Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

Trends and frontiers in signal amplification for aptamer-based tumor detection:A bibliometric analysis

BACKGROUND Malignant tumors are one of the leading causes of death worldwide,imposing a substantial economic and social burden.Early detection is the key to improving cure rates and reducing mortality rates,which requires the development of sensitive early detection technologies.Signal amplification techniques play a crucial role in aptamer-based early detection of tumors and are increasingly garnering attention from researchers.AIM To investigate the current research status,developmental trajectories,and hotspots in signal amplification for aptamer-based tumor detection through bibliometric analysis.METHODS English publications pertaining to signal amplification in aptamer-based tumor detection were retrieved from the Web of Science Core Collection database.VOSviewer and CiteSpace software were employed to analyze various information within this field,including countries,institutions,authors,co-cited authors,journals,co-cited journals,cited references,and keywords.RESULTS A total of 757 publications were included in this study.China accounted for 85.47%of all publications,with Nanjing University(China)emerging as the institution with the highest publication output.The most influential authors and journals were Hasanzadeh M.from Iran and"Biosensors and Bioelectronics",respectively.Exosomes and carcinoembryonic antigen(CEA)stood out as the most researched tumor-related molecules.Currently,the predominant signal amplification technique,nanomaterial,and signal transduction method were identified as hybridization chain reactions,gold nanoparticles,and electrochemical methods,respectively.Over the past 3 years,exosomes,CEA,electrochemical biosensors,and nanosheets have emerged as research hotspots,exhibiting a robust burst of intensity.CONCLUSION This study is the first bibliometric analysis of literature on signal amplification in aptamer-based tumor detection and elucidates the current status,hotspots,and prospective research directions within this realm.Additionally,it provides an important reference for researchers.

Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysis.In this study,speech samples are categorized for both training and testing purposes based on their geographical origin.Category 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian speakers.Testing speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the speakers.Significantly,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas.Indian speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers.The gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of India.In the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech data.The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.

TCGA-based analysis of oncogenic signaling pathways underlying oral squamous cell carcinoma

Background:Oral squamous cell carcinoma(OSCC)represents a prevalent malignancy in the oral and maxillofacial area,having a considerable negative impact on both the quality of life and overall survival of affected individuals.Our research endeavors to leverage bioinformatic approaches to elucidate oncogenic signaling pathways,with the ultimate goal of gaining deeper insights into the molecular underpinnings of OSCC pathogenesis,and thus laying the groundwork for the development of more effective therapeutic and preventive strategies.Methods:Differential expression analysis was performed on mRNA data from tumor and normal tissue groups to identify genes associated with OSCC,using The Cancer Genome Atlas database.Predictions of oncogenic signaling pathways linked to differentially expressedmRNAs were made,and these results were presented visually using R software,using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichments.Results:GO and KEGG analyses of 2938 differentially expressed genes in OSCC highlighted their significant involvement in various biological processes.Notably,these processes were related to the extracellular matrix,structural organization,connective tissue development,and cell cycle regulation.Conclusions:The comprehensive exploration of gene expression patterns provides valuable insights into potential oncogenic mechanisms in OSCC.

Analysis of clinical characteristics of leptomeningeal metastasis with band-like high signal in the brainstem

