Network Patterns Research Articles

Investigating the relationship between microbial network features of giant kelp "seedbank" cultures and subsequent farm performance.

Microbial inoculants can increase the yield of cultivated crops and are successful in independent trials; however, efficacy drops in large-scale applications due to insufficient consideration of microbial community dynamics. The structure of microbiomes, in addition to the impact of individual taxa, is an important factor to consider when designing growth-promoting inoculants. Here, we investigate the microbial network and community assembly patterns of Macrocystis pyrifera gametophyte germplasm cultures (collectively referred to as a "seedbank") used to cultivate an offshore farm in Santa Barbara, California, and identify network features associated with increased biomass of mature sporophytes. We found that [1] several network features, such as clustering coefficient and edge ratios, significantly vary with biomass outcomes; [2] gametophytes that become low- or high-biomass sporophytes have different hub taxa; and [3] microbial community assembly of gametophyte germplasm cultures is niche-driven. Overall, this study describes microbial community dynamics in M. pyrifera germplasm cultures and ultimately supports the development of early life stage inoculants that can be used on seaweed cultivars to increase biomass yield.

Does Formal Interorganizational Relationship Shape Interpersonal Knowledge Exchange in Multiteam Construction Projects? A Multi-Level Network Analysis

This study investigates the differences in structural patterns of intra-and inter-team knowledge exchange networks in multiteam construction projects and explores the cross-levelinteraction between knowledge exchange networks and interorganizational formal networks, as represented by networks of contractual and task interdependence relationships. An empirical study is developed by using multiple exponential random graph models. The findings show that a discrepancy between intra- and inter-team knowledge exchange structural patterns arises with respect to reciprocity. Interorganizational task interdependencies are found to align with cross-team knowledge exchange. Significant homophily in work experience is found in cross-team knowledge exchange. Interorganizational task interdependence promotes cross-team knowledge exchange among project members with heterogeneous expertise. This study expands the research on project-based multiteam system knowledge exchange networks from a multi-level network analysis perspective.

Elegans-AI: How the connectome of a living organism could model artificial neural networks

This paper introduces Elegans-AI models, a class of neural networks that leverage the connectome topology of the Caenorhabditis elegans to design deep and reservoir architectures. Utilizing deep learning models inspired by the connectome, this paper leverages the evolutionary selection process to consolidate the functional arrangement of biological neurons within their networks. The initial goal involves the conversion of natural connectomes into artificial representations. The second objective centers on embedding the complex circuitry topology of artificial connectomes into both deep learning and deep reservoir networks, highlighting their neural-dynamic short-term and long-term memory and learning capabilities. Lastly, our third objective aims to establish structural explainability by examining the heterophilic/homophilic properties within the connectome and their impact on learning capabilities. In our study, the Elegans-AI models demonstrate superior performance compared to similar models that utilize either randomly rewired artificial connectomes or simulated bio-plausible ones. Notably, these Elegans-AI models achieve a top-1 accuracy of 99.99% on both Cifar10 and Cifar100, and 99.84% on MNIST Unsup. They do this with significantly fewer learning parameters, particularly when reservoir configurations of the connectome are used. Our findings indicate a clear connection between bio-plausible network patterns, the small-world characteristic, and learning outcomes, emphasizing the significant role of evolutionary optimization in shaping the topology of artificial neural networks for improved learning performance.

Adolescent nicotine exposure induces long-term, sex-specific disturbances in mood and anxiety-related behavioral, neuronal and molecular phenotypes in the mesocorticolimbic system.

