The complex current systems of the Southern Ocean play a critical role in shaping the heterogeneity and distinctiveness of Antarctic habitats.Nonetheless,how Antarctic water masses influence ciliates,one of the most c...The complex current systems of the Southern Ocean play a critical role in shaping the heterogeneity and distinctiveness of Antarctic habitats.Nonetheless,how Antarctic water masses influence ciliates,one of the most common groups of protozoa in polar regions,remains largely unknown.The present study investigated how the ciliate communities are affected by com-plex Southern Ocean currents by analyzing the diversity distributions,community assembly mechanisms,and co-occurrence networks of ciliates across three distinct water masses in the Scotia Sea.The findings reveal that the hydrography of the Scotia Sea significantly affects the spatial patterns of planktonic ciliates,primarily through the combination of temperature,salinity,and depth.In contract to surface waters(Antarctic Surface Water and Antarctic Circumpolar Current),ciliates inhab-iting deep waters(Circumpolar Deep Water)exhibit stronger and more direct correlations with the environment parameters,alongside greater network stability.Community assembly in surface and deep-water masses is governed by stochastic and deterministic processes,respectively.Compared to other Antarctic regions documented in previous studies,the Scotia Sea demonstrated the lowest alpha diversity indices for ciliates while harboring the highest number of endemic species.A detailed re-evaluation of Antarctic ciliate community structure in the Antarctic from prior research offers valuable insights into how dynamic ocean currents shape the ecological dynamics of ciliate communities,thus providing a broader understanding of the environmental changes impacting polar marine ecosystems.展开更多
Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,for...Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,forest land and fallow land were investigated in six regions of northern China.Generic richness,diversity,abundance and biomass of soil nematodes was the lowest in crop land.The richness and diversity of soil nematodes were 28.8and 15.1%higher in fallow land than in crop land,respectively.No significant differences in soil nematode indices were found between forest land and fallow land,but their network keystone genera composition was different.Among the keystone genera,50%of forest land genera were omnivores-predators and 36%of fallow land genera were bacterivores.The proportion of fungivores in forest land was 20.8%lower than in fallow land.The network complexity and the stability were lower in crop land than forest land and fallow land.Soil pH,NH_(4)^(+)-N and NO_(3)^(–)-N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land.Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land.Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.展开更多
Objective:This study investigated trends in the study of phytochemical treatment of post-traumatic stress disorder(PTSD).Methods:The Web of Science database(2007-2022)was searched using the search terms“phytochemical...Objective:This study investigated trends in the study of phytochemical treatment of post-traumatic stress disorder(PTSD).Methods:The Web of Science database(2007-2022)was searched using the search terms“phytochemicals”and“PTSD,”and relevant literature was compiled.Network clustering co-occurrence analysis and qualitative narrative review were conducted.Results:Three hundred and one articles were included in the analysis of published research,which has surged since 2015 with nearly half of all relevant articles coming from North America.The category is dominated by neuroscience and neurology,with two journals,Addictive Behaviors and Drug and Alcohol Dependence,publishing the greatest number of papers on these topics.Most studies focused on psychedelic intervention for PTSD.Three timelines show an“ebb and flow”phenomenon between“substance use/marijuana abuse”and“psychedelic medicine/medicinal cannabis.”Other phytochemicals account for a small proportion of the research and focus on topics like neurosteroid turnover,serotonin levels,and brain-derived neurotrophic factor expression.Conclusion:Research on phytochemicals and PTSD is unevenly distributed across countries/regions,disciplines,and journals.Since 2015,the research paradigm shifted to constitute the mainstream of psychedelic research thus far,leading to the exploration of botanical active ingredients and molecular mechanisms.Other studies focus on anti-oxidative stress and anti-inflammation.展开更多
