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Romano, S.

Publications and source records attributed to Romano, S..

2 recordsLinked to original sources

The genome of the jellyfish Clytia hemisphaerica and the evolution of the cnidarian life-cycle

Jellyfish (medusae) are a distinctive life-cycle stage of medusozoan cnidarians. They are major marine predators, with integrated neurosensory, muscular and organ systems. The genetic foundations of this complex form are largely unknown. We report the draft genome of the hydrozoan jellyfish Clytia hemisphaerica and use multiple transcriptomes to determine gene use across life-cycle stages. Medusa, planula larva and polyp are each characterised by distinct transcriptome signatures reflecting abrupt life cycle transitions, and all deploy a mixture of phylogenetically old and new genes. Medusa specific transcription factors, including many with bilaterian orthologs, associate with diverse neurosensory structures. Compared to Clytia, the polyp-only hydrozoan Hydra has lost many of the medusa-expressed transcription factors, despite similar overall rates of gene content and sequence evolution. Absence of expression and gene loss among Clytia orthologs of genes patterning the anthozoan aboral pole, secondary axis and endomesoderm support simplification of planulae and polyps in Hydrozoa, including loss of bilateral symmetry. Consequently, although the polyp and planula are generally considered the ancestral cnidarian forms, in Clytia the medusa maximally deploys ancestral cnidarian-bilaterian transcription factor gene complexity.

evolutionary biology

Bayesian Selection Of Grammar Productions For The Language Of Thought

Probabilistic proposals of Language of Thoughts (LoTs) can explain learning across different domains as statistical inference over a compositionally structured hypothesis space. While frameworks may differ on how a LoT may be implemented computationally, they all share the property that they are built from a set of atomic symbols and rules by which these symbols can be combined. In this work we show how the set of productions of a LoT grammar can be effectively selected from a broad repertoire of possible productions by an inferential process starting from experimental data. We then test this method in the language of geometry, a specific LoT model (Amalric et al., 2017). Finally, despite the fact of the geometrical LoT not being a universal (i.e. Turing-complete) language, we show an empirical relation between a sequences probability and its complexity consistent with the theoretical relationship for universal languages described by Levins Coding Theorem.

neuroscience