scholarly journals A Scale-Free, Fully Connected Global Transition Network Underlies Known Microbiome Diversity

mSystems ◽  
2021 ◽  
Author(s):  
Gongchao Jing ◽  
Yufeng Zhang ◽  
Lu Liu ◽  
Zengbin Wang ◽  
Zheng Sun ◽  
...  
Keyword(s):  
Structural Similarity ◽  
Global Features ◽  
Scale Free ◽  
Microbiome Diversity ◽  
Transition Network ◽  
Fully Connected ◽  
Global Transition

Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here, we propose as a solution a search-based microbiome transition network.

scholarly journals A scale-free, fully connected global transition network underlies known microbiome diversity

2020 ◽  
Author(s):  
Gongchao Jing ◽  
Yufeng Zhang ◽  
Lu Liu ◽  
Zengbin Wang ◽  
Zheng Sun ◽  
...  
Keyword(s):  
Minimum Spanning Tree ◽  
Structural Similarity ◽  
Global Scale ◽  
Global Features ◽  
Origin And Evolution ◽  
Scale Free ◽  
Microbiome Diversity ◽  
Transition Network ◽  
Fully Connected ◽  
High Degree

AbstractMicrobiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here we propose as solution a search-based microbiome transition network. By traversing a composition-similarity based network of 177,022 microbiomes, we show that although the compositions are distinct by habitat, each microbiome is on-average only seven neighbors from any other microbiome on Earth, indicating the inherent homology of microbiome at the global scale. This network is scale-free, suggesting a high degree of stability and robustness in microbiome transition. By tracking the minimum spanning tree in this network, a global roadmap of microbiome dispersal was derived that tracks the potential paths of formulating and propagating microbiome diversity. Such search-based global microbiome networks, reconstructed within hours on just one computing node, provide a readily expanded reference for tracing the origin and evolution of existing or new microbiomes.

scholarly journals Dynamics of tax evasion through an epidemic-like model

2017 ◽  
Vol 28 (02) ◽  
pp. 1750023 ◽  
Author(s):  
Rafael M. Brum ◽  
Nuno Crokidakis
Keyword(s):  
Social Interactions ◽  
Tax Evasion ◽  
Nonequilibrium Phase Transition ◽  
Epidemic Spreading ◽  
Scale Free ◽  
Scale Free Networks ◽  
Random Complex ◽  
Fully Connected ◽  
Tax Payers ◽  
Fixed Population

In this work, we study a model of tax evasion. We considered a fixed population divided in three compartments, namely honest tax payers, tax evaders and a third class between the mentioned two, which we call susceptibles to become evaders. The transitions among those compartments are ruled by probabilities, similarly to a model of epidemic spreading. These probabilities model social interactions among the individuals, as well as the government’s fiscalization. We simulate the model on fully-connected graphs, as well as on scale-free and random complex networks. For the fully-connected and random graph cases, we observe that the emergence of tax evaders in the population is associated with an active-absorbing nonequilibrium phase transition, that is absent in scale-free networks.

scholarly journals How to plug out of the networks in jeopardy of ASF, Covid-19, social media or markets toxicity

E-methodology ◽  
2021 ◽  
Vol 7 (7) ◽  
pp. 71-84
Author(s):  
ANDRZEJ BUDA ◽  
KATARZYNA KUŹMICZ
Keyword(s):  
Social Media ◽  
Preferential Attachment ◽  
Global Network ◽  
Epidemic Threshold ◽  
Agent Based ◽  
Scale Free ◽  
Nearest Neighbours ◽  
Fully Connected ◽  
Node Removal ◽  
Parameter Values

Aim: In our research, we examine universal properties of the global network whose structure represents a real-world network that might be later extended to social media, commodity market or countries under the infl uence of diseases like Covid-19 or ASF.Methods: We propose quasi-epidemiological agent-based model of virus spread on a network. Firstly, we consider countries represented by subnetworks that have a scale-free structure achieved by the preferential attachment construction with a node hierarchy and binary edges. The global network of countries is a complete, directed, weighted network of thesesubnetworks connected by their capitals and divided into cultural and geographical proximity. Viruses with a defi ned strength or aggressiveness occur independently at one of the nodes of a selected subnetwork and correspond to a piece of products or messages or diseases.Results and conclusion: We analyse dynamics set by varying parameter values and observe a variety of phenomena including local and global pandemics and the existence of an epidemic threshold in the subnetworks. These phenomena have been also shown fromindividual users points of view because the node removal from the network might have impact on its nearest neighbours differently. The selective participation in global network is proposed here to avoid side effects when the global network has been fully connected and no longer divided into clusters.

scholarly journals Video Captioning Based on Channel Soft Attention and Semantic Reconstructor

2021 ◽  
Vol 13 (2) ◽  
pp. 55
Author(s):  
Zhou Lei ◽  
Yiyong Huang
Keyword(s):  
Temporal Structure ◽  
Video Content ◽  
Global Features ◽  
Time Step ◽  
Video Captioning ◽  
3 Dimensional ◽  
Spatial Features ◽  
Video Feature ◽  
Fully Connected

