Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Exploring the potential mechanism of WuFuYin against hypertrophic scar using network pharmacology and molecular docking

BACKGROUND Hypertrophic scar(HTS)is dermal fibroproliferative disorder,which may cause physiological and psychological problems.Currently,the potential mechanism of WuFuYin(WFY)in the treatment of HTS remained to be elucidated.AIM To explore the potential mechanism of WFY in treating HTS.METHODS Active components and corresponding targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.HTSrelated genes were obtained from the GeneCards,DisGeNET,and National Center for Biotechnology Information.The function of targets was analyzed by performing Gene Ontology and Kyoto Encyclopaedia of Genes and Genome(KEGG)enrichment analysis.A protein+IBM-protein interaction(PPI)network was developed using STRING database and Cytoscape.To confirm the high affinity between compounds and targets,molecular docking was performed.RESULTS A total of 65 core genes,which were both related to compounds and HTS,were selected from multiple databases.PPI analysis showed that CKD2,ABCC1,MMP2,MMP9,glycogen synthase kinase 3 beta(GSK3B),PRARG,MMP3,and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma(PIK3CG)were the hub targets and MOL004941,MOL004935,MOL004866,MOL004993,and MOL004989 were the key compounds of WFY against HTS.The results of KEGG enrichment analysis demonstrated that the function of most genes were enriched in the PI3K-Akt pathway.Moreover,by performing molecular docking,we confirmed that GSK3B and 8-prenylated eriodictyol shared the highest affinity.CONCLUSION The current findings showed that the GSK3B and cyclin dependent kinase 2 were the potential targets and MOL004941,MOL004989,and MOL004993 were the main compounds of WFY in HTS treatment.

Integrated network pharmacology and metabolomics to explore the mechanisms of Shenzao dripping pill against chronic myocardial ischemia

Background:Shenzao dripping pill(SZDP)is empirically prescribed for treating cardiac diseases.Nevertheless,there is a lack of comprehensive knowledge regarding the underlying mechanisms contributing to its therapeutic effects.The objective of this study is to investigate the underlying mechanism of SZDP against chronic myocardial ischemia(CMI)in a rat model.Methods:In this study,we utilized electrocardiographic and echocardiographic detection along with pathological tissue analysis to evaluate the efficacy of SZDP.The integration of network pharmacology and metabolomics was conducted to investigate the mechanisms.Molecular docking and molecular dynamics simulations were used to validate the binding energy between the compounds of SZDP and the associated targets.Results:The results showed that SZDP was able to improve T wave voltage,reverse CMI abnormalities in ejection fraction and fractional shortening,and restore histopathological heart damage.Metabolomics results indicated that disturbances of metabolic profile in CMI rats were partly corrected after SZDP administration,mainly affecting purine metabolism.13-Docosenamide may be the potential metabolic biomarker of the therapeutic application of SZDP for CMI.Integrating network pharmacology and metabolomics,thiopurine S-methyltransferase(TPMT),xanthine dehydrogenase/oxidase(XDH),bifunctional purine biosynthesis protein ATIC(ATIC),and cytochrome p4501A1(CYP1A1)were identified as possible targets of SZDP to exert therapeutic effects by enhancing the metabolic levels of L-Tryptophan,Deoxyribose 1-phosphate and Phosphoribosyl formamidocarboxamide.Conclusion:SZDP has a therapeutic effect on CMI by regulating metabolite levels,acting on the targets of TMPT,XDH,ATIC,and CYP1A1,and reducing cardiomyocyte injury and myocardial fibrosis.

