Analysis Of Network Structure Research Articles

Polygenic Score for Conscientiousness Is a Protective Factor for Reversion from Mild Cognitive Impairment to Normal Cognition.

Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS-C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue-specific enrichment analysis indicate that the protective effect of PGS-C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high-cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS-C.

The relationship between depression, self-control and meaning in life among young undergraduate students: Evidence from path analysis and network analysis

BackgroundLittle is known about the role of self-control in the relationship between depression and the meaning of life. In particular, when depression is viewed through the lens of symptom networks. It is also unclear how self-control (self-discipline and impulse control) and meaning in life relate to the symptoms of depression. MethodsThis study addressed this gap by estimating structural equation models and network analysis based on cross-sectional data representative of China (N = 936, surveyed from age 17 to 22, Mage = 18.27, SDage = 0.75, female = 65.7 %). ResultsThe findings revealed that depression is interrelated with self-control and meaning in life. Specifically, depression predicts levels of meaning in life through self-discipline, which is one component of self-control. There was a complex link between symptoms of depression, self-control, and meaning in life. However, there was no direct link between the core symptoms of depression and self-discipline or meaning in life. ConclusionsOverall, the evidence from this study suggests that emphasizing the presence of meaning among young undergraduate students who are at high risk for depression may decrease their levels of depression. Moreover, if this emphasis is coupled with an improvement in their self-discipline, it may also be beneficial for depression.

Structures of SenB and SenA enzymes from Variovorax paradoxus provide insights into carbon–selenium bond formation in selenoneine biosynthesis

Selenoneine, an ergothioneine analog, is important for antioxidation and detoxification. SenB and SenA are two crucial enzymes that form carbon–selenium bonds in the selenoneine biosynthetic pathway. To investigate their underlying catalytic mechanisms, we obtained complex structures of SenB with its substrate UDP-N-acetylglucosamine (UDP-GlcNAc) and SenA with N-α-trimethyl histidine (TMH). SenB adopts a type-B glycosyltransferase fold. Structural and functional analysis of the interaction network at the active center provide key information on substrate recognition and suggest a metal-ion-independent, inverting mechanism is utilized for SenB-mediated selenoglycoside formation. Moreover, the complex structure of SenA with TMH and enzymatic activity assays highlight vital residues that control substrate binding and specificity. Based on the conserved structure and substrate-binding pocket of the type I sulfoxide synthase EgtB in the ergothioneine biosynthetic pathway, a similar reaction mechanism was proposed for the formation of C–Se bonds by SenA. The structures provide knowledge on selenoneine synthesis and lay groundwork for further applications of this pathway.

Peran Unsur Meta-Utaut, Penerimaan Paylater dan Persepsi Penggunaan Terhadap Antropomofisme Indonesia

Purpose: Applying the integration of UTAUT and MTAM theories, this study investigates the acceptance and usage of paylater from the Indonesian anthropomorphism perspective. Methodology/approach: Quantitative research strategy using methods for gathering data via questionnaires. Using the Lameshow formula, which yields 384.16, the sample size was calculated and rounded to 400 individuals. The combined strategy of the SEM-ANN method is used in this study's data analysis. Findings: The research presented here demonstrates that behavioral intentions are mostly determined by their performance expectations. The elements of effort expectancy, social influence, facilitating condition, hedonic motivation, habit, and trust in shaping user behavioral intentions all support this. However, perceived risk and perceived cost have the effect of decreasing behavioral intention to use. One of the things that encourages usage behavior is its goal. Practical implications: This empirical study breaks out the paylater adoption-influencing aspects from an Indonesian social perspective. IT developers can create user-friendly or widely approved paylater applications in this approach. Originality/value: This study builds pricing value components by combining the UTAUT and MTAM methodologies with structural equation modeling (SEM) and analysis of neural network (ANN) techniques. In recognizing connections that are linear and non-linear.

Constrained Symmetric Non-Negative Matrix Factorization with Deep Autoencoders for Community Detection

Recently, community detection has emerged as a prominent research area in the analysis of complex network structures. Community detection models based on non-negative matrix factorization (NMF) are shallow and fail to fully discover the internal structure of complex networks. Thus, this article introduces a novel constrained symmetric non-negative matrix factorization with deep autoencoders (CSDNMF) as a solution to this issue. The model possesses the following advantages: (1) By integrating a deep autoencoder to discern the latent attributes bridging the original network and community assignments, it adeptly captures hierarchical information. (2) Introducing a graph regularizer facilitates a thorough comprehension of the community structure inherent within the target network. (3) By integrating a symmetry regularizer, the model’s capacity to learn undirected networks is augmented, thereby facilitating the precise detection of symmetry within the target network. The proposed CSDNMF model exhibits superior performance in community detection when compared to state-of-the-art models, as demonstrated by eight experimental results conducted on real-world networks.

