The influence of biofilm formation by Gardnerella vaginalis and other anaerobes on bacterial vaginosis

Bacterial vaginosis (BV) is the worldwide leading vaginal disorder in women of reproductive age. BV is characterized by the replacement of beneficial lactobacilli and the augmentation of anaerobic bacteria. Gardnerella vaginalis is a predominant bacterial species, however, BV is also associated with other numerous anaerobes, such as Atopobium vaginae, Mobiluncus mulieris, Prevotella bivia, Fusobacterium nucleatum and Peptoniphilus sp.. Currently, the role of G. vaginalis in the etiology of BV remains a matter of controversy. It is however known that, in BV patients, a biofilm is usually formed on the vaginal epithelium and G. vaginalis is typically the predominant species. So, the current paradigm is that the establishment of a biofilm plays a key role in the pathogenesis of BV. This review provides background on the influence of biofilm formation by G. vaginalis and other anaerobes in the polymicrobial etiology of BV, through its initial adhesion until biofilm formation and discusses the commensal and synergic interactions established between them to understand the phenotypic shift of G. vaginalis' biofilms into BV establishment.

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2016.00275

Concise server-wide causality management for eventually consistent data stores

Large scale distributed data stores rely on optimistic replication to scale and remain highly available in the face of net work partitions. Managing data without coordination results in eventually consistent data stores that allow for concurrent data updates. These systems often use anti-entropy mechanisms (like Merkle Trees) to detect and repair divergent data versions across nodes. However, in practice hash-based data structures are too expensive for large amounts of data and create too many false conflicts. Another aspect of eventual consistency is detecting write conflicts. Logical clocks are often used to track data causality, necessary to detect causally concurrent writes on the same key. However, there is a nonnegligible metadata overhead per key, which also keeps growing with time, proportional with the node churn rate. Another challenge is deleting keys while respecting causality: while the values can be deleted, perkey metadata cannot be permanently removed without coordination. Weintroduceanewcausalitymanagementframeworkforeventuallyconsistentdatastores,thatleveragesnodelogicalclocks(BitmappedVersion Vectors) and a new key logical clock (Dotted Causal Container) to provides advantages on multiple fronts: 1) a new efficient and lightweight anti-entropy mechanism; 2) greatly reduced per-key causality metadata size; 3) accurate key deletes without permanent metadata.