Analysis Of Network Patterns Research Articles

Size and Topography of the Brain’s Functional Networks with Psychotic Experiences, Schizophrenia, and Bipolar Disorder

BackgroundExisting functional connectivity studies of psychosis use population-averaged functional network maps, despite highly variable topographies of these networks across the brain surface. We aimed to define the functional network areas and topographies in the general population and the changes associated with psychotic experiences (PEs) and disorders. MethodsMaps of 8 functional networks were generated using an individual-specific template-matching procedure for each participant from the Human Connectome Project Young Adult cohort (n = 1003) and from a matched case cohort (schizophrenia [SCZ], n = 27; bipolar disorder, n = 35) scanned identically with the same Connectom scanner. In the Human Connectome Project Young Adult cohort, PEs were estimated based on scores from the Achenbach Self-Report Scale. The relationship of symptoms to the probability of network representation at each cortical vertex was assessed using logistic regression. ResultsIn Human Connectome Project Young Adult participants, PE severity on the Achenbach thought problems scale was predicted by increased language network (LAN) and dorsal attention network (DAN) areas and decreased cingulo-opercular network area (r < 0.12). Significant effects were found in SCZ, with a larger DAN and LAN and a smaller frontoparietal network. Network pattern analysis in SCZ showed an increased probability of LAN in the posterior region of the left superior temporal gyrus and of the visual network in the left insula. Regression analyses in SCZ found that mood dysregulation was related to increased DAN surface area. ConclusionsThose with PEs and SCZ showed abnormal functional network cortical topographies, particularly involving DAN and LAN. Network findings may predict psychosis progression and guide earlier intervention.

Analysis of network patterns and its influencing factors in Chengdu-Chongqing urban agglomeration based on multi-flow

With the strengthening of the cross-regional flows of the economy, information, innovation, and population, this paper constructs a network model of multi-flow integration and analyzes the spatial pattern and influencing factors of urban networks in Chengdu-Chongqing Urban Agglomeration using social network analysis and spatial analysis technology. The main conclusions are as follows. (1) The density and efficiency are in the transition stage from the primary level to the medium level in the comprehensive network. (2) The overall pattern keeps a polyhedral pyramid structure with Chengdu ↔ Chongqing as the core axis, and the grade of each axis has been significantly raised. (3) Four groups are formed using the social network method and show a geographic proximity effect. In addition, the connections within each group are relatively close, but the connections between the groups are significantly different. (4) Location conditions, economic development level, enterprise development level, scientific research investment, scientific and technological development level, and government support have a greater impact on the formation of the comprehensive network of Chengdu-Chongqing urban agglomeration. Information application level and transportation accessibility show a small impact and human capital level has not yet produced a significant impact.

Comparative ecological network pattern analysis: a case of Nanchang.

Urban-ecological landscape connectivity and pattern optimization can significantly enhance biodiversity and sustainable development capacity, which play an important role in continued ecosystem functioning. Previous studies identified ecological sources based on the area threshold method or combination with morphological spatial pattern analysis and the landscape connectivity index (CMSPACI) method, but few studies have compared the advantages, disadvantages, and applicability of the two methods. In this paper, taking Nanchang as the study area, we address the ecological sources via area threshold and the CMSPACI method. Then, the minimum cost distance method is used to generate potential corridors of different methods, and the differences in ecological networks are analyzed. Finally, the circuit theory is used to identify barriers, and we provide targeted recommendations for ecological network pattern optimization in the study area. The results show that (1) the ecological sources extracted by different methods are different. The ecological sources extracted by the area threshold are far away from the surrounding sources, and the landscape connectivity is low. The ecological sources identified by the CMSPACI method are closely related to the surrounding sources, and the landscape connectivity is high. (2) Compared with the area threshold method, the habitat quality of corridors under the CMSPACI method is better, and the interaction intensity between patches is larger. (3) There is little difference in the number of ecological barriers under different methods; all of them are located between patches or on the edge of patches, and most of them are roads or construction land. Overall, the area threshold method is simpler. Ecological sources can be effectively addressed through the CMSPACI method, and the landscape connectivity of the ecological network will be better. This study provides an important reference for the selection of ecological sources in the construction of ecological networks.

