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This article is concerned with the neural network (NN)-based event-triggered control problem for discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism (ETM) is used to reduce the communication load, and a Luenberger observer is introduced to estimate the unmeasured states. Two kinds of cyberattacks, denial-of-service (DoS) attacks and deception attacks, are investigated due to the vulnerability of cyberlayer. For the sake of mitigating the impact of these two types of cyberattacks on system performance, the ETM under DoS jamming attacks is discussed first, and a new estimation of such mechanism is given. Then, the NN technique is applied to approximate the injected false information. Some sufficient conditions are derived to guarantee the boundedness of the closed-loop system, and the observer and controller gains are presented by solving a set of matrix inequalities. The effectiveness of the presented control method is demonstrated by a numerical example.

The aim of this work is to investigate the differences in the measurement of foveal retinal thickness in myopic patients between two display modes (1:1 pixel and 1:1 micron) on optical coherence tomography (OCT).Horizontal OCT line scan through the central fovea was used for manual measurement of foveal retinal thickness under the two display modes, and the values were compared using Wilcoxon signed-rank test. Correlations between the OCT image tilting angle (OCT ITA) and differences in OCT measurement were analyzed by Spearman's test.127 participants with a median age of 28 years, a median spherical equivalent (SE) of - 8.5 D, and a median axial length (AL) of 27.04 mm. There were significant differences between the two display modes, with a median absolute difference (median relative difference) of 13.33 μm (2.75%) for the central foveal thickness (CFT), 5.33 μm (1.28%) for the Henle fiber and outer nuclear layer thickness (HFL + ONL), 3 μm (6.47%) for the external limiting membrane to ellipsoid zone distance (ELM-EZ), and 4 μm (8.77%) for the ellipsoid zone to retinal pigment epithelium distance (EZ-RPE) (all p 0.05). The differences in foveal retinal thickness between the two display modes were significantly correlated with the OCT ITA (r = 0.732 for CFT, 0.561 for HFL + ONL, 0.642 for ELM-EZ, and 0.471 for EZ-RPE, all p 0.05).Disparities between the two display modes were found in the manual measurement of foveal retinal thickness and correlated to the OCT ITA.

Di (2-ethylhexyl) phthalate (DEHP) is one of the most prevalent xenoestrogen endocrine disruptor in daily life. A growing number of studies showed that DEHP could exhibit long-term adverse health effects on the human body, particularly in the liver, kidneys, heart and reproductive systems. However, the impact of oral intake of DEHP on the nervous system is extremely limited. In the present study, the adult C57BL/6J male mice were intragastrically administered with two dosages of DEHP for 35 days. The behavioral parameters were assessed using the elevated plus maze and open-field test. The mRNA expression levels of neuropeptides and the oxidative stress-associated proteins were detected by qPCR and western blot seperately. The histopathologic alterations of the brain were observed by H&E and Nissl staining. The results demonstrated that DEHP exposure could result in neurobehavioral impairments such as locomotor increase and anxiety-like behavior. Furthermore, pathological damages were clearly observed in the cerebral cortex and hippocampus, accompanied by a decrease in neuropeptides and an increase in oxidative stress, which were all positively correlated with the dose of DEHP. Together, these findings provide valuable clues into the DEHP-induced neurotoxicity.

This paper is concerned with H control problem for discrete-time Takagi-Sugeno (T-S) fuzzy Markov jump systems with event-triggering scheme (ETS) and denial-of-service (DoS) attacks. Aperiodic DoS characterized by duration and frequency is introduced. In the light of DoS attacks and event-triggered fuzzy controller, the switched fuzzy Markov jump closed-loop system is established. Particularly, in order to address the issue of mismatched behavior between membership functions of fuzzy system and fuzzy controller, a novel ETS consisting of membership functions is developed. Then with the help of multiple Lyapunov function method and iterative technique, some sufficient conditions are provided to ensure the H performance of the resulting closed-loop system. Subsequently, the explicit parameter design of controller and ETS are provided. Finally, an example is employed to verify the validity of the proposed theoretical method.

