BACKGROUND:Prompt pathogen identification can have a substantial impact on the optimization of antimicrobial treatment.The objective of the study was to assess the diagnostic value of next-generation sequencing(NGS)fo...BACKGROUND:Prompt pathogen identification can have a substantial impact on the optimization of antimicrobial treatment.The objective of the study was to assess the diagnostic value of next-generation sequencing(NGS)for identifying pathogen and its clinical impact on antimicrobial intervention in immunocompromised patients with suspected infections.METHODS:This was a retrospective study.Between January and August 2020,47 adult immunocompromised patients underwent NGS testing under the following clinical conditions:1)prolonged fever and negative conventional cultures;2)new-onset fever despite empiric antimicrobial treatment;and 3)afebrile with suspected infections on imaging.Clinical data,including conventional microbial test results and antimicrobial treatment before and after NGS,were collected.Data were analyzed according to documented changes in antimicrobial treatment(escalated,no change,or deescalated)after the NGS results.RESULTS:The median time from hospitalization to NGS sampling was 19 d.Clinically relevant pathogens were detected via NGS in 61.7% of patients(29/47),more than half of whom suffered from fungemia(n=17),resulting in an antimicrobial escalation in 53.2% of patients(25/47)and antimicrobial de-escalation in 0.2% of patients(1/47).Antimicrobial changes were mostly due to the identification of fastidious organisms such as Legionella,Pneumocystis jirovecii,and Candida.In the remaining three cases,NGS detected clinically relevant pathogens also detected by conventional cultures a few days later.The antimicrobial treatment was subsequently adjusted according to the susceptibility test results.Overall,NGS changed antimicrobial management in 55.3%(26/47)of patients,and conventional culture detected clinically relevant pathogens in 14.9% of the patients(7/47).CONCLUSION:With its rapid identification and high sensitivity,NGS could be a promising tool for identifying relevant pathogens and enabling rapid appropriate treatment in immunocompromised patients with suspected infections.展开更多
Objective This study aimed to explore the diagnostic value of novel technique-targeted next-generation sequencing(tNGS)of bronchoalveolar lavage fluid(BALF)in pulmonary mycobacterial infections.Methods This retrospect...Objective This study aimed to explore the diagnostic value of novel technique-targeted next-generation sequencing(tNGS)of bronchoalveolar lavage fluid(BALF)in pulmonary mycobacterial infections.Methods This retrospective study was conducted on patients who underwent bronchoscopy and tNGS,smear microscopy,and mycobacterial culture of BALF.Patients with positive Mycobacterium tuberculosis(MTB)culture or GeneXpert results were classified into the tuberculosis case group.Those diagnosed with nontuberculous mycobacteria(NTM)-pulmonary disease(NTM-PD)composed the case group of NTM-PD patients.The control group comprised patients without tuberculosis or NTM-PD.Sensitivity,specificity,and receiver operating characteristic(ROC)curves were used to evaluate the diagnostic performance.Results For tuberculosis patients with positive mycobacterial culture results,the areas under the ROC curves(AUCs)for tNGS,GeneXpert,and smear microscopy were 0.975(95%CI:0.935,1.000),0.925(95%CI:0.859,0.991),and 0.675(95%CI:0.563,0.787),respectively.For tuberculosis patients with positive GeneXpert results,the AUCs of tNGS,culture,and smear microscopy were 0.970(95%CI:0.931,1.000),0.850(95%CI:0.770,0.930),and 0.680(95%CI:0.579,0.781),respectively.For NTM-PD,the AUCs of tNGS,culture,and smear-positive but GeneXpert-negative results were 0.987(95%CI:0.967,1.000),0.750(95%CI:0.622,0.878),and 0.615(95%CI:0.479,0.752),respectively.The sensitivity and specificity of tNGS in NTM-PD patients were 100%and 97.5%,respectively.Conclusion tNGS demonstrated superior diagnostic efficacy in mycobacterial infection,indicating its potential for clinical application.展开更多