The majority of lifetime smokers begin using nicotine during adolescence, a critical period of brain development wherein neural circuits critical for mood, affect and cognition are vulnerable to drug-related insults. Specifically, brain regions such as the medial prefrontal cortex (mPFC), the ventral tegmental area (VTA), nucleus accumbens (NAc) and hippocampus, are implicated in both nicotine dependence and pathological phenotypes linked to mood and anxiety disorders. Clinical studies report that females experience higher rates of mood/anxiety disorders and are more resistant to smoking cessation therapies, suggesting potential sex-specific responses to nicotine exposure and later-life neuropsychiatric risk. However, the potential neural and molecular mechanisms underlying such sex differences are not clear. In the present study, we compared the impacts of adolescent nicotine exposure in male vs. female rat cohorts. We performed a combination of behavioral, electrophysiological and targeted protein expression analyses along with matrix assisted laser deionization imaging (MALDI) immediately post-adolescent exposure and later in early adulthood. We report that adolescent nicotine exposure induced long-lasting anxiety/depressive-like behaviors, disrupted neuronal activity patterns in the mPFC-VTA network and molecular alterations in various neural regions linked to affect, anxiety and cognition. Remarkably, these phenotypes were only observed in males and/or were expressed in the opposite direction in females. These findings identify a series of novel, sex-selective biomarkers for adolescent nicotine-induced neuropsychiatric risk, persisting into adulthood.

Developing a novel algorithm for comparing cluster patterns in networks on journal articles during and after COVID-19: Bibliometric analysis.

Cluster analysis is vital in bibliometrics for deciphering large sets of academic data. However, no prior research has employed a cluster-pattern algorithm to assess the similarities and differences between 2 clusters in networks. The study goals are 2-fold: to create a cluster-pattern comparison algorithm tailored for bibliometric analysis and to apply this algorithm in presenting clusters of countries, institutes, departments, authors (CIDA), and keywords on journal articles during and after COVID-19. We analyzed 9499 and 5943 articles from the Journal of Medicine (Baltimore) during and after COVID-19 in 2020 to 2021 and 2022 to 2023, sourced from the Web of Science (WoS) Core Collection. Follower-leading clustering algorithm (FLCA) was compared to other 8 counterparts in cluster validation and effectiveness and a cluster-pattern-comparison algorithm (CPCA) was developed using the similarity coefficient, collaborative maps, and thematic maps to evaluate CIDA cluster patterns. The similarity coefficients were categorized as identical, similar, dissimilar, or different for values above 0.7, between 0.5 and 0.7, between 0.3 and 0.5, and below 0.3, respectively. Both stages displayed similar trends in annual publications and average citations, although these trends are decreasing. The peak publication year was 2020. Similarity coefficients of cluster patterns in these 2 stages for CIDA entities and keywords were 0.73, 0.35, 0.80, 0.02, and 0.83, respectively, suggesting the existence of identical patterns (>0.70) in countries, departments, and keywords plus, but dissimilar (<0.5) and different patterns (<0.3) found in institutes and 1st and corresponding authors, during and after COVID-19. This research effectively created and utilized CPCA to analyze cluster patterns in bibliometrics. It underscores notable identical patterns in country-/department-/keyword based clusters, but dissimilar and different in institute-/author- based clusters, between these 2 stages during and after COVID-19, offering a framework for future bibliographic studies to compare cluster patterns beyond just the CIDA entities, as demonstrated in this study.

Performance of microbial inoculation and tricalcium phosphate on nitrogen retention and conversion: Core microorganisms and enzyme activity during kitchen waste composting

The substantial release of NH3 during composting leads to nitrogen (N) losses and poses environmental hazards. Additives can mitigate nitrogen loss by adsorbing NH3/NH4, adjusting pH, and enhancing nitrification, thereby improving compost quality. Herein, we assessed the effects of combining bacterial inoculants (BI) (1.5%) with tricalcium phosphate (CA) (2.5%) on N retention, organic N conversion, bacterial biomass, functional genes, network patterns, and enzyme activity during kitchen waste (KW) composting. Results revealed that adding of 1.5%/2.5% (BI + CA) significantly (p < 0.05) improved ecological parameters, including pH (7.82), electrical conductivity (3.49 mS/cm), and N retention during composting. The bacterial network properties of CA (265 node) and BI + CA (341 node) exhibited a substantial niche overlap compared to CK (210 node). Additionally, treatments increased organic N and total N (TN) content while reducing NH4+-N by 65.42% (CA) and 77.56% (BI + CA) compared to the control (33%). The treatments, particularly BI + CA, significantly (p < 0.05) increased amino acid N, hydrolyzable unknown N (HUN), and amide N, while amino sugar N decreased due to bacterial consumption. Network analysis revealed that the combination expanded the core bacterial nodes and edges involved in organic N transformation. Key genes facilitating nitrogen mediation included nitrate reductase (nasC and nirA), nitrogenase (nifK and nifD), and hydroxylamine oxidase (hao). The structural equation model suggested that combined application (CA) and microbial inoculants enhance enzyme activity and bacterial interactions during composting, thereby improving nitrogen conversion and increasing the nutrient content of compost products.