Forward genetics combined with unsupervised classifications identified zebrafish mutants affecting biliary system formation

Impaired formation of the biliary network can lead to congenital cholestatic liver diseases; however, the genes responsible for proper biliary system formation and maintenance have not been fully identified. Combining computational network structure analysis algorithms with a zebrafish forward genetic screen, we identified 24 new zebrafish mutants that display impaired intrahepatic biliary network formation. Complementation tests suggested these 24 mutations affect 24 different genes. We applied unsupervised clustering algorithms to unbiasedly classify the recovered mutants into three classes. Further computational analysis revealed that each of the recovered mutations in these three classes has a unique phenotype on node-subtype composition and distribution within the intrahepatic biliary network. In addition, we found most of the recovered mutations are viable. In those mutant fish, which are already good animal models to study chronic cholestatic liver diseases, the biliary network phenotypes persist into adulthood. Altogether, this study provides unique genetic and computational toolsets that advance our understanding of the molecular pathways leading to biliary system malformation and cholestatic liver diseases.

Rail Transit Networks and Network Motifs: A Review and Research Agenda

The railway plays an essential role in urban and intercity transport of goods and people. Intercity and urban rail transit infrastructures contribute to the economic and environmental sustainability of global economies. Those infrastructures can be modeled as complex networks, so that we can evaluate system properties of the network structure. This stream of research has focused on the topological analysis of global network structure, but little research exists that examines how local network structures affect system properties. The local structure of complex networks can be examined with network motif analysis, as those network motifs are the building blocks of networked systems. Nevertheless, there has been scarce attention given to local network properties in rail transit networks. We contribute to covering this gap in the literature with a literature review of motif analysis research and its application to weighted and unweighted rail transit networks, also covering the current state-of-the-art of network motif decomposition and analysis. We demonstrate that network motif analysis is not only applicable, but also beneficial for the design and planning of rail transit networks, enhancing their sustainability by improving efficiency, reducing environmental impact, and optimizing resource allocation. Based on our findings, we propose future research directions that involve applying motif analysis to enhance the sustainability features of both unweighted and weighted rail transit networks.

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

Comparison of threshold algorithms for automatic processing of fat crystal microscopic images based on ImageJ

AbstractMicroscopic image analysis is a crucial tool in fat crystallization research, enabling the analysis of crystal size, network structure, fractal dimension and other parameters through binarization. It is essential to seek an appropriate thresholding algorithm to binarize fat crystal images, which plays a vital role in image segmentation. In this article, the effectiveness of 17 thresholding algorithms such as Default, Mean, IsoData, Otsu, Li and Triangle were analyzed in processing fat crystal images with different shapes, background colors and image intensities. This was expected to discover a stable and objective thresholding algorithm for the binarization of fat crystal images. The performance evaluation was conducted according to the peak signal noise ratio (PSNR), structural similarity index (SSIM) and region non‐uniformity (RNU) parameter. Moreover, the comparative analysis of crystal size error, crystal area fraction and intraclass correlation coefficients (ICC) for fractal dimension values would provide a foundation for the selection of thresholding techniques for fat crystal network images. The results indicated that the Default algorithm exhibited remarkable robustness and applicability with high‐quality and stable outputs in fat crystal image processing.

Detecting Overlapping Communities Based on Influence-Spreading Matrix and Local Maxima of a Quality Function

Community detection is a widely studied topic in network structure analysis. We propose a community detection method based on the search for the local maxima of an objective function. This objective function reflects the quality of candidate communities in the network structure. The objective function can be constructed from a probability matrix that describes interactions in a network. Different models, such as network structure models and network flow models, can be used to build the probability matrix, and it acts as a link between network models and community detection models. In our influence-spreading model, the probability matrix is called an influence-spreading matrix, which describes the directed influence between all pairs of nodes in the network. By using the local maxima of an objective function, our method can standardise and help in comparing different definitions and approaches of community detection. Our proposed approach can detect overlapping and hierarchical communities and their building blocks within a network. To compare different structures in the network, we define a cohesion measure. The objective function can be expressed as a sum of these cohesion measures. We also discuss the probability of community formation to analyse a different aspect of group behaviour in a network. It is essential to recognise that this concept is separate from the notion of community cohesion, which emphasises the need for varying objective functions in different applications. Furthermore, we demonstrate that normalising objective functions by the size of detected communities can alter their rankings.