Investigating the molecular mechanism of Chelidonii Herba against liver cancer using network pharmacology and molecular docking validation

Background:The molecular mechanism of Chelidonii Herba in treating hepatocellular carcinoma was investigated using network pharmacology and molecular docking validation.Methods:The main active components of Chelidonii Herba were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database,and the targets of these active ingredients were identified using the SwissTargetPrediction platform.Targets related to liver cancer were sourced from GeneCards,Therapeutic Targets Database,and Online Mendelian Inheritance in Man databases.Intersection targets between the active components of Chelidonii Herba and liver cancer were determined using the jvenn online platform.The protein interaction network was analyzed via STRING database and visualized using Cytoscape 3.9.1.Core targets were identified and further analyzed within the protein interaction network.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted for the intersection targets using the DAVID database to correlate gene functions.Sankey bubble diagrams for Gene Ontology enrichment analysis and circular diagrams for Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were generated using CNSknowall and SangerBox online platforms.Molecular docking and visualization were performed using AutoDockTools 1.5.7 and PyMOL 2.5.7 software,respectively.Overall survival and pan-cancer analysis of core targets were conducted using the GEPIA2 online platform.Results:Twelve active components of Chelidonii Herba were identified through screening.A total of 103 intersection targets and 12 core targets were found between these active constituents of Chelidonii Herba and liver cancer.Chelidonii Herba may exert its effects on liver cancer through these 12 core targets.Several signaling pathways are implicated,including chemical carcinogen-receptor activation,endocrine resistance,HIF-1 signaling pathway,and proteoglycans in cancer.Conclusion:Chelidonii Herba potentially intervenes in cancer-related signaling pathways for treating liver cancer by targeting AKT1,EGFR,and ERBB2.This action is facilitated by active ingredients such as(S)-chrysocorydaline,dihydrochelidonorubin,cryptopine,and oxysanguinarine.Chelidonii Herba may address liver cancer through a mechanism involving multiple components,targets,and pathways.

To explore the mechanism of Fuyang Jiebiao granules against viral pneumonia based on network pharmacology and pharmacodynamics

Objective:To investigate the mechanism of Fuyang Jiebiao granule(FYJBKL)in the treatment of viral pneumonia.Methods:Firstly,a network model was constructed using network pharmacology to study the target expression sites of FYJBKL viral pneumonia,so as to determine the main targets and important signal transduction pathways for the treatment of viral pneumonia.Secondly,the main components of the drug and the main target are docked.Then,the fever,sweating and inflammation rat models were established to explore the antipyretic,sweating and anti-inflammatory mechanisms of FYJBKL.Finally,the contents of IL-17,IL-1β,TNF-αand IL-6 in blood samples of rats were analyzed by ELISA method,and the morphological changes of lung tissue were observed by HE staining.Results:Quercetin,luteolin,kaempferol,etc.,and the main mechanism targets are IL-17,IL-1β,TNF-α,IL-6 and so on.Thirty signal pathways were identified by KEGG enrichment analysis,including interleukin-17 signaling pathway(IL-17 signaling pathway),human cytomegalovirus infection pathway(human cytomegalovirus infection),Kaposi's sarcoma associated herpesvirus infection pathway(Kaposi's sarcoma-as-sociated herpesvirus infection)and so on.After the study of molecular docking,we found that the contact efficiency between active substances and possible key targets is good.The high and middle concentration groups of FYJBKL significantly decreased the expression of IL-17,IL-1β,TNF-αand IL-6 in the blood of rats with inflammation(P<0.05).FYJBKL significantly reduced the foot swelling induced by egg white and inhibited the increase of body temperature induced by yeast in rats(P<0.05).HE staining showed that FYJBKL improved pulmonary fibrosis and inflammatory exudation to varying degrees.Conclusion:The effects of FuyangJiebiao granules on the related signal pathways of anti-virus,anti-immune and anti-inflammation as well as biological and cellular processes may be caused by the binding of quercetin,luteolin,kaempferol and other active ingredients to their shared targets.Fuyang Jiebiao granules can improve the related symptoms caused by viral pneumonia,and its mechanism may be related to the activities of TNF,IL-17,IL-6 and other related channels,which are multiple targets of inflammation regulation.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.

