The development of fungicides is time-consuming and costly.Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate.However,exist...The development of fungicides is time-consuming and costly.Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate.However,existing assessment methods are often plagued by low accuracy and poor generalization,while fragment-based design strategies commonly fail to account for synergistic effects between structural units.Therefore,based on a small-scale sample set,this study developed a more efficient global predictive model for fungicidal activity—-named APPf—by integrating multi-scale feature screening methods and machine learning algorithms,which also accounts for synergistic effects among different structural fragments.We utilized three independent external test sets for model validation:External Test Set 1 for general validation,External Test Set 2 for comparison with existing models,and External Test Set 3 for disease-specific fungicide evaluation.On External Test Set 1,the APPf model achieved a precision of 0.6454,a recall of 0.8535,and an F1 score of 0.7350,demonstrating its robust predictive performance.It also exhibited strong enrichment capability for positive samples in External Test Set 2.For External Test Set 3,APPf achieved a prediction accuracy exceeding 80%for each disease,suggesting its promising potential in practical fungicide development.Furthermore,we quantified the contribution of molecular descriptors to the model predictions using SHAP value analysis and identified nHdNH and NssssNp as strong indicative features for predicting fungicidal activity,thereby enhancing the interpretability of the model.APPf has been deployed on a public web server(http://pesticides.cau.edu.cn/APPf),providing a user-friendly online prediction service to support the discovery of novel fungicides.Meanwhile,we employed a molecular fragmentation strategy to analyze the co-occurrence relationships between fragments in fungicides and constructed a network map of fragment co-occurrence associated with fungicidal activity.This study provides both an active fragment library and a global fungicide-likeness assessment tool for AI-based de novo molecular generation aimed at discovering novel fungicidal leads,which is expected to enhance the efficiency of developing new fungicides.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
In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic q...In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic queries.Additionally,they typically rely on honest but curious cloud servers,which introduces the risk of repudiation.Furthermore,the combined operations of search and verification increase system load,thereby reducing performance.Traditional verification mechanisms,which rely on complex hash constructions,suffer from low verification efficiency.To address these challenges,this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification.Building on existing single and multi-keyword search methods,the scheme uses vector models to semantically train the dataset,enabling it to retain semantic information and achieve context-aware encrypted retrieval,significantly improving search accuracy.Additionally,a blockchain-based updatable master-slave chain storage model is designed,where the master chain stores encrypted keyword indexes and the slave chain stores verification information generated by zero-knowledge proofs,thus balancing system load while improving search and verification efficiency.Finally,an improved non-interactive zero-knowledge proof mechanism is introduced,reducing the computational complexity of verification and ensuring efficient validation of search results.Experimental results demonstrate that the proposed scheme offers stronger security,balanced overhead,and higher search verification efficiency.展开更多
The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issu...The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields.As a key technology for knowledge representation and sharing,knowledge graph...The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields.As a key technology for knowledge representation and sharing,knowledge graphs play a crucial role by constructing structured networks of relationships among entities.However,data sparsity and numerous unexplored implicit relations result in the widespread incompleteness of knowledge graphs.In static knowledge graph completion,most existing methods rely on linear operations or simple interaction mechanisms for triple encoding,making it difficult to fully capture the deep semantic associations between entities and relations.Moreover,many methods focus only on the local information of individual triples,ignoring the rich semantic dependencies embedded in the neighboring nodes of entities within the graph structure,which leads to incomplete embedding representations.To address these challenges,we propose Two-Stage Mixer Embedding(TSMixerE),a static knowledge graph completion method based on entity context.In the unit semantic extraction stage,TSMixerE leveragesmulti-scale circular convolution to capture local features atmultiple granularities,enhancing the flexibility and robustness of feature interactions.A channel attention mechanism amplifies key channel responses to suppress noise and irrelevant information,thereby improving the discriminative power and semantic depth of feature representations.For contextual information fusion,a multi-layer self-attentionmechanism enables deep interactions among contextual cues,effectively integrating local details with global context.Simultaneously,type embeddings clarify the semantic identities and roles of each component,enhancing the model’s sensitivity and fusion capabilities for diverse information sources.Furthermore,TSMixerE constructs contextual unit sequences for entities,fully exploring neighborhood information within the graph structure to model complex semantic dependencies,thus improving the completeness and generalization of embedding representations.展开更多
Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in th...Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in the residence-based(RB)or regional context,overlooking the mobility-based(MB)context in which people are exposed to air pollution.This study measures air pollution levels in MB,RB,and regional contexts and examines their relationships with perceived air quality across different neighborhoods and gender sub-groups of Hong Kong,China to investigate how people perceive air quality.The results indicate that particulate matter 2.5(PM_(2.5))measured in RB and the regional context significantly contributes to people’s perceived air quality compared to MB PM_(2.5).Individuals in Central and Western district of Hong Kong rely on RB,regional and MB PM_(2.5) to assess air pollution.In Sham Shui Po,RB PM_(2.5) exhibits the highest influence on people’s perceived air quality,followed by regional PM_(2.5).Women’s perceived air quality is strongly related to their RB PM_(2.5) exposure,while men’s perceived air quality is associated with both RB PM_(2.5) and regional PM_(2.5) levels.We conclude that neighborhood effects and mobility levels are the two most important factors influencing the association between meas-ured air pollution and perceived air quality.We reveal that the neighborhood effect averaging problem(NEAP)influences the associ-ation between perceived air quality and measured air pollution levels in a way that differs from health outcome-related studies.Effect-ive measures are needed to improve the public’s awareness of air pollution,and scientific control should be implemented to reduce pub-lic exposure.展开更多