Background:For patients with lung cancer,timely identification of new lung lesions as infectious or non-infectious,and accurate identification of pathogens is very important in improving OS of patients.As a new auxiliar...Background:For patients with lung cancer,timely identification of new lung lesions as infectious or non-infectious,and accurate identification of pathogens is very important in improving OS of patients.As a new auxiliary examination,metagenomic next-generation sequencing(mNGS)is believed to be more accurate in diagnosing infectious diseases in patients without underlying diseases,compared with conventional microbial tests(CMTs).We designed this study tofind out whether mNGS has better performance in distinguishing infectious and non-infectious diseases in lung cancer patients using bronchoalveolar lavagefluid(BALF).Materials and Methods:This study was a real-world retrospective review based on electronic medical records of lung cancer patients with bronchoalveolar lavage(BAL)and BALF commercial mNGS testing as part of clinical care from 1 April 2019 through 30 April 2022 at The First Affiliated Hospital of Sun Yat-sen University.164 patients were included in this study.Patients were categorized into the pulmonary non-infectious disease(PNID)group(n=64)and the pulmonary infectious disease(PID)group(n=100)groups based onfinal diagnoses.Results:BALF mNGS increased the sensitivity rate by 60%compared to CMTs(81%vs.21%,p<0.05),whereas there was no significant difference in specificity(75%vs.98.4%,p>0.1).Among the patients with PID,bacteria were the most common cause of infection.Fungal infections occurred in 32%of patients,and Pneumocystis Yersini was most common.Patients with Tyrosine kinase inhibitors(TKIs)therapy possess longer overall survival(OS)than other anti-cancer agents,the difference between TKIs and immuno-checkpoint inhibitors(ICIs)was insignificant(median OS TKIs vs.ICIs vs.Anti-angiogenic vs.Chemo vs.Radiotherapy=76 vs.84 vs.61 vs.58 vs.60).Conclusions:our study indicates that BALF mNGS can add value by improving overall sensitivity in lung cancer patients with potential pulmonary infection,and was outstanding in identifying Pneumocystis infection.It could be able to help physicians adjust the follow-up treatment to avoid the abuse of antibiotics.展开更多
BACKGROUND Brain abscess is a serious and potentially fatal disease caused primarily by microbial infection.Although progress has been made in the diagnosis and treatment of brain abscesses,the diagnostic timeliness o...BACKGROUND Brain abscess is a serious and potentially fatal disease caused primarily by microbial infection.Although progress has been made in the diagnosis and treatment of brain abscesses,the diagnostic timeliness of pathogens needs to be improved.CASE SUMMARY We report the case of a 54-year-old male with a brain abscess caused by oral bacteria.The patient recovered well after receiving a combination of metagenomic next-generation sequencing(mNGS)-assisted guided medication and surgery.CONCLUSION Therefore,mNGS may be widely applied to identify the pathogenic microor-ganisms of brain abscesses and guide precision medicine.展开更多
BACKGROUND Mycobacterium houstonense(M.houstonense)belongs to the nontuberculous mycobacterium group.Infection caused by M.houstonense is prone to recurrence.CASE SUMMARY We present a patient who was diagnosed with os...BACKGROUND Mycobacterium houstonense(M.houstonense)belongs to the nontuberculous mycobacterium group.Infection caused by M.houstonense is prone to recurrence.CASE SUMMARY We present a patient who was diagnosed with osteomyelitis caused by M.houstonense and treated with a combination of cefoxitin,and amikacin combined with linezolid.CONCLUSION The emergence of metagenomic next-generation sequencing(NGS)has brought new hope for the diagnosis and treatment of listeria meningitis.NGS can analyze a large number of nucleic acid sequences in a short time and quickly determine the pathogen species in the sample.Compared with traditional cerebrospinal fluid culture,NGS can greatly shorten the diagnosis time and provide strong support for the timely treatment of patients.Regarding treatment,NGS can also play an important role.Rapid and accurate diagnosis can enable patients to start targeted treatment as soon as possible and improve the treatment effect.At the same time,by monitoring the changes in pathogen resistance,the treatment plan can be adjusted in time to avoid treatment failure.展开更多
Query fever(Q fever)is a globally spread zoonotic disease caused by Coxiella burnetii,commonly found in natural foci but rarely seen in Hebei Province.The clinical manifestations of Q fever are diverse and nonspecific...Query fever(Q fever)is a globally spread zoonotic disease caused by Coxiella burnetii,commonly found in natural foci but rarely seen in Hebei Province.The clinical manifestations of Q fever are diverse and nonspecific,which often leads to missed or incorrect diagnoses in clinical practice.This article reports a case of acute Q fever diagnosed in an elderly patient using metagenomic next-generation sequencing.展开更多
