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.

Monocyte to high-density lipoprotein cholesterol ratio as a predictor of the activity of thyroid-associated ophthalmopathy

AIM:To evaluate the relationship between monocyte to high-density lipoprotein cholesterol ratio(MHR)and the disease activity of thyroid-associated ophthalmopathy(TAO).METHODS:A total of 87 patients were classified into two groups based on clinical activity score(CAS)scoring criteria:high CAS group(n=62,the CAS score was≥3);low CAS group(n=25,the CAS score was<3).In addition,a group of healthy people(n=114)were included to compared the MHR.Proptosis,MHR,average signal intensity ratio(SIR),average lacrimal gland(LG)-SIR,average extraocular muscles(EOM)area from 87 patients with TAO were calculated in magnetic resonance imaging(MRI),and compared between these two groups.Correlation testing was utilized to evaluate the association of parameters among the clinical variables.RESULTS:Patients in high CAS group had a higher proptosis(P=0.041)and MHR(P=0.048).Compared to the healthy group,the MHR in the TAO group was higher(P=0.001).Correlation testing declared that CAS score was strongly associated with proptosis and average SIR,and MHR was positively associated with CAS score,average SIR,and average LG-SIR.The area under the receiver operating characteristic curve(AUC)of MHR was 0.6755.CONCLUSION:MHR,a novel inflammatory biomarker,has a significant association with CAS score and MRI imaging(average SIR and LG-SIR)and it can be a new promising predictor during the active phase of TAO.

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.

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.

Correlation of Helicobacter pylori infection with high-density lipoprotein cholesterol levels and pulse wave conduction velocity

Background:Helicobacter pylori(HP)is associated with several gastrointestinal diseases,including peptic ulcer diseases and gastric cancer,and non-gastrointestinal diseases such as hypertension and Alzheimer's disease.However,the relationship between HP and lipid metabolism and atherosclerosis remains unclear.This study aims to investigate the association between H.pylori infection and high-density lipoprotein cholesterol levels and pulse wave conduction velocity.Methods:This is a report of a cross-sectional study that collected data from 2,827 participants.The data collected included results of life questionnaires,laboratory tests,13C-urea breath test(13C-UBT),and pulse wave conduction velocity test.Based on the results of the 13C-UBT test,the subjects were divided into two groups:the HP-uninfected group(HP−)and the HP-infected group(HP+).The study compared the differences in HDL-C levels and brachial-ankle pulse wave velocity(baPWV)between the two groups.One-way regression analysis was used to identify potential factors affecting HDL-C levels in the study population.Multiple regression equations were presented to analyze whether HP infection was an independent risk factor for abnormal HDL-C metabolism in the population.Results:Univariate analysis demonstrated that high-density lipoprotein cholesterol(HDL-C)levels were significantly lower in the HP+group compared to the HP−group,with a mean difference ofβ=−18.1 mg/dl(95%CI:−19.3 to−17.0,P<0.001).After adjusting for all variables,the HDL-C levels remained lower in the HP+group compared to the HP-group,with a mean difference ofβ=−17.4 mg/dl(95%CI:−18.2 to−16.7,P<0.001).These findings suggest that H.pylori infection is independently associated with abnormal HDL-C metabolism.Additionally,brachial-ankle pulse wave velocity(baPWV)was higher in the HP+group than in the HP−group on both sides.On the right side,the baPWV was 1,713.4±231.4 cm/s in the HP+group compared to 1,542.8±237.5 cm/s in the HP−group(t=−18.30,P<0.001).On the left side,the baPWV was 1,743.7±238.8 cm/s in the HP+group compared to 1,562.8±256.3 cm/s in the HP−group(t=−18.23,P<0.001).These results indicate a significant association between H.pylori infection and increased arterial stiffness,as measured by baPWV.Conclusion:Helicobacter pylori infection is associated with a decrease in high-density lipoprotein cholesterol levels and an increase in pulse wave conduction velocity.

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.

Monitoring Study of Long-Term Land Subsidence during Subway Operation in High-Density Urban Areas Based on DInSAR-GPS-GIS Technology and Numerical Simulation

During subway operation,various factors will cause long-term land subsidence,such as the vibration subsidence of foundation soil caused by train vibration load,incomplete consolidation deformation of foundation soil during tunnel construction,dense buildings and structures in the vicinity of the tunnel,and changes in water level in the stratum where the tunnel is located.The monitoring of long-term land subsidence during subway operation in high-density urban areas differs from that in low-density urban construction areas.The former is the gathering point of the entire urban population.There are many complex buildings around the project,busy road traffic,high pedestrian flow,and less vegetation cover.Several existing items requiremonitoring.However,monitoring distance is long,and providing early warning is difficult.This study uses the 2.8 km operation line between Wulin Square station and Ding’an Road station of Hangzhou Subway Line 1 as an example to propose the integrated method of DInSAR-GPS-GIS technology and the key algorithm for long-term land subsidence deformation.Then,it selects multiscene image data to analyze long-termland subsidence of high-density urban areas during subway operation.Results show that long-term land subsidence caused by the operation of Wulin Square station to Ding’an Road station of Hangzhou Subway Line 1 is small,with maximumsubsidence of 30.64 mm,and minimumsubsidence of 11.45 mm,and average subsidence ranging from 19.27 to 21.33 mm.And FLAC3D software was used to verify the monitoring situation,using the geological conditions of the soil in the study area and the tunnel profile to simulate the settlement under vehicle load,and the simulation results tended to be consistent with the monitoring situation.

Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.

