Introduction:Human brucellosis persists as a critical public health challenge in China.Understanding disease clusters and trends is essential for implementing effective control strategies.This study evaluates the epid...Introduction:Human brucellosis persists as a critical public health challenge in China.Understanding disease clusters and trends is essential for implementing effective control strategies.This study evaluates the epidemiological characteristics and spatiotemporal distribution of brucellosis in China from 2011 to 2023.Methods:Data were obtained from the National Notifiable Disease Reporting System(NNDRS).We conducted descriptive epidemiological analyses and employed SaTScan10.1 and ArcGIS10.7 software to identify disease clusters and generate county(district)-level incidence maps.Results:The incidence of human brucellosis in Chinese mainland increased substantially between 2011 and 2023,rising from 38,151 cases(2.8/100,000)across 834 counties(25.4%)to 70,439 cases(5.2/100,000)across 2,290 counties(76.9%).A significant upward trend in reported incidence emerged during 2018–2023(average annual percentage change(AAPC)=14.9%,P=0.01).Most cases(89.3%)occurred in individuals aged 25–69 years,with an increasing proportion among those aged over 60 years.While 96.1%of cases were reported in northern provincial-level administrative divisions(PLADs),southern regions demonstrated escalating incidence rates and expanding geographical spread.Southern PLADs exhibited a notable annual increase of 31.5%in reported incidence(P<0.01).Counties(districts)with incidence rates exceeding 10 per 100,000 expanded geographically from northwestern pastoral regions to southern areas and from rural to urban settings.Primary spatiotemporal clusters were concentrated in Inner Mongolia and adjacent provincial-level administrative divisions(PLADs),with emerging clusters identified in Yunnan,Guangdong,and Xizang.Conclusions:The human brucellosis epidemic in China continues to intensify,characterized by rebounding incidence rates and broader geographical distribution across counties(districts).While spatiotemporal clusters remain predominantly centered in Inner Mongolia and neighboring regions,targeted interventions and increased resource allocation for high-risk areas and populations are imperative.展开更多
Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster...Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.展开更多
Villager Pan Chunlin is witnessing a boom in his homestay business.More and more visitors are coming to his village,Yucun Village in Anji County,Huzhou City,Zhejiang Province.
Introduction:Chemotherapy-induced gastrointestinal symptom clusters in breast cancer impair quality of life and treatment adherence,yet lack effective interventions.While acupuncture mitigates isolated chemotherapy-in...Introduction:Chemotherapy-induced gastrointestinal symptom clusters in breast cancer impair quality of life and treatment adherence,yet lack effective interventions.While acupuncture mitigates isolated chemotherapy-induced symptoms,its mechanisms for multi-symptom clusters remain unclear.This study evaluates electroacupuncture's efficacy and explores its biological mechanisms in managing these clusters.Methods:This prospective,multicenter,block-randomized,double-blind,sham-controlled trial will enroll 388 patients with breast cancer undergoing neoadjuvant/adjuvant chemotherapy,to be randomly assigned(1:1)to electroacupuncture or sham electro-acupuncture groups.Both groups will receive the standard quadruple antiemetic regimen combined with electroacupuncture or sham intervention.The primary endpoint is the incidence of chemotherapy-induced gastrointestinal symptom clusters within 120 h after chemotherapy.Secondary endpoints include improvement in gastrointestinal symptom clusters post-first chemotherapy cycle,nausea-free rates during acute and delayed phases,vomiting-free rates during overall,acute,and delayed phases,complete response rate,complete protection rate,and quality of life.Adverse events will be documented throughout the study.Discussion:This study will assess the efficacy and safety of electroacupuncture in alleviating chemotherapy-induced gastro-intestinal symptom clusters in patients with breast cancer.By integrating multi-omics analyses,we aim to elucidate the biological mechanisms underlying its therapeutic effects.The findings may offer a robust clinical foundation for optimizing symptom cluster management in cancer care.Trial Registration:Clinical Trials ID:NCT06952920.Date of registration:April 16,2025.Prospectively registered.URL of Trial Registry Record:https://clinicaltrials.gov/study/NCT06952920cond=NCT06952920&rank=1.展开更多
Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic st...Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to p...Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.展开更多
An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the...An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.展开更多
Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as...Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.展开更多
