumab targeting tumor necrosis factor and ustekinumab targeting interleukin-12 subunit beta already being used to treat both skin and GIT diseases.Finally,Mendelian randomization analysis identified five significant causal associations,and subsequent mediation analysis identified three potential microbiota-GIT-skin pathways.Taken together,our study demonstrated that the shared genetic factors between skin and GIT diseases were widely distributed across the genome.These findings will enhance our understanding of the genetic mechanisms underlying GSA comorbidity.
基金supported by the Natural Science Foundation of Hunan Province (2022JJ30987)the Key Research and Development Project of Hunan Province (2024JK2107),China。
文摘Objective:The incidence and mortality of colorectal carcinoma(CRC)continue to rise globally,highlighting the need to identify modifiable risk factors for early detection and prevention.Previous studies have demonstrated significant associations between CRC risk and various serum metabolites as well as inflammatory cytokines;however,due to limitations in study design and potential confounding factors,the causal relationships remain unclear.This study aims to investigate the causal relationships between inflammatory cytokines,serum metabolites,and CRC risk,providing a theoretical basis for the development of novel early diagnostic biomarkers and therapeutic targets.Methods:A two-sample Mendelian randomization(MR)design was applied using summary statistics from genome-wide association studies(GWAS).Instrumental variables(IVs)were derived from:1)metabolomics GWAS data of 1400 serum metabolites(n=8299);2)cytokine GWAS data of 91 inflammatory factors(n=14824);and 3)CRC risk data from the FinnGen consortium(6847 cases and 314193 controls).The primary analysis was conducted using the inverse-variance weighted(IVW)method,with sensitivity analyses performed using MR Egger regression and the weighted median method.Effect estimates including odds ratios(OR),95%confidence intervals(CI),and false discovery rates(FDR)were calculated.Results:MR analysis indicated that higher levels of axin-1(AXIN1)(OR=0.84195%CI 0.714 to 0.991)and Fms-related tyrosine kinase 3 ligand(Flt3L)(OR=0.916,95%CI 0.844 to 0.994)were associated with a reduced risk of CRC.In contrast,higher levels of Delta/Notchlike epidermal growth factor-related receptor(DNER)(OR=1.119,95%CI 1.009 to 1.241)and vascular endothelial growth factor A(VEGF-A)(OR=1.078,95%CI 1.011 to 1.150)were associated with an increased risk of CRC(all P<0.05).Metabolomics association analysis further identified 144 serum metabolites significantly correlated with these four key inflammatory cytokines(FDR<0.05),suggesting that they may regulate CRC risk through inflammatory pathways.Conclusion:Specific inflammatory cytokines and serum metabolites have causal relationships with the risk of CRC.These findings provide insights for further exploration of potential risk factors and the development of effective prevention strategies for CRC.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
基金Computer Basic Education Teaching Research Project of Association of Fundamental Computing Education in Chinese Universities(Nos.2025-AFCEC-527 and 2024-AFCEC-088)Research on the Reform of Public Course Teaching at Nantong College of Science and Technology(No.2024JGG015).
文摘With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.
文摘Members of the British Textile Machinery Association(BTMA)can look back on 2025 as a year marked by notable technological advances and continued progress in global trade,despite an uncertain and volatile market.“Our members have been very active over the past 12 months and this has resulted in new technologies for the production of technical fibres and fabrics,the introduction of AI and machine learning into process control systems and significant advances in materials testing,”says BTMA CEO Jason Kent.“There’s real excitement about what can be achieved in 2026 as we look ahead to upcoming exhibitions such as JEC Composites in Paris in March and Techtextil in Frankfurt in April.”
基金supported by the Yanzhao Gold Talent Project of Hebei Province(NO.HJZD202506)。
文摘Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.
文摘[Objectives]To investigate the potential causal relationship between psoriasis and common mental disorders,and to provide genetic epidemiological evidence for the early intervention of mental comorbidities.[Methods]Based on publicly available large-scale GWAS data,a bidirectional Mendelian randomization(MR)approach was employed to assess the causal associations between psoriasis and major depressive disorder(MDD),bipolar disorder,schizophrenia,and anxiety disorders.The inverse variance weighted(IVW)method served as the primary analytical tool,supplemented by sensitivity analyses using MR-Egger and weighted median methods.Additionally,a subgroup analysis was conducted for psoriatic arthritis(PsA).[Results]Forward MR analysis revealed a significant positive causal association between the genetic predisposition to psoriasis and bipolar disorder as well as MDD,whereas no significant causal relationship was observed with schizophrenia or anxiety disorders.The reverse MR analysis found no causal effect of mental disorders on psoriasis.Subgroup analysis of PsA indicated that its genetic predisposition was significantly associated with the risk of bipolar disorder.The results of various sensitivity analyses and pleiotropy tests supported the robustness of the conclusions.[Conclusions]This study provides genetic evidence supporting a causal link between psoriasis and both MDD and bipolar disorder.In particular,patients with PsA are at a higher risk of developing bipolar disorder,highlighting the need to strengthen early screening and intervention for mental health in clinical management.