Video captioning is a popular task which automatically generates a natural-language sentence to describe video content. Previous video captioning works mainly use the encoder–decoder framework and exploit special techniques such as attention mechanisms to improve the quality of generated sentences. In addition, most attention mechanisms focus on global features and spatial features. However, global features are usually fully connected features. Recurrent convolution networks (RCNs) receive 3-dimensional features as input at each time step, but the temporal structure of each channel at each time step has been ignored, which provide temporal relation information of each channel. In this paper, a video captioning model based on channel soft attention and semantic reconstructor is proposed, which considers the global information for each channel. In a video feature map sequence, the same channel of every time step is generated by the same convolutional kernel. We selectively collect the features generated by each convolutional kernel and then input the weighted sum of each channel to RCN at each time step to encode video representation. Furthermore, a semantic reconstructor is proposed to rebuild semantic vectors to ensure the integrity of semantic information in the training process, which takes advantage of both forward (semantic to sentence) and backward (sentence to semantic) flows. Experimental results on popular datasets MSVD and MSR-VTT demonstrate the effectiveness and feasibility of our model.

scholarly journals A study on the effects of diffusion of information on epidemic spread

2019 ◽  
Vol 10 (03) ◽  
pp. 1950015 ◽  
Author(s):  
Semra Gündüç
Keyword(s):  
Direct Contact ◽  
Information Diffusion ◽  
Weighted Average ◽  
Scale Free ◽  
Diffusion Of Information ◽  
Scale Free Networks ◽  
Underlying Network ◽  
Fully Connected ◽  
Taking Action

In this work, the spread of a contagious disease on a society where the individuals may take precautions is modeled. The primary assumption is that the infected individuals transmit the infection to the susceptible members of the community through direct contact interactions. In the meantime, the susceptibles gather information from the adjacent sites which may lead to taking precautions. The SIR model is used for the diffusion of infection while the Bass equation models the information diffusion. The sociological classification of the individuals indicates that a small percentage of the population takes action immediately after being informed, while the majority expects to see some real advantage of taking action. The individuals are assumed to take two different precautions. The precursory measures are getting vaccinated or trying to avoid direct contact with the neighbors. A weighted average of states of the neighbors leads to the choice of action. The fully connected and scale-free Networks are employed as the underlying network of interactions. The comparison between the simple contagion diffusion and the diffusion of infection in a responsive society showed that a very limited precaution makes a considerable difference in the speed and the size of the spread of illness. Particularly, highly connected hub nodes play an essential role in the reduction of the spread of disease.

scholarly journals Graph-structured populations and the Hill–Robertson effect

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Peter A. Whigham ◽  
Hamish G. Spencer
Keyword(s):  
Population Structure ◽  
Structured Populations ◽  
Scale Free Network ◽  
Scale Free ◽  
Regular Structures ◽  
Diallelic Locus ◽  
Free Network ◽  
Fully Connected ◽  
Regular Networks ◽  
The Hill

The Hill–Robertson effect describes how, in a finite panmictic diploid population, selection at one diallelic locus reduces the fixation probability of a selectively favoured allele at a second, linked diallelic locus. Here we investigate the influence of population structure on the Hill–Robertson effect in a population of size N . We model population structure as a network by assuming that individuals occupy nodes on a graph connected by edges that link members who can reproduce with each other. Three regular networks (fully connected, ring and torus), two forms of scale-free network and a star are examined. We find that (i) the effect of population structure on the probability of fixation of the favourable allele is invariant for regular structures, but on some scale-free networks and a star, this probability is greatly reduced; (ii) compared to a panmictic population, the mean time to fixation of the favoured allele is much greater on a ring, torus and linear scale-free network, but much less on power-2 scale-free and star networks; (iii) the likelihood with which each of the four possible haplotypes eventually fix is similar across regular networks, but scale-free populations and the star are consistently less likely and much faster to fix the optimal haplotype; (iv) increasing recombination increases the likelihood of fixing the favoured haplotype across all structures, whereas the time to fixation of that haplotype usually increased, and (v) star-like structures were overwhelmingly likely to fix the least fit haplotype and did so significantly more rapidly than other populations. Last, we find that small ( N < 64) panmictic populations do not exhibit the scaling property expected from Hill & Robertson (1966 Genet. Res. 8 , 269–294. ( doi:10.1017/S0016672300010156 )).

scholarly journals Thermal Drift Correction for Laboratory Nano Computed Tomography via Outlier Elimination and Feature Point Adjustment

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8493
Author(s):  
Mengnan Liu ◽  
Yu Han ◽  
Xiaoqi Xi ◽  
Siyu Tan ◽  
Jian Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Structural Similarity ◽  
Single Step ◽  
Thermal Drift ◽  
Global Features ◽  
Low Contrast ◽  
Speeded Up Robust Features ◽  
Transmission Imaging ◽  
Feature Based

