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 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
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
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
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
Public Map Service Platforms(PMSPs)provide embedded map services in domains such as forests and rivers.Users from different domains(Domain Users)prefer specific spatial features,and extracting the Browsing Interests o...Public Map Service Platforms(PMSPs)provide embedded map services in domains such as forests and rivers.Users from different domains(Domain Users)prefer specific spatial features,and extracting the Browsing Interests of Domain Users(BIDUs)can help elucidate users’access intentions and provide suitable recommendations.Previous research has found that access frequency of spatial features is an indicator of users’browsing interests;however,highfrequency spatial features are sparsely distributed,resulting in inaccurate extraction of browsing interests.Our objective is to model the spatial co-occurrence of spatial features and employ BIDUs extraction to address this limitation.First,to extract spatial features in tiles,we proposed a k-nearest neighbor method for Point-of-Interest(POI)extraction and a template-based method for Land Uses/Land Covers extraction.Then,we developed the word2vec model to construct a POI semantic space to quantify spatial co-occurrence and employed multi-domain user classification to verify its effectiveness.Finally,a combined word2vec and singular value decomposition model is proposed to perform topic extraction as a representation of BIDUs.Compared with the baseline models,the proposed model integrates spatial co-occurrence from massive POIs to achieve high-accuracy BIDU extraction.Our findings can help construct domain user profiles and support the development of intelligent PMSPs.展开更多
Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the u...Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.展开更多
BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the ...BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited...Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.展开更多
For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models...For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.展开更多
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
Aiming at the problem that the current traffic safety helmet detection model can't balance the accuracy of detection with the size of the model and the poor generalization of the model,a method based on improving ...Aiming at the problem that the current traffic safety helmet detection model can't balance the accuracy of detection with the size of the model and the poor generalization of the model,a method based on improving you only look once version 5(YOLOv5) is proposed.By incorporating the lightweight Ghost Net module into the YOLOv5 backbone network,we effectively reduce the model size.The addition of the receptive fields block(RFB) module enhances feature extraction and improves the feature acquisition capability of the lightweight model.Subsequently,the high-performance lightweight convolution,GSConv,is integrated into the neck structure for further model size compression.Moreover,the baseline model's loss function is substituted with efficient insertion over union(EIoU),accelerating network convergence and enhancing detection precision.Experimental results corroborate the effectiveness of this improved algorithm in real-world traffic scenarios.展开更多
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 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 Natural Science Foundation of China[grant numbers:U20A209141771426]Zhizhuo Research Fund on Spatial-Temporal Artificial Intelligence[grant number ZZJJ202204]LIESMARS Special Research Funding.
文摘Public Map Service Platforms(PMSPs)provide embedded map services in domains such as forests and rivers.Users from different domains(Domain Users)prefer specific spatial features,and extracting the Browsing Interests of Domain Users(BIDUs)can help elucidate users’access intentions and provide suitable recommendations.Previous research has found that access frequency of spatial features is an indicator of users’browsing interests;however,highfrequency spatial features are sparsely distributed,resulting in inaccurate extraction of browsing interests.Our objective is to model the spatial co-occurrence of spatial features and employ BIDUs extraction to address this limitation.First,to extract spatial features in tiles,we proposed a k-nearest neighbor method for Point-of-Interest(POI)extraction and a template-based method for Land Uses/Land Covers extraction.Then,we developed the word2vec model to construct a POI semantic space to quantify spatial co-occurrence and employed multi-domain user classification to verify its effectiveness.Finally,a combined word2vec and singular value decomposition model is proposed to perform topic extraction as a representation of BIDUs.Compared with the baseline models,the proposed model integrates spatial co-occurrence from massive POIs to achieve high-accuracy BIDU extraction.Our findings can help construct domain user profiles and support the development of intelligent PMSPs.
文摘Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.
文摘BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金the National Natural Science Foundation of China(Grant No.62172132)Public Welfare Technology Research Project of Zhejiang Province(Grant No.LGF21F020014)the Opening Project of Key Laboratory of Public Security Information Application Based on Big-Data Architecture,Ministry of Public Security of Zhejiang Police College(Grant No.2021DSJSYS002).
文摘Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting.Stamped seal inspection is commonly audited manually to ensure document authenticity.However,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the seal.Traditional image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image recognition.However,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp datasets.Additionally,the fixed training data categories make handling new categories to be a challenging task.This paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these limitations.Firstly,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese network.Finally,we compare the results with the pre-stored standard seal template images in the database to obtain the seal type.To evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in total.The proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation processes.Furthermore,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.
基金supported by the Beijing Natural Science Foundation(Grant No.L223013)。
文摘For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
文摘Aiming at the problem that the current traffic safety helmet detection model can't balance the accuracy of detection with the size of the model and the poor generalization of the model,a method based on improving you only look once version 5(YOLOv5) is proposed.By incorporating the lightweight Ghost Net module into the YOLOv5 backbone network,we effectively reduce the model size.The addition of the receptive fields block(RFB) module enhances feature extraction and improves the feature acquisition capability of the lightweight model.Subsequently,the high-performance lightweight convolution,GSConv,is integrated into the neck structure for further model size compression.Moreover,the baseline model's loss function is substituted with efficient insertion over union(EIoU),accelerating network convergence and enhancing detection precision.Experimental results corroborate the effectiveness of this improved algorithm in real-world traffic scenarios.