One of the fundamental questions in community ecology is whether communities are random or formed by deterministic mechanisms. Although many efforts have been made to verify non-randomness in community structure, litt...One of the fundamental questions in community ecology is whether communities are random or formed by deterministic mechanisms. Although many efforts have been made to verify non-randomness in community structure, little is known with regard to co-occurrence patterns in above-ground and below-ground communities. In this paper, we used a null model to test non-randomness in the structure of the above-ground and below-ground mite communities in farmland of the Sanjiang Plain, Northeast China. Then, we used four tests for non-randomness to recognize species pairs that would be demonstrated as significantly aggregated or segregated co-occurrences of the above-ground and below-ground mite communities. The pattern of the above-ground mite commu- nity was significantly non-random in October, suggesting species segregation and hence interspecific competition. Additionally, species co-occurrence patterns did not differ from randomness in the above-ground mite community in August or in below-ground mite com- munities in August and October. Only one significant species pair was detected in the above-ground mite community in August, while no significant species pairs were recognized in the above-ground mite community in October or in the below-ground mite communities in August and October. The results indicate that non-randomness and significant species pairs may not be the general rule in the above-ground and below-ground mite communities in farmland of the Sanjiang Plain at the fine scale.展开更多
Inversion tillage with straw amendment is widely applied in northeastern China, and it can substantially increase the storage of carbon and improve multiple subsoil functions. Soil microorganisms are believed to be th...Inversion tillage with straw amendment is widely applied in northeastern China, and it can substantially increase the storage of carbon and improve multiple subsoil functions. Soil microorganisms are believed to be the key to this process,but research into their role in subsoil amelioration is limited. Therefore, a field experiment was conducted in 2018 in a region in northeastern China with Hapli-Udic Cambisol using four treatments: conventional tillage(CT, tillage to a depth of 15 cm with no straw incorporation), straw incorporation with conventional tillage(SCT, tillage to a depth of 15 cm),inversion tillage(IT, tillage to a depth of 35 cm) and straw incorporation with inversion tillage(SIT, tillage to a depth of 35 cm). The soils were managed by inversion to a depth of 15 or 35 cm every year after harvest. The results indicated that SIT improved soil multi-nutrient cycling variables and increased the availability of key nutrients such as soil organic carbon, total nitrogen, available nitrogen, available phosphorus and available potassium in both the topsoil and subsoil.In contrast to CT and SCT, SIT created a looser microbial network structure but with highly centralized clusters by reducing the topological properties of average connectivity and node number, and by increasing the average path length and the modularity. A Random Forest analysis found that the average path length and the clustering coefficient were the main determinants of soil multi-nutrient cycling. These findings suggested that SIT can be an effective option for improving soil multi-nutrient cycling and the structure of microbial networks, and they provide crucial information about the microbial strategies that drive the decomposition of straw in Hapli-Udic Cambisol.展开更多
AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix metho...AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.展开更多
Background:Disentangling the relative importance of environmental variables and interspecific interaction in modulating co-occurrence patterns of sympatric species is essential for understanding the mechanisms of comm...Background:Disentangling the relative importance of environmental variables and interspecific interaction in modulating co-occurrence patterns of sympatric species is essential for understanding the mechanisms of community assembly and biodiversity. For the two sympatric Galliformes, Silver Pheasants (Lophura nycthemera) and Whitenecklaced Partridges (Arborophila gingica), we know little about the role of habitat use and interspecific interactions in modulating their coexistence. Methods:We adopted a probabilistic approach incorporating habitat preference and interspecific interaction using occupancy model to account for imperfect detection,and used daily activity pattern analysis to investigate the cooccurrence pattern of these two sympatric Galliformes in wet and dry seasons. Results: We found that the detection probability of Silver Pheasant and White-necklaced Partridge were related to habitat variables and interspecific interaction. The presence of Silver Pheasant increases the detection probability of White-necklaced Partridge in both the wet and dry season. However, the presence of White-necklaced Partridges increases the detection probability of Silver Pheasants in the wet season, but decreases the probability in the dry season. Further, Silver Pheasants were detected frequently in the sites of high values of enhanced vegetable index (EVI) in both the wet and dry season, and in sites away from human residential settlement in the wet season. Whitenecklaced partridges were mainly detected in low EVI sites. The site use probabilities of two Galliformes were best explained by habitat variables, Silver Pheasants and White-necklaced Partridges preferred steeper areas during the wet and dry season. Both species mainly occurred in low EVI areas during the wet season and occupied sites away from the resident settlement during the dry season. Moreover, the site use probabilities of two species had opposite relationships with forest canopy coverage. Silver Pheasants preferred areas with high forest canopy coverage whereas White-necklaced Partridges preferred low forest canopy coverage in the dry season, and vice versa in the wet season. Species interaction factor (SIF)corroborated weak evidence of the dependence of the site use of one species on that of the other in the either dry or wet season.Temporally, high overlapping of daily activity pattern indicated no significantly temporal niche differentiation between sympatric Galliformes in both wet and dry seasons. Conclusions:Our results demonstrated that the presence of two species influenced the detection probability interactively and there was no temporal partitioning in activity time between Silver Pheasants and White-necklaced Partridges in the wet and dry seasons.The site use probability of two Galliformes was best explained by habitat variables, especially the forest canopy coverage.Therefore, environmental variables and interspecific interaction are the leading drivers regulating the detection and site use probability and promoting co-occurrence of Silver Pheasants and White-necklaced Partridges.展开更多