Exploring cybertechnology standards through bibliometrics: Case of National Institute of Standards and Technology

Cyber security is one of the topics that gain importance today. It is necessary to determine the basic components, basic dynamics, and main actors of the Cyber security issue, which is obvious that it will have an impact in many areas from social, social, economic, environmental, and political aspects, as a hot research topic. When the subject literature is examined, it has become a trend-forming research subject followed by institutions and organizations that produce R&D policy, starting from the level of governments. In this study, cybersecurity research is examined in the context of 5 basic cyber security functions specified in the cyber security standard (CSF) defined by the National Institute of Standards and Technology (NIST). It is aimed to determine the research topics emerging in the international literature, to identify the most productive countries, to determine the rankings created by these countries according to their functions, to determine the research clusters and research focuses. In the study, several quantitative methods were used, especially scientometrics, social network analysis (SNA) line theory and structural hole analysis. Statistical tests (Log-Likelihood Ratio) were used to reveal the prominent areas, and the text mining method was also used. we first defined a workflow according to the “Identify”, “Protect”, “Detect”, “Respond” and “Recover” setups, and conducted an online search on the Web of Science (WoS) to access the information on the publications on the relevant topics It is seen that actors, institutions and research create different densities according to various geographical regions in the 5 functions defined within the framework of cybersecurity. It is possible to say that infiltration detection, the internet of things and the concept of artificial intelligence are among the other prominent research focuses, although it is seen that smart grids are among the most prominent research topics. In the first clustering analysis we performed, we can say that 17 clusters are formed, especially when we look under the definition function. The largest of these clusters has 32 data points, so-called "decision making models".

Large-region image inpainting using adaptive Fourier Neural Network and space-frequency domain structural analysis

Due to its remarkable performance, the convolutional neural network (CNN) has gained widespread usage in image inpainting challenges. However, most of these CNN-based methods reconstruct images only in the spatial domain, which produces satisfactory outcomes for small-region inpainting tasks, but blurs the details and generates incomplete structures for large-region inpainting tasks with complex backgrounds. In this paper, we address the issue of large-region inpainting tasks by our novel Adaptive Fourier Neural Network. Specifically, in our network, a Fourier-based global receptive field module is introduced to incorporate frequency information and expand the receptive field by transforming local convolutions into global convolutions, enabling the proposed network to transmit global information to the missing region. Furthermore, to better fuse spatial and frequency features, an attention-based joint space-frequency module is proposed to combine spatial and frequency information. Finally, to validate the effectiveness and robustness of our proposed method, we conduct qualitative and quantitative experiments on two popular datasets Paris StreetView and Places. The experimental results demonstrate that our proposed method outperforms state-of-the-art methods by generating sharper, more coherent, and visually plausible inpainting results. Code will be released after this work published: https://github.com/langka9/AFNN.git.

Enhancing Threat Prioritization In Cybersecurity Using Avian Shield Optimizer (ASO) In Graph Forge Elite (GFE) Framework

In the ever-evolving field of cybersecurity, the ability to effectively prioritize threats is essential for organizations to allocate resources wisely and proactively mitigate potential risks. Avian Shield Optimizer (ASO) is a revolutionary optimization technique specifically designed for threat prioritization. ASO utilizes evolutionary-inspired mechanisms to dynamically adjust the prioritization of threats based on a comprehensive analysis of factors such as severity, relevance, and potential impact on organizational assets. By continuously monitoring and adapting to the dynamic threat landscape, ASO enables organizations to anticipate and respond quickly to emerging threats, ensuring that resources are allocated appropriately to address the most critical vulnerabilities. Moreover, ASO seamlessly integrates with existing threat intelligence platforms, providing cybersecurity teams with actionable insights and recommendations to comprehensively strengthen their defense strategies. Concurrently, the GraphForgeElite framework enhances this approach by facilitating the analysis of complex network data connections and structures, enabling the detection of subtle irregularities that may indicate potential cyber threats. ASO collaborates synergistically with GraphForgeElite, utilizing evolutionary-inspired methodologies to dynamically adapt the framework's design and settings, automatically tuning the parameters. Through rigorous experimentation, the performance of ASO-GraphForgeElite's Network is compared to other state-of-the-art classifiers. The results demonstrate the superior performance of ASO-GraphForgeElite's Network, surpassing 99% accuracy, precision, recall, and F1-score. Additionally, the framework exhibits efficiency in handling complex network data structures, enabling the identification of subtle patterns that indicate potential threats.