The elevational distributions of bacterial communities in natural mountain forests,especially along large elevational gradients,have been studied for many years.However,the distributional patterns that underlie variat...The elevational distributions of bacterial communities in natural mountain forests,especially along large elevational gradients,have been studied for many years.However,the distributional patterns that underlie variations in soil bacterial communities along small-scale elevational gradients in urban ecosystems are not yet well understood.Using Illumina MiSeq DNA sequencing,we surveyed soil bacterial communities at three elevations on Zijin Mountain in Nanjing City:the hilltop(300 m a.s.l.),the hillside(150 m a.s.l.),and the foot of the hill(0 m a.s.l.).The results showed that edaphic properties differed significantly with elevation.Bacterial community composition,rather than alpha diversity,strongly differed among the three elevations(Adonis:R2=0.12,P<0.01).Adonis and DistLM analyses demonstrated that bacterial community composition was highly correlated with soil pH,elevation,total nitrogen(TN),and dissolved organic carbon(DOC).The degree scores,betweenness centralities,and composition of keystone species were distinct among the elevations.These results demonstrate strong elevational partitioning in the distributions of soil bacterial communities along the gradient on Zijin Mountain.Soil pH and elevation together drove the smallscale elevational distribution of soil bacterial communities.This study broadens our understanding of distribution patterns and biotic co-occurrence associations of soil bacterial communities from large elevational gradients to short elevational gradients.展开更多
We identified a sporocarp as Agrocybe dura growing next to a living corn using PacBio sequencing.The mycoparasitism of Trichoderma spp.on A.dura were revealed by the co-occurrence network analysis.For long-read HTS da...We identified a sporocarp as Agrocybe dura growing next to a living corn using PacBio sequencing.The mycoparasitism of Trichoderma spp.on A.dura were revealed by the co-occurrence network analysis.For long-read HTS data,we updated a bioinformatic pipeline to enhance fungal taxonomic resolution.In forests,fungal sporocarps house the diverse fungicolous fungi;however,the relationships of sporocarps and associated fungal communities are rarely explored in agroecosystems.In a corn field near Gongzhuling City,Jilin Province,China,we found an epigeous sporocarp with agaricoid morphology that could grow next to the living corn plants.Using PacBio metabarcoding combined with an updated bioinformatic pipeline,we surveyed the fungal community profile along its cap,rhizomorph and hyphosphere soil at a much-improved taxonomic resolution.We identified the sporocarp,at a high probability,as Agrocybe dura,and this mushroom was significantly negatively correlated with Trichoderma hamatum and T.harzianum in the co-occurrence network.Fungal diversity in hyphosphere habitat was significantly higher than that in cap and rhizomorph habitats.Consistent with the pattern in fungal diversity,the node number,edge number,network diameter and average degree were significantly higher in hyphosphere habitat than other habitats.However,both the negative and positive cohesion were significantly higher in rhizomorph habitat than other habitats.Moreover,the z-c plot identified A.dura as the only network hub,linking multiple fungal species.The results give us a glimpse of the ecological relevance of saprobic mushrooms across the extensive northeastern black soil region of China.Our findings will aid in the assessment and forecasting of fungal diversity hotspots and their relationships with soil fertility in the‘Golden Corn Belt’of northeast China.展开更多
Understanding the fundamental drivers of large-scale species co-occurrence is a critical issue in ecology and conservation research. Here, we assessed foraging guilds, habitat type and disturbances as drivers of bird ...Understanding the fundamental drivers of large-scale species co-occurrence is a critical issue in ecology and conservation research. Here, we assessed foraging guilds, habitat type and disturbances as drivers of bird species co-occurrence in Ghana's Central Region over six months. Birds were sampled in 120 points across six different habitat types (farmland, forest reserve, urban area, coastal savannah, wetland, and mangrove), using the point-centred count technique. In total, 4060 individuals belonging to 216 species were recorded across all six habitat types. We found that co-occurring species were more similar in their foraging behaviour and habitat association. About 60% of the birds were found to co-occur randomly, 15% co-occurred negatively, and 25% co-occurred positively. Carnivores like the Black Heron (Egretta ardesiaca) and Spur-winged Lapwing (Vanellus spinosus) randomly co-occurred with other guild groups and were dominant in the mangroves and wetlands. Frugivores from forest reserves had only a 25% chance of randomly co-occurring with other birds and about a 60% chance of positively co-occurring with other birds. Our findings suggest that foraging guilds and habitat type are major factors driving bird co-occurrence and community assemblages in this West African suburban region.展开更多
