Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases

Background: As an important autoimmune disease, type 1 diabetes (T1D) is often diagnosed in children, but due to the complexity of the etiology of diabetes and many other factors, the disease pathogenesis of diabetes is still unclear. The intestinal microbiota has been proved to have close relationships with T1D in recent years, which is one of the most important molecular bases of pathogenesis and prognosis factors for T1D. Using the multi-omics and multicenter sample analysis method, a number of intestinal microbiota in T1D have been discovered and explained, which has provided comprehensive and rich information. However, how to find more useful information and get an intuitive understanding that people need conveniently in the huge data sea has become the focus of attention. Therefore, quantitative analysis and visualization of the interaction between intestinal microbiota and T1D in children are urgently needed.Methods: We retrieved the detailed original data from the National Center for Biotechnology Information, GMREPO, and gutMEGA databases and other authoritative multiple projects with related research; the ranking of intestinal microbiota abundance from healthy people, overall T1D patients, and T1D in children (0–18 years old) were detailed analyzed, classified, and visualized.Results: A total of 515 bacterial species and 161 related genera were fully analyzed. Also, Prevotella copri was led by 21.25% average abundance, followed by Clostridium tertium of 10.39% in all-cross T1D patients. For children with T1D, Bacteroides vulgatus has high abundance in all age periods, whereas the abundance of each intestinal microbiota was more uniform in female samples, with the ranking from high to low as Bacteroides dorei 9.56%, P. copri 9.53%, Streptococcus pasteurianus 8.15%, and C. tertium 7.53%, whereas in male samples, P. copri was accounted for the largest by 22.72%. The interaction between intestinal microbiota and comparison between healthy people and children with T1D was also detailed analyzed.Conclusions: This study provides a new method and comprehensive perspectives for the evaluation of the interaction between intestinal microbiota and T1D in children. A set of useful information of intestinal microbiota with its internal interaction and connections has been presented, which could be a compact, immediate, and practical scientific reference for further molecular biological and clinical translational research of T1D in children.

Hydrogels Classification According to the Physical or Chemical Interactions and as Stimuli-Sensitive Materials

Hydrogels are attractive biomaterials with favorable characteristics due to their water uptake capacity. However, hydrogel properties are determined by the cross-linking degree and nature, the tacticity, and the crystallinity of the polymer. These biomaterials can be sorted out according to the internal structure and by their response to external factors. In this case, the internal interaction can be reversible when the internal chains are led by physicochemical interactions. These physical hydrogels can be synthesized through several techniques such as crystallization, amphiphilic copolymers, charge interactions, hydrogen bonds, stereo-complexing, and protein interactions. In contrast, the internal interaction can be irreversible through covalent cross-linking. Synthesized hydrogels by chemical interactions present a high cross-linking density and are employed using graft copolymerization, reactive functional groups, and enzymatic methods. Moreover, specific smart hydrogels have also been denoted by their external response, pH, temperature, electric, light, and enzyme. This review deeply details the type of hydrogel, either the internal structure or the external response. Furthermore, we detail some of the main applications of these hydrogels in the biomedicine field, such as drug delivery systems, scaffolds for tissue engineering, actuators, biosensors, and many other applications.

Support Vector Regression Modelling of an Aerobic Granular Sludge in Sequential Batch Reactor

Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures. The complex internal interaction between the sludge characteristics and their influent were used to develop the models. The prediction becomes harder when dealing with a limited dataset due to the limitation of the experimental works. A radial basis function algorithm with selected kernel parameters of cost and gamma was used to developed SVR models. The kernel parameters were selected by using a grid search method and were further optimized by using particle swarm optimization and genetic algorithm. The SVR models were then compared with an artificial neural network. The prediction results R2 were within >90% for all predicted concentration of COD. The results showed the potential of SVR for simulating the complex aerobic granulation process and providing an excellent tool to help predict the behaviour in aerobic granular reactors of wastewater treatment.

Thin film growth by single and multi-center DLA model: Fractal analysis

A modified Diffusion Limited Aggregation (DLA) model has been established for single and multi-center fractal growth. Number of particles [Formula: see text], size of one step [Formula: see text], deposition probability [Formula: see text], growth direction, and interaction effect are had been take into consideration for fractal analysis. In addition, the effect of internal interaction in multi-center growth have been taken into consideration. Fractal growth morphology shows strong boundary and interaction effects.

