Robustness of dengue complex network under targeted versus random attack

Dengue virus infection is one of those epidemic diseases that require much consideration in order to save the humankind from its unsafe impacts. According to the World Health Organization (WHO), 3.6 billion individuals are at risk because of the dengue virus sickness. Researchers are striving to comprehend the dengue threat. This study is a little commitment to those endeavors. To observe the robustness of the dengue network, we uprooted the links between nodes randomly and targeted by utilizing different centrality measures. The outcomes demonstrated that 5% targeted attack is equivalent to the result of 65% random assault, which showed the topology of this complex network validated a scale-free network instead of random network. Four centrality measures (Degree, Closeness, Betweenness, and Eigenvector) have been ascertained to look for focal hubs. It has been observed through the results in this study that robustness of a node and links depends on topology of the network. The dengue epidemic network presented robust behaviour under random attack, and this network turned out to be more vulnerable when the hubs of higher degree have higher probability to fail. Moreover, representation of this network has been projected, and hub removal impact has been shown on the real map of Gombak (Malaysia)

Nature of complex network of dengue epidemic as a scale-free network

Objectives: Dengue epidemic is a dynamic and complex phenomenon that has gained considerable attention due to its injurious effects. The focus of this study is to statically analyze the nature of the dengue epidemic network in terms of whether it follows the features of a scale-free network or a random network. Methods: A multifarious network of Aedes aegypti is addressed keeping the viewpoint of a complex system and modelled as a network. The dengue network has been transformed into a one-mode network from a two-mode network by utilizing projection methods. Furthermore, three network features have been analyzed, the power-law, clustering coefficient, and network visualization. In addition, five methods have been applied to calculate the global clustering coefficient. Results: It has been observed that dengue epidemic follows a powerlaw, with the value of its exponent γ = –2.1. The value of the clustering coefficient is high for dengue cases, as weight of links. The minimum method showed the highest value among the methods used to calculate the coefficient. Network visualization showed the main areas. Moreover, the dengue situation did not remain the same throughout the observed period. Conclusions: The results showed that the network topology exhibits the features of a scale-free network instead of a random network. Focal hubs are highlighted and the critical period is found. Outcomes are important for the researchers, health officials, and policy makers who deal with arbovirus epidemic diseases. Zika virus and Chikungunya virus can also be modelled and analyzed in this manner. © 2019 The Korean Society of Medical Informatics

Network Formation and Analysis of Dengue Complex Network

Several efforts have been made and are constantly being made to keep the Aedes aegypti virus under control. Numerous scholars are involved in the study of medicine, while others are working in computer science and mathematics to model the spread of this disease. This study will help to comprehend how this epidemic sickness behaves. A complex network has been established from the complex dengue phenomenon. We have evaluated dengue network topology by pondering scale-free network properties. The network’s resilience in tracking the dengue epidemic is measured by systematically removing nodes and links. The primary hubs of this network are emphasized, and the vulnerability of the network structure has been examined through an in-depth investigation of the dengue virus’s spreading behavior. Understanding the intricate web of dengue outbreaks relies heavily on geographic representation. The applied method on the dengue epidemic network and the results will be added as scientific additions to the literature on complex networks. Different network analysis metrics have been applied (closeness centrality, betweenness centrality, eigenvector centrality, network density), and the network’s stability has been evaluated. This network is extremely vulnerable to targeted attacks; results showed that after removing 8% of focal hubs, 34% of the network is destroyed

The Effects of Spatio-Temporal Heterogeneities on the Emergence and Spread of Dengue Virus