[Objectives]To explore the spatiotemporal evolution and development drivers for Taobao villages in Cao County.[Methods]This paper employs GIS spatial analysis methods such as standard deviation ellipse and kernel dens...[Objectives]To explore the spatiotemporal evolution and development drivers for Taobao villages in Cao County.[Methods]This paper employs GIS spatial analysis methods such as standard deviation ellipse and kernel density to characterize the spatial distribution,agglomeration,and correlation features of Taobao villages in Cao County at the township scale.Additionally,geographic detectors are used to explore influencing factors.[Results](i)The number of Taobao villages in Cao County exhibits phased characteristics,progressing through initial,rapid expansion,and stable development stages.(ii)Taobao villages are unevenly distributed,with significant clustering features,primarily concentrated in the southeastern part of the county.In recent years,they have expanded toward the central and northwestern regions,potentially forming a"multi-core"spatial pattern in the future.(iii)The number of garment industries and e-commerce industrial parks consistently remains the primary factor influencing the spatial distribution of Taobao villages.After 2020,the number of wood processing industries and specialty agricultural product processing industries also exerted considerable influence.The number of logistics parks began to have an impact after 2018,albeit a weak one.Other factors had minimal influence on the differentiation of Taobao villages.[Conclusions]The spatiotemporal evolution exhibited diffusion along the"northwest-southeast"axis,with strengthened regional clustering.Government policies and industries significantly influenced the differentiation of Taobao villages,with varying primary factors across different periods.展开更多
Organic-inorganic hybrid clusters with strong X-ray radioluminescence have exhibited great potential in scintillator field.However,fabricating the X-ray imaging screens of the clusters without sacrificing the scintill...Organic-inorganic hybrid clusters with strong X-ray radioluminescence have exhibited great potential in scintillator field.However,fabricating the X-ray imaging screens of the clusters without sacrificing the scintillation performance is challenging.Herein,we report an effective way to prepare high-quality scintillation films of two synthesized Cu(Ⅰ)clusters through vacuum evaporation deposition.The developed Cu(Ⅰ)clusters with rigid molecular structures show excellent scintillation performance with a high light yield of up to 19356.7 photons/MeV and a low detection limit of 158 nGy/s.The scintillation film based on the Cu(Ⅰ)clusters made by vacuum evaporation deposition is highly uniform with a small surface roughness value of 1.04 nm,which can be applied to X-ray imaging for various objects.These results not only provide important guidance to develop high-performance organic-inorganic hybrid scintillators,but also pave a straightforward way to prepare non-doped scintillation screens for remarkable X-ray imaging applications.展开更多
In modern distributed systems and cloud computing architectures,high availability and high scalability are core requirements to ensure the continuous and stable operation of services.As key technologies for achieving ...In modern distributed systems and cloud computing architectures,high availability and high scalability are core requirements to ensure the continuous and stable operation of services.As key technologies for achieving these two goals,high-availability clusters and load-balancing clusters have significant differences in their design concepts and application scenarios,while also maintaining close connections.This paper aims to conduct an in-depth analysis of the core objectives,working principles,technical advantages and disadvantages,and typical application cases of high-availability clusters and load-balancing clusters.By introducing an analogical model of a“restaurant kitchen,”the differences between the two are intuitively explained,and their technical characteristics are compared in detail.Additionally,a detailed practical case is included to specifically demonstrate the collaborative work of high-availability and load-balancing technologies through the construction process of Keepalived and HAProxy.Finally,taking the architecture of a typical e-commerce website as an example,this paper demonstrates the best practice of organically combining the two cluster technologies in a production environment to build a robust and high-performance distributed system.Research shows that understanding the differences between the two and implementing collaborative deployment is the cornerstone of designing modern IT infrastructure.展开更多
Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integr...Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.展开更多
In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed p...In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.展开更多