Classifying the texture of granules in 2D images has aroused manifold research atten-tion for its technical challenges in image processing areas.This letter presents an aggregate texture identification approach by joi...Classifying the texture of granules in 2D images has aroused manifold research atten-tion for its technical challenges in image processing areas.This letter presents an aggregate texture identification approach by jointly using Gray Level Co-occurrence Probability(GLCP) and BP neural network techniques.First, up to 8 GLCP-associated texture feature parameters are defined and computed, and these consequent parameters next serve as the inputs feeding to the BP neural network to calculate the similarity to any of given aggregate texture type.A finite number of aggregate images of 3 kinds, with each containing specific type of mineral particles, are put to the identification test, experimentally proving the feasibility and robustness of the proposed method.展开更多
Background Bacteria,Archaea,and Microeukaryotes comprise taxonomic domains that interact in mediating biogeochemical cycles in coastal waters.Many studies have revealed contrasting biogeographic patterns of community ...Background Bacteria,Archaea,and Microeukaryotes comprise taxonomic domains that interact in mediating biogeochemical cycles in coastal waters.Many studies have revealed contrasting biogeographic patterns of community structure and assembly mechanisms in microbial communities from diferent domains in coastal ecosystems;however,knowledge of specifc biogeographic patterns on microbial co-occurrence relationships across complex coastal environmental gradients remains limited.Using a dense sampling scheme at the regional scale,SSU rRNA gene amplicon sequencing,and network analysis,we investigated intra-and inter-domain co-occurrence relationships and network topology-based biogeographic patterns from three microbial domains in coastal waters that show environmental gradients across the inshore-nearshore-ofshore continuum in the East China Sea.Results Overall,we found the highest complexity and connectivity in the bacterial network,the highest modularity in the archaeal network,and the lowest complexity,connectivity,and modularity in the microeukaryotic network.Although microbial co-occurrence networks from the three domains showed distinct topological features,they exhibited a consistent biogeographic pattern across the inshore-nearshore-ofshore continuum.Specifcally,the nearshore zones with intermediate levels of terrestrial impacts refected by multiple environmental factors(including water temperature,salinity,pH,dissolved oxygen,and nutrient-related parameters)had a higher intensity of microbial co-occurrence for all three domains.In contrast,the intensity of microbial co-occurrence was weaker in both the inshore and the ofshore zones at the two ends of the environmental gradients.Archaea occupied a central position in the microbial inter-domain co-occurrence network.In particular,members of the Thaumarchaeota Marine Group I(MGI,now placed within the Family Nitrosopumilaceae of the Phylum Thermoproteota)appeared to be the hubs in the biogeographic shift between inter-domain network modules across environmental gradients.Conclusions Our work ofers new insights into microbial biogeography by integrating network features into biogeographic patterns,towards a better understanding of the potential of microbial interactions in shaping biogeographic patterns of coastal marine microbiota.展开更多
Impact statement Habitat loss has been a primary threat to biodiversity.However,species do not function in isolation but often associate with each other and form complex networks.Thus,revealing how the network complex...Impact statement Habitat loss has been a primary threat to biodiversity.However,species do not function in isolation but often associate with each other and form complex networks.Thus,revealing how the network complexity and stability scale with habitat area will give us more insights into the effects of habitat loss on ecosystems.In this study,we explored the relationships between the island area and the network complexity and stability of soil microbes.We found that the complexity and stability of soil microbial co‐occurrence networks scale positively with island area,indicating that habitat loss will potentially simplify and destabilize soil microbial networks.展开更多
Microeukaryotes and bacteria are key drivers of primary productivity and nutrient cycling in aquaculture ecosystems.Although their diversity and composition have been widely investigated in aquaculture systems,the co-...Microeukaryotes and bacteria are key drivers of primary productivity and nutrient cycling in aquaculture ecosystems.Although their diversity and composition have been widely investigated in aquaculture systems,the co-occurrence bipartite network between microeukaryotes and bacteria remains poorly understood.This study used the bipartite network analysis of high-throughput sequencing datasets to detect the co-occurrence relationships between microeukaryotes and bacteria in water and sediment from coastal aquaculture ponds.Chlorophyta and fungi were dominant phyla in the microeukaryotic–bacterial bipartite networks in water and sediment,respectively.Chlorophyta also had overrepresented links with bacteria in water.Most