Co-occurrence pattern of fish species plays an important role in understanding the spatio-temporal structure and the stability of fish community.Species coexistence may vary with time and space.The co-occurrence patte...Co-occurrence pattern of fish species plays an important role in understanding the spatio-temporal structure and the stability of fish community.Species coexistence may vary with time and space.The co-occurrence patterns of fish species were examined using the C-score under fixed-fixed null model for fish communities in spring and autumn over different years in the Haizhou Bay,China.The results showed that fish assemblages in the whole bay had non-random patterns in spring and autumn over different years.However,the fish co-occurrence patterns were different for the northern and southern fish assemblages in spring and autumn.The northern fish assemblage showed structured pattern,whereas the southern assemblage were randomly assembled in spring.The co-occurrence patterns of fish communities were relatively stable over different years,and the number of significant species pairs in northern assemblage was more than that in the southern assemblage.Environmental heterogeneity played an important role in determining the distributions of fish species that formed significant species pairs,which might affect the co-occurrence patterns of northern and southern assemblages further in the Haizhou Bay.展开更多
Paddy soils are potential hotspots of combined contamination with arsenic(As) and antibiotics, which may induce co-selection of antibiotic resistance genes(ARGs) and As biotransformation genes (ABGs), resulting in dis...Paddy soils are potential hotspots of combined contamination with arsenic(As) and antibiotics, which may induce co-selection of antibiotic resistance genes(ARGs) and As biotransformation genes (ABGs), resulting in dissemination of antimicrobial resistance and modification in As biogeochemical cycling. So far, little information is available for these coselection processes and specific patterns between ABGs and ARGs in paddy soils. Here, the16S rRNA amplicon sequencing and high-throughput quantitative PCR and network analysis were employed to investigate the dynamic response of ABGs and ARGs to As stress and manure application. The results showed that As stress increased the abundance of ARGs and mobile genetic elements (MGEs), resulting in dissemination risk of antimicrobial resistance. Manure amendment increased the abundance of ABGs, enhanced As mobilization and methylation in paddy soil, posing risk to food safety. The frequency of the co-occurrence between ABGs and ARGs, the host bacteria carrying both ARGs and ABGs were increased by As or manure treatment, and remarkably boosted in soils amended with both As and manure. Multidrug resistance genes were found to have the preference to be co-selected with ABGs, which was one of the dominant co-occurring ARGs in all treatments, and manure amendment increased the frequency of Macrolide-Lincosamide-Streptogramin B resistance(MLSB) to co-occur with ABGs. Bacillus and Clostridium of Firmicutes are the dominant host bacteria carrying both ABGs and ARGs in paddy soils. This study would extend our understanding on the co-selection between genes for antibiotics and metals, also unveil the hidden environmental effects of combined pollution.展开更多
Denitrifying bacteria in epiphytic biofilms play a crucial role in nitrogen cycle in aquatic habitats.However,little is known about the connection between algae and denitrifying bacteria and their assembly processes i...Denitrifying bacteria in epiphytic biofilms play a crucial role in nitrogen cycle in aquatic habitats.However,little is known about the connection between algae and denitrifying bacteria and their assembly processes in epiphytic biofilms.Epiphytic biofilms were collected from submerged macrophytes(Patamogeton lucens and Najas marina L.)in the Caohai Lake,Guizhou,SW China,from July to November 2020 to:(1)investigate the impact of abiotic and biotic variables on denitrifying bacterial communities;(2)investigate the temporal variation of the algae-denitrifying bacteria co-occurrence networks;and(3)determine the contribution of deterministic and stochastic processes to the formation of denitrifying bacterial communities.Abiotic and biotic factors influenced the variation in the denitrifying bacterial community,as shown in the Mantel test.The co-occurrence network analysis unveiled intricate interactions among algae to denitrifying bacteria.Denitrifying bacterial community co-occurrence network complexity(larger average degrees representing stronger network complexity)increased continuously from July to September and decreased in October before increasing in November.The co-occurrence network complexity of the algae and nirS-encoding denitrifying bacteria tended to increase from July to November.The co-occurrence network complexity of the algal and denitrifying bacterial communities was modified by ammonia nitrogen(NH_(4)^(+)-N)and total phosphorus(TP),pH,and water temperature(WT),according to the ordinary least-squares(OLS)model.The modified stochasticity ratio(MST)results reveal that deterministic selection dominated the assembly of denitrifying bacterial communities.The influence of environmental variables to denitrifying bacterial communities,as well as characteristics of algal-bacterial co-occurrence networks and the assembly process of denitrifying bacterial communities,were discovered in epiphytic biofilms in this study.The findings could aid in the appropriate understanding and use of epiphytic biofilms denitrification function,as well as the enhancement of water quality.展开更多
In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of...In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.展开更多