In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperativ...In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.展开更多
The hardware and software architectures of core service platforms for next-generation networks were analyzed to compute the minimum cost hardware configuration of a core service platform. This method gives a closed fo...The hardware and software architectures of core service platforms for next-generation networks were analyzed to compute the minimum cost hardware configuration of a core service platform. This method gives a closed form expression for the optimized hardware cost configuration based on the service requirements, the processing features of the computers running the core service platform software, and the processing capabilities of the common object request broker architecture middleware. Three simulation scenarios were used to evaluate the model. The input includes the number of servers for the protocol mapping （PM）, Parlay gateway （PG）, application sever （AS）, and communication handling （CH） functions. The simulation results show that the mean delay meets requirements. When the number of servers for PM, PG, AS, and CH functions were not properly selected, the mean delay was excessive. Simulation results show that the model is valid and can be used to optimize investments in core service platforms.展开更多
The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users.Due to this popularity,there has been a huge rise in mobile data volume,applicatio...The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users.Due to this popularity,there has been a huge rise in mobile data volume,applications,types of services,and number of customers.Furthermore,due to the COVID-19 pandemic,the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home.This massive increase in demand for multi-class services has posed numerous challenges to wireless network frameworks.The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic,such as real-time live streaming of videos,audios,text,images etc.,at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers.Next-generation wireless networks(NGWNs,i.e.5G networks and beyond)are being developed to accommodate the service qualities mentioned above and many more.However,achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers.It requires the analysis of a huge volume of network data(structured and unstructured)received or collected from heterogeneous devices,applications,services,and customers and the effective and dynamic management of network parameters based on this analysis in real time.In the ever-increasing network heterogeneity and complexity,machine learning(ML)techniques may become an efficient tool for effectively managing these issues.In recent days,the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain.This study discusses current wireless network research,brief discussions on ML methods that can be effectively applied to the wireless networking domain,some tools available to support and customise efficient mobile system design,and some unresolved issues for future research directions.展开更多
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 c...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.展开更多
Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is s...Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).展开更多
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 expl...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.展开更多
Objective:To evaluate the value of next-generation sequencing(NGS)in the prevention and management of thalassemia.Methods:A systematic search was performed in eight databases including China Biomedical Literature Data...Objective:To evaluate the value of next-generation sequencing(NGS)in the prevention and management of thalassemia.Methods:A systematic search was performed in eight databases including China Biomedical Literature Database,Chinese National Knowledge Infrastructure,Chinese Scientific Journals Database,Wanfang database,PubMed,EMBASE,Web of Science,and Cochrane Library from the inception to 1 June 2022.Stata 17.0 and Review Manager 5.4 were used for the meta-analysis.Results:Nine studies containing 14794 participants were included in the meta-analysis.Compared with the routine genetic testing(including Gap-PCR and reverse dot blot),NGS had higher detection rates in screening thalassemia(RR 1.22,95%CI 1.13-1.31,P<0.01),particularly for theα-thalassaemia mutation carriers(RR 1.24,95%CI 1.07-1.44,P<0.01).However,no significant difference was found in the screening ofβ-thalassemia(RR 1.10,95%CI 0.99-1.23,P>0.05).Conclusions:Compared with routine genetic testing,NGS had a higher detection rate in general,particularly in the detection ofα-thalassemia.展开更多
