Abstract The morphological and histological features of the nasal cavity are diverse among animal species, and the nasal cavities of terrestrial and semiaquatic turtles possess 2 regions lined with each different type of sensory epithelium. Sea turtles can inhale both of volatile and water-soluble odorants with high sensitivity, but details of the architectural features and the distribution of the sensory epithelia within the sea turtle nasal cavity remain uncertain. The present study analyzed the nasal cavity of green sea turtles using morphological, computed tomographic, and histological methods. We found that the middle region of the sea turtle nasal cavity is divided into anterodorsal, anteroventral, and posterodorsal diverticula and a posteroventral excavation by connective tissue containing cartilages. The posterodorsal diverticulum was lined with a thin sensory epithelium, and the anterodorsal and anteroventral diverticula were occupied by a single thick sensory epithelium. In addition, a relatively small area on the posteroventral excavation was covered by independent sensory epithelium that differed from other 2 types of epithelia, and a single thin bundle derived from the posteroventral excavation comprised the most medial nerve that joins the anterior end of the olfactory nerve tract. These findings suggested that the posteroventral excavation identified herein transfers stimuli through an independent circuit and plays different roles when odorants arise from other nasal regions.

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Because the volume of data that must be managed in large-scale farming has become enormous, we developed a mobile field computer to record tractor operations for analysis by a cloud-computing-based farm management system. The newly developed field computer running Microsoft Windows is mounted in the cabin of a tractor and acquires data from sensors attached to implements and the tractor's GPS receiver via a controller area network, and sends the data to a cloud server through the Internet. The user of the system can view and use the data to aid farm management decision in a web browser. This paper describes the acquisition of spraying data and its analysis to verify the operation of the system. The server software simulated the change in the dispersion density of the spray from the path and working speed of the tractor, which was displayed in the web browser.