Nitrogen(N)deep placement has been found to reduce N leaching and increase N use efficiency in paddy fields.However,relatively little is known how bacterial consortia,especially abundant and rare taxa,respond to N dee...Nitrogen(N)deep placement has been found to reduce N leaching and increase N use efficiency in paddy fields.However,relatively little is known how bacterial consortia,especially abundant and rare taxa,respond to N deep placement,which is critical for understanding the biodiversity and function of agricultural ecosystem.In this study,lllumina sequencing and ecological models were conducted to examine the diversity patterns and underlying assembly mechanisms of abundant and rare taxa in rice rhizosphere soil under different N fertilization regimes at four rice growth stages in paddy fields.The results showed that abundant and rare bacteria had distinct distribution patterns in rhizosphere samples.Abundant bacteria showed ubiquitous distribution;while rare taxa exhibited uneven distribution across all samples.Stochastic processes dominated community assembly of both abundant and rare bacteria,with dispersal limitation playing a more vital role in abundant bacteria,and undominated processes playing a more important role in rare bacteria.The N deep placement was associated with a greater influence of dispersal limitation than the broadcast N fertilizer(BN)and no N fertilizer(NN)treatments in abundant and rare taxa of rhizosphere soil;while greater contributions from homogenizing dispersal were observed for BN and NN in rare taxa.Network analysis indicated that abundant taxa with closer relationships were usually more likely to occupy the central position of the network than rare taxa.Nevertheless,most of the keystone species were rare taxa and might have played essential roles in maintaining the network stability.Overall,these findings highlighted that the ecological mechanisms and co-occurrence patterns of abundant and rare bacteria in rhizosphere soil under N deep placement.展开更多
Interactions between dissolved organic matter(DOM)and bacteria are central in the biogeochemical cycles of aquatic ecosystems;however,the relative importance of biodegradable dissolved organic carbon(BDOC)compared wit...Interactions between dissolved organic matter(DOM)and bacteria are central in the biogeochemical cycles of aquatic ecosystems;however,the relative importance of biodegradable dissolved organic carbon(BDOC)compared with other environmental variables in structuring the bacterial communities needs further investigation.Here,we investigated bacterial communities,chromophoric DOM(CDOM)characteristics and physico-chemical parameters as well as examined BDOC via bioassay incubations in large eutrophic Lake Taihu,China,to explore the importance of BDOC for shaping bacterial community structures and co-occurrence patterns.We found that the proportion of BDOC(%BDOC)correlated significantly and positively with the DOC concentration and the index of the contribution of recent produced autochthonous CDOM(BIX).%BDOC,further correlated positively with the relative abundance of the tryptophan-like component and negatively with CDOM aromaticity,indicating that autochthonous production of protein-like CDOM was an important source of BDOC.The richness of the bacterial communities correlated negatively with%BDOC,indicating an enhanced number of species in the refractory DOC environments.%BDOC was identified as a significant stronger factor than DOC in shaping bacterial community composition and the co-occurrence network,suggesting that substrate biodegradability is more significant than DOC quantity determining the bacterial communities in a eutrophic lake.Environmental factors explained a larger proportion of the variation in the conditionally rare and abundant subcommunity than for the abundant and the rare bacterial subcommunities.Our findings emphasize the importance of considering bacteria with different abundance patterns and DOC biodegradability when studying the interactions between DOM and bacteria in eutrophic lakes.展开更多
In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic s...In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding distortions.For this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain.However,the distortion maps can be calculated for cover and stego images and the difference between them is significant.In this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence matrix.The proposed scheme first generates the corresponding distortion maps for cover and stego images.Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images.Finally,support vector machine,based on the gaussian kernel,is used to classify the features.Compared with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.展开更多
基金the National Key R&D Program of China(No.2022YFD1700200).
文摘The development of fungicides is time-consuming and costly.Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate.However,existing assessment methods are often plagued by low accuracy and poor generalization,while fragment-based design strategies commonly fail to account for synergistic effects between structural units.Therefore,based on a small-scale sample set,this study developed a more efficient global predictive model for fungicidal activity—-named APPf—by integrating multi-scale feature screening methods and machine learning algorithms,which also accounts for synergistic effects among different structural fragments.We utilized three independent external test sets for model validation:External Test Set 1 for general validation,External Test Set 2 for comparison with existing models,and External Test Set 3 for disease-specific fungicide evaluation.On External Test Set 1,the APPf model achieved a precision of 0.6454,a recall of 0.8535,and an F1 score of 0.7350,demonstrating its robust predictive performance.It also exhibited strong enrichment capability for positive samples in External Test Set 2.For External Test Set 3,APPf achieved a prediction accuracy exceeding 80%for each disease,suggesting its promising potential in practical fungicide development.Furthermore,we quantified the contribution of molecular descriptors to the model predictions using SHAP value analysis and identified nHdNH and NssssNp as strong indicative features for predicting fungicidal activity,thereby enhancing the interpretability of the model.APPf has been deployed on a public web server(http://pesticides.cau.edu.cn/APPf),providing a user-friendly online prediction service to support the discovery of novel fungicides.Meanwhile,we employed a molecular fragmentation strategy to analyze the co-occurrence relationships between fragments in fungicides and constructed a network map of fragment co-occurrence associated with fungicidal activity.This study provides both an active fragment library and a global fungicide-likeness assessment tool for AI-based de novo molecular generation aimed at discovering novel fungicidal leads,which is expected to enhance the efficiency of developing new fungicides.
