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Jellyfish have existed on the earth for around 600 million years and have evolved in response to environmental changes. Hydrozoan jellyfish, members of phylum Cnidaria, exist in multiple life stages, including planula larvae, vegetatively-propagating polyps, and sexually-reproducing medusae. Although free-swimming medusae display complex morphology and exhibit increase in body size and regenerative ability, their underlying cellular mechanisms are poorly understood. Here, we investigate the roles of cell proliferation in body-size growth, appendage morphogenesis, and regeneration using Cladonema pacificum as a hydrozoan jellyfish model. By examining the distribution of S phase cells and mitotic cells, we revealed spatially distinct proliferating cell populations in medusae, uniform cell proliferation in the umbrella, and clustered cell proliferation in tentacles. Blocking cell proliferation by hydroxyurea caused inhibition of body size growth and defects in tentacle branching, nematocyte differentiation, and regeneration. Local cell proliferation in tentacle bulbs is observed in medusae of two other hydrozoan species, Cytaeis uchidae and Rathkea octopunctata , indicating that it may be a conserved feature among hydrozoan jellyfish. Altogether, our results suggest that hydrozoan medusae possess actively proliferating cells and provide experimental evidence regarding the role of cell proliferation in body-size control, tentacle morphogenesis, and regeneration., Competing Interests: The authors declare that they have no competing interests.

Here, we propose an algorithm to automatically obtain extraction filters for the affected regions from cancer images. The proposed algorithm consists of two steps: extraction of affected region candidates and elimination of false positives. Useful features of cancer images, such as the area and degree of circularity of cancer nests, etc., are extracted using the derived filters. These features are useful for supporting pathological diagnosis. Automatic Construction of Tree-structural Image Transformation (ACTIT) was used to construct these filters to extract the affected regions from the image. The proposed algorithm was applied to a mouth cancer pathological image. The results confirmed that the proposed algorithm can obtain good filters capable of extracting cancer nests. The derived filters were also applied to other images from the same specimen. The results also indicated that the generated filters show general versatility in extracting cancer nest candidates. The area and degree of circularity of the cancer nets were also derived automatically.