Chiral metal-organic clusters(MOCs)integrating lanthanide ions(Ln^(3+))and organic luminophores present a promising platform for modulating circularly polarized luminescence(CPL).However,achieving dual-wavelength CPL ...Chiral metal-organic clusters(MOCs)integrating lanthanide ions(Ln^(3+))and organic luminophores present a promising platform for modulating circularly polarized luminescence(CPL).However,achieving dual-wavelength CPL in discrete cluster systems constitutes a considerable challenge.Herein,two enantiomeric pairs of heterometallic EuSn oxo clusters,designated as Sn_(2)EuL_(2)-R/S and Sn_(2)EuL_(4)-R/S,were strategically synthesized using axially chiral binaphthol-phosphonate ligands.These hybrid clusters exhibit dual emission,characterized by a broad ligand-derived fluorescence band superimposed with sharp,characteristic Eu^(3+)f-f transitions,which enables excitation-dependent luminescence color tuning.Their emission profiles and quantum yields are found to be exquisitely adjusted by the distinct coordination environments of Sn^(4+)centers.Notably,Sn_(2)EuL_(2)-R/S demonstrates CPL activity in both near-UV(|g_(lum)|=1.7×10^(-3))and visible(|g_(lum)|=3.1×10^(-2))regions.This work not only reports the first instance of dual-wavelength CPL in a lanthanide/tin oxo complex but also establishes a robust design strategy for fabricating color-tunable chiral photonic materials.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc...Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc-tion with the domain-based local pair natural orbital(DLPNO)method,has been applied to assess the average binding energies(ABEs)of large benzene clus-ters,specifically(C6H6)13,at the coupled cluster singles and doubles with perturbative triples correction[CCSD(T)]level and the complete basis set(CBS)limit.Utilizing GEBF-DLPNO-CCSD(T)/CBS ABEs as benchmarks,various DFT functionals were evaluated.It was found that several functionals with empirical dispersion correction,including M06-2X-D3,B3LYP-D3(BJ),and PBE-D3(BJ),provide accurate descriptions of the ABEs for(C6H6)13 clusters.Additionally,the M06-2X-D3 functional was used to calculate the ABEs and relative stabili-ties of(C6H6)n clusters for n=11,12,13,14,and 15 revealing that the(C6H6)13 cluster ex-hibits the highest relative stability.These findings align with experimental evidence suggest-ing that n=13 is one of the magic numbers for benzene clusters(C6H6)n,with n≤30.展开更多
Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaboratio...Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.展开更多
Objective This study aimed to investigate the prevalence of HIV pretreatment drug resistance(PDR)and the transmission clusters associated with PDR-related mutations in newly diagnosed,treatmentnaive patients between 2...Objective This study aimed to investigate the prevalence of HIV pretreatment drug resistance(PDR)and the transmission clusters associated with PDR-related mutations in newly diagnosed,treatmentnaive patients between 2020 and 2023 in Dehong prefecture,Yunnan province,China.Methods Demographic information and plasma samples were collected from study participants.PDR was assessed using the Stanford HIV Drug Resistance Database.The Tamura-Nei 93 model within HIVTRACE was employed to compute pairwise matches with a genetic distance of 0.015 substitutions per site.Results Among 948 treatment-naive individuals with eligible sequences,36 HIV subtypes were identified,with unique recombinant forms(URFs)being the most prevalent(18.8%,178/948).The overall prevalence of PDR was 12.4%(118/948),and resistance to non-nucleotide reverse transcriptase inhibitors(NNRTIs),nucleotide reverse transcriptase inhibitors(NRTIs),and protease inhibitors(PIs)was10.7%,1.3%,and 1.6%,respectively.A total of 91 clusters were identified,among which eight showed evidence of PDR strain transmission.The largest PDR-associated cluster consisted of six CRF01_AE drugresistant strains carrying K103N and V179T mutations;five of these individuals had initial CD4^(+)cell counts<200 cells/μL.Conclusion The distribution of HIV subtypes in Dehong is diverse and complex.PDR was moderately prevalent(12.4%)between 2020 and 2023.Evidence of transmission of CRF01_AE strains carrying K103N and V179T mutations was found.Routine surveillance of PDR and the strengthening of control measures are essential to limit the spread of drug-resistance HIV strains.展开更多
The first three months after lung transplantation are the clinical“vulnerable period”.Complications during this period often appear in the form of symptom clusters.The core includes primary graft dysfunction(PGD),in...The first three months after lung transplantation are the clinical“vulnerable period”.Complications during this period often appear in the form of symptom clusters.The core includes primary graft dysfunction(PGD),infection,inflammatory response and multiple organ dysfunction,and are interconnected to form a complex network.The symptom cluster shows a clear dynamic trajectory:the risk of PGD peaks at 24 hours after surgery,and its evolution trajectory(recovery,delay,deterioration)directly affects long-term graft function;infections show a“double peak distribution”,with bacteria/fungi dominant in the early stage(<1 month)and viruses/opportunistic infections dominant in the middle stage(1-6 months),and promote each other with PGD.Influencing factors include four dimensions:donor(smoking history,infection),recipient(weakness,immune status),perioperative period(surgical method,support strategy)and postoperative management(balance of immunosuppression).In the future,dynamic prediction models and individualized management paths need to be built to improve patient outcomes.展开更多
基金Supported by the Public Health Emergency Response Mechanism Operation Program of Chinese Center for Disease Control and Prevention(102393220020010000017).
