Many human diseases owe their pathology, to some degree, to the erroneous conversion of proteins from their soluble state into fibrillar, β-structured aggregates, often referred to as amyloid fibrils. Neurodegenerative diseases, such as Alzheimer and spongiform encephalopathies, as well as type 2 diabetes and both localized and systemic amyloid osis, are among the conditions that are associated with the formation of amyloid fibrils. Several mathe matical tools can rationalize and even predict important para meters of amyloid fibril formation. It is not clear, however, whether such algorithms have predictive powers for in vivo systems, in which protein aggregation is affected by the presence of other biological factors. In this review, we briefly describe the existing algorithms and use them to predict the effects of mutations on the aggregation of specific proteins, for which in vivo experimental data are available. The comparison between the theoretical predictions and the experimental data obtained in vivo is shown for each algorithm and experimental data set, and statistically significant correlations are found in most cases.

The cyanobacteria are the most important prokaryotic primary producers on Earth, inhabiting a great diversity of aquatic and terrestrial environments exposed to light. However, the evolutionary forces leading to their divergence and speciation remain largely enigmatic compared to macroorganisms due to their prokaryotic nature, including vast population sizes, and largely asexual reproduction. The advent of modern molecular techniques has facilitated an understanding of the important factors shaping cyanobacterial evolution, including horizontal gene transfer and homologous recombination. We review the forces shaping the evolution of cyanobacteria and discuss the role of cohesive forces on speciation. Further, while myriad species concepts and definitions are currently used, only a limited subset might be applied to cyanobacteria due to their asexual reproduction. Additionally, concepts based solely on phenotypes provide insufficient resolution. A monophyletic species concept which is universal may be ideal for cyanobacteria. Actual identification of the cyanobacteria is difficult due to cryptic diversity, lack of morphological variability, and frequent convergent evolutionary events. Thus, applied molecular techniques such as DNA barcoding will be useful for identifications of environmental samples. Lastly, we show that the real biodiversity of the cyanobacteria is widely underestimated, due in part to low sampling efforts, sensitivity to the molecular markers 123Biodivers Conserv (2015) 24:739-757 DOI 10.1007 employed, and the species definitions employed by researchers. In conclusion, we anticipate a rapid increase in cyanobacterial taxa described and large revisions of the system in the future as scientists adopt a common approach to cyanobacterial systematics.

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