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 in part by the National Natural Science Foundation of China under Grant 62262073in part by the Yunnan Provincial Ten Thousand People Program for Young Top Talents under Grant YNWR-QNBJ-2019-237in part by the Yunnan Provincial Major Science and Technology Special Program under Grant 202402AD080002.
文摘In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic queries.Additionally,they typically rely on honest but curious cloud servers,which introduces the risk of repudiation.Furthermore,the combined operations of search and verification increase system load,thereby reducing performance.Traditional verification mechanisms,which rely on complex hash constructions,suffer from low verification efficiency.To address these challenges,this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification.Building on existing single and multi-keyword search methods,the scheme uses vector models to semantically train the dataset,enabling it to retain semantic information and achieve context-aware encrypted retrieval,significantly improving search accuracy.Additionally,a blockchain-based updatable master-slave chain storage model is designed,where the master chain stores encrypted keyword indexes and the slave chain stores verification information generated by zero-knowledge proofs,thus balancing system load while improving search and verification efficiency.Finally,an improved non-interactive zero-knowledge proof mechanism is introduced,reducing the computational complexity of verification and ensuring efficient validation of search results.Experimental results demonstrate that the proposed scheme offers stronger security,balanced overhead,and higher search verification efficiency.
文摘The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金supported by the National Natural Science Foundation of China(No.62267005)the Chinese Guangxi Natural Science Foundation(No.2023GXNSFAA026493)+1 种基金Guangxi Collaborative Innovation Center ofMulti-Source Information Integration and Intelligent ProcessingGuangxi Academy of Artificial Intelligence.
文摘The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields.As a key technology for knowledge representation and sharing,knowledge graphs play a crucial role by constructing structured networks of relationships among entities.However,data sparsity and numerous unexplored implicit relations result in the widespread incompleteness of knowledge graphs.In static knowledge graph completion,most existing methods rely on linear operations or simple interaction mechanisms for triple encoding,making it difficult to fully capture the deep semantic associations between entities and relations.Moreover,many methods focus only on the local information of individual triples,ignoring the rich semantic dependencies embedded in the neighboring nodes of entities within the graph structure,which leads to incomplete embedding representations.To address these challenges,we propose Two-Stage Mixer Embedding(TSMixerE),a static knowledge graph completion method based on entity context.In the unit semantic extraction stage,TSMixerE leveragesmulti-scale circular convolution to capture local features atmultiple granularities,enhancing the flexibility and robustness of feature interactions.A channel attention mechanism amplifies key channel responses to suppress noise and irrelevant information,thereby improving the discriminative power and semantic depth of feature representations.For contextual information fusion,a multi-layer self-attentionmechanism enables deep interactions among contextual cues,effectively integrating local details with global context.Simultaneously,type embeddings clarify the semantic identities and roles of each component,enhancing the model’s sensitivity and fusion capabilities for diverse information sources.Furthermore,TSMixerE constructs contextual unit sequences for entities,fully exploring neighborhood information within the graph structure to model complex semantic dependencies,thus improving the completeness and generalization of embedding representations.
基金Under the auspices of the Hong Kong Research Grants Council(No.14605920,14606922,14603724,C4023-20GF,8601219,8601242,3110151)a Grant from the Research Committee on Research Sustainability of Major Research Grants Council Funding Schemes of the Chinese University of Hong Kong(CUHK,No.3133235)the Vice-Chancellor’s One-off Discretionary Fund of CUHK(No.4930787)。
文摘Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in the residence-based(RB)or regional context,overlooking the mobility-based(MB)context in which people are exposed to air pollution.This study measures air pollution levels in MB,RB,and regional contexts and examines their relationships with perceived air quality across different neighborhoods and gender sub-groups of Hong Kong,China to investigate how people perceive air quality.The results indicate that particulate matter 2.5(PM_(2.5))measured in RB and the regional context significantly contributes to people’s perceived air quality compared to MB PM_(2.5).Individuals in Central and Western district of Hong Kong rely on RB,regional and MB PM_(2.5) to assess air pollution.In Sham Shui Po,RB PM_(2.5) exhibits the highest influence on people’s perceived air quality,followed by regional PM_(2.5).Women’s perceived air quality is strongly related to their RB PM_(2.5) exposure,while men’s perceived air quality is associated with both RB PM_(2.5) and regional PM_(2.5) levels.We conclude that neighborhood effects and mobility levels are the two most important factors influencing the association between meas-ured air pollution and perceived air quality.We reveal that the neighborhood effect averaging problem(NEAP)influences the associ-ation between perceived air quality and measured air pollution levels in a way that differs from health outcome-related studies.Effect-ive measures are needed to improve the public’s awareness of air pollution,and scientific control should be implemented to reduce pub-lic exposure.