What Differentiates Believers and Non-Believers of Conspiracy Theories? A Structural Analysis of Semantic Networks Regarding the Social Representations of Covid-19 Pandemic

Belief in conspiracy theories is close related with the adoption of behavioral patterns often associated with a direct dispute of scientific research, institutional and governmental practices. The recent Covid-19 pandemic revealed an ecosystem of stories and narratives promoting alternative explanations about a real public threat. The exposure to these narratives and their persuasion towards people’s cognitive structures is a crucial issue to be examined to identify the different strategies implemented, by individuals and groups, in order to address a direct threat to society. In the current exploratory research, we examined whether believing in conspiracy theories about Covid-19 pandemic, is possible to predict semantic networks as cognitive structures. The analysis of the research findings provided different patterns of thought about individual exposure to Covid-19 conspiracy theories, their persuasiveness, and the different levels of compliance to (state-promoted) health measures.&#x0D; We discuss the importance of future research focusing on the level of acceptance of the role of different social institutions. A systematic analysis of conspiracy theories rationale is possible to address the cognitive bias organized on belief systems that offer a logical problem solving in social uncertainties. A more thorough study on the individual and group perception of the role of institutions, the level of their acceptance or dispute, as well as their direct and indirect impact on the development of socially responsible behaviors, within a shared social reality, could enhance our knowledge towards the development of effective social policies to confront the next public health crisis.

Eating disorder symptom dimensions and protective factors: A structural network analysis study

Eating Disorders (EDs) and related symptoms pose a substantial public health concern due to their widespread prevalence among both genders and associated negative outcomes, underscoring the need for effective preventive interventions. In this context, deepening our understanding of the interplay between ED symptoms and related protective factors appears crucial. Therefore, this study employed a structural network analysis approach considering both ED symptom dimensions (i.e., drive for thinness, bulimic symptoms, and body dissatisfaction) and related protective factors (i.e., body and functionality appreciation, intuitive eating, and self-esteem) to shed light on how these factors are interrelated. A community sample of 1391 individuals (34.4% men; Mage = 26.4 years) completed a socio-demographic schedule and self-report questionnaires. The network showed that the nodes with the highest positive expected influence were body and functionality appreciation, while those with the highest negative expected influence were eating for physical rather than emotional reasons and unconditional permission to eat (i.e., two components of intuitive eating). Crucially, the most relevant bridges between the conceptual communities “ED symptom dimensions” and “Protective factors” were the negative relations between (a) eating for physical rather than emotional reasons and bulimic symptoms, (b) unconditional permission to eat and drive for thinness, and (c) body appreciation and body dissatisfaction. Finally, age, gender, and body mass index did not moderate any edge in the network. The practical implications of these findings are discussed, especially in terms of preventive interventions for ED symptoms.

Correlation Analysis of Urban Road Network Structure and Spatial Distribution of Tourism Service Facilities at Multi-Scales Based on Tourists’ Travel Preferences

Harbin, as a popular tourist city in China, and the host of the 2025 Asian Winter Games, boasts rich tourism resources and significant potential for further development. The structure of the urban road network is a crucial factor influencing the spatial distribution of tourism service facilities. However, the current research on the correlation between the two, analyzed at multiple scales based on tourists’ travel preferences, is not sufficient. First, utilizing the questionnaire survey method to analyze tourists’ travel preferences and combining it with the theory of 15-min life circle, we determine the study scales at 500 m, 1000 m, 3000 m, and 5000 m. Secondly, the integration value and choice value of roads in the main urban area of Harbin are analyzed based on the theory of spatial syntax. The spatial distribution characteristics of tourism service facilities are then revealed through kernel density analysis. Finally, the correlation between the road network structure and the distribution of various types of tourism service facilities in Harbin at different scales is determined through buffer analysis and Pearson bivariate correlation analysis. The results show that: (1) Integration value plays a significant positive role in promoting the clustering of tourism service facilities, especially tending to cluster in areas with high integration value formed at a scale of 500 m; (2) At the scale of 3000 m, the distribution of tourism service facilities exhibits a significant correlation with the choice value; (3) The correlation between dining, shopping, and entertainment facilities and the integration value decreases with the increase in scale, whereas the spatial distribution of accommodation and attraction facilities does not exhibit a regular pattern with changes in integration value. In addition, this paper also puts forward targeted suggestions for optimizing the urban road network structure, reasonably locating tourism service facilities, and implementing balanced regional development. The contribution of this study is that it will help improve tourists’ travel experience in the city and provide scientific support for promoting the overall sustainable development of tourism in Harbin.