基金supported by the Open Foundation of Key Laboratory of Cyberspace Security,Ministry of Education of China(KLCS20240211)。
文摘With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raising increasing concerns about their security.Existing fuzz testing methods include random or dictionary-based input generation,which often fail to ensure both syntactic and semantic correctness,and OpenAPIbased approaches,which offer better accuracy but typically lack detailed descriptions of endpoints,parameters,or data formats.To address these issues,this paper proposes the APIDocX fuzz testing framework.It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions to trigger APIs,capturing and extracting parameter information from communication packets.A multi-endpoint parameter adaptation method based on improved Jaccard similarity is then used to generalize these parameters to other potential API endpoints,filling in gaps in OpenAPI specifications.Experimental results demonstrate that the extracted parameters can be generalized with 79.61%accuracy.Fuzz testing using the enriched OpenAPI documents leads to improvements in test coverage,the number of valid test cases generated,and fault detection capabilities.This approach offers an effective enhancement to automated REST API security testing.
基金supported in part by the National Science and Technology Council of Taiwan under the contract numbers NSTC 114-2221-E-019-055-MY2 and NSTC 114-2221-E-019-069.
文摘Cloud services,favored by many enterprises due to their high flexibility and easy operation,are widely used for data storage and processing.However,the high latency,together with transmission overheads of the cloud architecture,makes it difficult to quickly respond to the demands of IoT applications and local computation.To make up for these deficiencies in the cloud,fog computing has emerged as a critical role in the IoT applications.It decentralizes the computing power to various lower nodes close to data sources,so as to achieve the goal of low latency and distributed processing.With the data being frequently exchanged and shared between multiple nodes,it becomes a challenge to authorize data securely and efficiently while protecting user privacy.To address this challenge,proxy re-encryption(PRE)schemes provide a feasible way allowing an intermediary proxy node to re-encrypt ciphertext designated for different authorized data requesters without compromising any plaintext information.Since the proxy is viewed as a semi-trusted party,it should be taken to prevent malicious behaviors and reduce the risk of data leakage when implementing PRE schemes.This paper proposes a new fog-assisted identity-based PRE scheme supporting anonymous key generation,equality test,and user revocation to fulfill various IoT application requirements.Specifically,in a traditional identity-based public key architecture,the key escrow problem and the necessity of a secure channel are major security concerns.We utilize an anonymous key generation technique to solve these problems.The equality test functionality further enables a cloud server to inspect whether two candidate trapdoors contain an identical keyword.In particular,the proposed scheme realizes fine-grained user-level authorization while maintaining strong key confidentiality.To revoke an invalid user identity,we add a revocation list to the system flows to restrict access privileges without increasing additional computation cost.To ensure security,it is shown that our system meets the security notion of IND-PrID-CCA and OW-ID-CCA under the Decisional Bilinear Diffie-Hellman(DBDH)assumption.
文摘Although diet and gut microbial composition have been linked to chronic respiratory diseases,these associations remain difficult to interpret because of confounding and reverse causation.The gut-lung axis provides a plausible framework for this interaction,yet direct genetic evidence is limited.Using a two-step,two-sample Mendelian randomization(MR)framework,supplemented by multivariable MR(MVMR)to adjust for pleiotropic effects and Benjamini-Hochberg false discovery rate(FDR)correction for multiple testing,we assessed the causal contributions of dietary habits and gut microbial taxa to major chronic respiratory diseases.We identified 22 dietary factors with causal effects on disease risk and 225 microbial taxa that acted as independent risk or protective contributors.Mediation analyses further showed that the effects of 12 dietary habits were transmitted through 32 specific microbial taxa.Notably,genetically predicted pork intake increased the risk of chronic obstructive pulmonary disease(COPD)(OR=10.53,95%CI[8.54,13.00]),an effect partly mediated by elevated abundance of CAG-485 sp002404675.In contrast,bread consumption conferred protection against asthma(OR=0.68,95%CI[0.64,0.72]),whereas this benefit was offset by approximately 45%through a pathway involving reduced Veillonella abundance.Collectively,these findings provide genetic support for the gut-lung axis and demonstrate that the gut microbiome functions as a causal mediator linking diet to chronic respiratory disease risk.However,since this study was based on individuals of European ancestry,caution is warranted when generalizing these causal estimates to non-European populations,such as East Asian groups.This work suggests new opportunities for microbiota-targeted prevention and therapeutic strategies.
