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Background: Congenital myasthenic syndromes (CMS) are a heterogeneous group of inherited neuromuscular disorders sharing the common feature of fatigable weakness due to defective neuromuscular transmission. Despite rapidly increasing knowledge about the genetic origins, specific features and potential treatments for the known CMS entities, the lack of standardized classification at the most granular level has hindered the implementation of computer-based systems for knowledge capture and reuse. Where individual clinical or genetic entities do not exist in disease coding systems, they are often invisible in clinical records and inadequately annotated in information systems, and features that apply to one disease but not another cannot be adequately differentiated., Results: We created a detailed classification of all CMS disease entities suitable for use in clinical and genetic databases and decision support systems. To avoid conflict with existing coding systems as well as with expert-defined group-level classifications, we developed a collaboration with the Orphanet nomenclature for rare diseases, creating a clinically understandable name for each entity and placing it within a logical hierarchy that paves the way towards computer-aided clinical systems and improved knowledge bases for CMS that can adequately differentiate between types and ascribe relevant expert knowledge to each., Conclusions: We suggest that data science approaches can be used effectively in the clinical domain in a way that does not disrupt preexisting expert classification and that enhances the utility of existing coding systems. Our classification provides a comprehensive view of the individual CMS entities in a manner that supports differential diagnosis and understanding of the range and heterogeneity of the disease but that also enables robust computational coding and hierarchy for machine-readability. It can be extended as required in the light of future scientific advances, but already provides the starting point for the creation of FAIR (Findable, Accessible, Interoperable and Reusable) knowledge bases of data on the congenital myasthenic syndromes.

The Neotropical freshwater ichthyofauna has among the highest species richness and density of any vertebrate fauna on Earth, with more than 5,600 species compressed into less than 12% of the world's land surface area, and less than 0.002% of the world's total liquid water supply. How have so many species come to co-exist in such a small amount of total habitat space? Here we report results of an aquatic faunal survey of the Fitzcarrald region in southeastern Peru, an area of low-elevation upland (200-500 m above sea level) rainforest in the Western Amazon, that straddles the headwaters of four large Amazonian tributaries; the Juruá (Yurúa), Ucayali, Purús, and Madre de Dios rivers. All measures of fish species diversity in this region are high; there is high alpha diversity with many species coexisting in the same locality, high beta diversity with high turnover between habitats, and high gamma diversity with high turnover between adjacent tributary basins. Current data show little species endemism, and no known examples of sympatric sister species, within the Fitzcarrald region, suggesting a lack of localized or recent adaptive divergences. These results support the hypothesis that the fish species of the Fitzcarrald region are relatively ancient, predating the Late Miocene-Pliocene (c. 4 Ma) uplift that isolated its several headwater basins. The results also suggest that habitat specialization (phylogenetic niche conservatism) and geographic isolation (dispersal limitation) have contributed to the maintenance of high species richness in this region of the Amazon Basin.