基金Under the auspices of National Natural Science Foundation of China(No.41101049,40601047,41371072,31101617,41171047)China Postdoctoral Science Foundation(No.2012M511361)+2 种基金Excellent Youth Scholars of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(No.DLSYQ2012004)Fund for Distinguished Young Scholar of Harbin Normal University(No.KGB201204)Scientific Innovation Project for Doctoral Candidate of Harbin Normal University(No.HSDBSCX2012-07)
文摘One of the fundamental questions in community ecology is whether communities are random or formed by deterministic mechanisms. Although many efforts have been made to verify non-randomness in community structure, little is known with regard to co-occurrence patterns in above-ground and below-ground communities. In this paper, we used a null model to test non-randomness in the structure of the above-ground and below-ground mite communities in farmland of the Sanjiang Plain, Northeast China. Then, we used four tests for non-randomness to recognize species pairs that would be demonstrated as significantly aggregated or segregated co-occurrences of the above-ground and below-ground mite communities. The pattern of the above-ground mite commu- nity was significantly non-random in October, suggesting species segregation and hence interspecific competition. Additionally, species co-occurrence patterns did not differ from randomness in the above-ground mite community in August or in below-ground mite com- munities in August and October. Only one significant species pair was detected in the above-ground mite community in August, while no significant species pairs were recognized in the above-ground mite community in October or in the below-ground mite communities in August and October. The results indicate that non-randomness and significant species pairs may not be the general rule in the above-ground and below-ground mite communities in farmland of the Sanjiang Plain at the fine scale.
基金funded by the National Key Research and Development Program of China (2022YFD1500100)the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28070100)+1 种基金the National Natural Science Foundation of China (41807085)the earmarked fund for China Agriculture Research System (CARS04)。
文摘Inversion tillage with straw amendment is widely applied in northeastern China, and it can substantially increase the storage of carbon and improve multiple subsoil functions. Soil microorganisms are believed to be the key to this process,but research into their role in subsoil amelioration is limited. Therefore, a field experiment was conducted in 2018 in a region in northeastern China with Hapli-Udic Cambisol using four treatments: conventional tillage(CT, tillage to a depth of 15 cm with no straw incorporation), straw incorporation with conventional tillage(SCT, tillage to a depth of 15 cm),inversion tillage(IT, tillage to a depth of 35 cm) and straw incorporation with inversion tillage(SIT, tillage to a depth of 35 cm). The soils were managed by inversion to a depth of 15 or 35 cm every year after harvest. The results indicated that SIT improved soil multi-nutrient cycling variables and increased the availability of key nutrients such as soil organic carbon, total nitrogen, available nitrogen, available phosphorus and available potassium in both the topsoil and subsoil.In contrast to CT and SCT, SIT created a looser microbial network structure but with highly centralized clusters by reducing the topological properties of average connectivity and node number, and by increasing the average path length and the modularity. A Random Forest analysis found that the average path length and the clustering coefficient were the main determinants of soil multi-nutrient cycling. These findings suggested that SIT can be an effective option for improving soil multi-nutrient cycling and the structure of microbial networks, and they provide crucial information about the microbial strategies that drive the decomposition of straw in Hapli-Udic Cambisol.
基金Supported by the Priming Scientific Research Foundation for the Junior Researcher in Beijing Tongren Hospital,Capital Medical University
文摘AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.
基金supported by the National Key Research and Development Program of China(2017YFC0503802)China Postdoctoral Science Foundation(2017M 620905)
文摘Background:Disentangling the relative importance of environmental variables and interspecific interaction in modulating co-occurrence patterns of sympatric species is essential for understanding the mechanisms of community assembly and biodiversity. For the two sympatric Galliformes, Silver Pheasants (Lophura nycthemera) and Whitenecklaced Partridges (Arborophila gingica), we know little about the role of habitat use and interspecific interactions in modulating their coexistence. Methods:We adopted a probabilistic approach incorporating habitat preference and interspecific interaction using occupancy model to account for imperfect detection,and used daily activity pattern analysis to investigate the cooccurrence pattern of these two sympatric Galliformes in wet and dry seasons. Results: We found that the detection probability of Silver Pheasant and White-necklaced Partridge were related to habitat variables and interspecific interaction. The presence of Silver Pheasant increases the detection probability of White-necklaced Partridge in both the wet and dry season. However, the presence of White-necklaced Partridges increases the detection probability of Silver Pheasants in the wet season, but decreases the probability in the dry season. Further, Silver Pheasants were detected frequently in the sites of high values of enhanced vegetable index (EVI) in both the wet and dry season, and in sites away from human residential settlement in the wet season. Whitenecklaced partridges were mainly detected in low EVI sites. The site use probabilities of two Galliformes were best explained by habitat variables, Silver Pheasants and White-necklaced Partridges preferred steeper areas during the wet and dry season. Both species mainly occurred in low EVI areas during the wet season and occupied sites away from the resident settlement during the dry season. Moreover, the site use probabilities of two species had opposite relationships with forest canopy coverage. Silver Pheasants preferred areas with high forest canopy coverage whereas White-necklaced Partridges preferred low forest canopy coverage in the dry season, and vice versa in the wet season. Species interaction factor (SIF)corroborated weak evidence of the dependence of the site use of one species on that of the other in the either dry or wet season.Temporally, high overlapping of daily activity pattern indicated no significantly temporal niche differentiation between sympatric Galliformes in both wet and dry seasons. Conclusions:Our results demonstrated that the presence of two species influenced the detection probability interactively and there was no temporal partitioning in activity time between Silver Pheasants and White-necklaced Partridges in the wet and dry seasons.The site use probability of two Galliformes was best explained by habitat variables, especially the forest canopy coverage.Therefore, environmental variables and interspecific interaction are the leading drivers regulating the detection and site use probability and promoting co-occurrence of Silver Pheasants and White-necklaced Partridges.
