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 power-law, 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.
Administrative Personnel ; Aedes ; Arboviruses ; Chikungunya virus ; Critical Period (Psychology) ; Dengue Virus ; Dengue ; Humans ; Methods ; Zika Virus

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

Purpose: Allgrove syndrome, also known as “triple A” syndrome, is characterized by adrenal insufficiency, achalasia, and alacrimia. When neurological signs are also present, the condition is referred to as “4 A” syndrome. Methods: We conducted a retrospective analysis of three patients with 4 A syndrome confirmed genetically. A complete neurological exam was carried out by an experimented neurologist. Results: Herein, we describe the neurological characteristics often associated with this condition, through the clinical and electrophysiological analysis of three patients. All patients exhibited a mutation in AAAS, the gene coding for ALADIN. While these individuals presented with the classic features of triple-A syndrome, neurological symptoms were not prominent. Conclusion The neurological manifestations of Allgrove syndrome have historically been overlooked and inadequately explored. Due to the condition’s rarity and substantial phenotypic heterogeneity, only recently have a variety of symptoms been recognized and described.