Peer Influence and Factors Associated with Personal Network Size of Underage Drinkers Recruited Through Respondent-driven Sampling in Dibrugarh District of Assam, India

Personal networks significantly contributed to initiating risky behaviours among emerging youth. The study aimed to assess the personal network characteristics and factors associated with the personal network size of underage alcohol users. 200 underage drinkers were recruited through respondent-driven sampling in Dibrugarh, Assam using five seeds. Transition probability matrices and homophily indices were used to describe underage drinkers’ network characteristics and network patterns. The weighted logistic regression model was performed to observe the association. Personal network size differed significantly with respect to demographic profile and alcohol consumption patterns of the respondents. A higher proportion of participants used cannabis (77.0%), drank for more than 3 years (40.7%) and liked to drink with peers (51.1%). The affiliation pattern of educational status shows a trend of homophily and interaction between new and older underage drinkers. Students (OR 3.17, 95% CI 1.32, 7.62) who like to drink with peers (OR 3.45, 95% CI 1.24, 9.62) were found significantly associated with larger personal network size in weighted multiple binary logistic regression. The findings will be helpful to understand the network pattern of underage drinkers. The study highlighted the need for effective prevention and intervention programmes for this population and suggested further research in this area.

Synchrony patterns in Laplacian networks

Synchrony patterns in Laplacian networks

TENAGA DAN KELESTARIAN DALAM PENDIDIKAN: ANALISIS BIBLIOMETRIK TERHADAP ARAH TUJU PENYELIDIKAN (2019-2023)

This bibliometric analysis (2019-2023) explores the integration of energy sustainability in education. Utilizing data from Scopus, the study employs VOSviewer to map emerging trends and interconnections within this research domain. Focusing on the role of sustainability and energy conservation in educational contexts, particularly in lower and higher education, the study highlights the evolving discourse and its impact. It underscores the importance of incorporating sustainable energy concepts into educational curricula, preparing students for future environmental challenges. The analysis reveals key patterns in publication trends, subject distribution, and collaborative networks, offering a comprehensive overview of scholarly attention on sustainability in the field of education. This comprehensive bibliometric review contributes to understanding the pedagogical strategies and policy implications for embedding sustainability and energy awareness in educational systems globally.

Investigating the temporal dynamics of motor vehicle collision density patterns in urban road networks – A case study of New York

Introduction: Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time. Method: This study proposes an identification and characterization framework that profiles collision-prone roads with various collision density variations. We first employ the spatio-temporal network kernel density estimation (STNKDE) method and time-series clustering to identify road segments with different collision density patterns. Next, we characterize collision-prone road segments based on spatio-temporal information, consequences, vehicle types, and contributing factors to collisions. The proposed method is applied to two-year motor vehicle collision records for New York City. Results: Seven clusters of road segments with different collision density patterns were identified. Road segments frequently determined as collision-prone were primarily found in Lower Manhattan and the center of the Bronx borough. Furthermore, collisions near road segments that exhibit greater collision densities over time result in more fatalities and injuries, many of which are caused by both human and vehicle factors. Conclusions: Collision-prone road segments with various collision density patterns over time have distinct differences in the spatio-temporal domain and the collisions that occur on them. Practical Applications: The proposed method can help policymakers understand how collision-prone road segments change over time, and can serve as a reference for more targeted traffic treatment.