Impact of environmental changes on the dynamics of temporal networks

Dynamics of complex social systems has often been described in the framework of temporal networks, where links are considered to exist only at the moment of interaction between nodes. Such interaction patterns are not only driven by internal interaction mechanisms, but also affected by environmental changes. To investigate the impact of the environmental changes on the dynamics of temporal networks, we analyze several face-to-face interaction datasets using the multiscale entropy (MSE) method to find that the observed temporal correlations can be categorized according to the environmental similarity of datasets such as classes and break times in schools. By devising and studying a temporal network model considering a periodically changing environment as well as a preferential activation mechanism, we numerically show that our model could successfully reproduce various empirical results by the MSE method in terms of multiscale temporal correlations. Our results demonstrate that the environmental changes can play an important role in shaping the dynamics of temporal networks when the interactions between nodes are influenced by the environment of the systems.

Comprehensive nonmodularity and interaction indices for decision analysis

Nonmodularity is a prominent property of capacity that deeply links to the internal interaction phenomenon of multiple decision criteria. Following the common architectures of the simultaneous interaction indices as well as of the bipartition interaction indices, in this paper, we construct and study the notion of probabilistic nonmodularity index and also its particular cases, such as Shapely and Banzhaf nonmodularity indices, which can be used to describe the comprehensive interaction situations of decision criteria. The connections and differences among three categories of interaction indices are also investigated and compared theoretically and empirically. It is shown that three types of interaction indices have the same roots in their first and second orders, but meanwhile the nonmodularity indices have involved less amount of subsets and can be adopted to describe the interaction phenomenon in decision analysis.

GREATER EURASIA AND GREATER EUROPE: THE GEOPOLITICS OF COOPERATION PERSPECTIVES

In world history, 2020 will forever remain a year of serious humanitarian and economic upheavals that have further changed the geopolitical alignment of key actors in the system of international relations. In this regard, the concepts of Greater Eurasia and Greater Europe, which were held as two significant narratives present in the international academic and political discussion, are particularly interesting. The main questions of this research are whether consensus is possible and what are the prospects for geopolitical interaction between the projects of Greater Eurasia and Greater Europe, taking into account the interests of Russia, how will this affect the system of international relations and the environment of macro-regional security? The article discusses the opportunities for cooperation between Greater Eurasia and Greater Europe, as well as the key principles of internal interaction between the participants. Russia, being a key link in this context, can act as a guarantor of trade and economic, geostrategic and political stability. The authors conclude that Greater Eurasia and Greater Europe are geopolitical representations created by the key actors of the European and Eurasian space for various options for adapting their plans to reality, and conflicts and security challenges that arise on the periphery of these two associations and their great powers can jeopardize not only European but also the entire global security. To prevent this, countries need to review the existing Eurasian and European security architecture, based on the principles of mutual respect, cooperation and parity. The article is based entirely on qualitative analysis and does not refer to quantitative arguments or statistical data. The methodological platform for the research is the analysis based on the concept of geopolitics.

Third-Party Agreements

Third-party agreements can take many forms and can be a difficult area in which to determine clear-cut definitions. It is intended that this chapter will clarify some of the differences and identify some of the similarities.Agreements can be between any two or more organisations. It would be unusual, though not inappropriate, for there to be agreements between two departments within the same organisation. Agreements may occur in this case when, for example, a new process is introduced on a trial basis and it has not yet become ‘business as usual’ and part of the normal internal interaction.There will always be local variations in the naming of contract types, so this should be considered in interpretation.Any organisation with which there is an agreement, of whatever structure, should be validated and audited.

Center mass theorem in three dimensional spaces with constant curvature

In this paper, based on the definition of the center of mass given in [1, 2], its immobility is postulated in spaces with a constant curvature, and the problem of two particles with an internal interaction, described by a potential depending on the distance between points on a three-dimensional sphere, is considered. This approach, justified by the absence of a principle similar to the Galileo principle on the one hand and the property of isotropy of space on the other, allows us to consider the problem in the map system for the center of mass. It automatically ensures dependence only on the relative variables of the considered points. The Hamilton – Jacobi equation of the problem is formulated, its solutions and the equations of trajectories are found. It is shown that the reduced mass of the system depends on the relative distance. Given this circumstance, a modified system metric is written out.

Multicriteria correlation preference information (MCCPI) with nonadditivity index for decision aiding

MCCPI (Multiple Criteria Correlation Preference Information) is a kind of 2 dimensional decision preference information obtained by pairwise comparison on the importance and interaction of decision criteria. In this paper, we introduce the nonadditivity index to replace the Shapley simultaneous interaction index and construct an undated MCCPI based decision scheme. We firstly propose a diagram to help decision maker obtain the nonadditivity index type MCCPI, then establish transform equations to normalize them into desired capacity and finally adopt a random generation MCCPI based comprehensive decision aid algorithm to explore the dominance relationships and creditable ranking orders of all decision alternatives. An illustrative example is also given to demonstrate the feasibility and effectiveness of the proposed decision scheme. It’s shown that based on some good properties of nonadditivity index in practice, the updated MCCPI model can deal with the internal interaction among decision criteria with relatively less model construction and calculation effort.