Serum cystatin C,monocyte/high-density lipoprotein-C ratio,and uric acid for the diagnosis of coronary heart disease and heart failure

BACKGROUND Coronary heart disease(CHD)and heart failure(HF)are the major causes of morbidity and mortality worldwide.Early and accurate diagnoses of CHD and HF are essential for optimal management and prognosis.However,conventional diagnostic methods such as electrocardiography,echocardiography,and cardiac biomarkers have certain limitations,such as low sensitivity,specificity,availability,and cost-effectiveness.Therefore,there is a need for simple,noninvasive,and reliable biomarkers to diagnose CHD and HF.AIM To investigate serum cystatin C(Cys-C),monocyte/high-density lipoprotein cholesterol ratio(MHR),and uric acid(UA)diagnostic values for CHD and HF.METHODS We enrolled 80 patients with suspected CHD or HF who were admitted to our hospital between July 2022 and July 2023.The patients were divided into CHD(n=20),HF(n=20),CHD+HF(n=20),and control groups(n=20).The serum levels of Cys-C,MHR,and UA were measured using immunonephelometry and an enzymatic method,respectively,and the diagnostic values for CHD and HF were evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS Serum levels of Cys-C,MHR,and UA were significantly higher in the CHD,HF,and CHD+HF groups than those in the control group.The serum levels of Cys-C,MHR,and UA were significantly higher in the CHD+HF group than those in the CHD or HF group.The ROC curve analysis showed that serum Cys-C,MHR,and UA had good diagnostic performance for CHD and HF,with areas under the curve ranging from 0.78 to 0.93.The optimal cutoff values of serum Cys-C,MHR,and UA for diagnosing CHD,HF,and CHD+HF were 1.2 mg/L,0.9×10^(9),and 389μmol/L;1.4 mg/L,1.0×10^(9),and 449μmol/L;and 1.6 mg/L,1.1×10^(9),and 508μmol/L,respectively.CONCLUSION Serum Cys-C,MHR,and UA are useful biomarkers for diagnosing CHD and HF,and CHD+HF.These can provide information for decision-making and risk stratification in patients with CHD and HF.

Tandem hydroalkylation and deoxygenation of lignin-derived phenolics to synthesize high-density fuels

Lignin is the most abundant naturally phenolic biomass,and the synthesis of high-performance renewable fuel from lignin has attracted significant attention.We propose the efficient synthesis of high-density fuels using simulated lignin cracked oil in tandem with hydroalkylation and deoxygenation reactions.First,we investigated the reaction pathway for the hydroalkylation of phenol,which competes with the hydrodeoxygenation form cyclohexane.And then,we investigated the effects of metal catalyst types,the loading amount of metallic,acid dosage,and reactant ratio on the reaction results.The phenol hydroalkylation and hydrodeoxygenation were balanced when 180℃ and 5 MPa H_(2)with the alkanes yield of 95%.By extending the substrate to other lignin-derived phenolics and simulated lignin cracked oil,we obtained the polycyclic alkane fuel with high density of 0.918 g·ml^(-1)and calorific value of41.2 MJ·L^(-1).Besides,the fuel has good low-temperature properties(viscosity of 9.3 mm^(2)·s^(-1)at 20℃ and freezing point below-55℃),which is expected to be used as jet fuel.This work provides a promising way for the easy and green production of high-density fuel directly from real lignin oil.

Kinetic-Thermodynamic Promotion Engineering toward High-Density Hierarchical and Zn-Doping Activity-Enhancing ZnNiO@CF for High-Capacity Desalination