microeukaryotes and bacteria were classified as generalists,and tended to have symmetric positive and negative links with bacteria in both water and sediment.However,some microeukaryotes with high density of links showed asymmetric links with bacteria in water.Modularity detection in the bipartite network indicated that four microeukaryotes and twelve uncultured bacteria might be potential keystone taxa among the module connections.Moreover,the microeukaryotic–bacterial bipartite network in sediment harbored significantly more nestedness than that in water.The loss of microeukaryotes and generalists will more likely lead to the collapse of positive co-occurrence relationships between microeukaryotes and bacteria in both water and sediment.This study unveils the topology,dominant taxa,keystone species,and robustness in the microeukaryotic–bacterial bipartite networks in coastal aquaculture ecosystems.These species herein can be applied for further management of ecological services,and such knowledge may also be very useful for the regulation of other eutrophic ecosystems.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY o...Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.展开更多
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ...Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.展开更多
With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion det...With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.展开更多
The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.H...The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.展开更多
The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio...The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
基金supported by the Science and Technology Innovation Project of Laoshan Laboratory(LSKJ202203205)National Natural Science Foundation of China(NFSC)(Nos.42276156,42206147,32100404)Natural Science Foundation of Shandong Province of China(No.ZR2021QC045).
文摘The complex current systems of the Southern Ocean play a critical role in shaping the heterogeneity and distinctiveness of Antarctic habitats.Nonetheless,how Antarctic water masses influence ciliates,one of the most common groups of protozoa in polar regions,remains largely unknown.The present study investigated how the ciliate communities are affected by com-plex Southern Ocean currents by analyzing the diversity distributions,community assembly mechanisms,and co-occurrence networks of ciliates across three distinct water masses in the Scotia Sea.The findings reveal that the hydrography of the Scotia Sea significantly affects the spatial patterns of planktonic ciliates,primarily through the combination of temperature,salinity,and depth.In contract to surface waters(Antarctic Surface Water and Antarctic Circumpolar Current),ciliates inhab-iting deep waters(Circumpolar Deep Water)exhibit stronger and more direct correlations with the environment parameters,alongside greater network stability.Community assembly in surface and deep-water masses is governed by stochastic and deterministic processes,respectively.Compared to other Antarctic regions documented in previous studies,the Scotia Sea demonstrated the lowest alpha diversity indices for ciliates while harboring the highest number of endemic species.A detailed re-evaluation of Antarctic ciliate community structure in the Antarctic from prior research offers valuable insights into how dynamic ocean currents shape the ecological dynamics of ciliate communities,thus providing a broader understanding of the environmental changes impacting polar marine ecosystems.
基金supported by the National Natural Science Foundation of China(U22A20501)the National Key Research and Development Plan of China(2022YFD1500601)+4 种基金the National Science and Technology Fundamental Resources Investigation Program of China(2018FY100304)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28090200)the Liaoning Province Applied Basic Research Plan Program,China(2022JH2/101300184)the Shenyang Science and Technology Plan Program,China(21-109-305)the Liaoning Outstanding Innovation Team,China(XLYC2008015)。
文摘Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,forest land and fallow land were investigated in six regions of northern China.Generic richness,diversity,abundance and biomass of soil nematodes was the lowest in crop land.The richness and diversity of soil nematodes were 28.8and 15.1%higher in fallow land than in crop land,respectively.No significant differences in soil nematode indices were found between forest land and fallow land,but their network keystone genera composition was different.Among the keystone genera,50%of forest land genera were omnivores-predators and 36%of fallow land genera were bacterivores.The proportion of fungivores in forest land was 20.8%lower than in fallow land.The network complexity and the stability were lower in crop land than forest land and fallow land.Soil pH,NH_(4)^(+)-N and NO_(3)^(–)-N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land.Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land.Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.