Community social network pattern analysis: Development of a novel methodology using a complex, multi-level health intervention

Community social networks (CSN) include individuals and groups, and those with strong partnerships and relationships are well situated for implementing community-based interventions. However, information on the nature of CSN relationships required for multilevel community-based interventions is not present in the literature. Using data from the multi-level Children’s Healthy Living (CHL) trial to reduce child obesity in nine Pacific communities, this study aimed to develop a methodology based on Social Network Analysis (SNA) to understand how CSN evolved over the course of a two-year trial, as well as the characteristics of CSN most successful in impacting indicators of childhood obesity. &#x0D; The two-year trial was considered in four six-month intervals. Within each interval, implemented activities, as recorded in CHL monthly reports, were coded by activity implementer(s), e.g. government agency, school, or community-based group, as well as for collective efficacy impact of the activity, e.g. to leverage resources from outside the CSN or to facilitate civic engagement. Coded data were used to create CSN maps for the four time intervals, and SNA techniques examined the CSN characteristics. CSN density increased over time, as measured by the number of ties within the network. Schools, community-based groups and large organizations were identified as the primary implementers of the CHL intervention and formed a community implementer backbone. Social leveraging, i.e. linking local groups to people with authority over outside resources, was shown to be a central component in intervention success. It took time to develop strong CSN, and stronger (denser) CSN were more successful in building social cohesion and enacting community change. Findings illustrate a methodology that can be useful for tracking the development and impact of CSN.

Detecting coexisting oscillatory patterns in delay coupled Lur'e systems.

This work addresses the problem of pattern analysis in networks consisting of delay-coupled identical Lur'e systems. We study a class of nonlinear systems, which, being isolated, are globally asymptotically stable. Assembling such systems into a network via time-delayed coupling may result in the change of network equilibrium stability under parameter variation in the coupling. In this work, we focus on cases where a Hopf bifurcation causes the change of stability of the network equilibrium and leads to the occurrence of oscillatory modes (patterns). Moreover, some of these patterns can co-exist for the same set of coupling parameters, which makes the analysis by means of common methods, such as the Lyapunov-Krasovskii method or the analysis of Poincaré maps, cumbersome. A numerically efficient algorithm, aiming at the computation of the oscillatory patterns occurring in such networks, is presented. Moreover, we show that our approach is able to deal with co-existing patterns, and both stable and unstable regimes can be simultaneously computed, which gives deep insight into the network dynamics. In order to illustrate the efficiency of the method, we present two examples in which the instability of the network equilibria is caused by a subcritical and a supercritical Hopf bifurcation. In addition, a bifurcation analysis of the subcritical case is performed in order to further explain the occurrence of the detected coexisting modes.

Building Decision-making Indicators Through Network Analysis of Big Data

In recent years we have witnessed a growing concern in scientific research to understand and improve actionable analytics-driven decision processes, mostly focused on online data. Many researchers have focused their attention on computational and Information and Communications Technology issues in this matter. Only a small share of literature is concerned with how indicators can be improved by Big Data analytics. In this paper, we propose an innovative methodological approach to building indicators by combining Big Data analytics with the analysis of network patterns. Our study aims to define relational structures in a Big Data set, implementing measurements and clustering methods by Network Analysis in order to build decision-making indicators. We describe an audience model both to collect a large amount of online data from large online newspapers and to structure those in a relational form. By analysing readers’ comments, we can derive proxies of reliable indicators about specific topics discussed on an online newspaper blog. We show the effectiveness of such an approach in detecting and building indicators to support policy-makers in complex decision-making processes.

Microbial community composition, co-occurrence network pattern and nitrogen transformation genera response to biochar addition in cattle manure-maize straw composting

A better understanding of the microbial group influencing nitrogen (N) dynamics and cycling in composting matrix is critical in achieving good management to alleviate N loss and improve final compost quality. This study investigated the bacterial composition, structure, co-occurrence network patterns and topological roles of N transformation in cattle manure-maize straw composting using high-throughput sequencing. The two treatments used in this experiment were cattle manure and maize straw mixture (CM) and CM with 10% biochar addition (CMB). In both treatments, the bacterial community composition varied during composting and the major phyla included Actinobacteria, Firmicutes, Proteobacteria, Bacteroidetes and Chloroflexi. The phyla Actinobacteria and Proteobacteria were more abundant in CMB treatment while Firmicutes was abundant in CM piles. The metabolic functional profiles of bacteria was predicted using the “phylogenetic investigation of communities by reconstruction of unobserved states” (PICRUSt) which revealed that except for cellular processes pathway, CMB had slight higher abundance in metabolism, genetic information processing and environmental information processing than the CM. Pearson correlation revealed more significant relationship between the important bacteria communities and N transformation in CMB piles compared with CM. Furthermore, network pattern analysis revealed that the bacterial networks in biochar amended piles are more complex and harbored more positive links than that of no biochar piles. Corresponding agreement of multivariate analyses (correlation heatmap, stepwise regression, Path and network analyses) revealed that Psychrobacter, Thermopolyspora and Thermobifida in CM while Corynebacterium_1, Thermomonospora and Streptomyces in CMB were key bacterial genera affecting NH4+-N, NO3−-N and total nitrogen (TN) transformation respectively during composting process. These results provide insight into nitrogen transformation and co-occurrence patterns mediating microbes and bacterial metabolism which could be useful in enhancing compost quality and mitigating N loss during composting.