Despite the promising potential of transition metal oxides(TMOs)as capacitive deionization(CDI)electrodes,the actual capacity of TMOs electrodes for sodium storage is significantly lower than the theoretical capacity,posing a major obstacle.Herein,we prepared the kinetically favorable Zn_(x)Ni_(1−x)O electrode in situ growth on carbon felt(Zn_(x)Ni_(1−x)O@CF)through constraining the rate of OH^(−)generation in the hydrothermal method.Zn_(x)Ni_(1−x)O@CF exhibited a high-density hierarchical nanosheet structure with three-dimensional open pores,benefitting the ion transport/electron transfer.And tuning the moderate amount of redox-inert Zn-doping can enhance surface electroactive sites,actual activity of redox-active Ni species,and lower adsorption energy,promoting the adsorption kinetic and thermodynamic of the Zn_(0.2)Ni_(0.8)O@CF.Benefitting from the kinetic-thermodynamic facilitation mechanism,Zn_(0.2)Ni_(0.8)O@CF achieved ultrahigh desalination capacity(128.9 mgNaCl g^(-1)),ultra-low energy consumption(0.164 kW h kgNaCl^(-1)),high salt removal rate(1.21 mgNaCl g^(-1) min^(-1)),and good cyclability.The thermodynamic facilitation and Na^(+)intercalation mechanism of Zn_(0.2)Ni_(0.8)O@CF are identified by the density functional theory calculations and electrochemical quartz crystal microbalance with dissipation monitoring,respectively.This research provides new insights into controlling electrochemically favorable morphology and demonstrates that Zn-doping,which is redox-inert,is essential for enhancing the electrochemical performance of CDI electrodes.

2D DOA Estimation of Coherent Signals with a Separated Linear Acoustic Vector-Sensor Array

In this paper, a two-dimensional(2D) DOA estimation algorithm of coherent signals with a separated linear acoustic vector-sensor(AVS) array consisting of two sparse AVS arrays is proposed. Firstly,the partitioned spatial smoothing(PSS) technique is used to construct a block covariance matrix, so as to decorrelate the coherency of signals. Then a signal subspace can be obtained by singular value decomposition(SVD) of the covariance matrix. Using the signal subspace, two extended signal subspaces are constructed to compensate aperture loss caused by PSS.The elevation angles can be estimated by estimation of signal parameter via rotational invariance techniques(ESPRIT) algorithm. At last, the estimated elevation angles can be used to estimate automatically paired azimuth angles. Compared with some other ESPRIT algorithms, the proposed algorithm shows higher estimation accuracy, which can be proved through the simulation results.

Low Stress TSV Arrays for High-Density Interconnection

In three-dimensional(3D)stacking,the thermal stress of through-silicon via(TSV)has a significant influence on chip performance and reliability,and this problem is exacerbated in high-density TSV arrays.In this study,a novel hollow tungsten TSV(W-TSV)is presented and developed.The hollow structure provides space for the release of thermal stress.Simulation results showed that the hollow W-TSV structure can release 60.3%of thermal stress within the top 2 lm from the surface,and thermal stress can be decreased to less than 20 MPa in the radial area of 3 lm.The ultra-high-density(1600 TSV∙mm2)TSV array with a size of 640×512,a pitch of 25 lm,and an aspect ratio of 20.3 was fabricated,and the test results demonstrated that the proposed TSV has excellent electrical and reliability performances.The average resistance of the TSV was 1.21 X.The leakage current was 643 pA and the breakdown voltage was greater than 100 V.The resistance change is less than 2%after 100 temperature cycles from40 to 125℃.Raman spectroscopy showed that the maximum stress on the wafer surface caused by the hollow W-TSV was 31.02 MPa,which means that there was no keep-out zone(KOZ)caused by the TSV array.These results indicate that this structure has great potential for applications in large-array photodetectors and 3D integrated circuits.

Application of the CatBoost Model for Stirred Reactor State Monitoring Based on Vibration Signals

Stirred reactors are key equipment in production,and unpredictable failures will result in significant economic losses and safety issues.Therefore,it is necessary to monitor its health state.To achieve this goal,in this study,five states of the stirred reactor were firstly preset:normal,shaft bending,blade eccentricity,bearing wear,and bolt looseness.Vibration signals along x,y and z axes were collected and analyzed in both the time domain and frequency domain.Secondly,93 statistical features were extracted and evaluated by ReliefF,Maximal Information Coefficient(MIC)and XGBoost.The above evaluation results were then fused by D-S evidence theory to extract the final 16 features that are most relevant to the state of the stirred reactor.Finally,the CatBoost algorithm was introduced to establish the stirred reactor health monitoring model.The validation results showed that the model achieves 100%accuracy in detecting the fault/normal state of the stirred reactor and 98%accuracy in diagnosing the type of fault.