基金funded by the National Natural Science Foundation of China (No. 31772852)the Fundamental Research Funds for the Central Universities (Nos. 2015 62030, 201612004)the Public Science and Technology Research Funds Projects of Ocean (No. 201305030)
文摘Co-occurrence pattern of fish species plays an important role in understanding the spatio-temporal structure and the stability of fish community.Species coexistence may vary with time and space.The co-occurrence patterns of fish species were examined using the C-score under fixed-fixed null model for fish communities in spring and autumn over different years in the Haizhou Bay,China.The results showed that fish assemblages in the whole bay had non-random patterns in spring and autumn over different years.However,the fish co-occurrence patterns were different for the northern and southern fish assemblages in spring and autumn.The northern fish assemblage showed structured pattern,whereas the southern assemblage were randomly assembled in spring.The co-occurrence patterns of fish communities were relatively stable over different years,and the number of significant species pairs in northern assemblage was more than that in the southern assemblage.Environmental heterogeneity played an important role in determining the distributions of fish species that formed significant species pairs,which might affect the co-occurrence patterns of northern and southern assemblages further in the Haizhou Bay.
基金supported by the National Natural Science Foundation of China (Nos. 41991332, 41977323 and 42090063)。
文摘Paddy soils are potential hotspots of combined contamination with arsenic(As) and antibiotics, which may induce co-selection of antibiotic resistance genes(ARGs) and As biotransformation genes (ABGs), resulting in dissemination of antimicrobial resistance and modification in As biogeochemical cycling. So far, little information is available for these coselection processes and specific patterns between ABGs and ARGs in paddy soils. Here, the16S rRNA amplicon sequencing and high-throughput quantitative PCR and network analysis were employed to investigate the dynamic response of ABGs and ARGs to As stress and manure application. The results showed that As stress increased the abundance of ARGs and mobile genetic elements (MGEs), resulting in dissemination risk of antimicrobial resistance. Manure amendment increased the abundance of ABGs, enhanced As mobilization and methylation in paddy soil, posing risk to food safety. The frequency of the co-occurrence between ABGs and ARGs, the host bacteria carrying both ARGs and ABGs were increased by As or manure treatment, and remarkably boosted in soils amended with both As and manure. Multidrug resistance genes were found to have the preference to be co-selected with ABGs, which was one of the dominant co-occurring ARGs in all treatments, and manure amendment increased the frequency of Macrolide-Lincosamide-Streptogramin B resistance(MLSB) to co-occur with ABGs. Bacillus and Clostridium of Firmicutes are the dominant host bacteria carrying both ABGs and ARGs in paddy soils. This study would extend our understanding on the co-selection between genes for antibiotics and metals, also unveil the hidden environmental effects of combined pollution.
基金Supported by the National Natural Science Foundation of China(No.41867056)the Guizhou Provincial Key Technology R&D Program(Nos.2021470,2023216)。
文摘Denitrifying bacteria in epiphytic biofilms play a crucial role in nitrogen cycle in aquatic habitats.However,little is known about the connection between algae and denitrifying bacteria and their assembly processes in epiphytic biofilms.Epiphytic biofilms were collected from submerged macrophytes(Patamogeton lucens and Najas marina L.)in the Caohai Lake,Guizhou,SW China,from July to November 2020 to:(1)investigate the impact of abiotic and biotic variables on denitrifying bacterial communities;(2)investigate the temporal variation of the algae-denitrifying bacteria co-occurrence networks;and(3)determine the contribution of deterministic and stochastic processes to the formation of denitrifying bacterial communities.Abiotic and biotic factors influenced the variation in the denitrifying bacterial community,as shown in the Mantel test.The co-occurrence network analysis unveiled intricate interactions among algae to denitrifying bacteria.Denitrifying bacterial community co-occurrence network complexity(larger average degrees representing stronger network complexity)increased continuously from July to September and decreased in October before increasing in November.The co-occurrence network complexity of the algae and nirS-encoding denitrifying bacteria tended to increase from July to November.The co-occurrence network complexity of the algal and denitrifying bacterial communities was modified by ammonia nitrogen(NH_(4)^(+)-N)and total phosphorus(TP),pH,and water temperature(WT),according to the ordinary least-squares(OLS)model.The modified stochasticity ratio(MST)results reveal that deterministic selection dominated the assembly of denitrifying bacterial communities.The influence of environmental variables to denitrifying bacterial communities,as well as characteristics of algal-bacterial co-occurrence networks and the assembly process of denitrifying bacterial communities,were discovered in epiphytic biofilms in this study.The findings could aid in the appropriate understanding and use of epiphytic biofilms denitrification function,as well as the enhancement of water quality.