基金Supported by the National Natural Science Foundation of China(No.82501261)Medical Research Projects of the Jiangsu Provincial Health Commission(No.M2024041).
文摘AIM:To investigate the causal effect of obesity on cataract risk and explores the potential mediating roles of metabolites using Mendelian randomization(MR).METHODS:Summary-level data from large-scale genome-wide association studies to examine the relationship between obesity and cataract were utilized.Obesity-related traits,including body mass index(BMI),waist-to-hip ratio(WHR),and waist circumference(WC).A two-sample MR approach was employed to assess the causal effect of obesity on cataract risk,while potential mediators were identified from suitable genome-wide association studies(GWAS)datasets.Additionally,a metabolic pathway analysis was conducted.RESULTS:An increase of 1 standard deviation(SD)in BMI,WHR,and WC was associated with a significantly higher risk of cataract(BMI:odds ratio(OR)1.0017,95%confidence interval(CI):1.0001-1.0032,P=0.0320;WHR:OR 1.0029,95%CI:1.0006-1.0051,P=0.0129;WC:OR 1.0020,95%CI:1.0001-1.0038,P=0.0390].These associations remained robust after adjusting for confounding factors in multivariable MR analysis.Furthermore,a two-step MR analysis identified eight potential metabolic mediators,with one mediator showing a significant causal role in the relationship between obesity and cataract.CONCLUSION:This work highlights the importance of addressing obesity as a modifiable risk factor for cataracts,particularly through metabolic pathways.
基金Supported by the Hainan Provincial Natural Science Foundation of China(No.825RC898)Hainan Province Clinical Medical Center。
文摘AIM:To comprehensively assess the relationship between asthma and myopia based on the National Health and Nutrition Examination Survey(NHANES)database combined with Mendelian randomization(MR).METHODS:Initially,20497 subjects from the complete questionnaire cycle in the NHANES database from 2005 to 2008 were included.By exclusion criteria,8460 subjects were screened with 1676 myopia samples and 6784 control samples.Subsequently,baseline characteristics,association analyses,risk stratification analyses,and receive operating characteristic curve(ROC)were used to investigate the associations between covariates and myopia.Then,the causal relationship was explored in depth by MR analysis,and was estimated the reliability by sensitivity analyses and directionality tests.RESULTS:Baseline characteristics illustrated a significant difference between myopia and controls for both asthma and covariates(excluding gender;P<0.05).The results in all three models indicated that asthma was strongly associated with myopia and the effect on myopia was not significantly confounded by other covariates[model 3:odd ratio(OR)=1.31;95%CI=1.07-1.62;P=0.0133].The risk stratification analysis again verified that asthma remained strongly associated with myopia and was a risk factor for myopia(P<0.05,OR>1).ROC proved that the model was accurate in its prediction[area under curve(AUC)=0.7].Subsequently,the causal relationship between them was statistically significant(P<0.05)according to the inverse variance weighted(IVW)method in MR.Scatterplot showed that asthma and myopia had significant positive causality and were not affected by confounders.Forest plot displayed an increasing risk of myopia on asthma(OR>1).The funnel plot demonstrated compliance with Mendel’s second law.Sensitivity analysis and directional analysis further confirmed the confidence of the MR analysis results and a unidirectional causal relationship between them.CONCLUSION:A significant association and causality between asthma and myopia is found through the NHANES database and MR analysis,which is important implications for public health policy development and clinical practice.
基金Supported by the Central High Level Hospital Clinical Research Funding(No.BJ-2024-089).
文摘AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analysis.METHODS:The causal effects of several behavioral factors,including screen time,education time,time spent outdoors,and physical activity,on the risk of HM using univariable Mendelian randomization(MR)and MVMR analyses were first assessed.Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM.RESULTS:MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM.Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM.Additionally,MR analyses identified 13 serum metabolites significantly associated with HM,of which 12 were lipids and one was an amino acid derivative.Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM,with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC(p-16:0;30.83%).CONCLUSION:This study suggests that in addition to outdoor time,moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity.These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.