The dengue virus (DENV) remains a considerable global public health concern. The interactions between the virus, its mosquito vectors and the human host are complex and only partially understood. Dependencies of vector ecology on environmental attributes, such as temperature and rainfall, together with host population density, introduce strong spatiotemporal heterogeneities, resulting in irregular epidemic outbreaks and asynchronous oscillations in serotype prevalence. Human movements across different spatial scales have also been implicated as important drivers of dengue epidemiology across space and time, and further create the conditions for the geographic expansion of dengue into new habitats. Previously proposed transmission models often relied on strong, unrealistic assumptions regarding key epidemiological and ecological interactions to elucidate the effects of these spatio-temporal heterogeneities on the emergence, spread and persistence of dengue. Furthermore, the computational limitations of individual based models have hindered the development of more detailed descriptions of the influence of vector ecology, environment and human mobility on dengue epidemiology. In order to address these shortcomings, the main aim of this thesis was to rigorously quantify the effects of ecological drivers on dengue epidemiology within a robust and computational efficient framework. The individual based model presented included an explicit spatial structure, vector and human movement, spatio-temporal heterogeneity in population densities, and climate effects. The flexibility of the framework allowed robust assessment of the implications of classical modelling assumptions on the basic reproduction number, R₀, demonstrating that traditional approaches grossly inflate R₀ estimates. The model's more realistic meta-population formulation was then exploited to elucidate the effects of ecological heterogeneities on dengue incidence which showed that sufficient levels of community connectivity are required for the spread and persistence of dengue virus. By fitting the individual based model to empirical data, the influence of climate and on dengue was quantified, revealing the strong benefits that cross-sectional serological data could bring to more precisely inferring ecological drivers of arboviral epidemiology. Overall, the findings presented here demonstrate the wide epidemiological landscape which ecological drivers induce, forewarning against the strong implications of generalising interpretations from one particular setting across wider spatial contexts. These findings will prove invaluable for the assessment of vector-borne control strategies, such as mosquito elimination or vaccination deployment programs

Molecular signatures of silencing suppression degeneracy from a complex RNA virus

[EN] As genomic architectures become more complex, they begin to accumulate degenerate and redundant elements. However, analyses of the molecular mechanisms underlying these genetic architecture features remain scarce, especially in compact but sufficiently complex genomes. In the present study, we followed a proteomic approach together with a computational network analysis to reveal molecular signatures of protein function degeneracy from a plant virus (as virus-host protein-protein interactions). We employed affinity purification coupled to mass spectrometry to detect several host factors interacting with two proteins of Citrus tristeza virus (p20 and p25) that are known to function as RNA silencing suppressors, using an experimental system of transient expression in a model plant. The study was expanded by considering two different isolates of the virus, and some key interactions were confirmed by bimolecular fluorescence complementation assays. We found that p20 and p25 target a common set of plant proteins including chloroplastic proteins and translation factors. Moreover, we noted that even specific targets of each viral protein overlap in function. Notably, we identified argonaute proteins (key players in RNA silencing) as reliable targets of p20. Furthermore, we found that these viral proteins preferentially do not target hubs in the host protein interactome, but elements that can transfer information by bridging different parts of the interactome. Overall, our results demonstrate that two distinct proteins encoded in the same viral genome that overlap in function also overlap in their interactions with the cell proteome, thereby highlighting an overlooked connection from a degenerate viral system.This work was supported by the grants PROMETEO/2019/012 from the Valencian Regional Government (to SA), as well as grants AGL2010-20221/AGR, BIO2017-83184-R, and PGC2018-101410-B-I00 from the Spanish Ministry of Science, Innovation, and Universities (to SA, JAD, and GR, respectively). The two latter grants were co-financed by the European Regional Development Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ambrós, S.; Gómez-Muñoz, N.; Giménez-Santamarina, S.; Sánchez-Vicente, J.; Navarro-López, J.; Martínez, F.; Daròs, J.... (2021). Molecular signatures of silencing suppression degeneracy from a complex RNA virus. PLoS Computational Biology. 17(6):1-21. https://doi.org/10.1371/journal.pcbi.1009166S12117

The Impact of Social Media on the Personality Trait of Undergraduates students: A Descriptive Analytical Approach