In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.T...In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.This research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in WSNs.The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination.Employing a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication chain.Through comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced performance.Furthermore,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.展开更多
The genes of the major histocompatibility complex(MHC) encode cell surface proteins that are essential for adaptive immunity. MHC genes show the most prominent genetic diversity in vertebrates,reflecting the adaptatio...The genes of the major histocompatibility complex(MHC) encode cell surface proteins that are essential for adaptive immunity. MHC genes show the most prominent genetic diversity in vertebrates,reflecting the adaptation of populations to their evolving environment, population survival and reproduction. In the present study, we used nextgeneration sequencing(NGS) to study the loci polymorphism of exon 3 of the MHC class Ⅰ genes in an ovoviviparous skink, the many-lined sun skink,Eutropis multifasciata and five other species of Scincidae, to quantify genetic variation. In addition,we genotyped the same MHC class Ⅰ genes of E.multifasciata using clone sequencing, to directly compare the effectiveness of both analytical techniques for MHC genotyping. NGS detected 20MHC class Ⅰ alleles in E. multifasciata, and 2 to 15 alleles in the other five Scincidae species. However,clone sequencing detected only 15 of those MHC class Ⅰ alleles in E. multifasciata. In addition, transspecies polymorphism of MHC class Ⅰ genes was studied by constructing a phylogenetic tree using the gene sequences obtained by NGS. Phylogenetic analysis revealed that MHC class I alleles were shared among different species of Scincidae with trans-species polymorphism, and did not exhibit specific genealogical inheritance. These results have important implications for understanding polymorphism interspecies diversity in the MHC genes of Scincidae, and the evolution of the MHC more broadly.展开更多
Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely exp...Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.展开更多
BACKGROUND Pulmonary nocardiosis is difficult to diagnose by culture and other conventional testing,and is often associated with lethal disseminated infections.This difficulty poses a great challenge to the timeliness...BACKGROUND Pulmonary nocardiosis is difficult to diagnose by culture and other conventional testing,and is often associated with lethal disseminated infections.This difficulty poses a great challenge to the timeliness and accuracy of clinical detection,especially in susceptible immunosuppressed individuals.Metagenomic nextgeneration sequencing(mNGS)has transformed the conventional diagnosis pattern by providing a rapid and precise method to assess all microorganisms in a sample.CASE SUMMARY A 45-year-old male was hospitalized for cough,chest tightness and fatigue for 3 consecutive days.He had received a kidney transplant 42 d prior to admission.No pathogens were detected at admission.Chest computed tomography showed nodules,streak shadows and fiber lesions in both lung lobes as well as right pleural effusion.Pulmonary tuberculosis with pleural effusion was highly suspected based on the symptoms,imaging and residence in a high tuberculosisburden area.However,anti-tuberculosis treatment was ineffective,showing no improvement in computed tomography imaging.Pleural effusion and blood samples were subsequently sent for mNGS.The results indicated Nocardia farcinica as the major pathogen.After switching to sulphamethoxazole combined with minocycline for antinocardiosis treatment,the patient gradually improved and was finally discharged.CONCLUSION A case of pulmonary nocardiosis with an accompanying bloodstream infection was diagnosed and promptly treated before the dissemination of the infection.This report emphasizes the value of mNGS in the diagnosis of nocardiosis.mNGS may be an effective method for facilitating early diagnosis and prompt treatment in infectious diseases,which overcomes the shortcomings of conventional testing.展开更多
Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes i...Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.展开更多
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 complexi...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.展开更多
With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these...With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.展开更多
基金supported by National Natural Science Foundation of China(72274067)。
文摘BACKGROUND:Prompt pathogen identification can have a substantial impact on the optimization of antimicrobial treatment.The objective of the study was to assess the diagnostic value of next-generation sequencing(NGS)for identifying pathogen and its clinical impact on antimicrobial intervention in immunocompromised patients with suspected infections.METHODS:This was a retrospective study.Between January and August 2020,47 adult immunocompromised patients underwent NGS testing under the following clinical conditions:1)prolonged fever and negative conventional cultures;2)new-onset fever despite empiric antimicrobial treatment;and 3)afebrile with suspected infections on imaging.Clinical data,including conventional microbial test results and antimicrobial treatment before and after NGS,were collected.Data were analyzed according to documented changes in antimicrobial treatment(escalated,no change,or deescalated)after the NGS results.RESULTS:The median time from hospitalization to NGS sampling was 19 d.Clinically relevant pathogens were detected via NGS in 61.7% of patients(29/47),more than half of whom suffered from fungemia(n=17),resulting in an antimicrobial escalation in 53.2% of patients(25/47)and antimicrobial de-escalation in 0.2% of patients(1/47).Antimicrobial changes were mostly due to the identification of fastidious organisms such as Legionella,Pneumocystis jirovecii,and Candida.In the remaining three cases,NGS detected clinically relevant pathogens also detected by conventional cultures a few days later.The antimicrobial treatment was subsequently adjusted according to the susceptibility test results.Overall,NGS changed antimicrobial management in 55.3%(26/47)of patients,and conventional culture detected clinically relevant pathogens in 14.9% of the patients(7/47).CONCLUSION:With its rapid identification and high sensitivity,NGS could be a promising tool for identifying relevant pathogens and enabling rapid appropriate treatment in immunocompromised patients with suspected infections.
文摘Objective This study aimed to explore the diagnostic value of novel technique-targeted next-generation sequencing(tNGS)of bronchoalveolar lavage fluid(BALF)in pulmonary mycobacterial infections.Methods This retrospective study was conducted on patients who underwent bronchoscopy and tNGS,smear microscopy,and mycobacterial culture of BALF.Patients with positive Mycobacterium tuberculosis(MTB)culture or GeneXpert results were classified into the tuberculosis case group.Those diagnosed with nontuberculous mycobacteria(NTM)-pulmonary disease(NTM-PD)composed the case group of NTM-PD patients.The control group comprised patients without tuberculosis or NTM-PD.Sensitivity,specificity,and receiver operating characteristic(ROC)curves were used to evaluate the diagnostic performance.Results For tuberculosis patients with positive mycobacterial culture results,the areas under the ROC curves(AUCs)for tNGS,GeneXpert,and smear microscopy were 0.975(95%CI:0.935,1.000),0.925(95%CI:0.859,0.991),and 0.675(95%CI:0.563,0.787),respectively.For tuberculosis patients with positive GeneXpert results,the AUCs of tNGS,culture,and smear microscopy were 0.970(95%CI:0.931,1.000),0.850(95%CI:0.770,0.930),and 0.680(95%CI:0.579,0.781),respectively.For NTM-PD,the AUCs of tNGS,culture,and smear-positive but GeneXpert-negative results were 0.987(95%CI:0.967,1.000),0.750(95%CI:0.622,0.878),and 0.615(95%CI:0.479,0.752),respectively.The sensitivity and specificity of tNGS in NTM-PD patients were 100%and 97.5%,respectively.Conclusion tNGS demonstrated superior diagnostic efficacy in mycobacterial infection,indicating its potential for clinical application.
基金This study was funded by Science and Technology Projects in Guangzhou(No.202002030023).