Objective:The aim was to analyse the clinical features of leptomeningeal metastasis with banded high signal in the brainstem.Methods:In this paper,we report two cases of lung adenocarcinoma with soft meningeal metastasis,collected from the First Affiliated Hospital of Hainan Medical College,and searched the databases of CNKI,Wanfang,VIP,PubMed,Web of Science,and other databases which reported the MRI manifestation of"brainstem bandlike high signal",and collected the patients'past medical history,symptoms,signs,genetic findings,cerebrospinal fluid manifestation,treatment,and prognosis.Result:A total of 28 patients were included,of whom 26 had a history of lung adenocarcinoma and 2 were found to have occupational changes in the lungs.Magnetic resonance imaging(MRI)showed a band-like high signal in the ventral part of the brainstem on T2-FLAIR,symmetrical on both sides,which could extend to the cerebellar peduncles,with high signals on diffusion-weighted imaging(DWI),low signals on apparent diffusion coefficient(ADC),and long T1 signals on T1-weighted imaging,long T2 signals on T2 weighted imaging,and no long T2 signals on enhancement scan.T1-weighted imaging was a long T1 signal,T2-weighted imaging was a long T2 signal,and no enhancement was seen on enhanced scanning.Conclusion:It is important to recognize leptomeningeal metastasis of lung cancer,and the non-enhancing band of high signal in the brainstem on T2-FLAIR and DWI is likely to be the characteristic manifestation of leptomeningeal metastasis of non-small cell lung cancer.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Analysis of Aluminum Alloy Double-Pulse MIG Welding Arc Signal Characteristics Based on Broadband Mode Decomposition

Welding voltage and current in arc signals are directly related to arc stability and welding quality.Process experiments with different parameters were organized according to the orthogonal experimental design method by constructing an aluminum alloy double-pulse metal inert gas(MIG)welding arc electric signal test platform.The data acquisition system of the aluminum alloy MIG welding process was established to obtain real-time arc signal information reflecting the welding process.The aluminum alloy’s collected double-pulse arc current signals are decomposed adaptively by broadband mode decomposition(BMD).The direct current(DC)signal,pulse signal,distortion signal,ripple signal,and noise signal are separated and extracted,and the composite multiscale fuzzy entropy(CMFE)is calculated for the component set of the electrical signal.The experimental results show that the current waveform obtained by the double-pulse MIG welding current signal is consistent with the corresponding weld forming diagram.Simultaneously,the composite multiscale fuzzy entropy is calculated for the arc characteristic parameters.The rationality of matching process parameters and arc stability of aluminum alloy’s double-pulse MIG welding were evaluated.

MAPOD Analysis in Eddy Current Testing of Flaws Considering Multiple Response Signals and Multiple Flaw Parameters

The reliability of the eddy current testing (ECT) in flaw detection is quantitatively evaluated by theprobability of detection (POD). Precise and efficient modeling of POD gives direction for the implement of ECTon sites to avoid false or missing flaw detection. Traditional POD analysis focuses on single uncertain factor orsingle response signal with limited credibility in engineering. This paper considers multiple response signals andmultiple flaw parameters to perform POD. The flaw length, the flaw depth, the coil impedance, and the magneticflux density are comprehensively studied under various lift-off distances. A finite element model (FEM) of ECT isestablished and verified with experiments to obtain sufficient simulation data for discrete POD modeling. Thecontinuous POD function is then fitted based on the discrete values to show the superiority of integrating multiplefactors. A comparison with conventional POD analysis further demonstrates the higher reliability of ECT flawdetection considering multiple flaw parameters and multiple response signals, especially for small flaws.

Design of a Technology Verification Platform for Space Electromagnetic Interference Signal Testing and Analysis

This paper designs a space electromagnetic interference signal test and analysis technology verification platform.The article firstly introduces the general scheme of the technical verification platform and then describes each component unit of the hardware and the overall structure of the software in detail.The platform can achieve a 10 MHz~50 GHz working frequency band,1.2 GHz acquisition and real-time recording bandwidth,6 GB/s recording rate,and 12 TB recording capacity.

Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

Personal glucose meters coupled with signal amplification technologies for quantitative detection of non-glucose targets:Recent progress and challenges in food safety hazards analysis