Personality development of an individual is a continuous process that involves multiple methods and techniques. Study on a person’s personality is conducted by examining inherited and adopted personality traits during their life span. Higher education institutes play a vital role in flourishing student’s personality and significant revolution is noticed by the researchers in this regard. Individual’s personality is being affected by use of social media including Facebook, Twitter, LinkedIn and many more. Facebook (FB) is widely used as an online social network to communicate with other individuals. It is observed that students are also users of FB for learning, interacting, recreating and browsing purposes. Our research falls in the same dimension to study student’s personality traits through their FB use and eventually analyze the change in their personality. For the same, four higher education institutes of Pakistan located in Lahore are targeted. Data is collected for five years from under graduate students from 2015-2019. It has two main purpose; firstly, to present analytical results and secondly, to examine the changes in person’s personality traits (PT) during degree program at the university. Data is collected from FB profiles through an application and Big Five Personality Trait model is used to conduct personality assessment. Five PT are studied namely; extraversion, agreeableness, openness, conscientiousness, and neuroticism. Results interpret the direct relationship of the personality trait with the use of FB and it has shown increased impact on student’s personality over the progress of the degree program. Students of all four universities came up with almost identical results that depict the similar role of an institute in the development of the student’s personality. Research findings contribute towards data science by proving analytical data on student’s personality traits. Also, it acts as guide for educational institutes to deeply cope with targeted PT of a student. Eventually, it facilitates the students to use OSN more effectively in development of their positive personality

Customer Relationship Management Practices of Megamart in the Kingdom of Bahrain

The study aims to assess Customer Relationship Management (CRM) practices of MegaMart in the Kingdom of Bahrain. In this research, to adequately address the research questions, a variety of data collection methods and instruments were used and done by conducting an empirical investigation into Customer Relationship Management (CRM), an adoption behavior of 15 branches of MegaMart in the Kingdom of Bahrain. The purpose of the study was to evaluate the Customer Relationship Management (CRM) practices of MegaMart in the Kingdom of Bahrain. The effectiveness of Customer Relationship Management (CRM) was conducted through a survey, using the questionnaire as the research instrument. The respondents of the study are employees and customers of MegaMart in the Kingdom of Bahrain. The research instrument used the five-point Likert Scale. The statistical tools utilized in the study are multiple regression analysis and weighted mean. The study provides recommendations by the researcher in assessing those questions around Customer Relationship Management (CRM) methods in the Kingdom of Bahrain. Customer Relationship Management (CRM) can be applied as a tool to enhance the practices of MegaMart in the Kingdom of Bahrain. From the study, the researcher concluded that Customer Relationship Management (CRM) findings further depict managerial implications and opportunities for future research in the same area. Keywords: Customer Relationship Management, MegaMart in the Kingdom of Bahrain, practices DOI: 10.7176/EJBM/12-12-07 Publication date: April 30th 202

Network analysis of spreading of dengue, Zika and chikungunya in the state of Bahia based on notified, confirmed and discarded cases

Despite successful results of using complex networks to model and characterize the spread of dengue cases, works to date have mainly used data from primarily reported cases, without further consideration whether they were later confirmed or not. On the other hand, a study of the interdependence of confirmed and discarded cases of arboviruses have emphasized that the co-circulation of three arboviruses—dengue, Zika and chikungunya—may have led to false diagnoses due to several similarities in the early symptoms of the three diseases on acute phase. This implies that case notifications of one disease could be confirmed cases of others, and that discarded cases must be taken into account to avoid misinterpretations of the phenomenon. In this work we investigated the consequences of including information from discarded and confirmed cases in the analysis of arbovirus networks. This is done by firstly evaluating the possible changes in the networks after removing the discarded cases from the database of each arbovirus, and secondly by verifying the cross-relationship of the indices of the networks of confirmed and discarded cases of arboviruses. As will be detailed later on, our results reveal changes in the network indices when compared to when only confirmed cases are considered. The magnitudes of the changes are directly proportional to the amount of discarded cases. The results also reveal a strong correlation between the average degree of the networks of discarded cases of dengue and confirmed cases of Zika, but only a moderate correlation between that for networks of discarded cases of dengue and confirmed cases of chikungunya. This finding is compatible with the fact that dengue and Zika diseases are caused by closely related flaviviruses, what is not the case of the chikungunya caused by a togavirus