Identification of the dominant loop of a dual-loop macro-reentry left atrial flutter without prior intervention using high-density mapping technology:A case report

BACKGROUND Left atrial flutter without prior cardiac interventions is uncommon,especially dual-loop macro-reentry atrial flutter.The critical step to ablate dual-loop macroreentry atrial flutter is to identify the dominant loop and key isthmus.Although entrainment mapping could help identify the dominant loop and key isthmus,it may alter or terminate tachycardia.High-density mapping allows the generation of electroanatomic maps without altering or terminating tachycardia.CASE SUMMARY Here,we report a case of symptomatic left atrial flutter without prior intervention.In this case,high-density mapping revealed a dual-loop macro-reentry around the mitral annulus and central scar of the anterior wall.The propagation result showed that the dominant loop was around the mitral annulus,and the key isthmus was between the central scar and mitral annulus.The atrial flutter terminated successfully after ablation was performed.CONCLUSION In this case,we demonstrate that high-density mapping technology may help identify the dominant loop of dual-loop atrial flutter without entrainment,which makes ablation easier.

Research and Application of High-Density Resistivity Method in Seawater Intrusion Investigation in Laizhou Bay

The disaster of seawater intrusion seriously affects people's lives and restricts economic development,so the detection and treatment of seawater intrusion is a long-term task.On the basis of field investigation and water quality analysis,according to the change characteristics of apparent resistivity of groundwater after Cl-reaches 250 mg/L,the theoretical basis for the application of high-density resistivity method was determined,and the characteristic values of apparent resistivity for seawater intrusion interfaces in different geological characteristic regions in Laizhou Bay area were determined by typical profile tests.Combined with water quality investigation and other means,profiles for the high-density resistivity method were arranged,and the interfaces between saline and fresh water were accurately divided.

Urban Traffic Control Meets Decision Recommendation System:A Survey and Perspective

Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and expertise.However,this process is cumbersome,labor-intensive,and cannot be applied on a large network scale.Recent studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and scalability.Such a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been conducted.In this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.

Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.

HGNN-ETC: Higher-Order Graph Neural Network Based on Chronological Relationships for Encrypted Traffic Classification

Encrypted traffic plays a crucial role in safeguarding network security and user privacy.However,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic essential.Existing methods for detecting encrypted traffic face two significant challenges.First,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic.Second,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential characteristics.To address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed HGNN-ETC.This approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context information.HGNN-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate classification.We select the ISCXVPN and the USTC-TK2016 dataset for our experiments.The results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.

Segment routing for traffic engineering and effective recovery in low-earth orbit satellite constellations

Low-Earth Orbit Satellite Constellations(LEO-SCs)provide global,high-speed,and low latency Internet access services,which bridges the digital divide in the remote areas.As inter-satellite links are not supported in initial deployment(i.e.the Starlink),the communication between satellites is based on ground stations with radio frequency signals.Due to the rapid movement of satellites,this hybrid topology of LEO-SCs and ground stations is time-varying,which imposes a major challenge to uninterrupted service provisioning and network management.In this paper,we focus on solving two notable problems in such a ground station-assisted LEO-SC topology,i.e.,traffic engineering and fast reroute,to guarantee that the packets are forwarded in a balanced and uninterrupted manner.Specifically,we employ segment routing to support the arbitrary path routing in LEO-SCs.To solve the traffic engineering problem,we proposed two source routings with traffic splitting algorithms,Delay-Bounded Traffic Splitting(DBTS)and DBTS+,where DBTS equally splits a flow and DBTS+favors shorter paths.Simu-lation results show that DBTS+can achieve about 30%lower maximum satellite load at the cost of about 10%more delay.To guarantee the fast recovery of failures,two fast reroute mechanisms,Loop-Free Alternate(LFA)and LFA+,are studied,where LFA pre-computes an alternate next-hop as a backup while LFA+finds a 2-segment backup path.We show that LFA+can increase the percentage of protection coverage by about 15%.

Network Traffic Synthesis and Simulation Framework for Cybersecurity Exercise Systems

In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats.To address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems.Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field.The cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data patterns.This technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments.By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual scenarios.An integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic patterns.This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations.Our initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and adjustment.By providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity professionals.This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient.

RUFY4 deletion prevents pathological bone loss by blocking endo-lysosomal trafficking of osteoclasts

Mature osteoclasts degrade bone matrix by exocytosis of active proteases from secretory lysosomes through a ruffled border.However,the molecular mechanisms underlying lysosomal trafficking and secretion in osteoclasts remain largely unknown.Here,we show with GeneChip analysis that RUN and FYVE domain-containing protein 4(RUFY4)is strongly upregulated during osteoclastogenesis.Mice lacking Rufy4 exhibited a high trabecular bone mass phenotype with abnormalities in osteoclast function in vivo.Furthermore,deleting Rufy4 did not affect osteoclast differentiation,but inhibited bone-resorbing activity due to disruption in the acidic maturation of secondary lysosomes,their trafficking to the membrane,and their secretion of cathepsin K into the extracellular space.Mechanistically,RUFY4 promotes late endosome-lysosome fusion by acting as an adaptor protein between Rab7 on late endosomes and LAMP2 on primary lysosomes.Consequently,Rufy4-deficient mice were highly protected from lipopolysaccharide-and ovariectomy-induced bone loss.Thus,RUFY4 plays as a new regulator in osteoclast activity by mediating endo-lysosomal trafficking and have a potential to be specific target for therapies against bone-loss diseases such as osteoporosis.

Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.