文摘Introduction:Human brucellosis persists as a critical public health challenge in China.Understanding disease clusters and trends is essential for implementing effective control strategies.This study evaluates the epidemiological characteristics and spatiotemporal distribution of brucellosis in China from 2011 to 2023.Methods:Data were obtained from the National Notifiable Disease Reporting System(NNDRS).We conducted descriptive epidemiological analyses and employed SaTScan10.1 and ArcGIS10.7 software to identify disease clusters and generate county(district)-level incidence maps.Results:The incidence of human brucellosis in Chinese mainland increased substantially between 2011 and 2023,rising from 38,151 cases(2.8/100,000)across 834 counties(25.4%)to 70,439 cases(5.2/100,000)across 2,290 counties(76.9%).A significant upward trend in reported incidence emerged during 2018–2023(average annual percentage change(AAPC)=14.9%,P=0.01).Most cases(89.3%)occurred in individuals aged 25–69 years,with an increasing proportion among those aged over 60 years.While 96.1%of cases were reported in northern provincial-level administrative divisions(PLADs),southern regions demonstrated escalating incidence rates and expanding geographical spread.Southern PLADs exhibited a notable annual increase of 31.5%in reported incidence(P<0.01).Counties(districts)with incidence rates exceeding 10 per 100,000 expanded geographically from northwestern pastoral regions to southern areas and from rural to urban settings.Primary spatiotemporal clusters were concentrated in Inner Mongolia and adjacent provincial-level administrative divisions(PLADs),with emerging clusters identified in Yunnan,Guangdong,and Xizang.Conclusions:The human brucellosis epidemic in China continues to intensify,characterized by rebounding incidence rates and broader geographical distribution across counties(districts).While spatiotemporal clusters remain predominantly centered in Inner Mongolia and neighboring regions,targeted interventions and increased resource allocation for high-risk areas and populations are imperative.
基金Under the auspices of National Natural Science Foundation of China(No.42571219)Key Project of Zhejiang Province Soft Science Research Plan(No.2023C25014)。
文摘Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.
文摘Villager Pan Chunlin is witnessing a boom in his homestay business.More and more visitors are coming to his village,Yucun Village in Anji County,Huzhou City,Zhejiang Province.
基金Noncommunicable Chronic Diseases-National Science and Technology Major Project,Grant/Award Numbers:2024ZD0521400,2024ZD0521404Affiliated Hospital of Qinghai University。
文摘Introduction:Chemotherapy-induced gastrointestinal symptom clusters in breast cancer impair quality of life and treatment adherence,yet lack effective interventions.While acupuncture mitigates isolated chemotherapy-induced symptoms,its mechanisms for multi-symptom clusters remain unclear.This study evaluates electroacupuncture's efficacy and explores its biological mechanisms in managing these clusters.Methods:This prospective,multicenter,block-randomized,double-blind,sham-controlled trial will enroll 388 patients with breast cancer undergoing neoadjuvant/adjuvant chemotherapy,to be randomly assigned(1:1)to electroacupuncture or sham electro-acupuncture groups.Both groups will receive the standard quadruple antiemetic regimen combined with electroacupuncture or sham intervention.The primary endpoint is the incidence of chemotherapy-induced gastrointestinal symptom clusters within 120 h after chemotherapy.Secondary endpoints include improvement in gastrointestinal symptom clusters post-first chemotherapy cycle,nausea-free rates during acute and delayed phases,vomiting-free rates during overall,acute,and delayed phases,complete response rate,complete protection rate,and quality of life.Adverse events will be documented throughout the study.Discussion:This study will assess the efficacy and safety of electroacupuncture in alleviating chemotherapy-induced gastro-intestinal symptom clusters in patients with breast cancer.By integrating multi-omics analyses,we aim to elucidate the biological mechanisms underlying its therapeutic effects.The findings may offer a robust clinical foundation for optimizing symptom cluster management in cancer care.Trial Registration:Clinical Trials ID:NCT06952920.Date of registration:April 16,2025.Prospectively registered.URL of Trial Registry Record:https://clinicaltrials.gov/study/NCT06952920cond=NCT06952920&rank=1.
基金Under the auspices of National Natural Science Foundation of China(No.41771537)Fundamental Research Funds for the Central Universities
文摘Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
基金supported by the Specific Research Fund for Top-notch Talents of Guangdong Provincial Hospital of Chinese Medicine(No.2022KT1188).
文摘Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.
文摘An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.
文摘Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.