文摘Background:For patients with lung cancer,timely identification of new lung lesions as infectious or non-infectious,and accurate identification of pathogens is very important in improving OS of patients.As a new auxiliary examination,metagenomic next-generation sequencing(mNGS)is believed to be more accurate in diagnosing infectious diseases in patients without underlying diseases,compared with conventional microbial tests(CMTs).We designed this study tofind out whether mNGS has better performance in distinguishing infectious and non-infectious diseases in lung cancer patients using bronchoalveolar lavagefluid(BALF).Materials and Methods:This study was a real-world retrospective review based on electronic medical records of lung cancer patients with bronchoalveolar lavage(BAL)and BALF commercial mNGS testing as part of clinical care from 1 April 2019 through 30 April 2022 at The First Affiliated Hospital of Sun Yat-sen University.164 patients were included in this study.Patients were categorized into the pulmonary non-infectious disease(PNID)group(n=64)and the pulmonary infectious disease(PID)group(n=100)groups based onfinal diagnoses.Results:BALF mNGS increased the sensitivity rate by 60%compared to CMTs(81%vs.21%,p<0.05),whereas there was no significant difference in specificity(75%vs.98.4%,p>0.1).Among the patients with PID,bacteria were the most common cause of infection.Fungal infections occurred in 32%of patients,and Pneumocystis Yersini was most common.Patients with Tyrosine kinase inhibitors(TKIs)therapy possess longer overall survival(OS)than other anti-cancer agents,the difference between TKIs and immuno-checkpoint inhibitors(ICIs)was insignificant(median OS TKIs vs.ICIs vs.Anti-angiogenic vs.Chemo vs.Radiotherapy=76 vs.84 vs.61 vs.58 vs.60).Conclusions:our study indicates that BALF mNGS can add value by improving overall sensitivity in lung cancer patients with potential pulmonary infection,and was outstanding in identifying Pneumocystis infection.It could be able to help physicians adjust the follow-up treatment to avoid the abuse of antibiotics.
文摘BACKGROUND Brain abscess is a serious and potentially fatal disease caused primarily by microbial infection.Although progress has been made in the diagnosis and treatment of brain abscesses,the diagnostic timeliness of pathogens needs to be improved.CASE SUMMARY We report the case of a 54-year-old male with a brain abscess caused by oral bacteria.The patient recovered well after receiving a combination of metagenomic next-generation sequencing(mNGS)-assisted guided medication and surgery.CONCLUSION Therefore,mNGS may be widely applied to identify the pathogenic microor-ganisms of brain abscesses and guide precision medicine.
基金Supported by The National Natural Science Foundation of China,No.82100631.
文摘BACKGROUND Mycobacterium houstonense(M.houstonense)belongs to the nontuberculous mycobacterium group.Infection caused by M.houstonense is prone to recurrence.CASE SUMMARY We present a patient who was diagnosed with osteomyelitis caused by M.houstonense and treated with a combination of cefoxitin,and amikacin combined with linezolid.CONCLUSION The emergence of metagenomic next-generation sequencing(NGS)has brought new hope for the diagnosis and treatment of listeria meningitis.NGS can analyze a large number of nucleic acid sequences in a short time and quickly determine the pathogen species in the sample.Compared with traditional cerebrospinal fluid culture,NGS can greatly shorten the diagnosis time and provide strong support for the timely treatment of patients.Regarding treatment,NGS can also play an important role.Rapid and accurate diagnosis can enable patients to start targeted treatment as soon as possible and improve the treatment effect.At the same time,by monitoring the changes in pathogen resistance,the treatment plan can be adjusted in time to avoid treatment failure.
基金Baoding Science and Technology Program Project:“Clinical Study Analysis on the Effect of Vitamin D Supplementation in Improving Prognosis of Elderly Patients with H-type Hypertension”(Project No.2341ZF140)。
文摘Query fever(Q fever)is a globally spread zoonotic disease caused by Coxiella burnetii,commonly found in natural foci but rarely seen in Hebei Province.The clinical manifestations of Q fever are diverse and nonspecific,which often leads to missed or incorrect diagnoses in clinical practice.This article reports a case of acute Q fever diagnosed in an elderly patient using metagenomic next-generation sequencing.
文摘In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.