Therapeutic Effects and Potential Mechanisms of Glyasperin A against Myocardial Ischemia Based on Network Pharmacology and Molecular Docking

[Objectives]To explore the therapeutic effects and potential mechanisms of Glyasperin A(GAA)on myocardial ischemia(MI)based on network pharmacology and molecular docking.[Methods]The molecular structure of GAA was downloaded from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),and all targets of GAA were predicted by converting 3D model molecules into SMILES online tool and Swiss target prediction.Genecards database and DisGeNET database were used to find the targets related to MI,and then Venny 2.1.0 was used to generate the corresponding Wayne diagram,and then Cytoscape 3.9.1 software was used to construct the protein-protein interaction(PPI)network.With the help of DAVID database and Microbiology,the selected core targets were enriched and analyzed by gene ontology(GO),biological process(BP),and Kyoto Encyclopedia of Genes and Genomes(KEGG),and then the molecular docking between GAA and core targets was verified by AutoDock and Pymol software.[Results]A total of 1883 MI targets were screened,and in the protein-protein interaction network,AKT1,PTGS2,PPARG,ESR1,GSK3B were the proteins with higher values.Gene ontology and KEEG enrichment analysis showed that the biological processes involved mainly included inflammatory response,negative regulation of gene expression,and response to exogenous stimuli.Signaling pathways mainly include IL-17 signaling pathway,HIF-1 signaling pathway,and so on.The results of molecular docking showed that the binding energy of GAA and core protein was less than-5 Kcal/mol in four groups.These indicated that GAA with good binding had a certain therapeutic effect on myocardial ischemia.[Conclusions]Based on the systematic network pharmacology method,this study predicts the basic pharmacological effects and potential mechanisms of GAA in the treatment of MI,and reveals that GAA may treat MI through multiple targets and signaling pathways.It is expected to provide a basis for further study of its pharmacological mechanisms.

Protective mechanism of Paeoniae Radix Alba against chemical liver injury based on network pharmacology,molecular docking,and in vitro experiments

Objective:To explore and validate the potential targets of Paeoniae Radix Alba(P.Radix,Bai Shao)in protecting against chemical liver injury through network pharmacology,molecular docking technology,and in vitro cell experiments.Methods:Network pharmacology was used to identify the common potential targets of P.Radix and chemical liver injury.Molecular docking was used to fit the components,which were subsequently verified in vitro.A cell model of hepatic fibrosis was established by activating hepatic stellate cell(HSC)-LX2 cells with 10 ng/mL transforming growth factor-β1.The cells were exposed to different concentrations of total glucosides of paeony(TGP),the active substance of P.Radix,and then evaluated using the cell counting kit-8 assay,enzyme-linked immunosorbent assay,and western blot.Results:Analysis through network pharmacology revealed 13 key compounds of P.Radix,and the potential targets for preventing chemical liver injury were IL-6,AKT serine/threonine kinase 1,jun protooncogene,heat shock protein 90 alpha family class A member 1(HSP90AA1),peroxisome proliferator activated receptor gamma(PPARG),PTGS2,and CASP3.Gene Ontology(GO)enrichment analysis indicated the involvement of response to drugs,membrane rafts,and peptide binding.Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis revealed that the main pathways involved lipid and atherosclerosis and chemical carcinogenesis-receptor activation.Paeoniflorin and albiflorin exhibited strong affinity for HSP90AA1,PTGS2,PPARG,and CASP3.Different concentrations of TGP can inhibit the expression of COL-I,COL-III,IL-6,TNF-a,IL-1β,HSP-90a,and PTGS2 while increasing the expression of PPAR-γand CASP3 in activated HSC-LX2 cells.Conclusion:P.Radix primarily can regulate targets such as HSP90AA1,PTGS2,PPARG,CASP3.TGP,the main active compound of P.Radix,protects against chemical liver injury by reducing the inflammatory response,activating apoptotic proteins,and promoting the apoptosis of activated HSCs.