基金financially supported by ARC Linkage project(LP210200642)ARC Center of Excellence for Quantum Biotechnology(grant no.CE230100021)+1 种基金National Health and Medical Research Council Investigator Fellowship—(grant no.APP2017499)Chan Zuckerberg Initiative Deep Tissue Imaging Phase 2(grant no.DT12-0000000182).
文摘Lateral flow immunoassays(LFIAs)are low-cost,rapid,and easy to use for pointof-care testing(POCT),but the majority of the available LFIA tests are indicative,rather than quantitative,and their sensitivity in antigen tests are usually limited at the nanogram range,which is primarily due to the passive capillary fluidics through nitrocellulose membranes,often associated with non-specific bindings and high background noise.To overcome this challenge,we report a Beads-on-a-Tip design by replacing nitrocellulose membranes with a pipette tip loaded with magnetic beads.The beads are pre-conjugated with capture antibodies that support a typical sandwich immunoassay.This design enriches the low-abundant antigen proteins and allows an active washing process to significantly reduce non-specific bindings.To further improve the detection sensitivity,we employed upconversion nanoparticles(UCNPs)as luminescent reporters and SARS-CoV-2 spike(S)antigen as a model analyte to benchmark the performance of this design against our previously reported methods.We found that the key to enhance the immunocomplex formation and signal-to-noise ratio lay in optimizing incubation time and the UCNP-to-bead ratio.We therefore successfully demonstrated that the new method can achieve a very large dynamic range from 500 fg/mL to 10μg/mL,across over 7 digits,and a limit of detection of 706 fg/mL,nearly another order of magnitude lower than the best reported LFIA using UCNPs in COVID-19 spike antigen detection.Our system offers a promising solution for ultra-sensitive and quantitative POCT diagnostics.
基金supported by the National Social Science Foundation of China under Grant No.23&ZD126National Science Foundation of China under Grant No.12471256+1 种基金Natural Science Foundation of Shanxi Province under Grant No.202203021221219Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi under Grant No.2023L164。
文摘The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients.The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives.Based on the locally concerned U-statistic,the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero.A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic.Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.
基金supported by Natural Science Foundation of China(NSFC),Grant number 5247052693.
文摘To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.
基金supported by the National Key R&D Program of China(2024YFA1802300)the Major Science and Technology Program of Hainan Province(ZDKJ2021037)+4 种基金the Regional Innovation and Development Joint Fund of the National Natural Science Foundation of China(U24A20677)Hainan Province Science and Technology Special Fund(ZDYF2020117,ZDY2024SHFZ143)Hainan Province Science and TechnologyProject(LCXY202102,LCYX202203,LCYX202301,LCYx202502)Innovative research project for postgraduate students in Hainan Medical University(HYYB2021A05)the Hainan Province Clinical Medical Center,and the specific research fund of The Innovation Platform for Academicians of Hainan Province(YSPTZX202310).
文摘Patients affected by monogenic diseases impose a substantial burden on both themselves and their families.The primary preventive measure,i.e.,invasive prenatal diagnosis,carries a risk of miscarriage and cannot be performed early in pregnancy.Hence,there is a need for non-invasive prenatal testing(NIPT)for monogenic diseases.By utilizing enriched cell-free fetal DNA(cffDNA)from maternal plasma,we refine the NIPT method,which combines targeted region capture technology,haplotyping,and analysis of informative site frequency.We apply this method to 93 clinical families at genetic risk for thalassemia,encompassing various genetic variant types,to establish a workflow and evaluate its efficiency.Our approach requires only 3 ng of DNA input to generate 0.1 Gb informative target genomic data and leverages a minimum of 3%cffDNA.This method has a 98.16%success rate and 100%concordance with conventional invasive methods.Furthermore,we demonstrate the ability to analyze fetal genotypes as early as eight weeks of gestation.This study establishes an optimized NIPT method for the early detection of various thalassemia disorders during pregnancy.This technique demonstrates high accuracy and potential for clinical application in prenatal diagnosis.
基金supported in part by the Tianjin Natural Science Foundation Project(24JCZDJC01000)the Fundamental Research Funds for the Central Universities of China(No.3122025091).