基金the National Natural Science Foundation of China(No.81573150)Military Key Discipline Construction Projects of China(No.HL21JD1206).
文摘Objective:This study investigated trends in the study of phytochemical treatment of post-traumatic stress disorder(PTSD).Methods:The Web of Science database(2007-2022)was searched using the search terms“phytochemicals”and“PTSD,”and relevant literature was compiled.Network clustering co-occurrence analysis and qualitative narrative review were conducted.Results:Three hundred and one articles were included in the analysis of published research,which has surged since 2015 with nearly half of all relevant articles coming from North America.The category is dominated by neuroscience and neurology,with two journals,Addictive Behaviors and Drug and Alcohol Dependence,publishing the greatest number of papers on these topics.Most studies focused on psychedelic intervention for PTSD.Three timelines show an“ebb and flow”phenomenon between“substance use/marijuana abuse”and“psychedelic medicine/medicinal cannabis.”Other phytochemicals account for a small proportion of the research and focus on topics like neurosteroid turnover,serotonin levels,and brain-derived neurotrophic factor expression.Conclusion:Research on phytochemicals and PTSD is unevenly distributed across countries/regions,disciplines,and journals.Since 2015,the research paradigm shifted to constitute the mainstream of psychedelic research thus far,leading to the exploration of botanical active ingredients and molecular mechanisms.Other studies focus on anti-oxidative stress and anti-inflammation.
基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB15010101)the National Natural Science Foundation of China(41907039)the China Biodiversity Observation Networks(Sino BON).
文摘The elevational distributions of bacterial communities in natural mountain forests,especially along large elevational gradients,have been studied for many years.However,the distributional patterns that underlie variations in soil bacterial communities along small-scale elevational gradients in urban ecosystems are not yet well understood.Using Illumina MiSeq DNA sequencing,we surveyed soil bacterial communities at three elevations on Zijin Mountain in Nanjing City:the hilltop(300 m a.s.l.),the hillside(150 m a.s.l.),and the foot of the hill(0 m a.s.l.).The results showed that edaphic properties differed significantly with elevation.Bacterial community composition,rather than alpha diversity,strongly differed among the three elevations(Adonis:R2=0.12,P<0.01).Adonis and DistLM analyses demonstrated that bacterial community composition was highly correlated with soil pH,elevation,total nitrogen(TN),and dissolved organic carbon(DOC).The degree scores,betweenness centralities,and composition of keystone species were distinct among the elevations.These results demonstrate strong elevational partitioning in the distributions of soil bacterial communities along the gradient on Zijin Mountain.Soil pH and elevation together drove the smallscale elevational distribution of soil bacterial communities.This study broadens our understanding of distribution patterns and biotic co-occurrence associations of soil bacterial communities from large elevational gradients to short elevational gradients.
基金supported by the National Program on Key Basic Research Project(Grant No.2022YFD1500202)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA28020202)the National Natural Science Foundation of China(Grant No.42277308).
文摘We identified a sporocarp as Agrocybe dura growing next to a living corn using PacBio sequencing.The mycoparasitism of Trichoderma spp.on A.dura were revealed by the co-occurrence network analysis.For long-read HTS data,we updated a bioinformatic pipeline to enhance fungal taxonomic resolution.In forests,fungal sporocarps house the diverse fungicolous fungi;however,the relationships of sporocarps and associated fungal communities are rarely explored in agroecosystems.In a corn field near Gongzhuling City,Jilin Province,China,we found an epigeous sporocarp with agaricoid morphology that could grow next to the living corn plants.Using PacBio metabarcoding combined with an updated bioinformatic pipeline,we surveyed the fungal community profile along its cap,rhizomorph and hyphosphere soil at a much-improved taxonomic resolution.We identified the sporocarp,at a high probability,as Agrocybe dura,and this mushroom was significantly negatively correlated with Trichoderma hamatum and T.harzianum in the co-occurrence network.Fungal diversity in hyphosphere habitat was significantly higher than that in cap and rhizomorph habitats.Consistent with the pattern in fungal diversity,the node number,edge number,network diameter and average degree were significantly higher in hyphosphere habitat than other habitats.However,both the negative and positive cohesion were significantly higher in rhizomorph habitat than other habitats.Moreover,the z-c plot identified A.dura as the only network hub,linking multiple fungal species.The results give us a glimpse of the ecological relevance of saprobic mushrooms across the extensive northeastern black soil region of China.Our findings will aid in the assessment and forecasting of fungal diversity hotspots and their relationships with soil fertility in the‘Golden Corn Belt’of northeast China.