BackgroundBoth acute and persistent pain is associated with anxiety in clinical observations, but whether the underlying neural mechanisms differ is poorly understood.MethodsWe used formalin or complete Freund’s adjuvant (CFA) to induce acute or persistent pain. Behavioral performance was assessed by the paw withdrawal threshold (PWT), open field (OF), and elevated plus maze (EPM) tests. C-Fos staining was used to identify the activated brain regions. Chemogenetic inhibition was further performed to examine the necessity of brain regions in behaviors. RNA sequencing (RNA-seq) was used to identify the transcriptomic changes.ResultsBoth acute and persistent pain could lead to anxiety-like behavior in mice. The c-Fos expression indicates that the bed nucleus of the stria terminalis (BNST) is activated only in acute pain, whereas the medial prefrontal cortex (mPFC) is activated only in persistent pain. Chemogenetic manipulation reveals that the activation of the BNST excitatory neurons is required for acute pain-induced anxiety-like behaviors. In contrast, the activation of the prelimbic mPFC excitatory neurons is essential for persistent pain-induced anxiety-like behaviors. RNA-seq reveals that acute and persistent pain induces differential gene expression changes and protein–protein interaction networks in the BNST and prelimbic mPFC. The genes relevant to neuronal functions might underline the differential activation of the BNST and prelimbic mPFC in different pain models, and be involved in acute and persistent pain-related anxiety-like behaviors.ConclusionDistinct brain regions and gene expression patterns are involved in acute and persistent pain-related anxiety-like behaviors.

Risk analysis of dam slopes is crucial for ensuring the safety and stability of hydraulic engineering. To improve the accuracy and reliability of risk analysis, we adopt the cloud theory approach and conduct a study on the distribution types of soil shear strength indicators based on indoor geotechnical tests. We propose a “cloud model-Monte Carlo” coupling model that uses the cloud model to describe the uncertainty of risk factors and determine the probability distribution types of shear strength parameters, while the Monte Carlo method is used to simulate random variables in the model. The effectiveness of the proposed model is validated through a risk analysis of a slope of an earth-rock dam, with results showing significantly greater accuracy and reliability compared to traditional methods. The calculation results show that the risk probability corresponding to the design flood level of the dam is 9.01×10-6, exceeding its allowable risk standard of 0.5×10-6, hence the need for reinforcement treatment. The proposed model can accurately evaluate the risk of dams and provide the scientific basis for decision-making in dam safety management.

The wetting deformation of coarse-grained materials can seriously affect the safety of earth and rock dams during initial water storage. The wetting model formulas are expressed in various forms and have complex parameters. Only a small amount of test data is fitted by mathematical statistics, and the universality of the obtained wetting model is unknown. Duncan-Chang E-B constitutive model cannot accurately reflect the wetting deformation characteristics of coarse-grained materials. Through the double-line wetting test of coarse-grained materials, the wetting model proposed by predecessors was verified and analyzed. Based on the indoor wetting test data, the parameters of each wetting model were fitted to analyze the accuracy of each wetting model in describing the wetting deformation characteristics. According to the P-Z model in the elastic-plastic theory and the wetting model formula, the P-Z wetting model is established, and the BP artificial neural network is introduced to establish the artificial neural network wetting deformation prediction model based on the P-Z model. The results show that the relationship between wetting axial strain and wetting stress level is best expressed by the exponential function. The relationship between wetting volumetric strain and wetting stress level is best described by Cheng’s linear function. The relative errors between the predicted and experimental values of the proposed neural network prediction model are all within 6%. The relationship between wetting axial strain and wetting stress level is exponential function, and the relationship between wetting volumetric strain and wetting stress level is linear function. The P-Z wetting model proposed in this research can better reflect the wetting deformation characteristics of coarse-grained materials under complex stress paths. The artificial neural network prediction model based on P-Z wetting model is more reliable and accurate, which can meet the prediction requirements of actual engineering for wetting deformation of coarse-grained materials.

