The Evasive Enemy: Insights into the Virulence and Epidemiology of the Emerging Attaching and Effacing PathogenEscherichia albertii

ABSTRACTThe diarrheic attaching and effacing (A/E) pathogenEscherichia albertiiwas first isolated from infants in Bangladesh in 1991, although the bacterium was initially classified asHafnia alvei. Subsequent genetic and biochemical interrogation of these isolates raised concerns about their initial taxonomic placement. It was not until 2003 that these isolates were reassigned to the novel taxonEscherichia albertiibecause they were genetically more closely related toE. coli, although they had diverged sufficiently to warrant a novel species name. Unfortunately, new isolates continue to be mistyped as enteropathogenicE. coli(EPEC) or enterohemorrhagicE. coli(EHEC) owing to shared traits, most notably the ability to form A/E lesions. Consequently,E. albertiiremains an underappreciated A/E pathogen, despite multiple reports demonstrating that many provisional EPEC and EHEC isolates incriminated in disease outbreaks are actuallyE. albertii. Metagenomic studies on dozens ofE. albertiiisolates reveal a genetic architecture that boasts an arsenal of candidate virulence factors to rival that of its better-characterized cousins, EPEC and EHEC. Beyond these computational comparisons, studies addressing the regulation, structure, function, and mechanism of action of its repertoire of virulence factors are lacking. Thus, the paucity of knowledge about the epidemiology, virulence, and antibiotic resistance ofE. albertii, coupled with its misclassification and its ability to develop multidrug resistance in a single step, highlights the challenges in combating this emerging pathogen. This review seeks to synthesize our current but incomplete understanding of the biology ofE. albertii.

An Efficient Adaptive Strategy for Melody Search Algorithm

An efficient adaptive version of Melody Search algorithm (EAMS) is introduced in this study, which is a powerful tool to solve optimization problems in continuous domains. Melody search (MS) algorithm is a recent newly improved version of harmony search (HS), while the algorithm performance strongly depends on fine-tuning of its parameters. Although MS is more efficient for solving continuous optimization problems than most of other HS-based algorithms, the large number of algorithm parameters makes it difficult to use. Hence, the main objective in this study is to reduce the number of algorithm parameters and improving its efficiency. To achieve this, a novel improvisation scheme is introduced to generate new solutions, a useful procedure is developed to determine the possible variable ranges in different iterations and an adaptive strategy is employed to calculate proper parameters' values and choose suitable memory consideration rules during the evolution process. Extensive computational comparisons are carried out by employing a set of eighteen well-known benchmark optimization problems with various characteristics from the literature. The obtained results reveal that EAMS algorithm can achieve better solutions compared to some other HS variants, basic MS algorithms and certain cases of well-known robust optimization algorithms.

An Efficient Adaptive Strategy for Melody Search Algorithm

An efficient adaptive version of Melody Search algorithm (EAMS) is introduced in this study, which is a powerful tool to solve optimization problems in continuous domains. Melody search (MS) algorithm is a recent newly improved version of harmony search (HS), while the algorithm performance strongly depends on fine-tuning of its parameters. Although MS is more efficient for solving continuous optimization problems than most of other HS-based algorithms, the large number of algorithm parameters makes it difficult to use. Hence, the main objective in this study is to reduce the number of algorithm parameters and improving its efficiency. To achieve this, a novel improvisation scheme is introduced to generate new solutions, a useful procedure is developed to determine the possible variable ranges in different iterations and an adaptive strategy is employed to calculate proper parameters' values and choose suitable memory consideration rules during the evolution process. Extensive computational comparisons are carried out by employing a set of eighteen well-known benchmark optimization problems with various characteristics from the literature. The obtained results reveal that EAMS algorithm can achieve better solutions compared to some other HS variants, basic MS algorithms and certain cases of well-known robust optimization algorithms.