Integration of network pharmacology and bone marrow mesenchymal stem cells experimental research to reveal the molecular mechanisms for Hai Honghua medicinal liquor against osteoporosis

Background:Hai Honghua medicinal liquor(HHML)formula has been used in clinical practice for a long time,mainly for the treatment of freshly closed fractures,to promote osteogenesis,to increase bone mass,and thus to promote fracture healing.However,the underlying mechanisms of HHML in the treatment of osteoporosis(OP)are still unclear.Methods:Firstly,Traditional Chinese Medicines Systems Pharmacology Database and Analysis Platform and The Encyclopedia of Traditional Chinese Medicine were used to screen the targets of the active compounds of HHML.At the same time,OP targets were identified through GeneCards,Online Mendelian Inheritance in Man,DisGeNET,Therapeutic Target Database,Comparative Toxicogenomics Database and Human Phenotype Ontology databases.Next,protein-protein interaction and pathway networks were constructed for compound-disease common targets,and core targets and compounds were screened for molecular docking.Furthermore,rat bone marrow mesenchymal stem cells were extracted as model cells,and the osteogenic effects of HHML were verified via in vitro experiments.Results:Total of 343 common targets of HHML-OP and the top 10 target proteins in the protein-protein interaction network are TP53,AKT1,STAT3,HSP90AA1,ESR1,TNF,IL6,MAPK1,MAPK3 and MAPK8.Enrichment analysis yielded that the key molecular pathway was the PI3K/Akt signaling pathway.Molecular docking analysis showed that baicalein,erysodienone and naringenin docked with the target proteins AKT1,STAT3 and TP53,respectively,with low binding energy and high affinity.In addition,In vitro experiments demonstrated that HHML promoted the proliferation of bone marrow mesenchymal stem cells;compared with the control group,HHML-treated cells showed enhanced alkaline phosphatase staining,promoted the expression of OCN,RUNX2,BSP,and COL1 mRNAs as well as the expression of PI3K and AKT phosphorylated proteins.Secondly,the expression of target proteins revealed that HHML promoted the phosphorylation of STAT3 protein and inhibited the expression of P53.Conclusions:Our study investigated that HHML treatment with OP promotes bone formation possibly through activation of the PI3K/Akt signaling pathway and may involve STAT3 and TP53 target interactions.

Integrated data mining and network pharmacology to discover a novel traditional Chinese medicine prescription against diabetic retinopathy and reveal its mechanism

Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.

Exploring the targets and molecular mechanism of glycyrrhetinic acid against diabetic nephropathy based on network pharmacology and molecular docking

BACKGROUND Diabetic nephropathy(DN)stands as the most prevalent chronic microvascular complication of diabetes mellitus.Approximately 50%of DN patients progress to end-stage renal disease,posing a substantial health burden.AIM To employ network pharmacology and molecular docking methods to predict the mechanism by which glycyrrhetinic acid(GA)treats DN,subsequently validating these predictions through experimental means.METHODS The study initially identified GA targets using Pharm Mapper and the TCMSP database.Targets relevant to DN were obtained from the Genecards,OMIM,and TTD databases.The Venny database facilitated the acquisition of intersecting targets between GA and DN.The String database was used to construct a protein interaction network,while DAVID database was used to conducted Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis and Gene Ontology(GO)analysis.Molecular docking experiments were performed using Autodock software with selected proteins.Experimental validation was conducted using renal proximal tubular cells(HK-2)as the study subjects.A hyperglycemic environment was simulated using glucose solution,and the effect of GA on cell viability was assessed through the cell counting kit-8 method.Flow cytometry was employed to detect cell cycle and apoptosis,and protein immunoblot(western blot)was used to measure the expression of proteins of the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)signaling pathway and insulin resistance pathway,including insulin receptor(INSR),PI3K,p-PI3K,AKT,p-AKT,and glycogen synthase kinase-3(GSK3).RESULTS A total of 186 intersecting targets between GA and DN were identified,which were associated with 144 KEGGrelated enrichment pathways,375 GO biological process entries,45 GO cellular component entries,and 112 GO cellular function entries.Molecular docking demonstrated strong binding of GA to mitogen-activated protein kinase(MAPK)-1,SRC,PIK3R1,HSP90AA1,CASPASE9,HARS,KRAS,and MAPK14.In vitro experiments revealed that GA inhibited HK-2 cell viability,induced cell cycle arrest at the G2/M phase,and reduced apoptosis with increasing drug concentration.Western blot analysis showed that GA differentially up-regulated GSK3 protein expression,up-regulated AKT/p-AKT expression,down-regulated INSR,AKT,p-AKT,PI3K,and p-PI3K protein expression,and reduced p-PI3K/PI3K levels under high glucose conditions.CONCLUSION GA may protect renal intrinsic cells by modulating the PI3K/AKT signaling pathway,thereby inhibiting HK-2 cell viability,reducing HK-2 cell apoptosis,and inducing cell cycle arrest at the G0/G1 phase.