The isomerism of glucaric acids and the complexity of the composition of Leonurus japonicus Houtt. increased the difficulty of the separation of glucaric acids from the herb. In the present study, three glucaric acids were isolated from Leonurus japonicus Houtt. by using high-speed countercurrent chromatography combined with semi-preparative high-performance liquid chromatography. Cation exchange resin chromatography was applied to remove the alkaloids and enrich the glucaric acid fractions. Preliminary separation of the glucaric acid extract by high-speed countercurrent chromatography was carried out at 45℃ by using an optimized solvent system of ethyl acetate/n-butanol/formic acid/water (1:1:0.01:2, v/v/v/v) with satisfied stationary phase retention and separation factor. The semi-preparative high-performance liquid chromatography was used for further separation and purification of the target fractions, and three monomeric compounds were obtained with purities of 90.0, 91.0, and 95.3%. UV spectroscopy, NMR spectroscopy, and mass spectrometry were employed to identify their structures, which were assigned as 2-syringyl glucaric acid, 2,4-disyringyl glucaric acid, and 3,4-disyringyl glucaric acid, respectively, and 2,4-disyringyl glucaric acid was reported for the first time.

Cysteine (Cys) plays vital roles in various physiological and pathological functions. Either a deficiency or excess of Cys could lead to severe ailments in human. The identification and determination of Cys are the key issues for the early diagnosis of relevant diseases. This contribution has presented a promising potential of fluorescent gold nanoclusters (AuNCs) for Cys determination and D-/L-Cys enantiomer discrimination. Cys determination and discrimination are involved three steps. First, as a reducing and capping ligand, glutathione was applied to fabricate weak fluorescent AuNCs. Second, Ag

We propose self-adaptive training -- a unified training algorithm that dynamically calibrates and enhances training processes by model predictions without incurring an extra computational cost -- to advance both supervised and self-supervised learning of deep neural networks. We analyze the training dynamics of deep networks on training data that are corrupted by, e.g., random noise and adversarial examples. Our analysis shows that model predictions are able to magnify useful underlying information in data and this phenomenon occurs broadly even in the absence of any label information, highlighting that model predictions could substantially benefit the training processes: self-adaptive training improves the generalization of deep networks under noise and enhances the self-supervised representation learning. The analysis also sheds light on understanding deep learning, e.g., a potential explanation of the recently-discovered double-descent phenomenon in empirical risk minimization and the collapsing issue of the state-of-the-art self-supervised learning algorithms. Experiments on the CIFAR, STL, and ImageNet datasets verify the effectiveness of our approach in three applications: classification with label noise, selective classification, and linear evaluation. To facilitate future research, the code has been made publicly available at https://github.com/LayneH/self-adaptive-training., Comment: Accepted at T-PAMI. Journal version of arXiv:2002.10319 [cs.LG] (NeurIPS2020). 22 pages, 15 figures, 13 tables

A novel nonlinear time-varying model termed as the fuzzy parameter varying (FPV) system is proposed in this research, which inherits both advantages of the conventional T-S fuzzy system in dealing with nonlinear plants and strengths of the linear parameter varying (LPV) system in handling time-varying features. It is, therefore, an attractive mathematical model to efficiently approximate a nonlinear time-varying plant or to serve as a type of time-varying controller. Using the full block S-procedure, sufficient stability conditions have been derived in the form of linear matrix inequalities (LMIs) to test quadratic stability of the open-loop FPV system. Moreover, sufficient conditions have been derived on synthesizing both state feedback and dynamical output feedback fuzzy gain-scheduling controllers that can stabilize the FPV system. An inverted pendulum with a variable length pole is utilized to demonstrate advantages of the FPV system compared to the conventional T-S fuzzy system in representing a practical time-varying nonlinear plant and to validate the controller synthesis conditions.