Pattern Analysis in Networks of Diffusively Coupled Lur’e Systems

In this paper, a method for pattern analysis in networks of diffusively coupled nonlinear systems of Lur’e form is presented. We consider a class of nonlinear systems which are globally asymptotically stable in isolation. Interconnecting such systems into a network via diffusive coupling can result in persistent oscillatory behavior, which may lead to pattern formation in the coupled systems. Some of these patterns may coexist and can even all be locally stable, i.e. the network dynamics can be multistable. Multistability makes the application of common analysis methods, such as the direct Lyapunov method, highly involved. We develop a numerically efficient method in order to analyze the oscillatory behavior occurring in such networks. We focus on networks of Lur’e systems in which the oscillations appear via a Hopf bifurcation with the (diffusively) coupling strength as a bifurcation parameter and therefore display sinusoidal-like behavior in the neighborhood of the bifurcation point. Using the describing function method, we replace nonlinearities with their linear approximations. Then we analyze the system of linear equations by means of the multivariable harmonic balance method. We show that the multivariable harmonic balance method is able to accurately predict patterns that appear in such a network, even if multiple patterns coexist.

Network-scape metric analysis: a new approach for the pattern analysis of urban road networks

ABSTRACTThe spatial patterns of road networks reflect the morphological and structural characteristics of cities. Previous studies have focused mainly on seeking universal laws in road networks rather than explaining their differences. A new approach for road network pattern analysis is proposed that has been inspired by landscape metric analysis. The utility of this approach is illustrated through (but is not limited to) the extraction of the main factors in network landscapes, or network-scapes for short. Twenty-four metrics were calculated for network-scapes of 100 cities worldwide, before an exploratory analysis is performed to detect the main factors. Four main factors were revealed and may be regarded as the characteristic indicators of road networks, which were identified to be evenness, richness-density, shape irregularity, and size and shape variation. The meanings of these factors are explained, and their spatial distributions are illustrated. Compared to existing road network analytics, these factors depict better the characteristic differences of road networks. The proposed approach provides a new framework for road network pattern analysis from a cellular perspective.

Learning Hyperedge Replacement Grammars for Graph Generation

The discovery and analysis of network patterns are central to the scientific enterprise. In the present work, we developed and evaluated a new approach that learns the building blocks of graphs that can be used to understand and generate new realistic graphs. Our key insight is that a graph's clique tree encodes robust and precise information. We show that a Hyperedge Replacement Grammar (HRG) can be extracted from the clique tree, and we develop a fixed-size graph generation algorithm that can be used to produce new graphs of a specified size. In experiments on large real-world graphs, we show that graphs generated from the HRG approach exhibit a diverse range of properties that are similar to those found in the original networks. In addition to graph properties like degree or eigenvector centrality, what a graph "looks like" ultimately depends on small details in local graph substructures that are difficult to define at a global level. We show that the HRG model can also preserve these local substructures when generating new graphs.

Neurohemodynamic correlates of antonym generation in bilinguals

Objectives: Bilingualism and multilingualism are the norms of the society. The study was undertaken to assess the neural systems of language in bilinguals by means of antonym generation in Tamil to Tamil (TT) language, English to English (EE) language, and code-switching between Tamil to English (TE) using functional magnetic resonance imaging (fMRI). Methods: Proficient 16 Tamil-native speakers from rural Tamil Nadu, South India, participated in the study. Tamil (L1) was the first language and English (L2) was the second language in Cognitive Neuro Centre, NIMHANS. Single assessment design was used. Antonym generation task was used for the study with fMRI. Results: TT task uniquely activated right frontotemporal gyrus along with left caudate and lentiform nucleus. TE activated left parahippocampal and right cerebellar tonsil. Using conjunction analysis, it was found that during TT task, robust activations were present in multiple bilateral prefrontal areas, premotor area, bilateral insula, bilateral lingual gyrus, claustrum, and bilateral cerebellum. Common areas for TT and EE are precentral gyrus, cingulate gyrus, and right dentate. However, EE and TE activated bilateral parietal gyrus, cingulate gyrus, bilateral fusiform gyrus, angular gyrus bilateral thalamus, bilateral culmen, and right inferior semilunar lobule on the blood oxygenation level dependent of fMRI. Conclusions: The nature of the language produces unique neural circuits. The language processing in the brain requires executive processes and cognitive controls. The study has implications for cognitive network pattern analysis which could possibly aid in rehabilitation.

