Yonosuke Kobatake

TL;DR: The kinetics for uptake by mitochondria of TPP+ and DDA+ were analyzed, and it was found that TPP+ permeated the mitochondrial membrane about 15 times faster than DDA+.

TL;DR: It was found that a photorepellent system other than photosystem 370 (PS370) also controls the behavior of Halobacterium halobium and a mutant strain showed the repellent response due to the new photosystem.

Abstract: Olfaction is a sense that is vital for many living organisms. Animals have been relying on smell to sample the environment and gather information from it. Olfaction enables the identification of food, mates, and predators as well as communication (Mykytowycz 1985) not only between members of the same or different species but also between animals and the environment.Nevertheless, olfaction has not been as widely studied as vision or the auditory system. A deeper understanding of the biological olfactory system would allow us to develop novel artificial olfactory systems for real-world robotic applications such as environmental monitoring (Trincavelli et al. 2008), land mine detection (Bermudez i Badia et al. 2007), as well as detection of explosives and other hazardous substances (Rachkov et al. 2005; Distante et al. 2009). Although there have been several attempts to implement the sense of smell on robots, biological olfaction outperforms its artificial counterparts in robustness, size, response time, precision, and complexity. Animals, and more specifically insects with relatively simple nervous systems, are able to unravel the problem of odor localization and classification with great efficiency: bees use odor to localize nests, ants use pheromone trails to organize foraging in swarms, male moths use olfaction to locate mates (Baker and Haynes 1987), and so on.Despite the technological advances in the field of artificial olfaction, a robust solution for the task of odor source localization and classification utilizing a fully autonomous robot has not yet been demonstrated. The main challenge is thus to develop an intelligent system able to robustly encode and decode odors as well as navigate autonomously in natural environments and successfully locate an odor source.Artificial olfaction remains a challenging field in research, as it postulates the development of chemical sensors that are able to reliably capture information from the environment. In the field of artificial chemical sensing there is a wide diversity of technologies; however, the most widely used chemical sensors are made of thin-film metal oxide (MOX). These chemical sensors provide a broad spectrum of sensitivity to volatile chemical compounds with low power consumption. When employed on a robotic platform, they are usually structured in arrays of different types of chemical sensors—widely known as e-noses—which provide less error rates and a larger scale of chemical detection. Nevertheless, they are still less efficient than the sensory modalities of animals. As an alternative to an artificial chemical sensor Kuwana et al. (1999) have used its biological counterpart, which is the actual antennal lobe of a living silkworm moth connected to a mobile robot so as to perform pheromone search.Equipping a robot with reliable chemical sensors is not enough to perform the odor classification and localization task. This task requires the development of robust odor classification models as well as odor source localization strategies that handle and exploit the information acquired from both the classification model and other sensory modalities. Early attempts to achieve the odor localization task are demonstrated by the Braitenberg’s vehicles (Gomez-Marin et al. 2010; Lilienthal and Duckett 2003) or high-level processes that include a planner and symbolic reasoning (Loutfi and Coradeschi 2008). In the past two decades, several attempts have been made to model animals’ behaviors and techniques to achieve a robust odor localization and classification system. For instance, to determine the direction of a gas source, Hiroshi Ishida and Atsushi Kohnotoh (2008) based their model on the dog’s nose. Frank Grasso et al. (2009) have modeled the behavior of a lobster and built a robot that performs the odor localization task in an underwater environment. The list of studies that approach artificial olfaction by modeling animal olfaction is constantly increasing, with an emphasis on insect chemolocalization, and most specifically, the chemical search based on the behavior and neural substrates of the male moth (Pyk et al. 2006). In fact, in a comparative study of robot-based odor source localization strategies (Bermudez i Badia and Vershure 2009), the authors compare reactive approaches with strategies employed by the male moth, concluding that the latter are more efficient in correct localizations.Nonetheless, to locate the source of a chemical compound in real-world applications is a rather difficult task. Odors are chemical volatiles in the atmosphere that are mainly transported by airflow, creating a plume. However, the plume dispersion dynamics vary greatly depending on the medium, as the interaction of the airflow with other surfaces produces turbulence. This dispersion is best described by the so-called Reynolds number. In fluid mechanics, the Reynolds number can be characterized by different conditions, where a fluid may be in relative motion to a surface. It includes density and viscosity and measures the ratio of inertial forces to viscous forces. With low Reynolds numbers where viscosity prevails, there is a smooth constant fluid motion with a monotonic decrease of the chemical concentration. At medium or high values, however, turbulence dominates, producing flow instabilities. To address the problem of odor localization and classification, Kowaldo has proposed to divide the task of odor localization in three general steps: (1) search for and identify the chemical compound of interest, (2) track the odor using several sensory modalities (such as chemical), and (3) identify the source of the odor (by either vision or olfaction). Consequently, different search and classification strategies need to be employed for different environments (Kowaldo and Russell 2008).Our aim is to achieve a novel olfactory-based system that will allow an autonomous mobile robot to navigate within a given environment and locate the source of the desired odor. We propose two models for classification and localization based on the neural substrates and mechanisms employed by a biological system that is known to perform the task of odor localization and classification in a robust way—the male moth. To assess our models, we have conducted experiments using two different chemical compounds: ethanol and ammonia. Our results show the first steps toward a stable odor localization and classification system.

TL;DR: It was found that the liposome described above is a good model for the generation of membrane potentials in mitochondria and the electrode lifetime was remarkably prolonged.

TL;DR: The spectral properties of this pigment and the content of the cells correlate with the sensitivity of photo-repellent response to the light around 480 nm and a name 'phoborhodopsin' is suggested for this new pigment.