基金Supported by National Natural Science Foundation of China(42171206&42071159).
文摘[Objectives]To explore the spatiotemporal evolution and development drivers for Taobao villages in Cao County.[Methods]This paper employs GIS spatial analysis methods such as standard deviation ellipse and kernel density to characterize the spatial distribution,agglomeration,and correlation features of Taobao villages in Cao County at the township scale.Additionally,geographic detectors are used to explore influencing factors.[Results](i)The number of Taobao villages in Cao County exhibits phased characteristics,progressing through initial,rapid expansion,and stable development stages.(ii)Taobao villages are unevenly distributed,with significant clustering features,primarily concentrated in the southeastern part of the county.In recent years,they have expanded toward the central and northwestern regions,potentially forming a"multi-core"spatial pattern in the future.(iii)The number of garment industries and e-commerce industrial parks consistently remains the primary factor influencing the spatial distribution of Taobao villages.After 2020,the number of wood processing industries and specialty agricultural product processing industries also exerted considerable influence.The number of logistics parks began to have an impact after 2018,albeit a weak one.Other factors had minimal influence on the differentiation of Taobao villages.[Conclusions]The spatiotemporal evolution exhibited diffusion along the"northwest-southeast"axis,with strengthened regional clustering.Government policies and industries significantly influenced the differentiation of Taobao villages,with varying primary factors across different periods.
文摘Organic-inorganic hybrid clusters with strong X-ray radioluminescence have exhibited great potential in scintillator field.However,fabricating the X-ray imaging screens of the clusters without sacrificing the scintillation performance is challenging.Herein,we report an effective way to prepare high-quality scintillation films of two synthesized Cu(Ⅰ)clusters through vacuum evaporation deposition.The developed Cu(Ⅰ)clusters with rigid molecular structures show excellent scintillation performance with a high light yield of up to 19356.7 photons/MeV and a low detection limit of 158 nGy/s.The scintillation film based on the Cu(Ⅰ)clusters made by vacuum evaporation deposition is highly uniform with a small surface roughness value of 1.04 nm,which can be applied to X-ray imaging for various objects.These results not only provide important guidance to develop high-performance organic-inorganic hybrid scintillators,but also pave a straightforward way to prepare non-doped scintillation screens for remarkable X-ray imaging applications.
文摘In modern distributed systems and cloud computing architectures,high availability and high scalability are core requirements to ensure the continuous and stable operation of services.As key technologies for achieving these two goals,high-availability clusters and load-balancing clusters have significant differences in their design concepts and application scenarios,while also maintaining close connections.This paper aims to conduct an in-depth analysis of the core objectives,working principles,technical advantages and disadvantages,and typical application cases of high-availability clusters and load-balancing clusters.By introducing an analogical model of a“restaurant kitchen,”the differences between the two are intuitively explained,and their technical characteristics are compared in detail.Additionally,a detailed practical case is included to specifically demonstrate the collaborative work of high-availability and load-balancing technologies through the construction process of Keepalived and HAProxy.Finally,taking the architecture of a typical e-commerce website as an example,this paper demonstrates the best practice of organically combining the two cluster technologies in a production environment to build a robust and high-performance distributed system.Research shows that understanding the differences between the two and implementing collaborative deployment is the cornerstone of designing modern IT infrastructure.
基金supported by the National Natural Science Foundation of China(Grant No.72571150)Beijing Natural Science Foundation(Grant No.9182015)。
文摘Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.
基金supported by the National Office for Philosophy and Social Sciences(grant reference 22&ZD067).
文摘In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.
基金financially supported by the National Natural Science Foundation of China(22471268)the National Key Research and Development Project(2022YFA1503900)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB1170000)the Natural Science Foundation of Fujian Province(2022J05090,2024T3003)the Self-deployment Project Research Program of Haixi Institutes,Chinese Academy of Sciences(CXZX-2022-GH03,CXZX-2024-JQ08).
