Modeling the Internet of Things,Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach

Background

Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems.

Purpose

It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem.

Method

We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy.

Results

The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.     

Studies of plant colonisation by closely related Bacillus amyloliquefaciens biocontrol agents using strain specific quantitative PCR assays

Certain strains of Bacillus amyloliquefaciens can colonize plants and improve growth and stress management. In order to study these effects, bacterial growth dynamics on plants and in the rhizosphere are of interest calling for specific analytical tools. For that purpose, quantitative real-time PCR (qPCR) assays were developed in order to differentiate among three closely related B. amyloliquefaciens subsp. plantarum strains (UCMB5033, UCMB5036, UCMB5113) and to determine their levels with high accuracy. Oligonucleotide primers were designed for strain unique gene sequences and used for SYBR green based qPCR analysis. Standard curves covered a wide linear range (10⁶) of DNA amounts with the lowest detection level at 50 fg. Post-reaction melting curve analysis showed only a single product. Accurate threshold cycles were obtained, even in the presence of high excess of related Bacillus strains and total bacterial DNA from soil. Analysis of Bacillus colonisation after seed treatment of two oilseed rape cultivars (Oase and Ritz) grown on agar support showed a time dependent effect but that the bacteria mostly were found on root tissues and little on green tissues. The colonisation on plants grown in soil varied among the Bacillus strains where Oase seemed to house more bacteria than Ritz. Applied as a mixture, all three Bacillus strains co-existed on the roots of plants grown in soil. The qPCR assay in combination with other techniques will be a powerful tool to study plant interactions of these B. amyloliquefaciens biocontrol agents to further understand the requirements for successful interactions and improvement of plant properties.     

Molecular docking analysis of bioactive compounds from Plectranthus amboinicus with glucokinase

Natural remedies from medicinal plants are known to be effective and reliable appropriate medicine for illnesses. The current research examined Plectranthus amboinicus'' anti diabetic property by docking the bioactive compounds of certain target proteins. We document the molecular docking analysis of bioactive compounds from Plectranthus amboinicus with protein Glucokinase. Molecular docking experiments were carried out in PyRx software. Results of these docking experiments showed that most of the compounds showed very strong interaction with the target protein Glucokinase. Based on the scoring parameters we have selected best four compounds (Rutin, Salvianolic acid, Luteolin and Salvigenin) which showed very good docking score and hydrogen bond interaction for diabetics.     

Role of HLA DRB1*15 and HLA DRB1*16alleles in the genetic susceptibility to develop systemic lupus erythematosus (SLE) after Chikungunya and Zika viruses infection in México

Systemic lupus erythematosus (SLE) is a clinically and genetically heterogeneous disease particularly prevalent in Mexico. Althoughits etiology is unknown, genetic factors strongly influence its presenceas well as triggering factors, such as viral infections, including Cytomegalovirus and Epstein-Barr virus. Here,the study presents the appearance of de novoSLE (patients who did not present SLE before de virus infection, corroborated by serological analysis and negative for antinuclear antibodies) cases in Mexicans who live near the southern border of Mexico, who presented clinical symptoms of arthritic, hematological, mucocutaneous and renal SLE, after Zika and/ or Chikungunya virus infection. Low resolution class Ⅱ HLA typing was performed, which found a significantly increased frequency of HLA DRB1*02 (15 and 16)when compared to a group of 99 healthy individuals (P =0.001, OR=4.5, IC95% 1.8~11.0). All the patients were diagnosed with SLE 1 to 3 years after being confirmed with the Zika, and/or Chikungunya infection. At the point of acute viral infection, none of the patients presented clinical signs or symptoms of autoimmunity or were negative for antinuclear antibodies. In genetically susceptible individuals, Zika and Chikungunya viral infection can trigger SLE.     

Monitoring diabetes in patients with and without rheumatoid arthritis: a Medicare study

Introduction

Diabetes mellitus is a key predictor of mortality in rheumatoid arthritis (RA) patients. Both RA and diabetes increase the risk of cardiovascular disease (CVD), yet understanding of how comorbid RA impacts the receipt of guideline-based diabetes care is limited. The purpose of this study was to examine how the presence of RA affected hemoglobin A1C (A1c) and lipid measurement in older adults with diabetes.

Methods

Using a retrospective cohort approach, we identified beneficiaries ≥65 years old with diabetes from a 5% random national sample of 2004 to 2005 Medicare patients (N = 256,331), then examined whether these patients had comorbid RA and whether they received guideline recommended A1c and lipid testing in 2006. Multivariate logistic regression was used to examine the effect of RA on receiving guideline recommended testing, adjusting for baseline sociodemographics, comorbidities and health care utilization.

Results

Two percent of diabetes patients had comorbid RA (N = 5,572). Diabetes patients with comorbid RA were more likely than those without RA to have baseline cardiovascular disease (such as 17% more congestive heart failure), diabetes-related complications including kidney disease (19% higher), lower extremity ulcers (77% higher) and peripheral vascular disease (32% higher). In adjusted models, diabetes patients with RA were less likely to receive recommended A1c testing (odds ratio (OR) 0.84, CI 0.80 to 0.89) than those without RA, but were slightly more likely to receive lipid testing (OR 1.08, CI 1.01 to 1.16).

Conclusions

In older adults with diabetes, the presence of comorbid RA predicted lower rates of A1c testing but slightly improved lipid testing. Future research should examine strategies to improve A1c testing in patients with diabetes and RA, in light of increased CVD and microvascular risks in patients with both conditions.     

Towards a Methodology for Validation of Centrality Measures in Complex Networks

Background

Living systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important?

Purpose

The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets.

Method

We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary''s Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes.

Results

Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.     

Formal modeling and analysis of the MAL-associated biological regulatory network: insight into cerebral malaria

The discrete modeling formalism of René Thomas is a well known approach for the modeling and analysis of Biological Regulatory Networks (BRNs). This formalism uses a set of parameters which reflect the dynamics of the BRN under study. These parameters are initially unknown but may be deduced from the appropriately chosen observed dynamics of a BRN. The discrete model can be further enriched by using the model checking tool HyTech along with delay parameters. This paves the way to accurately analyse a BRN and to make predictions about critical trajectories which lead to a normal or diseased response. In this paper, we apply the formal discrete and hybrid (discrete and continuous) modeling approaches to characterize behavior of the BRN associated with MyD88-adapter-like (MAL)--a key protein involved with innate immune response to infections. In order to demonstrate the practical effectiveness of our current work, different trajectories and corresponding conditions that may lead to the development of cerebral malaria (CM) are identified. Our results suggest that the system converges towards hyperinflammation if Bruton's tyrosine kinase (BTK) remains constitutively active along with pre-existing high cytokine levels which may play an important role in CM pathogenesis.