The Critical Role of Networks to Describe Disease Spreading Dynamics in Social Systems: A Perspective

This review underscores the critical significance of incorporating networks science in epidemiology. Classic mathematical compartmental models (CMs) employed to describe epidemic spreading may fail to capture the intricacies of real disease dynamics. Rooted in the mean-field assumption, CMs oversimplify by assuming that every individual has the potential to “infect” any other, neglecting the inherent complexity of underlying network structures. Since social interactions follow a networked pattern with specific links between individuals based on social behaviors, joining classic CMs and network science in epidemiology becomes essential for a more authentic portrayal of epidemic spreading. This review delves into noteworthy research studies that, from various perspectives, elucidate how the synergy between networks and CMs can enhance the accuracy of epidemic descriptions. In conclusion, we explore research prospects aimed at further elevating the integration of networks within the realm of epidemiology, recognizing its pivotal role in refining our understanding of disease dynamics.

Association between resting-state connectivity patterns in the defensive system network and treatment response in spider phobia—a replication approach

Although highly effective on average, exposure-based treatments do not work equally well for all patients with anxiety disorders. The identification of pre-treatment response-predicting patient characteristics may enable patient stratification. Preliminary research highlights the relevance of inhibitory fronto-limbic networks as such. We aimed to identify pre-treatment neural signatures differing between exposure treatment responders and non-responders in spider phobia and to validate results through rigorous replication. Data of a bi-centric intervention study comprised clinical phenotyping and pre-treatment resting-state functional connectivity (rsFC) data of n = 79 patients with spider phobia (discovery sample) and n = 69 patients (replication sample). RsFC data analyses were accomplished using the Matlab-based CONN-toolbox with harmonized analyses protocols at both sites. Treatment response was defined by a reduction of >30% symptom severity from pre- to post-treatment (Spider Phobia Questionnaire Score, primary outcome). Secondary outcome was defined by a reduction of >50% in a Behavioral Avoidance Test (BAT). Mean within-session fear reduction functioned as a process measure for exposure. Compared to non-responders and pre-treatment, results in the discovery sample seemed to indicate that responders exhibited stronger negative connectivity between frontal and limbic structures and were characterized by heightened connectivity between the amygdala and ventral visual pathway regions. Patients exhibiting high within-session fear reduction showed stronger excitatory connectivity within the prefrontal cortex than patients with low within-session fear reduction. Whereas these results could be replicated by another team using the same data (cross-team replication), cross-site replication of the discovery sample findings in the independent replication sample was unsuccessful. Results seem to support negative fronto-limbic connectivity as promising ingredient to enhance response rates in specific phobia but lack sufficient replication. Further research is needed to obtain a valid basis for clinical decision-making and the development of individually tailored treatment options. Notably, future studies should regularly include replication approaches in their protocols.

Microstructure changes and miRNA-mRNA network in a developmental dysplasia of the hip rat model

MicroRNAs (miRNAs) interact with mRNAs in various pathophysiological processes. In developmental dysplasia of the hip (DDH), the miRNA-mRNA pairs affecting acetabular cartilage (AC) development remain unknown. We investigated dynamic microstructure changes and mRNA and miRNA expression profiles in the AC proliferative zone in a DDH rat model. Abnormal chondrocyte proliferation was observed, and several differentially expressed mRNAs and miRNAs were identified. Downregulated mRNAs and target genes of upregulated miRNAs were primarily enriched in bone and cartilage development. Six hub genes were identified using the predicted miRNA-mRNA interaction network and gene expression pattern analysis. The expression levels of these hub genes and paired miRNAs aligned with our predictions, and most of the pairs were significantly negatively correlated. Excessive chondrocyte proliferation in the AC proliferative zone can delay AC ossification, which might be crucial to DDH development. Specific miRNA-mRNA interaction pairs may serve as diagnostic biomarkers and therapeutic targets.