Ensuring food safety is paramount worldwide.Developing effective detection methods to ensure food safety can be challenging owing to trace hazards,long detection time,and resource-poor sites,in addition to the matrix effects of food.Personal glucose meter(PGM),a classic point-of-care testing device,possesses unique application advantages,demonstrating promise in food safety.Currently,many studies have used PGM-based biosensors and signal amplification technologies to achieve sensitive and specific detection of food hazards.Signal amplification technologies have the potential to greatly improve the analytical performance and integration of PGMs with biosensors,which is crucial for solving the challenges associated with the use of PGMs for food safety analysis.This review introduces the basic detection principle of a PGM-based sensing strategy,which consists of three key factors:target recognition,signal transduction,and signal output.Representative studies of existing PGM-based sensing strategies combined with various signal amplification technologies(nanomaterial-loaded multienzyme labeling,nucleic acid reaction,DNAzyme catalysis,responsive nanomaterial encapsulation,and others)in the field of food safety detection are reviewed.Future perspectives and potential opportunities and challenges associated with PGMs in the field of food safety are discussed.Despite the need for complex sample preparation and the lack of standardization in the field,using PGMs in combination with signal amplification technology shows promise as a rapid and cost-effective method for food safety hazard analysis.

Deep learning-based LPI radar signals analysis and identification using a Nyquist Folding Receiver architecture

Nyquist Folding Receiver(NYFR)is a perceptron structure that realizes a low probability of intercept(LPI)signal analog to information.Aiming at the problem of LPI radar signal receiving,the time domain,frequency domain,and time-frequency domain problems of signals intercepted by NYFR structure are studied.Combined with the time-frequency analysis(TFA)method,a radar recognition scheme based on deep learning(DL)is introduced,which can reliably classify common LPI radar signals.First,the structure of NYFR and its characteristics in the time domain,frequency domain,and time and frequency domain are analyzed.Then,the received signal is then converted into a time-frequency image(TFI).Finally,four kinds of DL algorithms are used to classify LPI radar signals.Simulation results demonstrate the correctness of the NYFR structure,and the effectiveness of the proposed recognition method is verified by comparison experiments.

Zuo Gui Wan Promotes Osteogenesis via PI3K/AKT Signaling Pathway:Network Pharmacology Analysis and Experimental Validation

Objective Osteogenesis is vitally important for bone defect repair,and Zuo Gui Wan(ZGW)is a classic prescription in traditional Chinese medicine(TCM)for strengthening bones.However,the specific mechanism by which ZGW regulates osteogenesis is still unclear.The current study is based on a network pharmacology analysis to explore the potential mechanism of ZGW in promoting osteogenesis.Methods A network pharmacology analysis followed by experimental validation was applied to explore the potential mechanisms of ZGW in promoting the osteogenesis of bone marrow mesenchymal stem cells(BMSCs).Results In total,487 no-repeat targets corresponding to the bioactive components of ZGW were screened,and 175 target genes in the intersection of ZGW and osteogenesis were obtained.And 28 core target genes were then obtained from a PPI network analysis.A GO functional enrichment analysis showed that the relevant biological processes mainly involve the cellular response to chemical stress,metal ions,and lipopolysaccharide.Additionally,KEGG pathway enrichment analysis revealed that multiple signaling pathways,including the phosphatidylinositol-3-kinase/protein kinase B(PI3K/AKT)signaling pathway,were associated with ZGW-promoted osteogensis.Further experimental validation showed that ZGW could increase alkaline phosphatase(ALP)activity as well as the mRNA and protein levels of ALP,osteocalcin(OCN),and runt related transcription factor 2(Runx 2).What’s more,Western blot analysis results showed that ZGW significantly increased the protein levels of p-PI3K and p-AKT,and the increases of these protein levels significantly receded after the addition of the PI3K inhibitor LY294002.Finally,the upregulated osteogenic-related indicators were also suppressed by the addition of LY294002.Conclusion ZGW promotes the osteogenesis of BMSCs via PI3K/AKT signaling pathway.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Analysis of Additional Damping Control Strategy and Parameter Optimization for Improving Small Signal Stability of VSC-HVDC System