Thermal drift of nano-computed tomography (CT) adversely affects the accurate reconstruction of objects. However, feature-based reference scan correction methods are sometimes unstable for images with similar texture and low contrast. In this study, based on the geometric position of features and the structural similarity (SSIM) of projections, a rough-to-refined rigid alignment method is proposed to align the projection. Using the proposed method, the thermal drift artifacts in reconstructed slices are reduced. Firstly, the initial features are obtained by speeded up robust features (SURF). Then, the outliers are roughly eliminated by the geometric position of global features. The features are refined by the SSIM between the main and reference projections. Subsequently, the SSIM between the neighborhood images of features are used to relocate the features. Finally, the new features are used to align the projections. The two-dimensional (2D) transmission imaging experiments reveal that the proposed method provides more accurate and robust results than the random sample consensus (RANSAC) and locality preserving matching (LPM) methods. For three-dimensional (3D) imaging correction, the proposed method is compared with the commonly used enhanced correlation coefficient (ECC) method and single-step discrete Fourier transform (DFT) algorithm. The results reveal that proposed method can retain the details more faithfully.

A syntactic dependency network approach to the study of translational language

2020 ◽  
Author(s):  
Lu Fan ◽  
Yue Jiang
Keyword(s):  
Complex Network ◽  
Native Language ◽  
Small World ◽  
Network Approach ◽  
Global Features ◽  
Scale Free ◽  
Source Language ◽  
Network Parameters ◽  
Dependency Network ◽  
Syntactic Dependency

Abstract Complex network approach provides language research with quantitative measures that can capture global features of language. Although translational language has been recognized as a ‘third code’ by some researchers, its independence still calls for further and quantitative validation in an overall manner. In this study, we intend to examine this independence and explore comprehensively its features. We investigated macroscopically translational language from English into Chinese and from Chinese into English by comparing with its source language and native language through syntactic dependency networks. The results show that: (1) translational language presents small-world and scale-free properties like most languages do; (2) however, it is independent of and different from both source language and native language in terms of its network parameters; (3) its network parameters show values eclectic between source language and native language, and this eclectic tendency may be regarded as a new candidate for universal features of translational language, which certainly needs further validation in other genres and language pairs. This study also corroborates that quantitative linguistic method of complex network approach can be well utilized in the study of translational language.

scholarly journals A Sequence-based Antibody Paratope Prediction Model Through Combing Local-Global Information and Partner Features

2021 ◽  
Author(s):  
Shuai Lu ◽  
Yuguang Li ◽  
Xiaofei Nan ◽  
Shoutao Zhang
Keyword(s):  
Short Term Memory ◽  
Vital Role ◽  
Superior Performance ◽  
Global Features ◽  
Benchmark Datasets ◽  
Local And Global Features ◽  
Antibody Sequence ◽  
Antigen Interaction ◽  
Window Approach ◽  
Fully Connected

Antibodies are proteins which play a vital role in the immune system by recognizing and neutralizing antigens. The region on the antibody binds to the antigens, also known as paratope, mediates antibody-antigen interaction with high affinity and specificity. And the accurate prediction of those regions from antibody sequence contributes to the design of therapeutic antibodies and remains challenging. However, the experimental methods are time-consuming and expensive. In this article, we propose a sequence-based method for antibody paratope prediction by combing the local and global features of antibody sequence and global features of partner antigen sequence. For extracting local features, we use Convolution Neural Networks(CNNs) and a sliding window approach on antibody sequence. For extracting global features, we use Attention-based Bidirectional Long Short-Term Memory(Att-BLSTM) networks on antibody sequence. For extracting partner features, we employ Att-BLSTM on the partner antigen sequence as well. And then, all features are combined to predict antibody paratope by fully-connected networks. The experiments show that our proposed method achieves superior performance over the state-of-the-art sequenced-based antibody paratope prediction methods on benchmark datasets.

Approaching textual coherence of machine translation with complex network

2020 ◽  
Vol 31 (12) ◽  
pp. 2050175
Author(s):  
Jiang Niu ◽  
Yue Jiang ◽  
Yadong Zhou
Keyword(s):  
Complex Networks ◽  
Machine Translation ◽  
Complex Network ◽  
Systemic Functional Linguistics ◽  
Small World ◽  
Building Blocks ◽  
Network Construction ◽  
Scale Free ◽  
Network Diameter ◽  
Fully Connected

This study analyzes topological properties of complex networks of textual coherence, and investigates the textual coherence of machine translation by contrasting these properties in machine-translated texts with those in a human-translated text. The complex networks of textual coherence are built by drawing on the knowledge from Systemic Functional Linguistics, with Themes and Rhemes denoted as vertices and the semantic connections between them as edges. It is found that the coherence networks are small-world, assortatively mixed, scale-free with an exponential cut-off, and hub-dependent. The basic building blocks consist of fully-connected triads and fully-connected squares, with the latter playing a more significant role in the network construction. Compared with the complex network of human translation, the networks of machine translations have fewer vertices and edges, lower average degree, smaller network diameter, shorter average path length, larger cluster coefficient, bigger assortativeness coefficient and more types of motifs. Thus, we suggest that the machine-translated texts are sparsely, locally, unevenly and monotonously connected, which may account for why and how machine translation is weak in coherence. This study is the first effort ever to employ complex networks to explore textual coherence of machine translations. It may hopefully promote the cross-disciplinary interaction between linguistics, computer science and network science.

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