Exploring the relationship of transport network and land use patterns: an approach through weighted centrality assessment

ABSTRACT This study investigates the relationship between transport networks and land use patterns in the Delhi Metropolitan Area by employing a weighted centrality assessment approach. Recognizing the limitations of traditional monocentric and accessibility-based urban models, this study introduces a multimodal perspective that integrates both road and rail systems. Land use patterns are scrutinized in terms of spatial distribution of Residential, commercial, Public semipublic (PSP)/institutional, industrial and other landuse. Weighted centrality measures are assessed in terms of closeness, betweenness, and straightness, applied within a weighted framework to reflect the multimodal transport characteristics. This study introduces a path evaluation function (PEF) that incorporates transport network capacity and travel time and is calibrated with a turning parameter (α). Through multinomial logistic regression, we explore the complex relationship between weighted centrality of transport network and land use patterns. Results reveal a significant relationship, underscoring that higher centrality tends to favor commercial and public-semi-public/institutional uses, whereas centrality at lower spectrum favors the development of residential areas. This research not only advances the theoretical understanding of urban planning but also offers practical insights for policymakers, emphasizing the importance of multimodal transport considerations in urban development.

Effects of microtopography on soil microbial communities in alpine meadows on the Qinghai-Tibetan Plateau

Heterogeneous habitats generated by diverse microtopography are critical in shaping soil microbial communities and their ecological functions. In this study, a combination of high-throughput sequencing technology and functional prediction was used to assess the effects of three microtopographic types (the shady slope, ravine, and sunny slope) on community structure, diversity, functional guilds, and the co-occurrence networks of soil microbes in alpine meadows. The results showed that the microbial α-diversity varied significantly across the three microtopographies, and fungal community structure was more responsive to microtopography than that of bacteria. In contrast, the co-occurrence network pattern of bacteria was more sensitive to environmental changes induced by microtopography. Mantel tests revealed that bacterial community structure was mainly impacted by plant diversity, belowground biomass, soil total phosphorus content and soil pH, and variations in fungal community structure were correlated with plant diversity, soil pH, and soil NH4+–N content. Meanwhile, regression analyses indicated that nitrogen fixers, ectomycorrhizal fungi, and endophytes relative abundances were positively linked to plant diversity, and soil pH negatively affected nitrogen fixers, ectomycorrhizal fungi, and methane oxidizers. Furthermore, different functional guilds with similar functions (e.g., ectomycorrhizal fungi vs. arbuscular mycorrhizal fungi) responded inconsistently to the microtopography, suggesting ecological niche differentiation of microorganisms in microtopographies. These findings highlight the importance of microtopography for driving bacterial and fungal diversity, community structure, functional profiles and co-occurrence networks in alpine ecosystems.

Neotectonic landforms of the Neoproterozoic basement of Southeastern Brazil: The case of the north border of the São Paulo Basin

Recent studies have demonstrated that the current tectonics activity plays an important role also in intraplate setting, such as in the Brazilian territory as a whole. The Continental Rift of Southeastern Brazil, which comprises the Neoproterozoic basement and some Paleogene basins (e.g. Taubaté, São Paulo, and Curitiba basins), is one of the most important features affected by Neotectonics, the tectonic regime acting since Neogene. However, information about the effects of the current tectonic regime on the rift surrounding areas is still missing. In this way, the objective of this research is to evaluate the possible effects of Neotectonics on the landform of the Neoproterozoic basement, close to the São Paulo sedimentary basin. In order to achieve this objective, general descriptive characteristics of the drainage system were analyzed, such as: network pattern, drainage and hydrographic densities, junction angles, trophy, and anomalies. These data were compared with the aeromagnetic data and the results of the Stream Length-Gradient (SL), Asymmetry Factor (AF), and Drainage Basin Shape (BS) indexes of the main rivers. In the field, 1330 measurements of tectonic features were surveyed in 122 outcrops. The paleostress orientations were calculated by means of fault and fracture inversions by Daisy3 software. In addition, Geologic-geomorphological Maps (1:25,000) of 3 key areas were prepared. The results suggest that tectonic structures have influenced the general arrangement of hills, ridges, and floodplains, as well as the general characteristics of the drainage system, responsible for the dissection of landforms. Recent deformations indicate Neotectonic activity causing tilting of blocks and channel migration, and formation of escarpments, triangular facets, abandoned meanders, fluvial terraces, knickpoints, and elbows. On the other hand, the field data suggest that the most of faults are related to the Pre Cenozoic tectonics. In this way, Neotectonics is not the principal structural element on the landform evolution of the study area, but it has an important role on the relief formation of some regions.