基金the China Postdoctoral Science Foundation (No. 20060390463)the Basic Research Foundation of Tsinghua National Laboratory for Information Science and Technology
文摘The hardware and software architectures of core service platforms for next-generation networks were analyzed to compute the minimum cost hardware configuration of a core service platform. This method gives a closed form expression for the optimized hardware cost configuration based on the service requirements, the processing features of the computers running the core service platform software, and the processing capabilities of the common object request broker architecture middleware. Three simulation scenarios were used to evaluate the model. The input includes the number of servers for the protocol mapping （PM）, Parlay gateway （PG）, application sever （AS）, and communication handling （CH） functions. The simulation results show that the mean delay meets requirements. When the number of servers for PM, PG, AS, and CH functions were not properly selected, the mean delay was excessive. Simulation results show that the model is valid and can be used to optimize investments in core service platforms.
文摘The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users.Due to this popularity,there has been a huge rise in mobile data volume,applications,types of services,and number of customers.Furthermore,due to the COVID-19 pandemic,the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home.This massive increase in demand for multi-class services has posed numerous challenges to wireless network frameworks.The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic,such as real-time live streaming of videos,audios,text,images etc.,at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers.Next-generation wireless networks(NGWNs,i.e.5G networks and beyond)are being developed to accommodate the service qualities mentioned above and many more.However,achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers.It requires the analysis of a huge volume of network data(structured and unstructured)received or collected from heterogeneous devices,applications,services,and customers and the effective and dynamic management of network parameters based on this analysis in real time.In the ever-increasing network heterogeneity and complexity,machine learning(ML)techniques may become an efficient tool for effectively managing these issues.In recent days,the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain.This study discusses current wireless network research,brief discussions on ML methods that can be effectively applied to the wireless networking domain,some tools available to support and customise efficient mobile system design,and some unresolved issues for future research directions.
基金The authors acknowledge the funding provided by the National Key R&D Program of China(2021YFA1401200)Beijing Outstanding Young Scientist Program(BJJWZYJH01201910007022)+2 种基金National Natural Science Foundation of China(No.U21A20140,No.92050117,No.62005017)programBeijing Municipal Science&Technology Commission,Administrative Commission of Zhongguancun Science Park(No.Z211100004821009)This work was supported by the Synergetic Extreme Condition User Facility(SECUF).
文摘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.
基金supported by the Notional Natural Science Foundation of China,No.81960417 (to JX)Guangxi Key Research and Development Program,No.GuiKeA B20159027 (to JX)the Natural Science Foundation of Guangxi Zhuang Autonomous Region,No.2022GXNSFBA035545 (to YG)。
文摘Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).
文摘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.
基金supported by the Hainan Provincial Natural Science Foundation(No.ZDKJ2021037)the China Postdoctoral Science Foundation(No.2021M691466)National Natural Science Foundation of China(No.8220061871).
文摘Objective:To evaluate the value of next-generation sequencing(NGS)in the prevention and management of thalassemia.Methods:A systematic search was performed in eight databases including China Biomedical Literature Database,Chinese National Knowledge Infrastructure,Chinese Scientific Journals Database,Wanfang database,PubMed,EMBASE,Web of Science,and Cochrane Library from the inception to 1 June 2022.Stata 17.0 and Review Manager 5.4 were used for the meta-analysis.Results:Nine studies containing 14794 participants were included in the meta-analysis.Compared with the routine genetic testing(including Gap-PCR and reverse dot blot),NGS had higher detection rates in screening thalassemia(RR 1.22,95%CI 1.13-1.31,P<0.01),particularly for theα-thalassaemia mutation carriers(RR 1.24,95%CI 1.07-1.44,P<0.01).However,no significant difference was found in the screening ofβ-thalassemia(RR 1.10,95%CI 0.99-1.23,P>0.05).Conclusions:Compared with routine genetic testing,NGS had a higher detection rate in general,particularly in the detection ofα-thalassemia.
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)in part by the NRF Grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401).
文摘In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.This research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in WSNs.The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination.Employing a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication chain.Through comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced performance.Furthermore,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.