The voltage source converter based high voltage direct current(VSC-HVDC)system is based on voltage source converter,and its control system is more complex.Also affected by the fast control of power electronics,oscillation phenomenon in wide frequency domain may occur.To address the problem of small signal stability of the VSCHVDC system,a converter control strategy is designed to improve its small signal stability,and the risk of system oscillation is reduced by attaching a damping controller and optimizing the control parameters.Based on the modeling of the VSC-HVDC system,the general architecture of the inner and outer loop control of the VSCHVDC converter is established;and the damping controllers for DC control and AC control are designed in the phase-locked loop and the inner and outer loop control parts respectively;the state-space statemodel of the control system is established to analyze its performance.And the electromagnetic transient simulation model is built on the PSCAD/EMTDC simulation platform to verify the accuracy of the small signal model.The influence of the parameters of each control part on the stability of the system is summarized.The main control parts affecting stability are optimized for the phenomenon of oscillation due to changes in operation mode occurring on the AC side due to faults and other reasons,which effectively eliminates system oscillation and improves system small signal stability,providing a certain reference for engineering design.

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.

Timosaponin AⅢ induces drug-metabolizing enzymes by activating constitutive androstane receptor (CAR) via dephosphorylation of the EGFR signaling pathway

The current study aimed to assess the effect of timosaponin AⅢ(T-AⅢ)on drug-metabolizing enzymes during anticancer therapy.The in vivo experiments were conducted on nude and ICR mice.Following a 24-day administration of T-AⅢ,the nude mice exhibited an induction of CYP2B10,MDR1,and CYP3A11 expression in the liver tissues.In the ICR mice,the expression levels of CYP2B10 and MDR1 increased after a three-day T-AⅢ administration.The in vitro assessments with HepG2 cells revealed that T-AⅢ induced the expression of CYP2B6,MDR1,and CYP3A4,along with constitutive androstane receptor(CAR)activation.Treatment with CAR siRNA reversed the T-AⅢ-induced increases in CYP2B6 and CYP3A4 expression.Furthermore,other CAR target genes also showed a significant increase in the expression.The up-regulation of murine CAR was observed in the liver tissues of both nude and ICR mice.Subsequent findings demonstrated that T-AⅢ activated CAR by inhibiting ERK1/2 phosphorylation,with this effect being partially reversed by the ERK activator t-BHQ.Inhibition of the ERK1/2 signaling pathway was also observed in vivo.Additionally,T-AⅢ inhibited the phosphorylation of EGFR at Tyr1173 and Tyr845,and suppressed EGF-induced phosphorylation of EGFR,ERK,and CAR.In the nude mice,T-AⅢ also inhibited EGFR phosphorylation.These results collectively indicate that T-AⅢ is a novel CAR activator through inhibition of the EGFR pathway.

Digital signal acquisition system for complex nuclear reaction experiments

A digital data-acquisition system based on XIA LLC products was used in a complex nuclear reaction experiment using radioactive ion beams.A flexible trigger system based on a field-programmable gate array(FPGA)parametrization was developed to adapt to different experimental sizes.A user-friendly interface was implemented,which allows converting script language expressions into FPGA internal control parameters.The proposed digital system can be combined with a conventional analog data acquisition system to provide more flexibility.The performance of the combined system was veri-fied using experimental data.

Advances in microfluidic-based DNA methylation analysis

DNA methylation has been extensively investigated in recent years,not least because of its known relationship with various diseases.Progress in analytical methods can greatly increase the relevance of DNA methylation studies to both clinical medicine and scientific research.Microflu-idic chips are excellent carriers for molecular analysis,and their use can provide improvements from multiple aspects.On-chip molecular analysis has received extensive attention owing to its advantages of portability,high throughput,low cost,and high efficiency.In recent years,the use of novel microfluidic chips for DNA methylation analysis has been widely reported and has shown obvious superiority to conventional methods.In this review,wefirst focus on DNA methylation and its applications.Then,we discuss advanced microfluidic-based methods for DNA methylation analysis and describe the great progress that has been made in recent years.Finally,we summarize the advantages that microfluidic technology brings to DNA methylation analysis and describe several challenges and perspectives for on-chip DNA methylation analysis.This review should help researchers improve their understanding and make progress in developing microfluidic-based methods for DNA methylation analysis.