A Multi-Graph Cross-Attention-Based Region-Aware Feature Fusion Network Using Multi-Template for Brain Disorder Diagnosis.

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

On the information-theoretic formulation of network participation

The participation coefficient is a widely used metric of the diversity of a node’s connections with respect to a modular partition of a network. An information-theoretic formulation of this concept of connection diversity, referred to here as participation entropy, has been introduced as the Shannon entropy of the distribution of module labels across a node’s connected neighbors. While diversity metrics have been studied theoretically in other literatures, including to index species diversity in ecology, many of these results have not previously been applied to networks. Here we show that the participation coefficient is a first-order approximation to participation entropy and use the desirable additive properties of entropy to develop new metrics of connection diversity with respect to multiple labelings of nodes in a network, as joint and conditional participation entropies. The information-theoretic formalism developed here allows new and more subtle types of nodal connection patterns in complex networks to be studied.

A lightweight Intrusion Detection for Internet of Things‐based smart buildings

AbstractThe integration of Internet of Things (IoT) devices into commercial or industrial buildings to create smart environments, such as Smart Buildings (SBs), has enabled real‐time data collection and processing to effectively manage building operations. Due to poor security design and implementation in IoT devices, SB networks face an array of security challenges and threats (e.g., botnet malware) that leverage IoT devices to conduct Distributed Denial of Service (DDoS) attacks on the Internet infrastructure. Machine Learning (ML)‐based traffic classification systems aim to automatically detect such attacks by effectively differentiating attacks from benign traffic patterns in IoT networks. However, there is an inherent accuracy‐efficiency tradeoff in network traffic classification tasks. To balance this tradeoff, we develop an accurate yet lightweight device‐specific traffic classification model. This model classifies SB traffic flows into four types of coarse‐grained flows, based on the locations of traffic sources and the directions of traffic transmissions. Through these four types of coarse‐grained flows, the model can extract simple yet effective flow rate features to conduct learning and predictions. Our experiments find the model to achieve an overall accuracy of 96%, with only 32 features to be learned by the ML model.

Distinctive Phase Interdependency Model for Retinal Vasculature Delineation in OCT-Angiography Images.

Automatic detection of retinal vasculature in optical coherence tomography angiography (OCTA) images faces several challenges such as the closely located capillaries, vessel discontinuity and high noise level. This paper introduces a new distinctive phase interdependency model to address these problems for delineating centerline patterns of the vascular network. We capture the inherent property of vascular centerlines by obtaining the inter-scale dependency information that exists between neighboring symmetrical wavelets in complex Poisson domain. In particular, the proposed phase interdependency model identifies vascular centerlines as the distinctive features that have high magnitudes over adjacent symmetrical coefficients whereas the coefficients caused by background noises are decayed rapidly along adjacent wavelet scales. The potential relationships between the neighboring Poisson coefficients are established based on the coherency of distinctive symmetrical wavelets. The proposed phase model is assessed on the OCTA-500 database (300 OCTA images + 200 OCT images), ROSE-1-SVC dataset (9 OCTA images), ROSE-1 (SVC+ DVC) dataset (9 OCTA images), and ROSE-2 dataset (22 OCTA images). The experiments on the clinically relevant OCTA images validate the effectiveness of the proposed method in achieving high-quality results. Our method produces average FScore of 0.822, 0.782, and 0.779 on ROSE-1-SVC, ROSE-1 (SVC+ DVC), and ROSE-2 datasets, respectively, and the FScore of 0.910 and 0.862 on OCTA_6mm and OCT_3mm datasets (OCTA-500 database), respectively, demonstrating its superior performance over the state-of-the-art benchmark methods.