基金the Yunnan Applied Basic Research Projects(No.2016FD039)the Talent Cultivation Project in Yunnan Province(No.KKSY201503063)
文摘In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.
基金the National Key Research and Development Program of China(2016YFD0200309 and 2018YFD0301104-01).
文摘Nitrogen(N)deep placement has been found to reduce N leaching and increase N use efficiency in paddy fields.However,relatively little is known how bacterial consortia,especially abundant and rare taxa,respond to N deep placement,which is critical for understanding the biodiversity and function of agricultural ecosystem.In this study,lllumina sequencing and ecological models were conducted to examine the diversity patterns and underlying assembly mechanisms of abundant and rare taxa in rice rhizosphere soil under different N fertilization regimes at four rice growth stages in paddy fields.The results showed that abundant and rare bacteria had distinct distribution patterns in rhizosphere samples.Abundant bacteria showed ubiquitous distribution;while rare taxa exhibited uneven distribution across all samples.Stochastic processes dominated community assembly of both abundant and rare bacteria,with dispersal limitation playing a more vital role in abundant bacteria,and undominated processes playing a more important role in rare bacteria.The N deep placement was associated with a greater influence of dispersal limitation than the broadcast N fertilizer(BN)and no N fertilizer(NN)treatments in abundant and rare taxa of rhizosphere soil;while greater contributions from homogenizing dispersal were observed for BN and NN in rare taxa.Network analysis indicated that abundant taxa with closer relationships were usually more likely to occupy the central position of the network than rare taxa.Nevertheless,most of the keystone species were rare taxa and might have played essential roles in maintaining the network stability.Overall,these findings highlighted that the ecological mechanisms and co-occurrence patterns of abundant and rare bacteria in rhizosphere soil under N deep placement.
基金supported by the National Natural Science Foundation of China (Nos.41930760, 41807362, and 41977322)the Provincial Natural Science Foundation of Jiangsu in China (No.BK20181104)+2 种基金the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (No.QYZDB-SSWDQC016)supported by WATEC (Centre for Water Technology, AU)the TüBITAK outstanding scientists program 2232 (project 118C250)。
文摘Interactions between dissolved organic matter(DOM)and bacteria are central in the biogeochemical cycles of aquatic ecosystems;however,the relative importance of biodegradable dissolved organic carbon(BDOC)compared with other environmental variables in structuring the bacterial communities needs further investigation.Here,we investigated bacterial communities,chromophoric DOM(CDOM)characteristics and physico-chemical parameters as well as examined BDOC via bioassay incubations in large eutrophic Lake Taihu,China,to explore the importance of BDOC for shaping bacterial community structures and co-occurrence patterns.We found that the proportion of BDOC(%BDOC)correlated significantly and positively with the DOC concentration and the index of the contribution of recent produced autochthonous CDOM(BIX).%BDOC,further correlated positively with the relative abundance of the tryptophan-like component and negatively with CDOM aromaticity,indicating that autochthonous production of protein-like CDOM was an important source of BDOC.The richness of the bacterial communities correlated negatively with%BDOC,indicating an enhanced number of species in the refractory DOC environments.%BDOC was identified as a significant stronger factor than DOC in shaping bacterial community composition and the co-occurrence network,suggesting that substrate biodegradability is more significant than DOC quantity determining the bacterial communities in a eutrophic lake.Environmental factors explained a larger proportion of the variation in the conditionally rare and abundant subcommunity than for the abundant and the rare bacterial subcommunities.Our findings emphasize the importance of considering bacteria with different abundance patterns and DOC biodegradability when studying the interactions between DOM and bacteria in eutrophic lakes.
基金This work is supported by the National Natural Science Foundation of China(No.U1736118)the Natural Science Foundation of Guangdong(No.2016A030313350)+3 种基金the Special Funds for Science and Technology Development of Guangdong(No.2016KZ010103)the Key Project of Scientific Research Plan of Guangzhou(No.201804020068)the Fundamental Research Funds for the Central Universities(No.16lgjc83 and No.17lgjc45)the Science and Technology Planning Project of Guangdong Province(Grant No.2017A040405051).
文摘In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding distortions.For this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain.However,the distortion maps can be calculated for cover and stego images and the difference between them is significant.In this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence matrix.The proposed scheme first generates the corresponding distortion maps for cover and stego images.Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images.Finally,support vector machine,based on the gaussian kernel,is used to classify the features.Compared with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.