基于GAIN-LSTM网络的雷达PRI序列还原及识别方法

开展脉冲重复间隔(Pulse Repetition Interval,PRI)模式识别工作是电子支援系统的一项重要任务。现代复杂电磁环境下,受雷达辐射源部署和接收设备本身影响,雷达脉冲丢失率极高,导致分选后PRI序列调制规律被破坏,现有的PRI模式识别方法准确率不足。针对上述问题,从PRI序列还原角度出发,并结合PRI序列本质是时序序列的特点,提出GAIN-LSTM(Generative Adversarial Imputation Nets and Long Short Term Memory)网络架构,其先对丢失脉冲位置进行补全操作,恢复PRI调制规律,然后对还原后PRI序列进行调制模式识别。仿真结果表明,提出的GAIN-LSTM网络架构在脉冲丢失率70%时仍保持95%的正确识别率。

Winter wheat yield improvement by genetic gain across different provinces in China

The replacement of winter wheat varieties has contributed significantly to yield improvement worldwide,with remarkable progress in China.Drawing on two sets of data,production yield from the National Bureau of Statistics of China and experimental yield from literature,this study aims to(1)illustrate the increasing patterns of production yield among different provinces from 1978 to 2018 in China,(2)explore the genetic gain in yield and yield relevant traits through the variety replacement based on experimental yield from 1937 to 2016 in China,and(3)compare the yield gap between experimental yield and production yield.The results show that both the production and experimental yields significantly increased along with the variety replacement.The national annual yield increase ratio for the production yield was 1.67%from 1978 to 2018,varying from 0.96%in Sichuan Province to 2.78%in Hebei Province;such ratio for the experimental yield was 1.13%from 1937 to 2016.The yield gap between experimental and production yields decreased from the 1970s to the 2010s.This study reveals significant increases in some yield components consequent to variety replacement,including thousand-grain weight,kernel number per spike,and grain number per square meter;however,no change is shown in spike number per square meter.The biomass and harvest index consistently and significantly increased,whereas the plant height decreased significantly.

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

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

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and community heterogeneity.A novel node influence ranking method,community-based Clustering-LeaderRank(CCL)algorithm,is first proposed to identify influential nodes in community networks.Simulation results show that the CCL method can effectively identify the influence of nodes.Based on node influence,a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks.Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process.The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities.When the initial load distribution and the load redistribution strategy based on the node influence are the same,the network shows better robustness against node failure.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Biodiversity metrics on ecological networks: Demonstrated with animal gastrointestinal microbiomes

Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

Intervention against information diffusion in static and temporal coupling networks

Information diffusion in complex networks has become quite an active research topic.As an important part of this field,intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers.In particular,it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks.For this purpose,we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks.First,individual interactions are described by a modified activitydriven network(ADN)model.Then,we establish a novel node-based susceptible-infected-recovered-susceptible(SIRS)model to characterize the information diffusion dynamics.On these bases,three synergetic intervention strategies are formulated.Second,we derive the critical threshold of the controlled-SIRS system via stability analysis.Accordingly,we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget.Third,we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense,in which the optimal intervention inputs are obtained through optimal control theory and a forward-backward sweep algorithm.Finally,extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.