The study aimed to improve polysaccharide-based packaging to extend the shelf life of fresh produce; a composite film with antimicrobial function was developed and tested. The packaging film is a blend of agar, a film-forming substrate; carrageenan, as a reinforcing agent; glycerol, as a plasticizer; and Nisin, an antimicrobial agent. The film was evaluated for its antibacterial, mechanical, and barrier properties at different humidity levels, using fresh-cut watermelon as the test food material. The film effectively inhibited the growth of Staphylococcus aureus and Listeria monocytogenes. Under relative humidity of 20%, 50%, and 90%, the tensile strength of the antibacterial film containing 0.28% Nisin was 23.08 ± 0.65, 16.09 ± 1.73, and 6.52 ± 0.56 MPa, respectively, and the film also had excellent barrier and heat-sealing properties. The packaging test using fresh-cut watermelon sealed in the antibacterial film containing 0.28% Nisin, under controlled atmosphere storage at either 4 °C or 20 °C, effectively inhibited microbial colonization in the melon and slowed the deterioration of the fruit, as indicated by measures of hardness, weight loss, and soluble solids. This method can extend the shelf life of fresh-cut fruit and provide a reference for further research on polysaccharide-based protective film for fresh produce.

This research was to explore antibiotic-induced drug resistance of Salmonella enteritidis and its biofilm formation mechanism. Kirby-Bauer (K-B) disk method recommended by Clinical and Laboratory Standards Institute (CLSI) was used to test drug sensitivity of Salmonella enteritidis to 16 kinds of antibiotics including ß-lactams, aminoglycosides, quinolones, sulfonamides, chloramphenicols, and tetracyclines. Polymerase chain reaction (PCR) was performed to detect carrying of drug resistance genes of 29 kinds of antibiotics including ß-lactams, aminoglycosides, quinolones, sulfonamides, chloramphenicols, and tetracyclines of Salmonella enteritidis. The expressions of esp, ebpA, ge1E, and fsrB genes in biofilm group and plankton group were detected when Salmonella was induced, and difference of gene expression was detected by FQ-PCR. The drug resistance rates of Salmonella enteritidis to nalidixic acid, ampicillin, streptomyces, and cefoperazone were high, which were 94.5%, 75%, 67%, and 52%, respectively. 94 strains of Salmonella enteritidis formed 22 kinds of drug resistance spectrum, the strains were generally resistant to 4-5 antibiotics, and some strains formed fixed drug resistance spectrum as follows: AMP-CFP-STR-NA-TE (22.6,21.7%), AMP-STR-NA-TE (17,16%), and AMP-CFP-STR-NA (11.1,10.6%). During biofilm formation, fsr can increase expression of ge1E and decrease expression of esp and ebpA. Consequently, Salmonella enteritidis was generally resistant to nalidixic acid, ampicillin, and streptomycin, and the multidrug resistance was severe. The drug resistance genes sul2, sul3, blaTEM-1-like, tet(A), and tet(G) were highly carried in Salmonella enteritidis. Esp, ebpA, ge1E, and fsrB genes were closely related to biofilm formation of Salmonella enteritidis., Graphical abstract

In the study of coal mining, the position and posture, the working status, and the straightness of the hydraulic support are critical to the safety of the coal mine. To detect and control the position of a hydraulic support, a model for solving the self-position and posture and a model for solving the relative position and pose of a hydraulic support based on the principle of three points determining a plane in space were established. Then, with the help of particle swarm optimization algorithm and Bang-Bang control algorithm, the hydraulic support position and the posture detection and control system are constructed. Finally, according to the requirements of sensor data collection and online monitoring of the position and posture of hydraulic support, a hydraulic support posture detection and control system is developed, and the simulation experiments of hydraulic support cylinder movement sample collecting, hydraulic support following motion and hydraulic support independent following machine are carried out on the self-developed three-machine intelligent control simulation experiment platform of the working face. However, the test under this simulation platform did not add the load of the top beam. The results show that the self-pose solution model and the relative-pose solution model of the hydraulic support can be used to quickly monitor the position and posture of the hydraulic support group. In the hydraulic support cylinder following motion movement, the largest following error of each cylinder is the balance cylinder, and its maximum error is 2.6 mm. In the hydraulic support independent following machine simulation, the maximum motion error of the support base is 2.2 mm. The position and posture detection and control system developed for hydraulic support can realize accurate detecting and control the position and posture of hydraulic support, as well as meet the requirements for industrial applications.