文摘Understanding the fundamental drivers of large-scale species co-occurrence is a critical issue in ecology and conservation research. Here, we assessed foraging guilds, habitat type and disturbances as drivers of bird species co-occurrence in Ghana's Central Region over six months. Birds were sampled in 120 points across six different habitat types (farmland, forest reserve, urban area, coastal savannah, wetland, and mangrove), using the point-centred count technique. In total, 4060 individuals belonging to 216 species were recorded across all six habitat types. We found that co-occurring species were more similar in their foraging behaviour and habitat association. About 60% of the birds were found to co-occur randomly, 15% co-occurred negatively, and 25% co-occurred positively. Carnivores like the Black Heron (Egretta ardesiaca) and Spur-winged Lapwing (Vanellus spinosus) randomly co-occurred with other guild groups and were dominant in the mangroves and wetlands. Frugivores from forest reserves had only a 25% chance of randomly co-occurring with other birds and about a 60% chance of positively co-occurring with other birds. Our findings suggest that foraging guilds and habitat type are major factors driving bird co-occurrence and community assemblages in this West African suburban region.
基金Funded by Ningbo Natural Science Foundation (No.2006A610016)
文摘Classifying the texture of granules in 2D images has aroused manifold research atten-tion for its technical challenges in image processing areas.This letter presents an aggregate texture identification approach by jointly using Gray Level Co-occurrence Probability(GLCP) and BP neural network techniques.First, up to 8 GLCP-associated texture feature parameters are defined and computed, and these consequent parameters next serve as the inputs feeding to the BP neural network to calculate the similarity to any of given aggregate texture type.A finite number of aggregate images of 3 kinds, with each containing specific type of mineral particles, are put to the identification test, experimentally proving the feasibility and robustness of the proposed method.
基金supported by the National Natural Science Foundation of China(41977192)Zhejiang Provincial Natural Science Foundation of China(LY21D060004)+2 种基金Natural Science Foundation of Ningbo(2021J060 and 2019A610449)Fundamental Research Funds for the Provincial Universities of Zhejiang(SJLY2022001)K.C.Wong Magna Fund in Ningbo University
文摘Background Bacteria,Archaea,and Microeukaryotes comprise taxonomic domains that interact in mediating biogeochemical cycles in coastal waters.Many studies have revealed contrasting biogeographic patterns of community structure and assembly mechanisms in microbial communities from diferent domains in coastal ecosystems;however,knowledge of specifc biogeographic patterns on microbial co-occurrence relationships across complex coastal environmental gradients remains limited.Using a dense sampling scheme at the regional scale,SSU rRNA gene amplicon sequencing,and network analysis,we investigated intra-and inter-domain co-occurrence relationships and network topology-based biogeographic patterns from three microbial domains in coastal waters that show environmental gradients across the inshore-nearshore-ofshore continuum in the East China Sea.Results Overall,we found the highest complexity and connectivity in the bacterial network,the highest modularity in the archaeal network,and the lowest complexity,connectivity,and modularity in the microeukaryotic network.Although microbial co-occurrence networks from the three domains showed distinct topological features,they exhibited a consistent biogeographic pattern across the inshore-nearshore-ofshore continuum.Specifcally,the nearshore zones with intermediate levels of terrestrial impacts refected by multiple environmental factors(including water temperature,salinity,pH,dissolved oxygen,and nutrient-related parameters)had a higher intensity of microbial co-occurrence for all three domains.In contrast,the intensity of microbial co-occurrence was weaker in both the inshore and the ofshore zones at the two ends of the environmental gradients.Archaea occupied a central position in the microbial inter-domain co-occurrence network.In particular,members of the Thaumarchaeota Marine Group I(MGI,now placed within the Family Nitrosopumilaceae of the Phylum Thermoproteota)appeared to be the hubs in the biogeographic shift between inter-domain network modules across environmental gradients.Conclusions Our work ofers new insights into microbial biogeography by integrating network features into biogeographic patterns,towards a better understanding of the potential of microbial interactions in shaping biogeographic patterns of coastal marine microbiota.