Corpus linguistics meets historical linguistics and construction grammar: how far have we come, and where do we go from here?

This paper aims to give an overview of corpus-based research that investigates processes of language change from the theoretical perspective of Construction Grammar. Starting in the early 2000s, a dynamic community of researchers has come together in order to contribute to this effort. Among the different lines of work that have characterized this enterprise, this paper discusses the respective roles of qualitative approaches, diachronic collostructional analysis, multivariate techniques, distributional semantic models, and analyses of network structure. The paper tries to contextualize these approaches and to offer pointers for future research.

Comparison of patenting trends and alliance patterns among countries with a majority religion: structural topic model and network analysis

ABSTRACT In our modern, technologically advanced society, 84% of the world’s population holds some form of religious faith. Our history has also witnessed religion and technology having an impact on each other. Assuming the underlying correlation between them, we question whether there are distinguished technological features among countries with strong religions. This study examines the technological trends in countries with a majority religion according to the dominant religion of each – namely, Judaism, Islam, and Hinduism. We applied structural topic modelling (STM) and co-patent network analysis to patents from those countries registered with the United States Patent and Trademark Office between 1965 and 2015. According to the STM results, technologies appear to reflect geographical environments, industrial concentrations, and investments led by governments and private sectors rather than religion. Moreover, countries with a majority religion in our study had mainly collaborated with the United States and European countries, regardless of religious tendency.

Biodiversity loss and inter-provincial cooperative protection in China based on input-output model

Along with habitat destruction and the intensification of climate change, economic and social development is threatening the living of species. In the context of China's domestic economic circulation, the spatial structure of biodiversity loss is complicated by the decentralization of production, with many regions affecting the survival of species in other regions. Based on the environmentally extended MRIO model, this paper calculated the subnational biodiversity loss footprint through structural path analysis and social network analysis, identified the regional linkages and spatial networks of species loss, and traced the conservation responsibility to specific sectors. Our findings indicate that Yunnan, Tibet, Guangdong, and Sichuan possess the most significant biodiversity footprints, comprising over 40% of the total footprint. In terms of threat patterns, Yunnan and Tibet have the largest intra-regional footprints, indicating their important impact on species within the region; Guangdong and Zhejiang have the largest extra-regional footprints, indicating their role in threatening other species through cross-regional trades. The primary industry, tap water and food and tobacco sectors are major biodiversity footprint producers. Overall, some eastern and coastal areas are the final drivers of biodiversity loss in China, driven by increasingly complex trade links. Notably, the directionality of species loss in biodiversity hotspots is evident, with Shandong and Guangdong significantly influencing the biodiversity of Yunnan and Guangxi, respectively. This study systematically analyzed the complete network of biodiversity loss and divided four biodiversity protection communities to promote the realization of trans-regional protection and compensation. Examining the biodiversity loss through the lens of networks, and connecting economic activities to species, serves as a warning to decision-making bodies that a future of increasingly intricate domestic trade could pose a critical threat to biodiversity, necessitating collaborative conservation efforts.

Phosphate additives promote humic acid carbon and nitrogen skeleton formation by regulating precursors and composting bacterial communities

This study aimed to compare the effect of different phosphate additives including superphosphate (CP) and MP [Mg(OH)2 + H3PO4] on nitrogen conversion, humus fractions formation and bacterial community in food waste compost. The results showed the ratio of humic acid nitrogen in total nitrogen (HA-N/TN) in CP increased by 49 %. Ammonium nitrogen accumulation was increased by 75 % (CP) and 44 % (MP). Spectroscopic techniques proved that phosphate addition facilitated the formation of complex structures in HA. CP enhanced the dominance of Saccharomonospora, while Thermobifida and Bacillus were improved in MP. Structural equation modeling and network analysis demonstrated that ammonium nitrogen can be converted to HA-N and has positive effects on bacterial composition, reducing sugars and amino acids, especially in CP with more clustered network and synergic bacterial interactions. Therefore, the addition of phosphate provides a new idea to regulate the retained nitrogen toward humification in composting.