In response to the new round of COVID-19 outbreaks since March 2022, universities with high outbreak rates around the country have taken quarantine measures to contain the epidemic. Evidence from previous coronavirus outbreaks has shown that people under quarantine are at risk for mental health disorders. To better understand the impacts of this round of COVID-19 quarantine on domestic college students and their responses, we conducted a systematic survey to assess the stress and anxiety, and to evaluate effective measurements in this population. We searched relevant documents and literature, and designed a questionnaire from six aspects, including psychological status, epidemic situation, study, daily life, sports, and interpersonal communication, with 51 items in total. We sent the questionnaire on the Wenjuanxing Web platform, from April 2 to 8, 2022. We evaluated the mental status according to parts of the Generalized Anxiety Disorder-7 (GAD-7) and Depression Anxiety Stress Scales-21 (DASS-21), and investigated the influencing risk factors and countermeasures. Statistical analysis was performed by using the Chi-square test and multi-variable logistic regression. In total, 508 college respondents were recruited in our survey, and the pooled prevalence of mild anxiety (GAD score ≽ 5, or DASS-21 anxiety score ≽ 8) or stress (DASS-21 pressure score ≽ 14) caused by the new round of COVID-19 pandemic quarantine was 19.69% (100/508). The prevalence of the anxiety or stress in college students with COVID-19 quarantine between different genders, regions, and majors was not significantly different. Independent risk factors for the mild anxiety or stress of undergraduates by COVID-19 quarantine included learning efficiency or duration [OR = 1.36, 95%CI (1.14-1.62)

The intestinal flora plays an important role in the development of many human and animal diseases. Microbiome association studies revealed the potential regulatory function of intestinal bacteria in many liver diseases, such as autoimmune hepatitis, viral hepatitis and alcoholic hepatitis. However, the key intestinal bacterial strains that affect pathological liver injury and the underlying functional mechanisms remain unclear. We found that the gut microbiota from gentamycin (Gen)-treated mice significantly alleviated concanavalin A (ConA)-induced liver injury compared to vancomycin (Van)-treated mice by inhibiting CD95 expression on the surface of hepatocytes and reducing CD95/CD95L-mediated hepatocyte apoptosis. Through the combination of microbiota sequencing and correlation analysis, we isolated 5 strains with the highest relative abundance, Bacteroides acidifaciens (BA), Parabacteroides distasonis (PD), Bacteroides thetaiotaomicron (BT), Bacteroides dorei (BD) and Bacteroides uniformis (BU), from the feces of Gen-treated mice. Only BA played a protective role against ConA-induced liver injury. Further studies demonstrated that BA-reconstituted mice had reduced CD95/CD95L signaling, which was required for the decrease in the L-glutathione/glutathione (GSSG/GSH) ratio observed in the liver. BA-reconstituted mice were also more resistant to alcoholic liver injury. Our work showed that a specific murine intestinal bacterial strain, BA, ameliorated liver injury by reducing hepatocyte apoptosis in a CD95-dependent manner. Determination of the function of BA may provide an opportunity for its future use as a treatment for liver disease.

In this paper, the emerging orthogonal time frequency space (OTFS) modulation is firstly restructured as a precoded orthogonal frequency division multiplexing (OFDM) system, so that the well-established frequency-domain approach can be applied to perform signal in fast fading channels. Then a frequency-domain minimum mean squared error (MMSE) equalizer for OTFS is introduced and its performance is analyzed based on the eigenvalue decomposition of the channel matrix. Inspired by the frequency-domain precoding structure, an adaptive transmission scheme with frequency-domain precoding matrix composed of the eigenvectors of the channel matrix is proposed to improve the system performance under MMSE equalization, and its optimized performance is derived with simple expression. Finally, considering two extreme channel conditions, the lower and upper bounds for the diversity performance of the adaptive transmission scheme are derived. Simulation results show that the proposed adaptive transmission achieves significantly better performance for short signal frames and can work well with imperfect channel state information (CSI). The derived performance bounds can serve as benchmarks for OTFS and other precoded OFDM systems.