基金This work was supported by grants from the National Natural Science Foundation of China(32171495 and 31971414)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘The genes of the major histocompatibility complex(MHC) encode cell surface proteins that are essential for adaptive immunity. MHC genes show the most prominent genetic diversity in vertebrates,reflecting the adaptation of populations to their evolving environment, population survival and reproduction. In the present study, we used nextgeneration sequencing(NGS) to study the loci polymorphism of exon 3 of the MHC class Ⅰ genes in an ovoviviparous skink, the many-lined sun skink,Eutropis multifasciata and five other species of Scincidae, to quantify genetic variation. In addition,we genotyped the same MHC class Ⅰ genes of E.multifasciata using clone sequencing, to directly compare the effectiveness of both analytical techniques for MHC genotyping. NGS detected 20MHC class Ⅰ alleles in E. multifasciata, and 2 to 15 alleles in the other five Scincidae species. However,clone sequencing detected only 15 of those MHC class Ⅰ alleles in E. multifasciata. In addition, transspecies polymorphism of MHC class Ⅰ genes was studied by constructing a phylogenetic tree using the gene sequences obtained by NGS. Phylogenetic analysis revealed that MHC class I alleles were shared among different species of Scincidae with trans-species polymorphism, and did not exhibit specific genealogical inheritance. These results have important implications for understanding polymorphism interspecies diversity in the MHC genes of Scincidae, and the evolution of the MHC more broadly.
基金the TCL Science and Technology Innovation Fundthe Youth Science and Technology Talent Promotion Project of Jiangsu Association for Science and Technology,Grant/Award Number:JSTJ‐2023‐017+4 种基金Shenzhen Municipal Science and Technology Innovation Council,Grant/Award Number:JSGG20220831105002004National Natural Science Foundation of China,Grant/Award Number:62201468Postdoctoral Research Foundation of China,Grant/Award Number:2022M722599the Fundamental Research Funds for the Central Universities,Grant/Award Number:D5000210966the Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2021A1515110079。
文摘Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.
文摘BACKGROUND Pulmonary nocardiosis is difficult to diagnose by culture and other conventional testing,and is often associated with lethal disseminated infections.This difficulty poses a great challenge to the timeliness and accuracy of clinical detection,especially in susceptible immunosuppressed individuals.Metagenomic nextgeneration sequencing(mNGS)has transformed the conventional diagnosis pattern by providing a rapid and precise method to assess all microorganisms in a sample.CASE SUMMARY A 45-year-old male was hospitalized for cough,chest tightness and fatigue for 3 consecutive days.He had received a kidney transplant 42 d prior to admission.No pathogens were detected at admission.Chest computed tomography showed nodules,streak shadows and fiber lesions in both lung lobes as well as right pleural effusion.Pulmonary tuberculosis with pleural effusion was highly suspected based on the symptoms,imaging and residence in a high tuberculosisburden area.However,anti-tuberculosis treatment was ineffective,showing no improvement in computed tomography imaging.Pleural effusion and blood samples were subsequently sent for mNGS.The results indicated Nocardia farcinica as the major pathogen.After switching to sulphamethoxazole combined with minocycline for antinocardiosis treatment,the patient gradually improved and was finally discharged.CONCLUSION A case of pulmonary nocardiosis with an accompanying bloodstream infection was diagnosed and promptly treated before the dissemination of the infection.This report emphasizes the value of mNGS in the diagnosis of nocardiosis.mNGS may be an effective method for facilitating early diagnosis and prompt treatment in infectious diseases,which overcomes the shortcomings of conventional testing.
基金supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI(JP22H03643)Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)(JPMJSP2145)+2 种基金JST Through the Establishment of University Fellowships Towards the Creation of Science Technology Innovation(JPMJFS2115)the National Natural Science Foundation of China(52078382)the State Key Laboratory of Disaster Reduction in Civil Engineering(CE19-A-01)。
文摘Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘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.
基金supported by the National Science Foundation of China under Grant 62271062 and 62071063by the Zhijiang Laboratory Open Project Fund 2020LCOAB01。
文摘With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.