文摘Chiral metal-organic clusters(MOCs)integrating lanthanide ions(Ln^(3+))and organic luminophores present a promising platform for modulating circularly polarized luminescence(CPL).However,achieving dual-wavelength CPL in discrete cluster systems constitutes a considerable challenge.Herein,two enantiomeric pairs of heterometallic EuSn oxo clusters,designated as Sn_(2)EuL_(2)-R/S and Sn_(2)EuL_(4)-R/S,were strategically synthesized using axially chiral binaphthol-phosphonate ligands.These hybrid clusters exhibit dual emission,characterized by a broad ligand-derived fluorescence band superimposed with sharp,characteristic Eu^(3+)f-f transitions,which enables excitation-dependent luminescence color tuning.Their emission profiles and quantum yields are found to be exquisitely adjusted by the distinct coordination environments of Sn^(4+)centers.Notably,Sn_(2)EuL_(2)-R/S demonstrates CPL activity in both near-UV(|g_(lum)|=1.7×10^(-3))and visible(|g_(lum)|=3.1×10^(-2))regions.This work not only reports the first instance of dual-wavelength CPL in a lanthanide/tin oxo complex but also establishes a robust design strategy for fabricating color-tunable chiral photonic materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金supported by the National Key R&D Program of China(No.2023YFB3712504)the National Natural Science Foundation of China(Nos.22273038,22073043,and 22033004)。
文摘Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc-tion with the domain-based local pair natural orbital(DLPNO)method,has been applied to assess the average binding energies(ABEs)of large benzene clus-ters,specifically(C6H6)13,at the coupled cluster singles and doubles with perturbative triples correction[CCSD(T)]level and the complete basis set(CBS)limit.Utilizing GEBF-DLPNO-CCSD(T)/CBS ABEs as benchmarks,various DFT functionals were evaluated.It was found that several functionals with empirical dispersion correction,including M06-2X-D3,B3LYP-D3(BJ),and PBE-D3(BJ),provide accurate descriptions of the ABEs for(C6H6)13 clusters.Additionally,the M06-2X-D3 functional was used to calculate the ABEs and relative stabili-ties of(C6H6)n clusters for n=11,12,13,14,and 15 revealing that the(C6H6)13 cluster ex-hibits the highest relative stability.These findings align with experimental evidence suggest-ing that n=13 is one of the magic numbers for benzene clusters(C6H6)n,with n≤30.
基金supported by the Beijing Natural Science Foundation(Certificate Number:L234025).
文摘Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.
基金supported by the National Key Research and Development Program of China(2022YFC2305201)National Natural Science Foundation of China(71874168)。
文摘Objective This study aimed to investigate the prevalence of HIV pretreatment drug resistance(PDR)and the transmission clusters associated with PDR-related mutations in newly diagnosed,treatmentnaive patients between 2020 and 2023 in Dehong prefecture,Yunnan province,China.Methods Demographic information and plasma samples were collected from study participants.PDR was assessed using the Stanford HIV Drug Resistance Database.The Tamura-Nei 93 model within HIVTRACE was employed to compute pairwise matches with a genetic distance of 0.015 substitutions per site.Results Among 948 treatment-naive individuals with eligible sequences,36 HIV subtypes were identified,with unique recombinant forms(URFs)being the most prevalent(18.8%,178/948).The overall prevalence of PDR was 12.4%(118/948),and resistance to non-nucleotide reverse transcriptase inhibitors(NNRTIs),nucleotide reverse transcriptase inhibitors(NRTIs),and protease inhibitors(PIs)was10.7%,1.3%,and 1.6%,respectively.A total of 91 clusters were identified,among which eight showed evidence of PDR strain transmission.The largest PDR-associated cluster consisted of six CRF01_AE drugresistant strains carrying K103N and V179T mutations;five of these individuals had initial CD4^(+)cell counts<200 cells/μL.Conclusion The distribution of HIV subtypes in Dehong is diverse and complex.PDR was moderately prevalent(12.4%)between 2020 and 2023.Evidence of transmission of CRF01_AE strains carrying K103N and V179T mutations was found.Routine surveillance of PDR and the strengthening of control measures are essential to limit the spread of drug-resistance HIV strains.
文摘The first three months after lung transplantation are the clinical“vulnerable period”.Complications during this period often appear in the form of symptom clusters.The core includes primary graft dysfunction(PGD),infection,inflammatory response and multiple organ dysfunction,and are interconnected to form a complex network.The symptom cluster shows a clear dynamic trajectory:the risk of PGD peaks at 24 hours after surgery,and its evolution trajectory(recovery,delay,deterioration)directly affects long-term graft function;infections show a“double peak distribution”,with bacteria/fungi dominant in the early stage(<1 month)and viruses/opportunistic infections dominant in the middle stage(1-6 months),and promote each other with PGD.Influencing factors include four dimensions:donor(smoking history,infection),recipient(weakness,immune status),perioperative period(surgical method,support strategy)and postoperative management(balance of immunosuppression).In the future,dynamic prediction models and individualized management paths need to be built to improve patient outcomes.