Proteus vulgaris is an important foodborne opportunistic pathogen, both environmentally and clinically. The use of appropriate antibiotics has significant therapeutic effects, but has led to the emergence and spread of drug-resistant strains. In this study, a P. vulgaris strain, designated "P3M," was isolated from Penaeus vannamei in Tianjin, China. The whole genome of P3M was sequenced, generating detailed information, including the key genes involved in important metabolic pathways and their physiological functions. A total of 218 antibiotic resistance genes (ARGs) were predicted in the genome. The determination of various minimum inhibitory concentrations indicated that P3M is a multidrug-resistant (MDR) bacterium, with significant resistance to 16 antibiotics in seven categories. Determination of fractional inhibitory concentration index showed that the combination of ciprofloxacin plus tetracycline exhibited synergistic antimicrobial activity. Bioinformatics and phylogenetic analyses detected the presence of two two-component systems that mediate multidrug resistance and several mobile genetic elements involved in the horizontal transfer of ARGs in P3M. P. vulgaris strains represent a serious challenge to clinicians and infection control teams for its ubiquity worldwide and close relevance with human life. To the best of our knowledge, we report the first isolation and characterization of an important foodborne MDR P. vulgaris strain, and this study will provide necessary theoretical basis for the selection and clinical use of the appropriate antibiotics.

In cooperative games, a key question is to find a division of payoffs to coalition members in a fair manner. Nucleolus is one of such solution concepts that provides a stable solution for the grand coalition. We study the computation of the nucleolus of a number of cooperative games, including fractional matching games and fractional edge cover games on general weighted graphs, as well as vertex cover games and clique games on weighted bipartite graphs. Our results are on the positive side---we give efficient algorithms to compute the nucleolus, as well as the least core, of all of these games.

BackgroundNeuroblastoma is the most common extracranial solid tumor of childhood, arising from the sympathetic nervous system. High-risk neuroblastoma (HRNB) remains a major therapeutic challenge with low survival rates despite the intensification of therapy. This study aimed to develop a malignant-cell marker gene signature (MMGS) that might serve as a prognostic indicator in HRNB patients.MethodsMulti-omics datasets, including mRNA expression (single-cell and bulk), DNA methylation, and clinical information of HRNB patients, were used to identify prognostic malignant cell marker genes. MMGS was established by univariate Cox analysis, LASSO, and stepwise multivariable Cox regression analysis. Kaplan–Meier (KM) curve and time-dependent receiver operating characteristic curve (tROC) were used to evaluate the prognostic value and performance of MMGS, respectively. MMGS further verified its reliability and accuracy in the independent validation set. Finally, the characteristics of functional enrichment, tumor immune features, and inflammatory activity between different MMGS risk groups were also investigated.ResultsWe constructed a prognostic model consisting of six malignant cell maker genes (MAPT, C1QTNF4, MEG3, NPW, RAMP1, and CDT1), which stratified patients into ultra-high-risk (UHR) and common-high-risk (CHR) group. Patients in the UHR group had significantly worse overall survival (OS) than those in the CHR group. MMGS was verified as an independent predictor for the OS of HRNB patients. The area under the curve (AUC) values of MMGS at 1-, 3-, and 5-year were 0.78, 0.693, and 0.618, respectively. Notably, functional enrichment, tumor immune features, and inflammatory activity analyses preliminarily indicated that the poor prognosis in the UHR group might result from the dysregulation of the metabolic process and immunosuppressive microenvironment.ConclusionThis study established a novel six-malignant cell maker gene prognostic model that can be used to predict the prognosis of HRNB patients, which may provide new insight for the treatment and personalized monitoring of HRNB patients.

The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.

Exosomes are considered as potential biomarkers that can reflect information from their parent cell-associated cancer microenvironment. Recently, aptasensors have been widely used for cancer and tumor exosome detection. Aptamers related to exosome surface proteins are usually used to introduce a sequence; the aptamer is used for exosome recognition, and the introduced sequence is used to form G-quadruplexes and for signal amplification. In this paper, we found that the EpCAM aptamer is rich in guanine and unimolecular G-quadruplex with a two-layer G-tetrad under acidic conditions, and we investigated its topology, thermal stability and dissociation constant with hemin. Based on this, our proposed colorimetric aptamer sensor combines the unmodified EpCAM aptamer with hemin to construct a hemin/G-quadruplex DNAzyme and catalyze the TMB-H