Formation of the Panama Isthmus, that had global oceanographic and biotic effects in the Neogene, is generally associated with tectonic uplift during collision of the Panama volcanic arc with South America. However, new field, geochemical and geochronological data from the Culebra Cut of the Panama Canal suggest that volcanism also contributed to the Isthmus emergence in the Early Miocene. This volcanism is recorded in a newly-recognised Central Panama volcanic field that includes several phases of development. Early activity of this field along the Panama Canal was associated with proximal effusive to explosive felsic products during formation of subaerial stratovolcanoes and possible domes ca. 21 Ma. This was followed by a period of marine transgression ca. 21–18 Ma, with more distal volcanism documented by tuffs that deposited in marine to terrestrial environments. Finally, proximal mafic volcanism formed tephra cones in a monogenetic field ca. 18(-?) Ma. This was associated with phreatomagmatic processes in a coastal environment, with remarkable kilometre-wide subvolcanic peperitic intrusions. We propose based on these observations that formation of the Central Panama volcanic field was critical in shaping regional topography, and that this could have actively contributed to obstruction and closure of an interoceanic strait in Central Panama.

Background: The notion of heme as a regulator of many physiological processes via transient binding to proteins is one that is recently being acknowledged. The broad spectrum of the effects of heme makes it important to identify further heme-regulated proteins to understand physiological and pathological processes. Moreover, several proteins were shown to be functionally regulated by interaction with heme, yet, for some of them the hemebinding site(s) remain unknown. The presented application HeMoQuest enables identification and qualitative evaluation of such heme-binding motifs from protein sequences. Results: We present HeMoQuest, an online interface (http://bit.ly/hemoquest) to algorithms that provide the user with two distinct qualitative benefits. First, our implementation rapidly detects transient heme binding to nonapeptide motifs from protein sequences provided as input. Additionally, the potential of each predicted motif to bind heme is qualitatively gauged by assigning binding affinities predicted by an ensemble learning implementation, trained on experimentally determined binding affinity data. Extensive testing of our implementation on both existing and new manually curated datasets reveal that our method produces an unprecedented level of accuracy (92%) in identifying those residues assigned "heme binding" in all of the datasets used. Next, the machine learning implementation for the prediction and qualitative assignment of binding affinities to the predicted motifs achieved 71% accuracy on our data. Conclusions: Heme plays a crucial role as a regulatory molecule exerting functional consequences via transient binding to surfaces of target proteins. HeMoQuest is designed to address this imperative need for a computational approach that enables rapid detection of heme-binding motifs from protein datasets. While most existing implementations attempt to predict sites of permanent heme binding, this application is to the best of our knowledge, the first of its kind to address the significance of predicting transient heme binding to proteins.

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Background:The integration of heterogeneous, multiscale, and multimodal knowledge and data has become a common prerequisite for joint analysis to unravel the mechanisms and aetiologies of complex diseases. Because of its unique ability to capture this variety, Biological Expression Language (BEL) is well suited to be further used as a platform for semantic integration and harmonization in networks and systems biology. 1/15Results: We have developed numerous independent packages capable of downloading, structuring, and serializing various biological data sources to BEL. Each Bio2BEL package is implemented in the Python programming language and distributed through GitHub ( https://github.com/bio2bel ) and PyPI. Conclusions:The philosophy of Bio2BEL encourages reproducibility, accessibility, and democratization of biological databases. We present several applications of Bio2BEL packages including their ability to support the curation of pathway mappings, integration of pathway databases, and machine learning applications.