文摘As deep learning(DL)models are increasingly deployed in sensitive domains(e.g.,healthcare),concerns over privacy and security have intensified.Conventional penetration testing frameworks,such asOWASP and NIST,are effective for traditional networks and applications but lack the capabilities to address DL-specific threats,such asmodel inversion,membership inference,and adversarial attacks.This review provides a comprehensive analysis of penetration testing for the privacy of DL models,examining the shortfalls of existing frameworks,tools,and testing methodologies.Through systematic evaluation of existing literature and empirical analysis,we identify three major contributions:(i)a critical assessment of traditional penetration testing frameworks’inadequacies when applied to DL-specific privacy vulnerabilities,(ii)a comprehensive evaluation of state-of-the-art privacy-preserving methods and their integration with penetration testing workflows,and(iii)the development of a structured framework that combines reconnaissance,threat modeling,exploitation,and post-exploitation phases specifically tailored for DL privacy assessment.Moreover,this review evaluates popular solutions such as IBMAdversarial Robustness Toolbox and TensorFlowPrivacy,alongside privacy-preserving techniques(e.g.,Differential Privacy,Homomorphic Encryption,and Federated Learning),which we systematically analyze through comparative studies of their effectiveness,computational overhead,and practical deployment constraints.While these techniques offer promising safeguards,their adoption is hindered by accuracy loss,performance overheads,and the rapid evolution of attack strategies.Our findings reveal that no single existing solution provides comprehensive protection,which leads us to propose a hybrid approach that strategically combines multiple privacy-preserving mechanisms.The findings of this survey underscore an urgent need for automated,regulationcompliant penetration testing frameworks specifically tailored to DL systems.We argue for hybrid privacy solutions that combinemultiple protectivemechanisms to ensure bothmodel accuracy and privacy.Building on our analysis,we present actionable recommendations for developing adaptive penetration testing strategies that incorporate automated vulnerability assessment,continuous monitoring,and regulatory compliance verification.
文摘AIM:To investigate the potential causal associations between 41 inflammatory cytokines and myopia using a two-sample Mendelian randomization(MR)approach.METHODS:Publicly available genome-wide association study(GWAS)datasets were utilized for this two-sample MR analysis.Inflammatory cytokine-related GWAS data were extracted from The University of Bristol’s Research Data Repository,and myopia-related GWAS data were obtained from the FinnGen project.Single nucleotide polymorphisms(SNPs)associated with inflammatory cytokines were systematically selected as instrumental variables(IVs)based on three rigorous criteria:relevance,independence,and exclusion of pleiotropy.Five MR methods were employed for causal inference:the inverse-variance weighted(IVW)method as the primary analysis,supplemented by MREgger regression,weighted median estimator,simple mode,and weighted mode approaches.Sensitivity analyses were performed to evaluate the robustness of the causal estimates.RESULTS:A total of 773 myopia-associated SNPs were identified.MR analysis revealed that higher levels of macrophage inflammatory protein 1-α(MIP-1α)were associated with a 17%reduced risk of myopia[odds ratio(OR)=0.83;95%confidence interval(CI):0.69-0.99;P<0.05].In contrast,elevated levels of eotaxin(OR=1.26;95%CI:1.07-1.47;P<0.01),stromal cell-derived factor-1α(SDF-1α;OR=1.68;95%CI:1.08-2.62;P<0.05),and interleukin-2 receptor subunit alpha(IL-2Rα;OR=1.25;95%CI:1.01-1.53;P<0.05)were significantly associated with an increased risk of myopia.Sensitivity analyses confirmed the reliability of these results.CONCLUSION:This study provides evidence supporting a causal relationship between specific inflammatory cytokines and myopia.MIP-1αmay act as a protective factor against myopia,while eotaxin,SDF-1α,and IL-2Rαare potential risk factors for myopia.These findings emphasize the critical role of inflammatory pathways in the pathogenesis of myopia,offering novel insights for the development of preventive and therapeutic strategies for myopia.
基金supported by the Fundamental Research Funds for the Central Universities in UIBE under Grant No.CXTD14-05。
文摘Smartphone-based electrocardiograms(ECGs)are increasingly utilized for monitoring atrial fibrillation(AF)recurrence after catheter ablation(CA),referred to as smartphone AF burden(SMURDEN).The SMURDEN data often exhibit complex patterns of zero AF episodes,which may arise from either true AF-free status(structural zeros)or missed AF episodes due to intermittent monitoring(random zeros).Such a mixture of AF-free and at-risk patients can lead to zero-inflation in the data.The authors propose a novel zero-inflation test for binomial regression models to identify recurrence-free AF populations.Unlike traditional approaches requiring fully specified zero-inflated models,the proposed test utilizes a weighted average of the discrepancies between observed and expected zero proportions,with weights determined by binomial sizes.A closed-form test statistic is developed,and its asymptotic distribution is derived using estimating equations.Simulations demonstrate superior performance over existing methods,and real-world AF monitoring data validate the practical utility of our proposed test.