Predictive value of intracranial high-density areas in neurological function

BACKGROUND Intracranial high-density areas(HDAs)have attracted considerable attention for predicting clinical outcomes;however,whether HDAs predict worse neurological function and mental health remains controversial and unclear,which requires further investigation.In this prospective study,96 patients with acute ischemic stroke(AIS)who accepted endovascular mechanical thrombectomy(EMT)were included.The enrolled patients underwent cranial computed tomography(CT)examination within 24 hours after EMT.Clinical data in terms of National Institutes of Health Stroke Scale(NIHSS),the 3-month modified Rankin Scale(mRS),self-rating depression scale(SDS),and self-rating anxiety scale(SAS)scores were collected and compared between patients with HDAs and non-HDAs and between patients with good and poor clinical prognosis.Compared to patients without HDAs,patients with HDAs presented severe neurological deficits(admission NIHSS score:18±3 vs 19±4),were more likely to have post-stroke disabilities(mRS<3:35%vs 62%),and suffered more severe depression(SDS score:58±16 vs 64±13)and anxiety disorder(SAS score:52±8 vs 59±10).Compared to patients with a good prognosis,patients with a poor prognosis presented severe neurological deficits(admission NIHSS score:17±4 vs 20±3),were more likely to have HDAs on CT images(64%vs 33%),and suffered more severe depression(SDS score:55±19 vs 65±11)and anxiety(SAS score:50±8 vs 58±12).Multivariate analysis revealed that HDAs were independent nega-tive prognostic factors.CONCLUSION In conclusion,HDAs on CT images predicted poor prognosis and severe depressive and anxiety symptoms in patients with AIS who underwent EMT.

N-th root slant stack for enhancing weak seismic signals

Seismic imaging of complicated underground structures with severe surface undulation(i.e.,double complex areas)is challenging owing to the difficulty of collecting the very weak reflected signal.Enhancing the weak signal is difficult even with state-of-the-art multi-domain and multidimensional prestack denoising techniques.This paper presents a time–space dip analysis of offset vector tile(OVT)domain data based on theτ-p transform.The proposed N-th root slant stack method enhances the signal in a three-dimensionalτ-p domain by establishing a zero-offset time-dip seismic attribute trace and calculating the coherence values of a given data sub-volume(i.e.,inline,crossline,time),which are then used to recalculate the data.After sorting,the new data provide a solid foundation for obtaining the optimal N value of the N-th root slant stack,which is used to enhance a weak signal.The proposed method was applied to denoising low signal-to-noise ratio(SNR)data from Western China.The optimal N value was determined for improving the SNR in deep strata,and the weak seismic signal was enhanced.The results showed that the proposed method effectively suppressed noise in low-SNR data.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

Subtraction of liposome signals in cryo-EM structural determination of protein-liposome complexes

Reconstituting membrane proteins in liposomes and determining their structure is a common method for determining membrane protein structures using single-particle cryo-electron microscopy(cryo-EM).However,the strong signal of liposomes under cryo-EM imaging conditions often interferes with the structural determination of the embedded membrane proteins.Here,we propose a liposome signal subtraction method based on single-particle two-dimensional(2D)classification average images,aimed at enhancing the reconstruction resolution of membrane proteins.We analyzed the signal distribution characteristics of liposomes and proteins within the 2D classification average images of protein–liposome complexes in the frequency domain.Based on this analysis,we designed a method to subtract the liposome signals from the original particle images.After the subtraction,the accuracy of single-particle three-dimensional(3D)alignment was improved,enhancing the resolution of the final 3D reconstruction.We demonstrated this method using a PIEZO1-proteoliposome dataset by improving the resolution of the PIEZO1 protein.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

Regulation of specific abnormal calcium signals in the hippocampal CA1 and primary cortex M1 alleviates the progression of temporal lobe epilepsy

Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.