Network patterns and social architecture in Massively Multiplayer Online Games: Mapping the social world of EverQuest II

This paper presents a critical examination of the social interactions among Massively Multiplayer Online Game (MMOG) participants. Drawing on the conceptual framework of “third places” and prior empirical studies, this study aims to map the social world of a popular MMOG, EverQuest II, primarily relying on unobtrusively collected behavioral server logs. Analysis of network patterns revealed that the social architecture of the world was quite effective in shaping the structure of interaction, as the involvement in various social networks was influenced by class choice and character level. However, sociability among players was quite diffuse, with a sizable number of players opting to play solo despite the built-in mechanisms that encourage collaborative play.

PredictingIn SilicoWhich Mixtures of the Natural Products of Plants Might Most Effectively Kill Human Leukemia Cells?

The aim of the analysis of just 13 natural products of plants was to predict the most likely effective artificial mixtures of 2-3 most effective natural products on leukemia cells from over 364 possible mixtures. The natural product selected included resveratrol, honokiol, chrysin, limonene, cholecalciferol, cerulenin, aloe emodin, and salicin and had over 600 potential protein targets. Target profiling used the Ontomine set of tools for literature searches of potential binding proteins, binding constant predictions, binding site predictions, and pathway network pattern analysis. The analyses indicated that 6 of the 13 natural products predicted binding proteins which were important targets for established cancer treatments. Improvements in effectiveness were predicted for artificial combinations of 2 or 3 natural products. That effect might be attributed to drug synergism rather than increased numbers of binding proteins bound (dose effects). Among natural products, the combinations of aloe emodin with mevinolin and honokiol were predicted to be the most effective combination for AML-related predicted binding proteins. Therefore, plant extracts may in future provide more effective medicines than the single purified natural products of modern medicine, in some cases.

A network view of the transmission of sexually transmitted infections in Manitoba, Canada

Diez Roux has used the concept of complex systems to describe approaches for the incorporation of social factors into health research. These systems consist of heterogeneous interdependent units that also...

BISON: bio-interface for the semi-global analysis of network patterns

BackgroundThe large amount of genomics data that have accumulated over the past decade require extensive data mining. However, the global nature of data mining, which includes pattern mining, poses difficulties for users who want to study specific questions in a more local environment. This creates a need for techniques that allow a localized analysis of globally determined patterns.ResultsWe developed a tool that determines and evaluates global patterns based on protein property and network information, while providing all the benefits of a perspective that is targeted at biologist users with specific goals and interests. Our tool uses our own data mining techniques, integrated into current visualization and navigation techniques. The functionality of the tool is discussed in the context of the transcriptional network of regulation in the enteric bacterium Escherichia coli. Two biological questions were asked: (i) Which functional categories of proteins (identified by hidden Markov models) are regulated by a regulator with a specific domain? (ii) Which regulators are involved in the regulation of proteins that contain a common hidden Markov model? Using these examples, we explain the gene-centered and pattern-centered analysis that the tool permits.ConclusionIn summary, we have a tool that can be used for a wide variety of applications in biology, medicine, or agriculture. The pattern mining engine is global in the way that patterns are determined across the entire network. The tool still permits a localized analysis for users who want to analyze a subportion of the total network. We have named the tool BISON (Bio-Interface for the Semi-global analysis Of Network patterns).

SOME OBSERVATIONS ON THE STRUCTURE OF SOCIO-ECONOMIC CELLULAR NETWORKS

The paper has presented an approach to cellular network pattern analysis which is novel in several respects. First, the approach emphasizes the consideration of the complete geometric structure of the pattern rather than the examination of summary statistics for individual variables or correlations between pairs or other combinations of variables. By comparing empirical structures with model-generated ones it is suggested that the approach offers a way of evaluating the relative impact of the three major forces thought to be responsible for the geometric structure of a network, i.e., the generating process, the phenomena expressed in the network, and the function of the network. It was demonstrated how different processes can lead to diverse geometric structures. Examination of two empirical networks composed of bus service centre hinterlands suggested that the generating process was the dominant determinant of physical structure in this particular instance. However, more research on other cellular networks consisting of different phenomena, and on different generative processes, must be undertaken before any firm conclusions can be reached. The approach taken here would seem to offer one way of undertaking such research.