基金This research was supported by the National Natural Science Foundation of China(31971553,32222051,and 31361123001)the National Science Foundation of the United States of America(DEB-1342754 and DEB-1856318)+1 种基金the Shanghai Rising-Star ProgramP.W.was supported by the research fund of the post-doctor who came to Shenzhen(szbo202306).
文摘Impact statement Habitat loss has been a primary threat to biodiversity.However,species do not function in isolation but often associate with each other and form complex networks.Thus,revealing how the network complexity and stability scale with habitat area will give us more insights into the effects of habitat loss on ecosystems.In this study,we explored the relationships between the island area and the network complexity and stability of soil microbes.We found that the complexity and stability of soil microbial co‐occurrence networks scale positively with island area,indicating that habitat loss will potentially simplify and destabilize soil microbial networks.
基金This study was supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2021SP203,313022004)the National Natural Science Foundation of China(32102821,92051120)+4 种基金the Yongjiang Talent Introduction Programme,the Natural Science Foundation of Ningbo(2022J050)the Zhejiang Major Program of Science and Technology(2021C02069-5-4)the Key Research and Development Program of Zhejiang Province(2019C02054)the Key Research and Development Program of Ningbo(2022Z172)China Agriculture Research System of MOF and MARA.
文摘Microeukaryotes and bacteria are key drivers of primary productivity and nutrient cycling in aquaculture ecosystems.Although their diversity and composition have been widely investigated in aquaculture systems,the co-occurrence bipartite network between microeukaryotes and bacteria remains poorly understood.This study used the bipartite network analysis of high-throughput sequencing datasets to detect the co-occurrence relationships between microeukaryotes and bacteria in water and sediment from coastal aquaculture ponds.Chlorophyta and fungi were dominant phyla in the microeukaryotic–bacterial bipartite networks in water and sediment,respectively.Chlorophyta also had overrepresented links with bacteria in water.Most microeukaryotes and bacteria were classified as generalists,and tended to have symmetric positive and negative links with bacteria in both water and sediment.However,some microeukaryotes with high density of links showed asymmetric links with bacteria in water.Modularity detection in the bipartite network indicated that four microeukaryotes and twelve uncultured bacteria might be potential keystone taxa among the module connections.Moreover,the microeukaryotic–bacterial bipartite network in sediment harbored significantly more nestedness than that in water.The loss of microeukaryotes and generalists will more likely lead to the collapse of positive co-occurrence relationships between microeukaryotes and bacteria in both water and sediment.This study unveils the topology,dominant taxa,keystone species,and robustness in the microeukaryotic–bacterial bipartite networks in coastal aquaculture ecosystems.These species herein can be applied for further management of ecological services,and such knowledge may also be very useful for the regulation of other eutrophic ecosystems.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金supported by grants from the National Natural Science Foundation of China(82004252)the Project of Administration of Traditional Chinese Medicine of Guangdong Province(202405112017596500)the Basic and Applied Basic Research Foundation of Guangzhou Municipal Science and Technology Bureau(202102020533).
文摘Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01296).
文摘Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.
基金supported by the High-Level Talent Foundation of Jinling Institute of Technology(grant number.JIT-B-202413).
文摘With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB2901501in part by the Science and Technology Innovation leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.
基金funded by the National Natural Science Foundation of China under Grant No.62371187the Open Program of Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center under Grant No.2024JS101.
文摘The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
