Extant eukaryote ecology is primarily sustained by oxygenic photosynthesis, in which chlorophylls play essential roles. The exceptional photosensitivity of chlorophylls allows them to harvest solar energy for photosynthesis, but on the other hand, they also generate cytotoxic reactive oxygen species. A risk of such phototoxicity of the chlorophyll must become particularly prominent upon dynamic cellular interactions that potentially disrupt the mechanisms that are designed to quench photoexcited chlorophylls in the phototrophic cells. Extensive examination of a wide variety of phagotrophic, parasitic, and phototrophic microeukaryotes demonstrates that a catabolic process that converts chlorophylls into nonphotosensitive 132,173-cyclopheophorbide enols (CPEs) is phylogenetically ubiquitous among extant eukaryotes. The accumulation of CPEs is identified in phagotrophic algivores belonging to virtually all major eukaryotic assemblages with the exception of Archaeplastida, in which no algivorous species have been reported. In addition, accumulation of CPEs is revealed to be common among phototrophic microeukaryotes (i.e., microalgae) along with dismantling of their secondary chloroplasts. Thus, we infer that CPE-accumulating chlorophyll catabolism (CACC) primarily evolved among algivorous microeukaryotes to detoxify chlorophylls in an early stage of their evolution. Subsequently, it also underpinned photosynthetic endosymbiosis by securing close interactions with photosynthetic machinery containing abundant chlorophylls, which led to the acquisition of secondary chloroplasts. Our results strongly suggest that CACC, which allowed the consumption of oxygenic primary producers, ultimately permitted the successful radiation of the eukaryotes throughout and after the late Proterozoic global oxygenation.

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We propose a system of detecting the position of a device embedded with an antenna by sensing the electrical power from a two-dimensional communication (2DC) sheet. The system obtains a characteristic power pattern at each position by sweeping the frequency of the microwave supplied to the 2DC sheet. Our system uses a machine learning technique to learn the accumulated power-pattern data to detect the position of a device. The position-detection accuracy of our system was 79.1% when the antenna was moved in 12 mm intervals. In addition to detecting the position of a device, we also estimated the direction.

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