Impact of correlated private signals on continuous-time insider trading

A model of continuous-time insider trading in which a risk-neutral in-sider possesses two imperfect correlated signals of a risky asset is studied.By conditional expectation theory and filtering theory,we first establish three lemmas:normal corre-lation,equivalent pricing and equivalent profit,which can guarantee to turn our model into a model with insider knowing full information.Then we investigate the impact of the two correlated signals on the market equilibrium consisting of optimal insider trading strategy and semi-strong pricing rule.It shows that in the equilibrium,(1)the market depth is constant over time;(2)if the two noisy signals are not linerly correlated,then all private information of the insider is incorporated into prices in the end while the whole information on the asset value can not incorporated into prices in the end;(3)if the two noisy signals are linear correlated such that the insider can infer the whole information of the asset value,then our model turns into a model with insider knowing full information;(4)if the two noisy signals are the same then the total ex ant profit of the insider is increasing with the noise decreasing,while down to O as the noise going up to infinity;(5)if the two noisy signals are not linear correlated then with one noisy signal fixed,the total ex ante profit of the insider is single-peaked with a unique minimum with respect to the other noisy signal value,and furthermore as the noisy value going to O it gets its maximum,the profit in the case that the real value is observed.

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.

Conversion of lignin oil and hemicellulose derivative into high-density jet fuel

Synthesizing high-density fuel from lignocellulose can not only achieve green and low-carbon development,but also expand the feedstock source of hydrocarbon fuel.Here,we reported a route of producing high-density fuel from lignin oil and hemicellulose derivative cyclopentanol through alkylation and hydrodeoxygenation,HY with SiO_(2)/Al_(2)O_(3) molar ratio of 5.3 was screened as the alkylation catalyst in the reaction of model phenolic compounds and mixtures,and the reaction conditions were optimized to achieve conversion of phenolic compounds higher than 87%and selectivity of bicyclic and tricyclic products higher than 99%.Then two phenolic pools simulating the composition of two typic lignin oils were studied to validate the alkylation and analyze the competition mechanism of phenolic compounds in mixture system.Finally,real lignin oil from depolymerized of beech powder was tested,and notably80%of phenolic monomers in the oil were converted into fuel precursor.After hydrodeoxygenation,the alkylated product was converted to fuel blend with a density of 0.91 g/mL at 20℃and a freezing point lower than-60℃,very promising as high density fuel.This work provides a facile and energyefficient way of synthesizing high-performance jet fuel directly from lignocellulosic derivatives,which decreases processing energy consumption and improve the utilization rate of feedstock.

QTL mapping for berry shape based on a high-density genetic map constructed by whole-genome resequencing in grape

Grape berry shape is an important agricultural trait.Clarifying its genetic basis is significant for cultivating grape varieties that meet market demands.However,the current study by forward genetics has not achieved in-depth results.Here,a high-density map was constructed to identify quantitative trait loci(QTLs)for berry shape.A total of 358709 polymorphic SNPs were obtained using whole-genome resequencing(WGS)based on 208 F2 individuals derived from round grape‘E42-6’and oblong grape‘Rizamat’.The 1635.65 cM high-density map was divided into 19 linkage groups with an average distance of 0.37 cM.Using this map,three significant QTLs for fruit shape index(ShI:ratio of berry length to berry width)identified over three years were mapped onto LG4 and LG5,including one stable QTL on Chr5 with the genomic region of 0.47–1.94 Mb.Combining with gene annotation and expression patterns based on RNA-seq data from two contrasting F2 individuals with round and oblong berry(their average ShI was 1.89 and 1.10,respectively)at four developmental stages,four candidate genes were selected from the above QTLs.They were mainly involved in DNA replication,cell wall modification,and phytohormone biosynthesis.Further analysis of RNA-seq data revealed that several important phytohormone synthesis and metabolic pathways were enriched based on differentially expressed genes(DEGs),which was consistent with the results of QTL mapping for genes related to plant hormone biosynthesis in the F2 population.Furthermore,a comparison of plant hormone content showed that there were significant differences in IAA and tZ content between the two contrasting F2 individuals at different developmental stages.Our findings provide molecular insights into the genetic variation in grape berry shape.Stable QTLs and their tightly linked markers offer the possibility of marker-assisted selection to accelerate berry shape breeding.

