Extrathymic differentiation of resident T cells in the joints of mice with collagen-induced arthritis

Murine collagen-induced arthritis (CIA) is known as a T cell-mediated autoimmune disease, although autoantibodies are also suspected to be associated with the onset of the disease. To determine the origin of such T cells in the joints of mice with CIA, their phenotypic properties as well as those of T cells in other immune organs were examined in DBA/1 mice. Since a significant number of mononuclear cells (MNC) was also yielded by the joints of normal DBA/1 mice, the properties of these T cells were examined in parallel. When CIA was induced by an intradermal injection of type II collagen at the base of the tail, the numbers of MNC yielded by the regional lymph nodes and the foot joints were doubled. Interestingly, regardless of the onset of CIA, the joints were always comprised of unique T cell populations, including IL-2(R)alpha- beta+ T cells, gammadelta T cells, CD8alpha+ beta- cells, and CD44+ L-selectin- cells. All these properties coincide with those of extrathymic T cells in liver and intestine. In the case of gammadelta T cells in joints, Vgamma and Vdelta usages were unique and different from those in the other organs. More importantly, Vgamma and Vdelta usages in gammadelta T cells in the joints of normal mice and in those of mice with CIA were essentially the same. Taken together with the expression of recombination-activating gene-1 and -2 mRNAs by MNC in mice with CIA, these findings raise the possibility that the joints have their own resident T cells that are extrathymically generated in situ.     

Modeling of the pharyngeal muscle in Caenorhabditis elegans based on FitzHugh-Nagumo equations

The pharyngeal pumping motion to send food to the bowel is a rhythmic movement in Caenorhabditis elegans. This paper proposes a simulation-based approach to investigate the mechanisms of rhythm phenomena in the pharyngeal pumping motion. To conduct the simulations, first, we developed a pharyngeal muscle model including 29 cell models which simulate the activity of each cell as a membrane potential based on FitzHugh-Nagumo equations. Then, to compare the response of the model with that of C. elegans, we calculated the electropharyngeogram (EPG), which represents the electrophysiological responses of the pharyngeal cells, using the simulated membrane potentials. The results confirmed that our model could generate the EPG similar to that measured from C. elegans. We proposed a computer simulation of the pumping motion to investigate the mechanisms of rhythm phenomena in living organisms.     

Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...     

Probabilistic nearest neighbor query processing on distributed uncertain data

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.     

Fabrication of micro-gelatin fiber utilizing coacervation method

Biotechnology has drastically been advanced by the development of iPS and ES cells, which are representative forms induced pluripotent stem cells. In the micro/nano bio field, the development of cells and Taylor-made medicine for a potential treatment of incurable diseases has been a center of attention. The melting point of gelatin is between 25 and 33 °C, and the sol–gel transition occurs in low temperature. This makes the deformation of this useful biomaterial easy. The examples of gelatin fiber applications are suture threads, blood vessel prosthesis, cell-growth-based materials, filter materials, and many others. Because the cell size differs depending on the species and applications, it is essential to fabricate gelatin fibers of different diameters. In this paper, we have developed a fabrication method for gelatin fibers the coacervation method. We fabricated narrow gelatin fibers having a diameter over 10 μm.     

Fundamental characteristics of printed gelatin utilizing micro 3D printer

Gelatin is useful for biofabrication, because it can be used for cell scaffolds and it has unique properties. Therefore, we attempted to fabricate biodevices of gelatin utilizing micro 3D printer which is able to print with high precision. However, it has been difficult to fabricate 3D structure of gelatin utilizing 3D printer, because a printed gelatin droplet on the metal plate electrode would spread before solidification. To clear this problem, we developed a new experimental set-up with a peltier device that can control temperature of the impact point. At an impact point temperature of 80 °C, the spreading of printed gelatin droplets was prevented. Therefore, we were able to print a ball gelatin. In addition, we were able to print a narrower gelatin line than at an impact point temperature of 20 °C.     

Theoretical and evolutionary parameter tuning of neural oscillators with a double-chain structure for generating rhythmic signals

A neural oscillator with a double-chain structure is one of the central pattern generator models used to simulate and understand rhythmic movements in living organisms. However, it is difficult to reproduce desired rhythmic signals by tuning an enormous number of parameters of neural oscillators. In this study, we propose an automatic tuning method consisting of two parts. The first involves tuning rules for both the time constants and the amplitude of the oscillatory outputs based on theoretical analyses of the relationship between parameters and outputs of the neural oscillators. The second involves an evolutionary tuning method with a two-step genetic algorithm (GA), consisting of a global GA and a local GA, for tuning parameters such as neural connection weights that have no exact tuning rule. Using numerical experiments, we confirmed that the proposed tuning method could successfully tune all parameters and generate sinusoidal waves. The tuning performance of the proposed method was less affected by factors such as the number of excitatory oscillators or the desired outputs. Furthermore, the proposed method was applied to the parameter-tuning problem of some types of artificial and biological wave reproduction and yielded optimal parameter values that generated complex rhythmic signals in Caenorhabditis elegans without trial and error.