Extraction of Strain Characteristic Signals from Wind Turbine Blades Based on EEMD-WT

Analyzing the strain signal of wind turbine blade is the key to studying the load of wind turbine blade,so as to ensure the safe and stable operation of wind turbine in natural environment.The strain signal of the wind turbine blade under continuous crosswind state has typical non-stationary and unsteady characteristics.The strain signal contains a lot of noise,which makes the analysis error.Therefore,it is very important to denoise and extract features of measured signals before signal analysis.In this paper,the joint algorithm of ensemble empirical mode decomposition(EEMD)and wavelet transform(WT)is used for the first time to achieve sufficient noise reduction and effectively extract the feature signals of non-stationary strain signals.The application process of EEMD-WT is optimized.This optimization can avoid the repeated selection of wavelet basis function and the number of decomposition layers due to different crosswind conditions.EEMD adaptively decomposes the strain signal into intrinsic mode functions,to judge the frequency of IMFs,remove the high-frequency noise components,retain the useful components.The useful components are denoised twice by the wavelet transform,the components and residual terms after the secondary denoising are reconstructed to obtain the characteristic signal.The EEMD-WT was applied to process the simulating signals andmeasured the strain signals.The results were compared with the results of the EEMD.The results showed that the EEMD-WTmethod has better noise reduction performance,and can effectively extract the characteristics of strain signals,which lays a solid foundation for accurate analysis of wind turbine blade strain signals under crosswind conditions.

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.

Open World Recognition of Communication Jamming Signals

To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.

Macrophage-derived SHP-2 inhibits the metastasis of colorectal cancer via Tie2-PI3K signals

This research aimed to explore the influence of Src homology-2 containing protein tyrosine phosphatase(SHP-2)on the functions of tyrosine kinase receptors with immunoglobulin and EGF homology domains 2(Tie2)-expressing monocyte/macrophages(TEMs)and the influence of the angiopoietin(Ang)/Tie2-phosphatidylinositol-3-kinase(PI3K)/protein kinase B(Akt)/mammalian target of rapamycin(mTOR)(Ang/Tie2-PI3K/Akt/mTOR)signaling pathway on the tumor microvascular remodeling in an immunosuppressive microenvironment.In vivo,SHP-2-deficient mice were used to construct colorectal cancer(CRC)liver metastasis models.SHP-2-deficient mice had significantly more metastatic cancer and inhibited nodules on the liver surface than wild-type mice,and the high-level expression of p-Tie2 was found in the liver tissue of the macrophages’specific SHP-2-deficient mice(SHP-2MACKO)+planted tumor mice.Compared with the SHP-2 wild type mice(SHP-2WT)+planted tumor group,the SHP-2MAC-KO+planted tumor group experienced increased expression of p-Tie2,p-PI3K,p-Akt,p-mTOR,vascular endothelial growth factor(VEGF),cyclooxygenase-2(COX-2),matrix metalloproteinase 2(MMP2),and MMP9 in the liver tissue.TEMs selected by in vitro experiments were co-cultured with remodeling endothelial cells and tumor cells as carriers.It was found that when Angpt1/2 was used for stimulation,the SHP-2MAC-KO+Angpt1/2 group displayed evident increases in the expression of the Ang/Tie2-PI3K/Akt/mTOR pathway.The number of cells passing through the lower chamber and the basement membrane and the number of blood vessels formed by cells compared with the SHP-2WT+Angpt1/2 group,while these indexes were subjected to no changes under the simultaneous stimulation of Angpt1/2+Neamine.To sum up,the conditional knockout of SHP-2 can activate the Ang/Tie2-PI3K/Akt/mTOR pathway in TEMs,thereby strengthening tumor micro angiogenesis in the microenvironment and facilitating CRC liver metastasis.